davanstrien/dataset_cards_with_metadata · Datasets at Hugging Face

datasetId large_stringlengths 6 116	author large_stringlengths 2 42	last_modified large_stringdate 2021-04-29 15:34:29 2025-06-25 02:40:10	downloads int64 0 3.97M	likes int64 0 7.74k	tags large listlengths 1 7.92k	task_categories large listlengths 0 48	createdAt large_stringdate 2022-03-02 23:29:22 2025-06-25 00:32:52	trending_score float64 0 64	card large_stringlengths 31 1.01M
abhinav302019/olympiad_data_320	abhinav302019	2025-03-05T16:53:53Z	14	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-05T16:53:50Z	0	--- dataset_info: features: - name: problem dtype: string - name: Known_Solution dtype: string - name: Known_Answer dtype: string - name: Generated_Solution dtype: string - name: Generated_Answer dtype: string - name: Judge_Evaluation dtype: string - name: Judge_Rating dtype: string - name: Judge_Justification dtype: string splits: - name: train num_bytes: 46504 num_examples: 10 download_size: 46114 dataset_size: 46504 configs: - config_name: default data_files: - split: train path: data/train-* ---
RLHF-And-Friends/tldr-thematic	RLHF-And-Friends	2025-05-19T23:44:12Z	351	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-28T18:38:31Z	0	--- dataset_info: - config_name: Advice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 3248777.2880519526 num_examples: 2088 - name: validation num_bytes: 361482.16246153845 num_examples: 232 download_size: 2218375 dataset_size: 3610259.450513491 - config_name: AskDocs features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 440327.5730453556 num_examples: 283 - name: validation num_bytes: 62324.51076923077 num_examples: 40 download_size: 312880 dataset_size: 502652.0838145864 - config_name: AskReddit features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 24023525.540001027 num_examples: 15440 - name: validation num_bytes: 2779673.1803076924 num_examples: 1784 download_size: 15633065 dataset_size: 26803198.72030872 - config_name: BreakUps features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1303867.513116636 num_examples: 838 - name: validation num_bytes: 168276.1790769231 num_examples: 108 download_size: 883490 dataset_size: 1472143.6921935591 - config_name: Cooking features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 177375.77147410085 num_examples: 114 - name: validation num_bytes: 10906.789384615384 num_examples: 7 download_size: 110570 dataset_size: 188282.56085871623 - config_name: Dogtraining features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 563245.8708212676 num_examples: 362 - name: validation num_bytes: 65440.73630769231 num_examples: 42 download_size: 397400 dataset_size: 628686.6071289598 - config_name: GetMotivated features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 262951.8015712548 num_examples: 169 - name: validation num_bytes: 37394.70646153846 num_examples: 24 download_size: 196817 dataset_size: 300346.50803279324 - config_name: Parenting features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 676828.60167749 num_examples: 435 - name: validation num_bytes: 74789.41292307692 num_examples: 48 download_size: 480742 dataset_size: 751618.0146005669 - config_name: Pets features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 569469.5821010607 num_examples: 366 - name: validation num_bytes: 68556.96184615385 num_examples: 44 download_size: 394199 dataset_size: 638026.5439472145 - config_name: askwomenadvice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1070478.3401243982 num_examples: 688 - name: validation num_bytes: 115300.34492307692 num_examples: 74 download_size: 686910 dataset_size: 1185778.6850474752 - config_name: books features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 250504.37901166876 num_examples: 161 - name: validation num_bytes: 34278.480923076924 num_examples: 22 download_size: 173603 dataset_size: 284782.8599347457 - config_name: cats features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 504120.613663234 num_examples: 324 - name: validation num_bytes: 62324.51076923077 num_examples: 40 download_size: 338338 dataset_size: 566445.1244324647 - config_name: college features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 410764.94446633884 num_examples: 264 - name: validation num_bytes: 70115.07461538461 num_examples: 45 download_size: 275869 dataset_size: 480880.01908172347 - config_name: dating_advice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 4432838.359032573 num_examples: 2849 - name: validation num_bytes: 500154.1989230769 num_examples: 321 download_size: 2859130 dataset_size: 4932992.55795565 - config_name: dogs features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 992681.9491269855 num_examples: 638 - name: validation num_bytes: 104393.55553846154 num_examples: 67 download_size: 686858 dataset_size: 1097075.504665447 - config_name: jobs features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1686625.7568239064 num_examples: 1084 - name: validation num_bytes: 166718.0663076923 num_examples: 107 download_size: 1115205 dataset_size: 1853343.8231315988 - config_name: legaladvice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 3107187.8564366614 num_examples: 1997 - name: validation num_bytes: 316296.89215384616 num_examples: 203 download_size: 2148216 dataset_size: 3423484.7485905075 - config_name: loseit features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 2259207.1945648636 num_examples: 1452 - name: validation num_bytes: 267995.3963076923 num_examples: 172 download_size: 1502741 dataset_size: 2527202.590872556 - config_name: needadvice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 821529.8889326777 num_examples: 528 - name: validation num_bytes: 81021.864 num_examples: 52 download_size: 566990 dataset_size: 902551.7529326777 - config_name: offmychest features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 2461477.8111581365 num_examples: 1582 - name: validation num_bytes: 232158.80261538463 num_examples: 149 download_size: 1752857 dataset_size: 2693636.613773521 - config_name: personalfinance features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 3597305.119720361 num_examples: 2312 - name: validation num_bytes: 422248.56046153844 num_examples: 271 download_size: 2364890 dataset_size: 4019553.6801818996 - config_name: pettyrevenge features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 852648.4453316428 num_examples: 548 - name: validation num_bytes: 96602.9916923077 num_examples: 62 download_size: 639836 dataset_size: 949251.4370239505 - config_name: relationship_advice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 13522568.683170268 num_examples: 8691 - name: validation num_bytes: 1480207.1307692307 num_examples: 950 download_size: 9038096 dataset_size: 15002775.813939499 - config_name: relationships features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 98527573.27040318 num_examples: 63324 - name: validation num_bytes: 10908347.497384615 num_examples: 7001 download_size: 65934652 dataset_size: 109435920.76778778 - config_name: running features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 882211.0739106595 num_examples: 567 - name: validation num_bytes: 110626.00661538461 num_examples: 71 download_size: 542869 dataset_size: 992837.0805260441 - config_name: self features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1630612.3553057692 num_examples: 1048 - name: validation num_bytes: 204112.77276923077 num_examples: 131 download_size: 1156745 dataset_size: 1834725.128075 - config_name: tifu features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 11957305.296302326 num_examples: 7685 - name: validation num_bytes: 1305698.5006153847 num_examples: 838 download_size: 8213592 dataset_size: 13263003.79691771 - config_name: travel features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 703279.3746166104 num_examples: 452 - name: validation num_bytes: 81021.864 num_examples: 52 download_size: 450229 dataset_size: 784301.2386166104 - config_name: weddingplanning features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 673716.7460375936 num_examples: 433 - name: validation num_bytes: 66998.84907692307 num_examples: 43 download_size: 471160 dataset_size: 740715.5951145167 configs: - config_name: Advice data_files: - split: train path: Advice/train-* - split: validation path: Advice/validation-* - config_name: AskDocs data_files: - split: train path: AskDocs/train-* - split: validation path: AskDocs/validation-* - config_name: AskReddit data_files: - split: train path: AskReddit/train-* - split: validation path: AskReddit/validation-* - config_name: BreakUps data_files: - split: train path: BreakUps/train-* - split: validation path: BreakUps/validation-* - config_name: Cooking data_files: - split: train path: Cooking/train-* - split: validation path: Cooking/validation-* - config_name: Dogtraining data_files: - split: train path: Dogtraining/train-* - split: validation path: Dogtraining/validation-* - config_name: GetMotivated data_files: - split: train path: GetMotivated/train-* - split: validation path: GetMotivated/validation-* - config_name: Parenting data_files: - split: train path: Parenting/train-* - split: validation path: Parenting/validation-* - config_name: Pets data_files: - split: train path: Pets/train-* - split: validation path: Pets/validation-* - config_name: askwomenadvice data_files: - split: train path: askwomenadvice/train-* - split: validation path: askwomenadvice/validation-* - config_name: books data_files: - split: train path: books/train-* - split: validation path: books/validation-* - config_name: cats data_files: - split: train path: cats/train-* - split: validation path: cats/validation-* - config_name: college data_files: - split: train path: college/train-* - split: validation path: college/validation-* - config_name: dating_advice data_files: - split: train path: dating_advice/train-* - split: validation path: dating_advice/validation-* - config_name: dogs data_files: - split: train path: dogs/train-* - split: validation path: dogs/validation-* - config_name: jobs data_files: - split: train path: jobs/train-* - split: validation path: jobs/validation-* - config_name: legaladvice data_files: - split: train path: legaladvice/train-* - split: validation path: legaladvice/validation-* - config_name: loseit data_files: - split: train path: loseit/train-* - split: validation path: loseit/validation-* - config_name: needadvice data_files: - split: train path: needadvice/train-* - split: validation path: needadvice/validation-* - config_name: offmychest data_files: - split: train path: offmychest/train-* - split: validation path: offmychest/validation-* - config_name: personalfinance data_files: - split: train path: personalfinance/train-* - split: validation path: personalfinance/validation-* - config_name: pettyrevenge data_files: - split: train path: pettyrevenge/train-* - split: validation path: pettyrevenge/validation-* - config_name: relationship_advice data_files: - split: train path: relationship_advice/train-* - split: validation path: relationship_advice/validation-* - config_name: relationships data_files: - split: train path: relationships/train-* - split: validation path: relationships/validation-* - config_name: running data_files: - split: train path: running/train-* - split: validation path: running/validation-* - config_name: self data_files: - split: train path: self/train-* - split: validation path: self/validation-* - config_name: tifu data_files: - split: train path: tifu/train-* - split: validation path: tifu/validation-* - config_name: travel data_files: - split: train path: travel/train-* - split: validation path: travel/validation-* - config_name: weddingplanning data_files: - split: train path: weddingplanning/train-* - split: validation path: weddingplanning/validation-* ---
ziyu3141/rf_newtrain_1_48	ziyu3141	2025-02-07T03:57:05Z	14	0	[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-07T03:57:03Z	0	--- dataset_info: features: - name: Filename dtype: string - name: Aesthetics score dtype: float64 - name: Artifact score dtype: float64 - name: Misalignment score dtype: float64 - name: Overall score dtype: float64 - name: Artifact heatmap sequence: sequence: sequence: int64 - name: Misalignment heatmap sequence: sequence: sequence: int64 - name: Misalignment token label dtype: string - name: is_uneven dtype: bool - name: preferred_image dtype: binary - name: unpreferred_image dtype: binary - name: revised_image dtype: binary - name: unrevised_id dtype: string - name: is_preferred dtype: bool splits: - name: train num_bytes: 135379387 num_examples: 20 download_size: 9833035 dataset_size: 135379387 configs: - config_name: default data_files: - split: train path: data/train-* ---
Hennara/Recap-DataComp-1B_split_4	Hennara	2024-11-28T20:08:01Z	46	0	[ "size_categories:100M<n<1B", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-28T19:00:44Z	0	--- dataset_info: features: - name: url dtype: string - name: re_caption dtype: string - name: org_caption dtype: string - name: sha256 dtype: string - name: key dtype: string - name: re_clip_score dtype: float64 - name: org_clip_score dtype: float64 - name: re_length dtype: int64 - name: org_length dtype: int64 - name: re_gpt4v_score dtype: int64 - name: org_gpt4v_score dtype: int64 splits: - name: train num_bytes: 67854510316 num_examples: 117611282 download_size: 41259522279 dataset_size: 67854510316 configs: - config_name: default data_files: - split: train path: data/train-* ---
fdschmidt93/synthetic-llama-8b	fdschmidt93	2025-03-21T18:55:21Z	17	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-21T18:55:10Z	0	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 657960740 num_examples: 478247 download_size: 353896912 dataset_size: 657960740 configs: - config_name: default data_files: - split: train path: data/train-* ---
ChaosAiVision/Medical_reasoning	ChaosAiVision	2025-06-22T13:04:27Z	31	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-20T11:02:46Z	0	--- dataset_info: features: - name: question dtype: string - name: anwser dtype: string - name: chain_of_though dtype: string splits: - name: train num_bytes: 6323164 num_examples: 2144 download_size: 2322394 dataset_size: 6323164 configs: - config_name: default data_files: - split: train path: data/train-* ---
sucharush/rag_mcqa_with_doc	sucharush	2025-06-08T03:03:07Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-08T02:43:40Z	0	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 240267317 num_examples: 122868 download_size: 122928694 dataset_size: 240267317 configs: - config_name: default data_files: - split: train path: data/train-* ---
philschmid/open-orca-10k-guidellm	philschmid	2024-10-09T11:45:44Z	33	1	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-09T11:45:42Z	0	--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 55855617.39201845 num_examples: 10000 download_size: 34695180 dataset_size: 55855617.39201845 configs: - config_name: default data_files: - split: train path: data/train-* ---
french-datasets/bismarck91_frA-enA-tokenised-qwen-part1	french-datasets	2025-06-21T14:19:03Z	0	0	[ "task_categories:audio-to-audio", "language:fra", "language:eng", "region:us" ]	[ "audio-to-audio" ]	2025-06-21T14:18:28Z	0	--- language: - fra - eng viewer: false task_categories: - audio-to-audio --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [bismarck91/frA-enA-tokenised-qwen-part1](https://huggingface.co/datasets/bismarck91/frA-enA-tokenised-qwen-part1).
mlfoundations-dev/multiple_samples_sympy_numina_aime	mlfoundations-dev	2025-02-04T05:33:24Z	14	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-03T23:22:34Z	0	--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: r1_distill_70b_response sequence: string - name: r1_distill_70b_extracted_answer sequence: string - name: sympy_code dtype: string - name: correct dtype: bool - name: execution_output dtype: string splits: - name: train num_bytes: 177622 num_examples: 3 download_size: 101520 dataset_size: 177622 configs: - config_name: default data_files: - split: train path: data/train-* ---
woshityj/xsum_dataset	woshityj	2024-12-11T08:49:23Z	15	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-11T08:49:14Z	0	--- dataset_info: features: - name: document dtype: string - name: summary dtype: string - name: id dtype: string splits: - name: train num_bytes: 69746054 num_examples: 30000 - name: test num_bytes: 8864589 num_examples: 3750 - name: validation num_bytes: 8637471 num_examples: 3750 download_size: 55382887 dataset_size: 87248114 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
Tobius/9e77d18e-3e9a-46af-ac54-7342711a98b6	Tobius	2024-11-29T11:14:06Z	13	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-29T11:14:05Z	0	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 108838.4 num_examples: 800 - name: test num_bytes: 27209.6 num_examples: 200 download_size: 11524 dataset_size: 136048.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
lighteval/RULER-131072-Lamma3-Instruct	lighteval	2025-06-18T12:10:44Z	0	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-18T12:03:29Z	0	--- dataset_info: features: - name: index dtype: int64 - name: input dtype: string - name: outputs sequence: string - name: length dtype: int64 splits: - name: vt num_bytes: 245243500 num_examples: 500 - name: fwe num_bytes: 160767200 num_examples: 500 - name: niah_single_1 num_bytes: 245447719 num_examples: 500 - name: qa_2 num_bytes: 278549005 num_examples: 500 - name: niah_multikey_1 num_bytes: 306810494 num_examples: 500 - name: niah_multivalue num_bytes: 306833986 num_examples: 500 - name: niah_multikey_3 num_bytes: 128503000 num_examples: 500 - name: niah_single_3 num_bytes: 306735516 num_examples: 500 - name: niah_single_2 num_bytes: 306711618 num_examples: 500 - name: qa_1 num_bytes: 312918473 num_examples: 500 - name: niah_multikey_2 num_bytes: 242897246 num_examples: 500 - name: niah_multiquery num_bytes: 306890263 num_examples: 500 - name: cwe num_bytes: 177649224 num_examples: 500 download_size: 1617169541 dataset_size: 3325957244 configs: - config_name: default data_files: - split: vt path: data/vt-* - split: fwe path: data/fwe-* - split: niah_single_1 path: data/niah_single_1-* - split: qa_2 path: data/qa_2-* - split: niah_multikey_1 path: data/niah_multikey_1-* - split: niah_multivalue path: data/niah_multivalue-* - split: niah_multikey_3 path: data/niah_multikey_3-* - split: niah_single_3 path: data/niah_single_3-* - split: niah_single_2 path: data/niah_single_2-* - split: qa_1 path: data/qa_1-* - split: niah_multikey_2 path: data/niah_multikey_2-* - split: niah_multiquery path: data/niah_multiquery-* - split: cwe path: data/cwe-* ---
RyanYr/reflect_collegemath-test_t4	RyanYr	2025-01-19T04:11:17Z	15	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-17T17:09:46Z	0	--- dataset_info: features: - name: data_source dtype: string - name: question_number dtype: string - name: problem dtype: string - name: answer dtype: string - name: license dtype: string - name: data_topic dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@3 sequence: string - name: response@4 sequence: string - name: response@5 sequence: string - name: response@6 sequence: string - name: response@7 sequence: string - name: response@8 sequence: string splits: - name: train num_bytes: 46205198 num_examples: 2818 download_size: 16907418 dataset_size: 46205198 configs: - config_name: default data_files: - split: train path: data/train-* ---
nvidia/Scoring-Verifiers	nvidia	2025-04-01T16:35:02Z	33	6	[ "task_categories:text-ranking", "license:other", "size_categories:1K<n<10K", "arxiv:2502.13820", "region:us", "code", "synthetic", "nvidia", "reasoning", "llms", "verifiers" ]	[ "text-ranking" ]	2025-04-01T15:55:46Z	0	--- license: other license_name: nsclv1 license_link: https://github.com/aleksficek/Scoring-Verifiers/blob/main/LICENSE task_categories: - text-ranking tags: - code - synthetic - nvidia - reasoning - llms - verifiers pretty_name: Scoring Verifiers size_categories: - 1K<n<10K --- # Scoring Verifiers Scoring Verifiers is a set of 4 benchmarks that evaluate the scoring and ranking capabilities of synthetic verifiers such as test case generation and reward modelling. You can find our paper [Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning](https://www.arxiv.org/abs/2502.13820) which explains in more detail our methodology, benchmark details and findings. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b8b0096c3182e96bf6cea1/HPZXg6QOjpI1h9NvujHQr.png) ## Datasets In this repository, we include 4 benchmarks that are code scoring and ranking versions of HumanEval and MBPP: - HE-R - HE-R+ - MBPP-R - MBPP-R+ Each dataset sample contains a question from HumanEval or MBPP following by several `gpt-4o` solutions and their rankings based on pre-defined test case execution scores. Alongside the keys found in the original benchmarks each sample contains the following keys: - `task_id` - `prompt` - `canonical_solution` - `all_solutions` (each solution contains the following) - `rank` - `average_test_score` - `average_time_taken` - `solution` For example, the following is a distribution of the test case scores for all solutions in HE-R+ and MBPP-R+ respectively. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b8b0096c3182e96bf6cea1/ZHYrzGVdirzifWoBJT518.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b8b0096c3182e96bf6cea1/LxcCczOl_BbG2UdFTthxy.png) ## Paper Overall our paper's contributions can be summarized as follows: 1. We provide a recipe to transform any coding benchmark with predefined test cases into a code scoring and ranking benchmark. 2. We certify our recipe by creating code scoring and ranking versions of HumanEval and MBPP datasets: HE-R, HE-R+, MBPP-R, MBPP-R+. 3. We use our benchmark to evaluate synthetic verification methods such as test case generation in standard, reward and reasoning LLM’s. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b8b0096c3182e96bf6cea1/s-mRwk11u_BYXvaeN9lq7.png) We also open-source the [code used to generate these benchmarks](https://github.com/aleksficek/Scoring-Verifiers). ## Citation ``` @misc{ficek2025scoringverifiersevaluatingsynthetic, title={Scoring Verifiers: Evaluating Synthetic Verification in Code and Reasoning}, author={Aleksander Ficek and Somshubra Majumdar and Vahid Noroozi and Boris Ginsburg}, year={2025}, eprint={2502.13820}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2502.13820}, } ```
ssktora/train-jqara-for-tevatron-1-all	ssktora	2025-04-16T06:17:45Z	16	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-16T06:17:41Z	0	--- dataset_info: features: - name: query_id dtype: string - name: query dtype: string - name: positive_passages list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string - name: negative_passages list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 643740 num_examples: 18 download_size: 406196 dataset_size: 643740 configs: - config_name: default data_files: - split: train path: data/train-* ---
french-datasets/ProfessorBob-text-embedding-dataset	french-datasets	2025-03-30T17:00:19Z	16	0	[ "language:fra", "region:us" ]	[]	2025-03-29T20:42:45Z	0	--- language: "fra" viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [ProfessorBob/text-embedding-dataset](huggingface.co/datasets/ProfessorBob/text-embedding-dataset).
jed351/Chinese-Common-Crawl-Filtered	jed351	2025-06-02T05:32:00Z	264	15	[ "language:zh", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2023-07-20T21:23:06Z	0	--- language: - zh --- # Traditional Chinese C4 ### Dataset Summary Data obtained from 2025-18 and 2025-13 Common Crawl. Downloaded and processed using [code](https://github.com/jedcheng/c4-dataset-script) based on another [project](https://github.com/shjwudp/c4-dataset-script) attempting to recreate the C4 dataset. The resultant dataset contains both simplified and traditional Chinese. It was then filtered using a [modified list](https://github.com/jedcheng/c4-dataset-script/blob/master/SC_filter/SC_list.txt) of simplified Chinese characters to obtain [another traditional Chinese dataset](https://huggingface.co/datasets/jed351/Traditional-Chinese-Common-Crawl-Filtered). I am still ironning out the process of filtering. The 2025-13 dataset was deduplicated without splitting into shards resulting in a smaller dataset than the 2025-18 dataset. Unfortunately, this process takes 2 CPU nodes for 4 hours. In addition, other people would have done a better job at cleaning the common crawl. To preserver time, money and resources, I will not work on this dataset anymore but instead shift my focus to the [Traditional Chinese Common Crawl dataset](https://huggingface.co/datasets/jed351/Traditional-Chinese-Common-Crawl).
sergiov2000/eval_wm_act_yellow3	sergiov2000	2025-05-30T10:17:50Z	114	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]	[ "robotics" ]	2025-05-30T10:17:34Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 2, "total_frames": 1728, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.above": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.side": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
mhr2004/NevIR-val	mhr2004	2025-04-25T05:29:58Z	30	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-25T05:29:56Z	0	--- dataset_info: features: - name: id dtype: string - name: version dtype: string - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 689464 num_examples: 450 download_size: 125405 dataset_size: 689464 configs: - config_name: default data_files: - split: train path: data/train-* ---
michsethowusu/dyula-kamba_sentence-pairs	michsethowusu	2025-04-02T13:14:12Z	11	0	[ "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-02T13:14:08Z	0	--- dataset_info: features: - name: score dtype: float32 - name: Dyula dtype: string - name: Kamba dtype: string splits: - name: train num_bytes: 2691554 num_examples: 22901 download_size: 2691554 dataset_size: 2691554 configs: - config_name: default data_files: - split: train path: Dyula-Kamba_Sentence-Pairs.csv --- # Dyula-Kamba_Sentence-Pairs Dataset This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks. This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php) ## Metadata - File Name: Dyula-Kamba_Sentence-Pairs - Number of Rows: 22901 - Number of Columns: 3 - Columns: score, Dyula, Kamba ## Dataset Description The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns: 1. `score`: The similarity score between the two sentences (range from 0 to 1). 2. `Dyula`: The first sentence in the pair (language 1). 3. `Kamba`: The second sentence in the pair (language 2). This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning. ## References Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications: [1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017 [2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018. [3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018 [4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018. [5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018. [6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018. [7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019. [8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB [9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761. [10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
SciFy/annotation_demo	SciFy	2025-03-10T16:39:57Z	17	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-10T16:39:55Z	0	--- dataset_info: features: - name: id dtype: string - name: metadata struct: - name: ocr_model dtype: string - name: python_version dtype: string splits: - name: annotations num_bytes: 89 num_examples: 2 download_size: 1735 dataset_size: 89 configs: - config_name: default data_files: - split: annotations path: data/annotations-* ---
taeyoon12321421/final_datasets_gorani	taeyoon12321421	2025-02-12T03:01:45Z	14	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-12T03:01:22Z	0	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: target_language dtype: string - name: metadata struct: - name: ENG dtype: string - name: JPN dtype: string - name: KO dtype: string splits: - name: train num_bytes: 10250793 num_examples: 21662 - name: test num_bytes: 2558493 num_examples: 5416 download_size: 4724881 dataset_size: 12809286 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sl-revised_NoQuant_32_32_0.05_64_BestF1	ferrazzipietro	2024-11-25T14:10:59Z	20	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-25T11:24:26Z	0	--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 241896 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
severo/fix-401	severo	2022-06-24T11:45:48Z	12	0	[ "region:us" ]	[]	2022-06-20T16:04:10Z	0	--- viewer: false --- # Try to include an iframe from observable: <iframe width="100%" height="635" frameborder="0" src="https://observablehq.com/embed/@d3/sortable-bar-chart?cell=viewof+order&cell=chart"></iframe> from an HF space: <iframe src="https://hf.space/embed/YoannLemesle/CLIPictionary/+?__theme=system" data-src="https://hf.space/embed/YoannLemesle/CLIPictionary/+" data-sdk="gradio" title="Gradio app" class="container p-0 flex-grow overflow-hidden space-iframe" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking" sandbox="allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads" scrolling="no" id="iFrameResizer0" style="overflow: hidden; height: 725px;"></iframe>
yunjae-won/mp_gemma9b_sft_ogd_rms_epoch4_multisample_2.5k	yunjae-won	2025-05-11T20:41:37Z	0	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-11T20:41:32Z	0	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: output_logps dtype: float64 splits: - name: train num_bytes: 58064068 num_examples: 20000 download_size: 20871352 dataset_size: 58064068 configs: - config_name: default data_files: - split: train path: data/train-* ---
a1031737/flickr8k_geometry3k_style	a1031737	2025-05-09T10:12:01Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-09T07:31:56Z	0	--- dataset_info: features: - name: images sequence: image - name: problem dtype: string - name: answer dtype: string splits: - name: train num_bytes: 12950265.6 num_examples: 135 - name: validation num_bytes: 671495.2533333333 num_examples: 7 - name: test num_bytes: 767423.1466666666 num_examples: 8 download_size: 14477291 dataset_size: 14389184.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
Rapidata/text-2-image-Rich-Human-Feedback-32k	Rapidata	2025-04-29T11:28:30Z	159	12	[ "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2312.10240", "region:us", "heatmap", "t2i", "human", "feedback", "rich", "annotation", "open-image-preferences" ]	[]	2025-04-24T15:55:37Z	12	--- dataset_info: features: - name: image_name dtype: image - name: sentence dtype: string - name: word_scores dtype: string - name: alignment_score_norm dtype: float32 - name: coherence_score_norm dtype: float32 - name: style_score_norm dtype: float32 - name: alignment_heatmap dtype: array2_d: shape: - 1024 - 1024 dtype: float32 - name: coherence_heatmap dtype: array2_d: shape: - 1024 - 1024 dtype: float32 - name: alignment_score dtype: float32 - name: coherence_score dtype: float32 - name: style_score dtype: float32 splits: - name: train num_bytes: 116617124714.976 num_examples: 32528 download_size: 91216762385 dataset_size: 116617124714.976 configs: - config_name: default data_files: - split: train path: data/train-* tags: - heatmap - t2i - human - feedback - rich - annotation - open-image-preferences license: apache-2.0 language: - en pretty_name: Text to image - Rich Annotation size_categories: - 10K<n<100K --- <a href="https://www.rapidata.ai"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/jfxR79bOztqaC6_yNNnGU.jpeg" width="250" alt="Rapidata Logo"> </a> Building upon Google's research [Rich Human Feedback for Text-to-Image Generation](https://arxiv.org/abs/2312.10240), and the [smaller, previous version of this dataset](https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback), we have collected over 3.7 million responses from 307'415 individual humans for the [open-image-preference-v1](https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1) dataset using Rapidata via the [Python API](https://docs.rapidata.ai/). Collection took less than 2 weeks. If you get value from this dataset and would like to see more in the future, please consider liking it ♥️ # Overview We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were asked to identify specific problematic areas. Additionally, for all images, participants identified words from the prompts that were not accurately represented in the generated images. If you want to replicate the annotation setup, the steps are outlined at the [bottom](#replicating-the-annotation-setup). This dataset and the annotation process is described in further detail in our blog post [Beyond Image Preferences](https://huggingface.co/blog/RapidataAI/beyond-image-preferences). # Usage Examples Accessing this data is easy with the Huggingface `dataset` library. For quick demos or previews, we recommend setting `streaming=True` as downloading the whole dataset can take a while. ```python from datasets import load_dataset ds = load_dataset("Rapidata/text-2-image-Rich-Human-Feedback-32k", split="train", streaming=True) ``` As an example, below we show how to replicate the figures below. <details> <summary>Click to expand Select Words example</summary> The methods below can be used to produce figures similar to the ones shownn below. Note however that the figures below were created using `matplotlib`, however we opt to use `opencv` here as it makes calculating the text spacing much easier. Methods ```python from PIL import Image from datasets import load_dataset import cv2 import numpy as np def get_colors(words): colors = [] for item in words: intensity = item / max(words) value = np.uint8((1 - intensity) * 255) color = tuple(map(int, cv2.applyColorMap(np.array([[value]]), cv2.COLORMAP_AUTUMN)[0][0])) colors.append(color) return colors def get_wrapped_text(text_color_pairs, font, font_scale, thickness, word_spacing, max_width): wrapped_text_color_pairs, current_line, line_width = [], [], 0 for text, color in text_color_pairs: text_size = cv2.getTextSize(text, font, font_scale, thickness)[0] if line_width + text_size[0] > max_width: wrapped_text_color_pairs.append(current_line) current_line, line_width = [], 0 current_line.append((text, color, text_size)) line_width += text_size[0] + word_spacing wrapped_text_color_pairs.append(current_line) return wrapped_text_color_pairs def add_multicolor_text(input, text_color_pairs, font_scale=1, thickness=2, word_spacing=20): image = cv2.cvtColor(np.array(input), cv2.COLOR_RGB2BGR) image_height, image_width, _ = image.shape font = cv2.FONT_HERSHEY_SIMPLEX wrapped_text = get_wrapped_text(text_color_pairs, font, font_scale, thickness, word_spacing, int(image_width0.95)) position = (int(0.025image_width), int(word_spacing2)) overlay = image.copy() cv2.rectangle(overlay, (0, 0), (image_width, int((len(wrapped_text)+1)word_spacing2)), (100,100,100), -1) out_img = cv2.addWeighted(overlay, 0.75, image, 0.25, 0) for idx, text_line in enumerate(wrapped_text): current_x, current_y = position[0], position[1] + int(idxword_spacing2) for text, color, text_size in text_line: cv2.putText(out_img, text, (current_x, current_y), font, font_scale, color, thickness) current_x += text_size[0] + word_spacing return Image.fromarray(cv2.cvtColor(out_img, cv2.COLOR_BGR2RGB)) ``` Create figures* ```python ds_words = ds.select_columns(["image","prompt", "word_scores"]) for example in ds_words.take(5): image = example["image"] prompt = example["prompt"] word_scores = [s[1] for s in eval(example["word_scores"])] words = [s[0] for s in eval(example["word_scores"])] colors = get_colors(word_scores) display(add_multicolor_text(image, list(zip(words, colors)), font_scale=1, thickness=2, word_spacing=20)) ``` </details> <details> <summary>Click to expand Heatmap example</summary> Methods ```python import cv2 import numpy as np from PIL import Image def overlay_heatmap(image, heatmap, alpha=0.3): cv2_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) heatmap_normalized = ((heatmap - heatmap.min()) / (heatmap.max() - heatmap.min())) heatmap_normalized = np.uint8(255 * (heatmap_normalized)) heatmap_colored = cv2.applyColorMap(heatmap_normalized, cv2.COLORMAP_HOT) overlaid_image = cv2.addWeighted(cv2_image, 1 - alpha, heatmap_colored, alpha, 0) return Image.fromarray(cv2.cvtColor(overlaid_image, cv2.COLOR_BGR2RGB)) ``` Create figures ```python ds_heatmap = ds.select_columns(["image","prompt", "alignment_heatmap"]) for example in ds_heatmap.take(5): image = example["image"] heatmap = example["alignment_heatmap"] if heatmap: display(overlay_heatmap(image, np.asarray(heatmap))) ``` </details> </br> # Data Summary ## Word Scores Users identified words from the prompts that were NOT accurately depicted in the generated images. Higher word scores indicate poorer representation in the image. Participants also had the option to select "[No_mistakes]" for prompts where all elements were accurately depicted. ### Examples Results: \| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/lzlWHmLKBvBJhjGWP8xZZ.png" width="500"> \| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/b38uskYWaGEgfeJQtKiaO.png" width="500"> \| \|---\|---\| \| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/4uWKVjZBA5aX2YDUYNpdV.png" width="500"> \| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/f9JIuwDoNohy7EkDYILFm.png" width="500"> \| ## Coherence The coherence score measures whether the generated image is logically consistent and free from artifacts or visual glitches. Without seeing the original prompt, users were asked: "Look closely, does this image have weird errors, like senseless or malformed objects, incomprehensible details, or visual glitches?" Each image received at least 21 responses indicating the level of coherence on a scale of 1-5, which were then averaged to produce the final scores where 5 indicates the highest coherence. Images scoring below 3.5 in coherence were further evaluated, with participants marking specific errors in the image. ### Example Results: \| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/sc-4ls9X0yO-hGN0VCDSX.png" width="500"> \| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/J77EmYp4oyRRakkcRnaF9.png" width="500"> \| \|---\|---\| \| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/mRDdoQdc4_iy2JcLhdI7J.png" width="500"> \| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/2N2KJyz4YOGT6N6tuUX8M.png" width="500"> \| ## Alignment The alignment score quantifies how well an image matches its prompt. Users were asked: "How well does the image match the description?". Again, each image received at least 21 responses indicating the level of alignment on a scale of 1-5 (5 being the highest), which were then averaged. For images with an alignment score below 3.2, additional users were asked to highlight areas where the image did not align with the prompt. These responses were then compiled into a heatmap. As mentioned in the google paper, aligment is harder to annotate consistently, if e.g. an object is missing, it is unclear to the annotators what they need to highlight. ### Example Results: <style> .example-results-grid { display: grid; grid-template-columns: repeat(2, 450px); gap: 20px; margin: 20px 0; justify-content: left; } .result-card { background-color: #fff; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); padding: 15px; width: 450px; } .prompt { margin-bottom: 10px; font-size: 18px; line-height: 1.4; color: #333; background-color: #f8f8f8; padding: 10px; border-radius: 5px; } .image-container img { width: 450px; height: auto; border-radius: 4px; } @media (max-width: 1050px) { .example-results-grid { grid-template-columns: 450px; } } </style> <div class="example-results-grid"> <div class="result-card"> <div class="prompt"> <strong>Prompt:</strong> Three cats and one dog sitting on the grass. </div> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/qCNWVSNjPsp8XQ3zliLcp.png" alt="Three cats and one dog"> </div> </div> <div class="result-card"> <div class="prompt"> <strong>Prompt:</strong> A brown toilet with a white wooden seat. </div> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/M3buzP-5k4pRCxOi_ijxM.png" alt="Brown toilet"> </div> </div> <div class="result-card"> <div class="prompt"> <strong>Prompt:</strong> Photograph of a pale Asian woman, wearing an oriental costume, sitting in a luxurious white chair. Her head is floating off the chair, with the chin on the table and chin on her knees, her chin on her knees. Closeup </div> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/ggYXUEbGppiTeL84pG-DP.png" alt="Asian woman in costume"> </div> </div> <div class="result-card"> <div class="prompt"> <strong>Prompt:</strong> A tennis racket underneath a traffic light. </div> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/mT7sAbnO-w6ySXaeEqEki.png" alt="Racket under traffic light"> </div> </div> </div> ## Style The style score reflects how visually appealing participants found each image, independent of the prompt. Users were asked: "How much do you like the way this image looks?" Each image received 21 responses grading on a scale of 1-5, which were then averaged. In contrast to other prefrence collection methods, such as the huggingface image arena, the preferences were collected from humans from around the world (156 different countries) from all walks of life, creating a more representative score. # About Rapidata Rapidata's technology makes collecting human feedback at scale faster and more accessible than ever before. Visit [rapidata.ai](https://www.rapidata.ai/) to learn more about how we're revolutionizing human feedback collection for AI development. # Other Datasets We run a benchmark of the major image generation models, the results can be found on our [website](https://www.rapidata.ai/leaderboard/image-models). We rank the models according to their coherence/plausiblity, their aligment with the given prompt and style prefernce. The underlying 2M+ annotations can be found here: - Link to the [Coherence dataset](https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset) - Link to the [Text-2-Image Alignment dataset](https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Alignment_Dataset) - Link to the [Preference dataset](https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3) We have also started to run a [video generation benchmark](https://www.rapidata.ai/leaderboard/video-models), it is still a work in progress and currently only covers 2 models. They are also analysed in coherence/plausiblity, alignment and style preference. # Replicating the Annotation Setup For researchers interested in producing their own rich preference dataset, you can directly use the Rapidata API through python. The code snippets below show how to replicate the modalities used in the dataset. Additional information is available through the [documentation](https://docs.rapidata.ai/) <details> <summary>Creating the Rapidata Client and Downloading the Dataset</summary> First install the rapidata package, then create the RapidataClient() this will be used create and launch the annotation setup ```bash pip install rapidata ``` ```python from rapidata import RapidataClient, LabelingSelection, ValidationSelection client = RapidataClient() ``` As example data we will just use images from the dataset. Make sure to set `streaming=True` as downloading the whole dataset might take a significant amount of time. ```python from datasets import load_dataset ds = load_dataset("Rapidata/text-2-image-Rich-Human-Feedback-32k", split="train", streaming=True) ds = ds.select_columns(["image","prompt"]) ``` Since we use streaming, we can extract the prompts and download the images we need like this: ```python import os tmp_folder = "demo_images" # make folder if it doesn't exist if not os.path.exists(tmp_folder): os.makedirs(tmp_folder) prompts = [] image_paths = [] for i, row in enumerate(ds.take(10)): prompts.append(row["prompt"]) # save image to disk save_path = os.path.join(tmp_folder, f"{i}.jpg") row["image"].save(save_path) image_paths.append(save_path) ``` </details> <details> <summary>Likert Scale Alignment Score</summary> To launch a likert scale annotation order, we make use of the classification annotation modality. Below we show the setup for the alignment criteria. The structure is the same for style and coherence, however arguments have to be adjusted of course. I.e. different instructions, options and validation set. ```python # Alignment Example instruction = "How well does the image match the description?" answer_options = [ "1: Not at all", "2: A little", "3: Moderately", "4: Very well", "5: Perfectly" ] order = client.order.create_classification_order( name="Alignment Example", instruction=instruction, answer_options=answer_options, datapoints=image_paths, contexts=prompts, # for alignment, prompts are required as context for the annotators. responses_per_datapoint=10, selections=[ValidationSelection("676199a5ef7af86285630ea6"), LabelingSelection(1)] # here we use a pre-defined validation set. See https://docs.rapidata.ai/improve_order_quality/ for details ) order.run() # This starts the order. Follow the printed link to see progress. ``` </details> <details> <summary>Alignment Heatmap</summary> To produce heatmaps, we use the locate annotation modality. Below is the setup used for creating the alignment heatmaps. ```python # alignment heatmap # Note that the selected images may not actually have severely misaligned elements, but this is just for demonstration purposes. order = client.order.create_locate_order( name="Alignment Heatmap Example", instruction="What part of the image does not match with the description? Tap to select.", datapoints=image_paths, contexts=prompts, # for alignment, prompts are required as context for the annotators. responses_per_datapoint=10, selections=[ValidationSelection("67689e58026456ec851f51f8"), LabelingSelection(1)] # here we use a pre-defined validation set for alignment. See https://docs.rapidata.ai/improve_order_quality/ for details ) order.run() # This starts the order. Follow the printed link to see progress. ``` </details> <details> <summary>Select Misaligned Words</summary> To launch the annotation setup for selection of misaligned words, we used the following setup ```python # Select words example from rapidata import LanguageFilter select_words_prompts = [p + " [No_Mistake]" for p in prompts] order = client.order.create_select_words_order( name="Select Words Example", instruction = "The image is based on the text below. Select mistakes, i.e., words that are not aligned with the image.", datapoints=image_paths, sentences=select_words_prompts, responses_per_datapoint=10, filters=[LanguageFilter(["en"])], # here we add a filter to ensure only english speaking annotators are selected selections=[ValidationSelection("6761a86eef7af86285630ea8"), LabelingSelection(1)] # here we use a pre-defined validation set. See https://docs.rapidata.ai/improve_order_quality/ for details ) order.run() ``` </details>
shreyas231219/new_dataset_1	shreyas231219	2025-02-09T03:06:24Z	17	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-09T03:06:20Z	0	--- dataset_info: features: - name: What dtype: string - name: When dtype: string - name: Where dtype: string - name: Who dtype: string - name: Category dtype: string splits: - name: train num_bytes: 118713 num_examples: 1500 download_size: 35429 dataset_size: 118713 configs: - config_name: default data_files: - split: train path: data/train-* ---
selfcorrexp2/llama31_first_wrong_and_20kfirst_corr_regular_norr_20k	selfcorrexp2	2024-12-25T21:08:39Z	13	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-25T21:08:10Z	0	--- dataset_info: features: - name: idx dtype: int64 - name: prompt dtype: string - name: answers sequence: string - name: first_round dtype: bool - name: gt dtype: string - name: rewards sequence: bool - name: my_solu sequence: string - name: flag dtype: bool - name: turn dtype: int64 - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 338968859.6649252 num_examples: 20000 download_size: 152955034 dataset_size: 338968859.6649252 configs: - config_name: default data_files: - split: train path: data/train-* ---
RobotisSW/eval_ffw_test_pc_8	RobotisSW	2025-04-28T14:37:43Z	28	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]	[ "robotics" ]	2025-04-28T14:37:36Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "ffw", "total_episodes": 2, "total_frames": 327, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 16 ], "names": [ "arm_right_waist", "arm_right_shoulder", "arm_right_shoulder_shadow", "arm_right_elbow", "arm_right_elbow_shadow", "arm_right_forearm_roll", "arm_right_wrist_angle", "arm_right_gripper", "arm_left_waist", "arm_left_shoulder", "arm_left_shoulder_shadow", "arm_left_elbow", "arm_left_elbow_shadow", "arm_left_forearm_roll", "arm_left_wrist_angle", "arm_left_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 16 ], "names": [ "arm_right_waist", "arm_right_shoulder", "arm_right_shoulder_shadow", "arm_right_elbow", "arm_right_elbow_shadow", "arm_right_forearm_roll", "arm_right_wrist_angle", "arm_right_gripper", "arm_left_waist", "arm_left_shoulder", "arm_left_shoulder_shadow", "arm_left_elbow", "arm_left_elbow_shadow", "arm_left_forearm_roll", "arm_left_wrist_angle", "arm_left_gripper" ] }, "observation.images.cam_head": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_wrist_1": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_wrist_2": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
UniDataPro/portuguese-speech-recognition-dataset	UniDataPro	2025-05-22T09:06:31Z	75	0	[ "license:cc-by-nc-nd-4.0", "size_categories:n<1K", "format:audiofolder", "modality:audio", "modality:text", "library:datasets", "library:mlcroissant", "region:us", "speech recognition", "machine learning", "NLP", "ASR", "audio", "speech", "portuguese" ]	[]	2025-03-17T21:47:11Z	0	--- license: cc-by-nc-nd-4.0 tags: - speech recognition - machine learning - NLP - ASR - audio - speech - portuguese size_categories: - 1K<n<10K --- # Portuguese Speech Dataset for recognition task Dataset comprises 406 hours of telephone dialogues in Portuguese, collected from 590 native speakers across various topics and domains. This dataset boasts an impressive 98% word accuracy rate, making it a valuable resource for advancing speech recognition technology. By utilizing this dataset, researchers and developers can advance their understanding and capabilities in automatic speech recognition (ASR) systems, transcribing audio, and natural language processing (NLP). - [Get the data](https://unidata.pro/datasets/portuguese-speech-recognition-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=portuguese-speech-recognition-dataset) The dataset includes high-quality audio recordings with text transcriptions, making it ideal for training and evaluating speech recognition models. # 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/portuguese-speech-recognition-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=portuguese-speech-recognition-dataset) to discuss your requirements and pricing options. ## Metadata for the dataset ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fa3f375fb273dcad3fe17403bdfccb63b%2Fssssssssss.PNG?generation=1739884059328284&alt=media) - Audio files: High-quality recordings in WAV format - Text transcriptions: Accurate and detailed transcripts for each audio segment - Speaker information: Metadata on native speakers, including gender and etc - Topics: Diverse domains such as general conversations, business and etc This dataset is a valuable resource for researchers and developers working on speech recognition, language models, and speech technology. # 🌐 [UniData](https://unidata.pro/datasets/portuguese-speech-recognition-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=portuguese-speech-recognition-dataset) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects
r2e-edits/14b_swebv_temp08_10_patch_verifier	r2e-edits	2025-02-26T03:54:00Z	19	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-26T03:53:54Z	0	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: docker_images dtype: string - name: rewards dtype: float64 splits: - name: train num_bytes: 356479621 num_examples: 4801 download_size: 106826836 dataset_size: 356479621 configs: - config_name: default data_files: - split: train path: data/train-* ---
justus27/lcbv5-test	justus27	2025-06-25T00:22:26Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-23T23:42:25Z	0	--- dataset_info: features: - name: problem_id dtype: string - name: task_type dtype: string - name: prompt dtype: string - name: verification_info dtype: string - name: responses sequence: string - name: response_lens sequence: int64 - name: avg_reward dtype: float64 splits: - name: train num_bytes: 3921596209 num_examples: 279 download_size: 2415217496 dataset_size: 3921596209 configs: - config_name: default data_files: - split: train path: data/train-* ---
Iess/chinese_modern_poetry	Iess	2023-06-25T16:39:13Z	281	25	[ "language:zh", "license:mit", "size_categories:100K<n<1M", "format:json", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "poetry", "chinese poetry", "modern poetry", "chinese modern poetry" ]	[]	2023-06-25T15:59:49Z	1	--- license: mit language: - zh tags: - poetry - chinese poetry - modern poetry - chinese modern poetry --- ### 简介 1. 数据集包括了近现代的中国诗人及外国诗人（中译版）作品，所有作品著作权归原作者所有，侵删请联系[email protected] 2. chinese_poems.jsonl为原数据，training_imagery2-5_maxlen256.json 分别是根据2-5个关键意象生成诗歌的相关数据集 3. 数据来源于网络，包括但不限于 + https://github.com/sheepzh/poetry + https://bedtimepoem.com/ + https://poemwiki.org/ + baidu、google、zhihu等 ### 一些作品使用此数据集训练ChatGLM、LLaMA7b模型生成的诗歌，更多诗歌查看poems目录 ![alt 云](https://huggingface.co/datasets/Iess/chinese_modern_poetry/resolve/main/poems/%E4%BA%91.jpg) ![alt 桃花潭](https://huggingface.co/datasets/Iess/chinese_modern_poetry/resolve/main/poems/%E6%A1%83%E8%8A%B1%E6%BD%AD.jpg) ![alt 荒原](https://huggingface.co/datasets/Iess/chinese_modern_poetry/resolve/main/poems/%E8%8D%92%E5%8E%9F.jpg)
test-gen/code_humaneval_qwen2.5-3b_t0.1_n8_tests_humaneval_qwen3-8b_t0.7_n1	test-gen	2025-05-16T15:32:56Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-16T15:32:55Z	0	--- dataset_info: features: - name: task_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: test dtype: string - name: entry_point dtype: string - name: generated_code sequence: string - name: gt_rewards sequence: float64 - name: rewards sequence: float64 - name: verification_info struct: - name: language dtype: string - name: test_cases sequence: string splits: - name: test num_bytes: 2299064 num_examples: 164 download_size: 396095 dataset_size: 2299064 configs: - config_name: default data_files: - split: test path: data/test-* ---
chiyuanhsiao/text_L2-regular-ASR_trivia_qa-audio	chiyuanhsiao	2025-04-28T16:02:06Z	13	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-28T15:57:00Z	0	--- dataset_info: features: - name: question dtype: string - name: question_id dtype: string - name: question_source dtype: string - name: entity_pages sequence: - name: doc_source dtype: string - name: filename dtype: string - name: title dtype: string - name: wiki_context dtype: string - name: search_results sequence: - name: description dtype: string - name: filename dtype: string - name: rank dtype: int32 - name: title dtype: string - name: url dtype: string - name: search_context dtype: string - name: answer struct: - name: aliases sequence: string - name: normalized_aliases sequence: string - name: matched_wiki_entity_name dtype: string - name: normalized_matched_wiki_entity_name dtype: string - name: normalized_value dtype: string - name: type dtype: string - name: value dtype: string - name: my_prediction_text dtype: string splits: - name: validation num_bytes: 51583519 num_examples: 1000 download_size: 30132540 dataset_size: 51583519 configs: - config_name: default data_files: - split: validation path: data/validation-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_41d6cd9b-6ceb-4990-a7dc-c6f7e62b549a	argilla-internal-testing	2024-12-18T10:23:25Z	16	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-18T10:23:24Z	0	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---
alea-institute/kl3m-filter-data-dotgov-www.usa.gov	alea-institute	2025-02-04T21:20:16Z	15	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-04T21:20:15Z	0	--- dataset_info: features: - name: identifier dtype: string - name: dataset dtype: string - name: mime_type dtype: string - name: score dtype: float64 - name: tokens sequence: int64 splits: - name: train num_bytes: 4093672 num_examples: 615 download_size: 602873 dataset_size: 4093672 configs: - config_name: default data_files: - split: train path: data/train-* ---
BoooomNing/Faces_Dataset	BoooomNing	2025-01-09T12:12:34Z	29	1	[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-09T12:12:16Z	0	--- dataset_info: features: - name: face_images dtype: image - name: target_images dtype: image - name: captions dtype: string splits: - name: train num_bytes: 144501219.0 num_examples: 470 download_size: 106181649 dataset_size: 144501219.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
serEzioAuditore/turkceVeriset3	serEzioAuditore	2025-03-16T19:51:48Z	15	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-16T19:51:34Z	0	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 245519628.3 num_examples: 90000 - name: validation num_bytes: 27279958.7 num_examples: 10000 download_size: 160223582 dataset_size: 272799587.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
JavisGPT-dev/InstUnd-Audio	JavisGPT-dev	2025-03-17T08:42:18Z	152	1	[ "size_categories:1M<n<10M", "format:json", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]	[]	2025-02-24T02:55:19Z	0	--- size_categories: - 100K<n<1M --- - After downloading the dataset, you need to concat the files and extract the files. ```shell cat data_part_* > data.tar.gz tar -xzvf data.tar.gz ```
ZhengGuangze/TAP-Vid	ZhengGuangze	2025-05-28T01:32:13Z	0	0	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:text", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]	[]	2025-05-28T01:03:50Z	0	--- license: apache-2.0 ---
thainq107/c4-small	thainq107	2025-05-23T02:32:41Z	0	0	[ "region:us" ]	[]	2025-05-23T02:32:24Z	0	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 194484214.8 num_examples: 90000 - name: test num_bytes: 21609357.2 num_examples: 10000 download_size: 131631260 dataset_size: 216093572.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
eliasfiz/emilia-snac-with-spk-emb-DE	eliasfiz	2025-05-06T23:08:35Z	0	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-06T22:57:00Z	0	--- dataset_info: features: - name: codes_list sequence: int64 - name: speaker_embedding sequence: float32 - name: text dtype: string splits: - name: part_82 num_bytes: 3004706 num_examples: 378 - name: part_33 num_bytes: 26900546 num_examples: 3853 - name: part_72 num_bytes: 11162071 num_examples: 1474 - name: part_73 num_bytes: 11241726 num_examples: 1485 - name: part_69 num_bytes: 16655292 num_examples: 2172 - name: part_36 num_bytes: 32814113 num_examples: 4402 - name: part_66 num_bytes: 16919071 num_examples: 2222 - name: part_77 num_bytes: 11260986 num_examples: 1461 - name: part_50 num_bytes: 25353469 num_examples: 3319 - name: part_59 num_bytes: 23748838 num_examples: 3179 - name: part_55 num_bytes: 24708418 num_examples: 3278 - name: part_47 num_bytes: 37466034 num_examples: 5054 - name: part_54 num_bytes: 24892316 num_examples: 3332 - name: part_57 num_bytes: 24722307 num_examples: 3336 - name: part_11 num_bytes: 86811637 num_examples: 14069 - name: part_12 num_bytes: 86629026 num_examples: 13529 - name: part_14 num_bytes: 89289674 num_examples: 14227 - name: part_8 num_bytes: 104772590 num_examples: 15144 - name: part_27 num_bytes: 92785970 num_examples: 15702 - name: part_16 num_bytes: 104976420 num_examples: 16303 - name: part_21 num_bytes: 103246091 num_examples: 16788 - name: part_13 num_bytes: 111827449 num_examples: 17857 - name: part_20 num_bytes: 103331829 num_examples: 16560 - name: part_5 num_bytes: 128579654 num_examples: 18538 download_size: 541210774 dataset_size: 1303100233 configs: - config_name: default data_files: - split: part_82 path: data/part_82-* - split: part_33 path: data/part_33-* - split: part_72 path: data/part_72-* - split: part_73 path: data/part_73-* - split: part_69 path: data/part_69-* - split: part_36 path: data/part_36-* - split: part_66 path: data/part_66-* - split: part_77 path: data/part_77-* - split: part_50 path: data/part_50-* - split: part_59 path: data/part_59-* - split: part_55 path: data/part_55-* - split: part_47 path: data/part_47-* - split: part_54 path: data/part_54-* - split: part_57 path: data/part_57-* - split: part_11 path: data/part_11-* - split: part_12 path: data/part_12-* - split: part_14 path: data/part_14-* - split: part_8 path: data/part_8-* - split: part_27 path: data/part_27-* - split: part_16 path: data/part_16-* - split: part_21 path: data/part_21-* - split: part_13 path: data/part_13-* - split: part_20 path: data/part_20-* - split: part_5 path: data/part_5-* ---
tthoma909/math-basics	tthoma909	2025-02-15T21:44:07Z	17	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-15T21:35:12Z	0	--- dataset_info: features: - name: nums sequence: int64 - name: expression dtype: string - name: target dtype: int64 splits: - name: train num_bytes: 5246324 num_examples: 100000 download_size: 2559611 dataset_size: 5246324 configs: - config_name: default data_files: - split: train path: data/train-* ---
junnystateofmind/conversational_ai_5_turns_only_ckp_3	junnystateofmind	2024-11-22T17:06:21Z	15	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-22T16:02:54Z	0	--- dataset_info: features: - name: narrative dtype: string - name: trajectory list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 7488 num_examples: 20 download_size: 7108 dataset_size: 7488 configs: - config_name: default data_files: - split: train path: data/train-* ---
laochengzi/stanford_df_rectified	laochengzi	2024-11-05T06:37:00Z	18	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-05T06:36:47Z	0	--- dataset_info: features: - name: Image_name dtype: int64 - name: Paragraph dtype: string - name: train dtype: bool - name: test dtype: bool - name: url dtype: string - name: val dtype: bool splits: - name: train num_bytes: 7510209 num_examples: 19561 download_size: 3505452 dataset_size: 7510209 configs: - config_name: default data_files: - split: train path: data/train-* ---
Ahmed-ibn-Harun/BrainHermorrhage	Ahmed-ibn-Harun	2024-10-23T11:12:58Z	18	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-23T11:03:46Z	0	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 0_no_hermorrhage '1': 1_hermorrhage splits: - name: train num_bytes: 286881574.338 num_examples: 7298 - name: validation num_bytes: 72656887.733 num_examples: 2029 - name: test num_bytes: 35044806.0 num_examples: 811 download_size: 433700277 dataset_size: 394583268.071 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
YYT-t/code_opencoder_edu-deepseek-coder-6.7b-instruct-iter1_sample_4000_tp	YYT-t	2025-04-24T13:23:47Z	19	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-24T13:23:46Z	0	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: rational_answer dtype: string splits: - name: train num_bytes: 5920468 num_examples: 4000 download_size: 2067106 dataset_size: 5920468 configs: - config_name: default data_files: - split: train path: data/train-* ---
magnifi/Phi3_intent_v56_2_w_unknown_remove_68_intents_upper_lower	magnifi	2025-03-10T01:02:42Z	14	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-10T01:02:40Z	0	--- dataset_info: features: - name: Query dtype: string - name: true_intent dtype: string splits: - name: train num_bytes: 1505486 num_examples: 20664 - name: validation num_bytes: 8109 num_examples: 113 download_size: 430246 dataset_size: 1513595 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
andreuka18/DeepSeek-R1-Distill-Llama-8B-OpenThoughts-114k-tokenized	andreuka18	2025-03-06T12:21:55Z	111	0	[ "size_categories:100K<n<1M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-06T12:20:29Z	0	--- dataset_info: features: - name: tokens sequence: int64 splits: - name: train num_bytes: 6166867104.0 num_examples: 752424 download_size: 1238722898 dataset_size: 6166867104.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
MisterEScholar/s50K_part_24	MisterEScholar	2025-02-09T06:07:00Z	16	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-09T06:06:56Z	0	--- dataset_info: features: - name: solution dtype: string - name: question dtype: string - name: cot_type dtype: string - name: source_type dtype: string - name: metadata dtype: string - name: cot dtype: 'null' splits: - name: train num_bytes: 1639387 num_examples: 1000 download_size: 894444 dataset_size: 1639387 configs: - config_name: default data_files: - split: train path: data/train-* ---
griffinpinney/Sort-4to6	griffinpinney	2025-02-24T03:53:42Z	15	0	[ "size_categories:100K<n<1M", "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-23T01:22:14Z	0	--- dataset_info: features: - name: target sequence: int64 - name: original sequence: int64 splits: - name: train num_bytes: 44009696 num_examples: 500000 download_size: 4058605 dataset_size: 44009696 configs: - config_name: default data_files: - split: train path: data/train-* ---
eagle0504/MedQuad-MedicalQnADataset-1024-synth-aug-1024-synth-aug-1024-synth-aug	eagle0504	2025-04-29T09:10:07Z	27	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-29T09:10:06Z	0	--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string splits: - name: train num_bytes: 165392 num_examples: 85 download_size: 82738 dataset_size: 165392 configs: - config_name: default data_files: - split: train path: data/train-* ---
WaltonFuture/GEOQA_R1V_Train_8K	WaltonFuture	2025-04-24T12:01:25Z	39	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-24T12:01:11Z	0	--- dataset_info: features: - name: images sequence: image - name: problem dtype: string - name: answer dtype: string splits: - name: train num_bytes: 28815465.18 num_examples: 8030 download_size: 35537104 dataset_size: 28815465.18 configs: - config_name: default data_files: - split: train path: data/train-* ---
AFZAL0008/mal-eng-translation	AFZAL0008	2024-12-20T05:08:01Z	70	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-20T04:22:38Z	0	--- dataset_info: features: - name: 'Unnamed: 0.1' dtype: int64 - name: 'Unnamed: 0' dtype: int64 - name: English dtype: string - name: Malayalam dtype: string splits: - name: train num_bytes: 84825396 num_examples: 361847 download_size: 33788639 dataset_size: 84825396 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/s1_ablation_diversity_sampling_1k	mlfoundations-dev	2025-02-12T16:02:43Z	16	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-02-10T15:52:35Z	0	--- dataset_info: features: - name: problem dtype: string - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: response dtype: string - name: math_class dtype: string - name: system dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 40463615 num_examples: 1000 download_size: 17218343 dataset_size: 40463615 configs: - config_name: default data_files: - split: train path: data/train-* ---
gaotang/sky_v02_processed_qwen	gaotang	2025-03-20T16:11:04Z	16	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-20T16:10:56Z	0	--- dataset_info: features: - name: context_messages list: - name: content dtype: string - name: role dtype: string - name: winner dtype: string splits: - name: train num_bytes: 503664912 num_examples: 77016 download_size: 192509566 dataset_size: 503664912 configs: - config_name: default data_files: - split: train path: data/train-* ---
Alex-xu/secalign-dbg-haiku-javascript-all	Alex-xu	2025-01-22T03:44:18Z	16	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-22T03:44:17Z	0	--- dataset_info: features: - name: lang dtype: string - name: cwe dtype: string - name: original_instruction dtype: string - name: original_code dtype: string - name: empty dtype: string - name: fixed_code dtype: string - name: benign dtype: bool splits: - name: train num_bytes: 60992724 num_examples: 17892 download_size: 27563981 dataset_size: 60992724 configs: - config_name: default data_files: - split: train path: data/train-* ---
chocckaka/CriticTool-Dataset	chocckaka	2025-06-24T02:53:49Z	0	0	[ "license:apache-2.0", "region:us" ]	[]	2025-06-24T02:49:53Z	0	--- license: apache-2.0 ---
DanqingZ/tic_tac_toe_5_raw_2	DanqingZ	2025-06-15T07:40:19Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]	[ "robotics" ]	2025-06-15T07:24:55Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 2, "total_frames": 715, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.on_robot": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.side_view": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
Pisethan/plp-ai-dataset	Pisethan	2025-05-26T09:08:01Z	0	0	[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-26T07:49:28Z	0	--- license: apache-2.0 ---
qrk-labs/QRK-Islam-Basic-Weak	qrk-labs	2025-05-12T14:12:43Z	73	0	[ "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-09T21:42:54Z	0	--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1334778 num_examples: 7099 download_size: 475144 dataset_size: 1334778 ---
Asap7772/omnimath-hint-v6-r1distill15b-respgen__1534_1705	Asap7772	2025-04-07T00:12:53Z	7	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-07T00:12:06Z	0	--- dataset_info: features: - name: hint_chosen dtype: string - name: hint_completion sequence: string - name: hint_completion_answer sequence: string - name: hint_completion_correct sequence: bool - name: hint_completion_succ_rate dtype: float64 - name: domain dtype: string - name: difficulty dtype: float64 - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: source dtype: string - name: completion sequence: string - name: completion_answer sequence: string - name: completion_correct sequence: bool - name: completion_succ_rate dtype: float64 - name: context dtype: string - name: hint1 dtype: string - name: hint2 dtype: string - name: hint3 dtype: string - name: hint4 dtype: string - name: hint5 dtype: string splits: - name: train num_bytes: 1311018061 num_examples: 1026 download_size: 466969384 dataset_size: 1311018061 configs: - config_name: default data_files: - split: train path: data/train-* ---
datasets-CNRS/CIENSFO	datasets-CNRS	2025-03-29T21:41:48Z	22	0	[ "language:fra", "license:cc-by-4.0", "region:us" ]	[]	2024-10-22T20:23:17Z	0	--- language: - fra viewer: false license: cc-by-4.0 --- > [!NOTE] > Dataset origin: https://www.ortolang.fr/market/corpora/ciensfo > # CIENSFO Corpus of Non-Standard Spoken French Subordinated Interrogatives (Corpus d'Interrogatives Enchâssées Non-Standards du Français Oral) ## Corpus content This corpus contains transcriptions of spoken French sentences which exhibit non-standard subordinated interrogatives. ex. ma façon de voir les choses c'est de faire le bilan de [pause] c'est quoi notre expertise More precisely, five types of subordinated clauses are present: 1. Interrogatives dependent on a noun * ex. moi je pouvais observer les différences de comment on était éduqués 2. Interrogatives dependent on a verb and introduced by a preposition * ex. ça a pratiquement tout de suite reposé sur qu'est-ce qu'on va inventer comme femme [...] 3. Interrogatives being verbal adjunct * ex. [...] ça peut être paronyme ou homonyme suivant comment vous le prononcez 4. Subordinated interrogatives using a non-standard form (ex. a marker unexpected in subordination) * ex. avant ça je me pose jamais la question de est-ce que j'aime faire ça 5. Interrogatives used in a nominal context * ex. avant de s'attaquer au à quoi ça sert commençons par le à quoi ça ne sert pas TW: Some sources mention sensitive questions (sex, sexism, etc.). Thus, some sentences may use explicit words. ## Corpus elaboration This corpus is constituted with transcribed sentences personally observed by the authors. There are two kinds of sources. 1. heard in a personnal conversations: sentences uttered by someone else, sometimes the author being an interlocutor, sometimes not 2. from an online (often free) material (podcast, YouTube video, series, etc.) 3. saw written on an online forum or in a text message 4. sentences taken from the CEFC (see `cefc.tsv`) Sentences of type 2. are provided with a complete description of their source (title, author, publisher, URL, time code), so that it is possible to check the transcription and obtain the actual prosody. Sentences of type 1. were transcribed on the fly from a spontaneous conversation. There was no recording. Therefore, it is not possible to check the accuracy of the transcription nor to obtain the actual prosody. As a consequence, sentences of type 1. and 3. are little trustworthy. Please take that into account in your analyses. Type 1. and 3. sentences were taken from persons of various age (but mostly French young adults) bewteen June 2022 and November 2023. Type 2 sentences are mostly extracted from materials put online between 2018 and November 2023 by persons of various age (but mostly French adults). ## Transcription choices Sentence segmentation is based on locutory units. A token `NAME` was substituted to proper names present in personal conversations. Transcription is performed using standard lexical spelling forms. In particular, silent `e`'s are not removed (ex. `tu as` instead of `t'as` for `/ta/`). However, "missing words" are not added back (ex. `y a` instead of `y'a` or `il y a` for `/ja/`). Punctuation and capital letters starting sentences are not considered. The transcription follows the 1990 French spelling reform. A `[pause]` symbol is added when there is a long enough pause between the predicate and the interrogative (only for type 2 sentences). Similarly, `euh`'s are not transcribed, except the ones close to the interrogative boarders. ex. [...] un de mes objectifs c'est de partager avec vous mes réflexions sur [pause] comment vous pouvez vous créer votre propre mindset A lot a extracts mentioned a list of interrogations following a first embedded interrogative. To avoid getting cumbersome lines, most of these additional interrogations were omitted. ## Structure The main document `ciensfo.json` is a json file. It contains a list of records. Each record contains fields: * id (mandatory): unique sentence identifier * source (mandatory): json description of the source of the sentence * time (mandatory, except for type 1. and 2. sentences): time code of the online material `(hours:)minutes:seconds` * text (mandatory): transcription * subtitles (optional): official subtitles given by the publisher / author(s), they may differ from the transcription * modality (optional): `written` or `spoken`, when absent, `spoken` is default * note (optional): e.g. `ungrammatical, humoristic`, `maybe subrodinated exclamative`, `maybe free relative`, `maybe reported` * variant (optional): variant of French, e.g. `Québec`, `Belgium`, when absent, default is European French The source field has the following fields: * id (mandatory): unique material identifier * title (mandatory) * type (mandatory): among: * type 1.: `conversation`, `scientific_conference` * type 2.: `online_podcast`, `series_epidose`, `radio_programme`, `recorded_speech`, `comedy_video`, `interview_video`, `newspaper_video`, `popularization_video`, `position_video`, `documentary_video`, `music`, `FAQ_video`, `tv_programme` * type 3.: `online_forum`, `comics`, `text_message` * date (mandatory): online publication date, vector date format ``[[year:month:day]]`` (may be underspecified) * duration (mandatory): `(hours:)minutes:seconds` * publisher (optional) * catalog (optional) * authors (mandatory): the authors on the list may be identified by their given name, family name, literal name (ex. YouTube channel) or a mix of them * URL (optional) * accessed (mandatory, except for written): vector date format * page (optional) * pages (optional) * booktitle (optional) * series (optional) * volume (optional) * ISSN (optional) Type 1. and 3. sentences only have source fields `id` and `type`. Note that the person saying the extracted sentence may not always be one of the authors, but e.g. an interviewed person. When some sentences are extracted from the same material, the source field of subsequent sentences may only contain the `id` field. Therefore, the couple "source" "id" + "time" (or just "source" "id" for type 1 sentences) constitute another possible unique sentence identifier. ## Annotation The file `classification_ciensfo.csv` contains, for each occurrence of a non-standard subordinated interrogatve, annotations about syntactic features. Theses labels have been added by hand. Columns: 1. sentence id, if a sentence has several of such patterns, we add a dot and a second id. (e.g `14.1`, `14.2`). The id of CEFC sentences begins with a `c` 2. sentence type: 1, 2 or 3 3. dependent on a noun: if so, the field contains the lemma of the noun 4. dependent on an adjective: if so, the field contains the lemma of the adjective 5. dependent on a verb (includes semi-fixed verbal expressions): if so, the field contains the lemma of the predicate (or `CONJ` if it is conjuncted with the previous interrogative in the same sentence) 6. negated: if dependent on a verb or attribute adjective, 1 if the predicate is negated or 0 is not 7. adverbial adjunct clause: if the interrogative is an adverbial modifier clause, the field contains the preposition(al locution) introducing it 8. introducing preposition: ("/" if no preposition) 9. graft: if the interrogative is a graft, the field contains the preceding word, typicaly a preposition or a determiner 10. non-standard type: if applicable: * `qecq`: occurrence of qu'est-ce que/qui * `ecq`: est-ce que instead of si or WH + est-ce que other than qu'est-ce que/qui * `in-situ`: e.g c'est quoi * `spp`: suffixed personal pronoun (aka. subject -verb inversion) 11. WH: interrogative word lemma 12. WH 2: second interrogative word lemma, if applicable 13. marker: interrogative marker 14. marker: additional morphosyntactic phenomenon which can hint at interrogativeness, e.g. oui ou non, ou pas, ou non 15. class: class according to [Coveney 2011] 16. additional note, e.g. `ungrammatical, humoristic`, `maybe subrodinated exclamative`, `maybe free relative`, `maybe reported` Note: columns 3, 4, 5 and 7 may contain the token `CONJ` to indicate that the interrogative is conjuncted with the previous line, under the same governor. ### Extended Coveney classification The classification `type` (direct interrogatives only) is based on: > Aidan Coveney. 2011. L’interrogation directe. Travaux de linguistique, 63(2):112–145. De Boeck Supérieur. We extend it to account for infinitival interrogatives, subordinated interrogatives, nominal and elliptical interrogatives. Note: Contrary to [Coveney 2011], `stats.py` considers expression qu'est-ce que/qui as an interrogative word, and not as `Q` + est-ce que pattern. The list of categories is: * yes-no interrogatives: * `ESV`: 'est-ce que', e.g. Est-ce que les autres / ils sont partis ? * `V-CL`: clitic inversion, e.g. Sont-ils partis ? * `GN V-CL`: complex inversion, e.g. Les autres sont-ils partis ? * `SV-ti`: '-ti' marker, C'est-ti pas fini ? * constituent (fr. partielle): * `SVQ`: in situ, e.g. Ils sont partis où ? * `QSV`: fronting (fr. antéposition), e.g. Où ils sont partis ? * `QV-CL`: qu + clitic inversion, e.g. Où sont-ils partis ? * `Q GN V-CL`: qu + complex inversion, e.g. Où les autres sont-ils partis ? * `QV GN`: qu + stylistic inversion, e.g. Où sont partis les autres ? * `seQkSV`: cleft, e.g. C’est où qu’ils sont partis ? * `QESV`: qu + 'est-ce que', e.g. Où est-ce qu’ils sont partis ? * `QsekSV`: qu + cleft variant, e.g. Où c’est qu’ils sont partis ? * `QkSV`: qu + complementizer, e.g. Où qu’ils sont partis ? * `Q=S V`: subject qu, e.g. Lesquels sont partis ? * hybrid (non-standard) * `QEV GN`: qu+ 'est-ce que' + stylistic inversion, e.g. Avec qui est-ce que travaille nicole Dupont ? * `Q=S V-CL`: subject qu + clitic inversion, e.g. De ces fillettes, lesquelles sont-elles les tiennes ? * `E GN V-CL`: 'est-ce que' + complex inversion, e.g. Est-ce que demain les sauveteurs pourront-ils s’approcher des alpinistes en détresse ? * `QE GN V-CL`: qu + 'est-ce que' + complex inversion e.g. Qu’est-ce que le rédacteur de la rubrique des chats écrasés entend-il par un pachyderme ? Our extension includes: * other case * `Q=S sekV`: subject qu + cleft c'est qui + verb, e.g. Qui c'est qui diffuse ça ? * infinitival * `QVinf`: qu + infinitival verb, e.g. Où partir ? * `Vinf Q`: infinitival verb + in-situ qu, e.g. Pour partir où ? * `QsekVinf`: qu + cleft variant + infinitival verb, e.g. Qu'est-ce que c'est qu'être une fille ? * multiple qu-words * `Q=S VQ`: double qu-interrogative with one qu subject, e.g. Qui veut intervenir dans quoi ? * `QSVQ`: double qu-interrogative, e.g. Combien d'infanteries tu envoies sur quelle planète ? * `QVinf QQ`: triple qu-interrogative + infinitival verb, e.g. Qui inviter à quel endroit sur quel sujet ? * Nominal or elliptical * `Q GN`: qu + noun phrase, e.g. Pourquoi Angiox ? * `Qsek GN`: qu + cleft variant + noun phrase, e.g. Qu'est-ce que c'est que l'énergie ? * `Q`: elliptical qu (interrogative phrase alone), e.g. Où ? * embedded yes-no * `si SV`: 'si', e.g. Je sais s'ils sont partis. * other regional variants * `SV-tu`: '-tu' marker, C'est-tu vraiment si pire que ça ? ## Corpus searches The pattern created to search for interrogative adverbial modifier clauses use [Grew](https://grew.fr/) (v. >= 1.14). Request files and found sentences are in the `searches` folder. * `fib_comp_prep.req` used on the [FIB](https://github.com/Valentin-D-Richard/UD_French-FIB) (on the enriched version) * `orfeo_prep_int.req` used on the [CEFC corpus](https://orfeo.grew.fr/?corpus=cefc-gold) The raw results can be found in the json files for the FIB and in the `CEFC_found` folder for the CEFC. The files with `_0` ending contain the pattern with the preposition immediately preceding the QU-word or morphosyntactic marking. The files with `_1` ending, where there is one word in between. ## License The data is collected under the Right to quote. It is distributed under the Creative Common [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. ## Publication Please cite this publication to mention CIENSFO. Richard, V. D. (2024). “selon coment vous vous positionnez” : Étude des circonstancielles à interrogative. to appear in 9e Congrès Mondial de Linguistique Française, Lausanne. ## Citation ``` @misc{11403/ciensfo/v1, title = {CIENSFO (Corpus d'Interrogatives Enchâssées Non-Standards du Fran\c{c}ais Oral)}, author = {Valentin D. Richard}, url = {https://hdl.handle.net/11403/ciensfo/v1}, note = {{ORTOLANG} ({Open} {Resources} {and} {TOols} {for} {LANGuage}) \textendash www.ortolang.fr}, copyright = {Licence Creative Commons - Attribution 4.0 International}, year = {2024} } ```
pragsri8/WildBenchGenv2-hard-matched	pragsri8	2025-05-11T00:11:53Z	0	0	[ "region:us" ]	[]	2025-05-10T23:56:08Z	0	--- dataset_info: - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390600 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390600 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390600 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390600 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390184 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390184 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390184 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390184 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390848 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390848 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390848 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390848 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 389845 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 389845 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 389845 dataset_size: 726930 configs: - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RRM_pairpm_wildbench/train-* ---
vlm-reasoning-cot/ciphers	vlm-reasoning-cot	2025-05-15T18:11:15Z	128	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-13T04:58:14Z	0	--- dataset_info: - config_name: chunk_0001 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 707261228.0 num_examples: 1000 download_size: 578886758 dataset_size: 707261228.0 - config_name: chunk_0002 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 714895362.0 num_examples: 1000 download_size: 586241826 dataset_size: 714895362.0 - config_name: chunk_0003 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 718190062.0 num_examples: 1000 download_size: 589335104 dataset_size: 718190062.0 - config_name: chunk_0004 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 704665360.0 num_examples: 1000 download_size: 577484472 dataset_size: 704665360.0 - config_name: chunk_0005 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 716389846.0 num_examples: 1000 download_size: 593459805 dataset_size: 716389846.0 - config_name: chunk_0006 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 705398322.0 num_examples: 1000 download_size: 583947568 dataset_size: 705398322.0 - config_name: chunk_0007 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 415705968.0 num_examples: 593 download_size: 340850442 dataset_size: 415705968.0 configs: - config_name: chunk_0001 data_files: - split: train path: chunk_0001/train-* - config_name: chunk_0002 data_files: - split: train path: chunk_0002/train-* - config_name: chunk_0003 data_files: - split: train path: chunk_0003/train-* - config_name: chunk_0004 data_files: - split: train path: chunk_0004/train-* - config_name: chunk_0005 data_files: - split: train path: chunk_0005/train-* - config_name: chunk_0006 data_files: - split: train path: chunk_0006/train-* - config_name: chunk_0007 data_files: - split: train path: chunk_0007/train-* ---
SarraChab/MNLP_M2_mcqa_dataset	SarraChab	2025-05-26T09:39:13Z	85	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-18T09:37:53Z	0	--- dataset_info: features: - name: id dtype: string - name: dataset dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2268293 num_examples: 10687 download_size: 1254741 dataset_size: 2268293 configs: - config_name: default data_files: - split: train path: data/train-* --- This dataset was constructed as part of the EPFL Modern NLP (MNLP) course project to train and evaluate large language models on multiple-choice question answering (MCQA) tasks focused on scientific reasoning. It aggregates and reformats 10,687 unique examples from five high-quality academic and biomedical QA datasets, applying consistent structure, question normalization, and cross-source deduplication. ### 📊 Dataset Composition \| Source Dataset \| Link \| Questions Used \| Description \| \|----------------\|------\|----------------\|-------------\| \| ARC-Challenge \| [ai2_arc](https://huggingface.co/datasets/ai2_arc) \| 1,119 \| Harder science exam questions requiring multi-step reasoning \| \| ARC-Easy \| [ai2_arc](https://huggingface.co/datasets/ai2_arc) \| 2,251 \| Simpler science questions at the elementary/middle school level \| \| QASC \| [qasc](https://huggingface.co/datasets/qasc) \| 3,000 (subset) \| A filtered and deduplicated subset of the QASC dataset, which was originally larger (~8,000+ examples). Only 3,000 unique and diverse questions were selected for balance \| \| OpenBookQA \| [openbookqa](https://huggingface.co/datasets/openbookqa) \| 3,317 \| 4-option science questions, filtered to keep `humanScore ≥ 1` \| \| PubMedQA \| [pubmed_qa](https://huggingface.co/datasets/pubmed_qa) \| 1,000 \| Biomedical questions with Yes/No/Maybe answers based on PubMed abstracts \| ### 🧪 Preprocessing Pipeline - Normalization: All questions were lowercased and stripped of whitespace for consistency. - Deduplication: Each question was hashed (`md5(lowercase question)`) to detect and eliminate duplicates across datasets. - Filtering: - OpenBookQA was filtered to retain only questions with `humanScore ≥ 1`. - PubMedQA was filtered to retain only labeled questions with answers in {yes, no, maybe}. - QASC was sampled and capped at 3,000 unique questions to ensure dataset balance. - Unified formatting: All entries follow the same JSON schema across sources. ### 📦 Format Each sample follows this structure: ```json { "id": "qasc_481", "dataset": "qasc", "question": "What do bees use to make honey?", "options": ["nectar", "pollen", "water", "leaves"], "answer": "A" }
extralit-dev/test_import_dataset_from_hub_with_classlabel_fc75f806-a0ee-4fad-a969-29e01309ac96	extralit-dev	2025-06-19T05:05:44Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-06-19T05:05:44Z	0	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1264 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---
TianHongZXY/MATH-test-Tulu-3-8B-SFT-beam_search-completions-temp_0.8-range_3800_to_3900	TianHongZXY	2025-01-19T21:15:18Z	7	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-18T00:46:18Z	0	--- dataset_info: config_name: TianHongZXY_MATH--T-0.8--top_p-1.0--n-32--m-4--iters-20--look-1--seed-42--agg_strategy--last features: - name: problem dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string splits: - name: train num_bytes: 2963248 num_examples: 100 download_size: 381984 dataset_size: 2963248 configs: - config_name: TianHongZXY_MATH--T-0.8--top_p-1.0--n-32--m-4--iters-20--look-1--seed-42--agg_strategy--last data_files: - split: train path: TianHongZXY_MATH--T-0.8--top_p-1.0--n-32--m-4--iters-20--look-1--seed-42--agg_strategy--last/train-* ---
fernandabufon/ukr_to_pt_json_gpt	fernandabufon	2025-01-15T13:43:17Z	18	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-15T13:43:15Z	0	--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: translation dtype: string - name: anger dtype: int64 - name: disgust dtype: int64 - name: fear dtype: int64 - name: joy dtype: int64 - name: sadness dtype: int64 - name: surprise dtype: int64 - name: inference_time dtype: float64 - name: inference_total_time dtype: float64 - name: inference_average_time dtype: float64 splits: - name: train num_bytes: 1016468 num_examples: 2466 download_size: 514168 dataset_size: 1016468 configs: - config_name: default data_files: - split: train path: data/train-* ---
zuozhuan/so100_close	zuozhuan	2024-12-16T02:08:01Z	26	0	[ "task_categories:robotics", "region:us", "LeRobot", "so100", "tutorial" ]	[ "robotics" ]	2024-12-16T02:03:20Z	0	--- task_categories: - robotics tags: - LeRobot - so100 - tutorial --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
pbvr/so101_test011	pbvr	2025-05-26T07:42:36Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so101", "tutorial" ]	[ "robotics" ]	2025-05-26T07:42:18Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so101", "total_episodes": 2, "total_frames": 1788, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.handeye": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.side": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.topdown": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
dylanebert/iso3d	dylanebert	2024-06-10T19:38:43Z	112	9	[ "license:mit", "size_categories:n<1K", "format:imagefolder", "modality:image", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]	[]	2024-06-07T21:02:18Z	1	--- license: mit --- # iso3d - Isolated Synthetic Objects 3D A dataset of isolated object images for evaluating [image-to-3D](https://huggingface.co/models?pipeline_tag=image-to-3d) models. ## Leaderboard Vote and view results at [3d-arena](https://huggingface.co/spaces/dylanebert/3d-arena). ## Curation Images are created using [dreamshaper-xl](https://huggingface.co/Lykon/dreamshaper-xl-v2-turbo) and [white background lora](https://civitai.com/models/119388/white-background) on [karlo-v1](https://huggingface.co/datasets/diffusers-parti-prompts/karlo-v1) prompts. 1. Each [karlo-v1](https://huggingface.co/datasets/diffusers-parti-prompts/karlo-v1) prompt is extended with `{prompt}, isolated object render, with a white background` and negative prompt `text, watermark, shadow, background`. 2. Images are generated using [ComfyUI](https://github.com/comfyanonymous/ComfyUI) with [dreamshaper-xl](https://huggingface.co/Lykon/dreamshaper-xl-v2-turbo) and [white background lora](https://civitai.com/models/119388/white-background). 3. Backgrounds are removed using [rembg](https://github.com/danielgatis/rembg). 4. 100 images are hand-selected from the 1.63k generated images. ## Contributing The leaderboard is automatically populated by the [3d-arena dataset](https://huggingface.co/datasets/dylanebert/3d-arena). To submit your model, [open a PR](https://huggingface.co/docs/hub/en/repositories-pull-requests-discussions) on the dataset. ## Citation ``` @misc{3d-arena, author = {Dylan Ebert} title = {3D Arena} year = {2024} publisher = {Hugging Face} howpublished = \url{https://huggingface.co/spaces/dylanebert/3d-arena} } ```
ganker5/so100_toy_20250402	ganker5	2025-04-02T03:33:53Z	56	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]	[ "robotics" ]	2025-04-02T02:36:36Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 10, "total_frames": 4946, "total_tasks": 1, "total_videos": 20, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:10" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
PhilSad/SCP-Wiki-Dataset	PhilSad	2024-12-14T15:09:58Z	20	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-14T15:09:52Z	0	--- dataset_info: features: - name: description dtype: string - name: content dtype: string splits: - name: train num_bytes: 17570353.015576325 num_examples: 2311 - name: test num_bytes: 1953950.984423676 num_examples: 257 download_size: 11421492 dataset_size: 19524304.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
RyanYr/reflect_mmlumathpro_nonmrkv-test_t4_crtc	RyanYr	2025-01-27T21:11:22Z	20	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-01-27T16:40:52Z	0	--- dataset_info: features: - name: question_id dtype: int64 - name: original_question dtype: string - name: options sequence: string - name: answer dtype: string - name: answer_index dtype: int64 - name: cot_content dtype: string - name: category dtype: string - name: src dtype: string - name: problem dtype: string - name: alt_answer dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@3 sequence: string - name: response@4 sequence: string - name: response@5 sequence: string - name: response@6 sequence: string - name: response@7 sequence: string splits: - name: train num_bytes: 21694922 num_examples: 1351 download_size: 8184506 dataset_size: 21694922 configs: - config_name: default data_files: - split: train path: data/train-* ---
MInference/SCBench	MInference	2024-12-13T07:06:57Z	104	0	[ "license:mit", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-12T18:43:55Z	0	--- license: mit dataset_info: - config_name: multi_turn_choice_eng features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: options sequence: string - name: id dtype: int64 splits: - name: train num_bytes: 46482955 num_examples: 58 download_size: 28590613 dataset_size: 46482955 - config_name: multi_turn_kv features: - name: id dtype: int64 - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string splits: - name: train num_bytes: 20071200 num_examples: 100 download_size: 18278186 dataset_size: 20071200 - config_name: multi_turn_many_shot features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: id dtype: int64 - name: task dtype: string splits: - name: train num_bytes: 4734315 num_examples: 54 download_size: 99406 dataset_size: 4734315 - config_name: multi_turn_mf features: - name: id dtype: int64 - name: context sequence: int64 - name: multi_turns list: - name: answer dtype: int64 - name: input dtype: string splits: - name: train num_bytes: 24065100 num_examples: 100 download_size: 3766479 dataset_size: 24065100 - config_name: multi_turn_prefix_suffix features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string splits: - name: train num_bytes: 17498600 num_examples: 100 download_size: 16417345 dataset_size: 17498600 - config_name: multi_turn_qa_chn features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180437341 num_examples: 35 download_size: 115936454 dataset_size: 180437341 - config_name: multi_turn_qa_eng features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 58359967 num_examples: 69 download_size: 35648660 dataset_size: 58359967 - config_name: multi_turn_repoqa features: - name: context dtype: string - name: id dtype: int64 - name: multi_turns list: - name: answer dtype: string - name: code_ratio dtype: float64 - name: description dtype: string - name: end_byte dtype: int64 - name: end_line dtype: int64 - name: func dtype: string - name: global_end_byte dtype: int64 - name: global_end_line dtype: int64 - name: global_start_byte dtype: int64 - name: global_start_line dtype: int64 - name: input dtype: string - name: name dtype: string - name: path dtype: string - name: start_byte dtype: int64 - name: start_line dtype: int64 - name: lang dtype: string - name: repo dtype: string splits: - name: train num_bytes: 24847710 num_examples: 88 download_size: 4427455 dataset_size: 24847710 - config_name: multi_turn_repoqa_and_kv features: - name: context dtype: string - name: id dtype: int64 - name: multi_turns list: - name: answer dtype: string - name: code_ratio dtype: float64 - name: description dtype: string - name: end_byte dtype: int64 - name: end_line dtype: int64 - name: func dtype: string - name: global_end_byte dtype: int64 - name: global_end_line dtype: int64 - name: global_start_byte dtype: int64 - name: global_start_line dtype: int64 - name: input dtype: string - name: name dtype: string - name: path dtype: string - name: start_byte dtype: int64 - name: start_line dtype: int64 - name: task dtype: string - name: lang dtype: string - name: repo dtype: string splits: - name: train num_bytes: 25019328 num_examples: 88 download_size: 8583611 dataset_size: 25019328 - config_name: multi_turn_summary features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 28622955 num_examples: 70 download_size: 14245669 dataset_size: 28622955 - config_name: multi_turn_summary_with_needles features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: task dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 28629718 num_examples: 70 download_size: 14233712 dataset_size: 28629718 - config_name: multi_turn_vt features: - name: index dtype: int64 - name: input dtype: string - name: length dtype: int64 - name: multi_turns list: - name: answer sequence: string - name: input dtype: string splits: - name: train num_bytes: 42549030 num_examples: 90 download_size: 2160077 dataset_size: 42549030 configs: - config_name: multi_turn_choice_eng data_files: - split: train path: multi_turn_choice_eng/train-* - config_name: multi_turn_kv data_files: - split: train path: multi_turn_kv/train-* - config_name: multi_turn_many_shot data_files: - split: train path: multi_turn_many_shot/train-* - config_name: multi_turn_mf data_files: - split: train path: multi_turn_mf/train-* - config_name: multi_turn_prefix_suffix data_files: - split: train path: multi_turn_prefix_suffix/train-* - config_name: multi_turn_qa_chn data_files: - split: train path: multi_turn_qa_chn/train-* - config_name: multi_turn_qa_eng data_files: - split: train path: multi_turn_qa_eng/train-* - config_name: multi_turn_repoqa data_files: - split: train path: multi_turn_repoqa/train-* - config_name: multi_turn_repoqa_and_kv data_files: - split: train path: multi_turn_repoqa_and_kv/train-* - config_name: multi_turn_summary data_files: - split: train path: multi_turn_summary/train-* - config_name: multi_turn_summary_with_needles data_files: - split: train path: multi_turn_summary_with_needles/train-* - config_name: multi_turn_vt data_files: - split: train path: multi_turn_vt/train-* --- # SCBench [[Paper]](https://drive.google.com/file/d/1_DFu11V7HbktvEMRqMUAWGm7DTkVXlOR/view?usp=drive_link) [[Code]](https://github.com/microsoft/MInference/SCBench) ![SCBench](./data/framework.png) SCBench (SharedContextBench) is a comprehensive benchmark to evaluate efficient long-context methods in a KV cache-centric perspective, analyzing their performance across the full KV cache lifecycle (generation, compression, retrieval, and loading) in real-world scenarios where context memory (KV cache) is shared and reused across multiple requests. ## Dataset ![SCBench](./data/overview.png) SCBench covers 12 diverse tasks that test four key long-context capabilities: string retrieval, semantic retrieval, global information processing, and multi-tasking. ### String Retrieval - Retr.KV: Tests key-value lookup in large JSON objects with random, incompressible content - Retr.Prefix-Suffix: Evaluates finding strings with specific prefix and suffix patterns - Retr.MultiHop: Assesses multi-hop variable tracing capabilities in long inputs ### Semantic Retrieval - Code.RepoQA: Function retrieval from large codebases based on natural language descriptions - Language QA: Includes English QA, Chinese QA, and multi-choice questions on long texts - Requires semantic understanding on length inputs ### Global Information Processing - Many-shot ICL: Tests in-context learning with hundreds of examples - Math.Find: Statistical tasks on large arrays - En.Sum: Summarization of documents - Requires global information processing or aggregation ### Multi-Tasking - Mix.Sum+NIAH: Combines summarization with needle-in-haystack search - Mix.RepoQA+KV: Integrates code function retrieval with key-value lookup - Requires multi-tasking or multi-step reasoning ## Two Shared Context Modes The benchmark evaluates these tasks across two shared context modes: - Multi-turn Mode: Caches context within single sessions - Multi-request Mode: Shares context across multiple sessions ## Compared to previous long-context benchmarks ![SCBench](./data/comparison.png) Our SCBench is the first long-context benchmark that covers single-turn, multi-turn, and multi-request scenarios. In addition, our impelmentation also involves KV cache reuse techniques, thereby providing a more comprehensive analysis on the full KV cache lifecycle of efficient long-context methods. ## Results and Findings ![SCBench](./data/results.png) Our SCBench reveals that the following key insights: ### Finding 1: Sub-O(n) Memory is Problematic in Multi-Request/Multi-Turn Decoding - Sparse decoding methods with sub-O(n) memory perform well on first queries but lose accuracy in subsequent requests - Methods maintaining O(n) memory with sub-O(n²) computation during pre-filling can better approximate full attention accuracy across multiple queries ### Finding 2: Task Performance Shows Varying Decline Patterns - Sparse KV cache methods excel in tasks requiring global information processing - O(n) memory is essential for tasks involving exact match retrieval ### Finding 3: Performance vs Compression Rate - All methods show performance degradation as compression rates increase - Sub-O(n) memory methods exhibit significant drop at 1/4 compression rate - Methods like RetrievalAttention and KIVI that maintain O(n) memory with sparse decoding show better resilience at higher compression rates ### Finding 4: Issues with Long-Generation Scenarios - Attention distribution shifts significantly as generation length and number of rounds increase - This out-of-distribution (OOD) issue impacts performance even for O(n) memory methods ### Finding 5: Dynamic vs Static Patterns - Dynamic sparse patterns generally outperform static patterns ## Citation ```bibtex @article{li2024scbench, title={SCBench: A KV cache-centric analysis of long-context methods}, author={Li, Yucheng and Jiang, Huiqiang and Wu, Qianhui and Luo, Xufang and Ahn, Surin and Zhang, Chengruidong and Abdi, Amir H and Li, Dongsheng and Gao, Jianfeng and Yang, Yuqing and Qiu, Lili}, journal={arXiv preprint arXiv:2412.}, year={2024} } ```
dgambettaphd/D_gen1_run2_llama2-7b_wiki_doc1000_real32_synt96_vuw	dgambettaphd	2024-12-17T10:01:08Z	17	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-17T10:01:05Z	0	--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 373990 num_examples: 1000 download_size: 211688 dataset_size: 373990 configs: - config_name: default data_files: - split: train path: data/train-* ---
dcml0714/StyleSet	dcml0714	2025-06-24T11:21:32Z	37	1	[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2506.05984", "region:us" ]	[]	2025-06-09T03:21:45Z	0	--- dataset_info: - config_name: role_playing features: - name: ID dtype: int64 - name: text_0 dtype: string - name: text_1 dtype: string - name: audio_0 dtype: audio: sampling_rate: 16000 - name: audio_1 dtype: audio: sampling_rate: 16000 - name: source dtype: string - name: speaker1 dtype: string - name: speaker2 dtype: string splits: - name: test num_bytes: 182310504.0 num_examples: 20 download_size: 148908359 dataset_size: 182310504.0 - config_name: voice_instruction_following features: - name: ID dtype: int64 - name: text_1 dtype: string - name: text_2 dtype: string - name: audio_1 dtype: audio: sampling_rate: 16000 - name: audio_2 dtype: audio: sampling_rate: 16000 splits: - name: test num_bytes: 36665909.0 num_examples: 20 download_size: 35109899 dataset_size: 36665909.0 configs: - config_name: role_playing data_files: - split: test path: role_playing/test-* - config_name: voice_instruction_following data_files: - split: test path: voice_instruction_following/test-* --- # StyleSet WARNING: This dataset contains some profane words. A spoken language benchmark for evaluating speaking-style-related speech generation Released in our paper, [Audio-Aware Large Language Models as Judges for Speaking Styles](https://arxiv.org/abs/2506.05984) This dataset is released by NTU Speech Lab under the MIT license. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/622326ae0129f2097d69a3e2/Q8Os1g5vfy22Y9myvSc7X.png) --- ## Tasks 1. Voice Style Instruction Following - Reproduce a given sentence verbatim. - Match specified prosodic styles (emotion, volume, pace, emphasis, pitch, non-verbal cues). 2. Role Playing - Continue a two-turn dialogue prompt in character. - Generate the next utterance with appropriate prosody and style. - The dataset is modified from IEMOCAP with the consent of the authors. Please refer to [IEMOCAP](https://sail.usc.edu/iemocap/) for details and the original data of IEMOCAP. We do not redistribute the data here. --- ## Evaluation We use ALLM-as-a-judge for evaluation. Currently, we found that `gemini-2.5-pro-0506` reaches the best agreement with human evaluators. The complete evaluation prompt and evaluation pipelines can be found in Table 3 to Table 5 in our paper. ## Citation If you use StyleSet or find ALLM-as-a-judge useful, please cite our paper by ``` @misc{chiang2025audioawarelargelanguagemodels, title={Audio-Aware Large Language Models as Judges for Speaking Styles}, author={Cheng-Han Chiang and Xiaofei Wang and Chung-Ching Lin and Kevin Lin and Linjie Li and Radu Kopetz and Yao Qian and Zhendong Wang and Zhengyuan Yang and Hung-yi Lee and Lijuan Wang}, year={2025}, eprint={2506.05984}, archivePrefix={arXiv}, primaryClass={eess.AS}, url={https://arxiv.org/abs/2506.05984}, } ```
YounesHouhou/filter	YounesHouhou	2025-05-22T12:43:35Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot" ]	[ "robotics" ]	2025-05-22T12:43:32Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": null, "total_episodes": 1, "total_frames": 600, "total_tasks": 1, "total_videos": 1, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.state": { "dtype": "float32", "shape": [ 9 ], "names": { "motors": [ "Measurement_RIGHT_ARM_SHOULDER_PITCH", "Measurement_RIGHT_ARM_SHOULDER_ROLL", "Measurement_RIGHT_ARM_BICEP_YAW", "Measurement_RIGHT_ARM_ELBOW_PITCH", "Measurement_RIGHT_ARM_WRIST_YAW", "Measurement_RIGHT_ARM_WRIST_PITCH", "Measurement_RIGHT_ARM_WRIST_ROLL", "Measurement_RIGHT_ARM_THUMB", "Measurement_RIGHT_ARM_FINGERS" ] } }, "action": { "dtype": "float32", "shape": [ 9 ], "names": { "motors": [ "MpcInput_RIGHT_ARM_SHOULDER_PITCH", "MpcInput_RIGHT_ARM_SHOULDER_ROLL", "MpcInput_RIGHT_ARM_BICEP_YAW", "MpcInput_RIGHT_ARM_ELBOW_PITCH", "MpcInput_RIGHT_ARM_WRIST_YAW", "MpcInput_RIGHT_ARM_WRIST_PITCH", "MpcInput_RIGHT_ARM_WRIST_ROLL", "Target_RIGHT_ARM_THUMB", "Target_RIGHT_ARM_FINGERS" ] } }, "next.done": { "dtype": "bool", "shape": [ 1 ] }, "observation.images.camera_head": { "dtype": "video", "shape": [ 3, 480, 640 ], "names": [ "channels", "height", "width" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
test-gen/livecodebench_qwen-0.5b-random_t0.0_n1_generated_tests_updated	test-gen	2025-05-23T02:45:12Z	24	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-15T02:30:33Z	0	--- dataset_info: features: - name: question_title dtype: string - name: question_content dtype: string - name: question_id dtype: string - name: contest_id dtype: string - name: test_id dtype: int64 - name: contest_date dtype: timestamp[us] - name: starter_code dtype: string - name: function_name dtype: string - name: difficulty dtype: string - name: test dtype: string - name: verification_info struct: - name: language dtype: string - name: test_cases sequence: string - name: new_verification_info struct: - name: language dtype: string - name: test_cases sequence: string splits: - name: test num_bytes: 183440 num_examples: 182 download_size: 77951 dataset_size: 183440 configs: - config_name: default data_files: - split: test path: data/test-* ---
DuongTrongChi/vov_crawler	DuongTrongChi	2024-10-25T15:22:36Z	29	0	[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-10-25T15:22:13Z	0	--- dataset_info: features: - name: title dtype: string - name: content sequence: string - name: metadata struct: - name: date dtype: string - name: url dtype: string splits: - name: train num_bytes: 1085141731 num_examples: 372528 download_size: 510136321 dataset_size: 1085141731 configs: - config_name: default data_files: - split: train path: data/train-* ---
flozi00/german-canary-asr-0324	flozi00	2024-03-19T10:48:50Z	270	6	[ "task_categories:automatic-speech-recognition", "language:de", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[ "automatic-speech-recognition" ]	2024-03-16T10:46:35Z	1	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 decode: false - name: transkription dtype: string - name: source dtype: string splits: - name: train num_bytes: 41511776468.673 num_examples: 985257 download_size: 142197574339 dataset_size: 41511776468.673 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - automatic-speech-recognition language: - de pretty_name: German Canary ASR --- # Dataset Beschreibung ## Allgemeine Informationen Dieser Datensatz ist eine Kombination aus drei verschiedenen Quellen für die deutsche Sprache: Commonvoice 16.1, Voxpopuli und Multilingual librispeech. Die Daten wurden gefiltert, normalisiert und grammatikalisch korrigiert. Die drei Datensätze wurden erneut transkribiert und mit den entsprechenden Audio-Daten abgeglichen, um genaue Transkriptionen zu erhalten. Anschließend wurde ein Abgleich mit den Originaltranskripten durchgeführt, um fehlerhafte Transkriptionen zu korrigieren oder zu entfernen, sofern dies möglich war. Für diese Aufgabe wurde das Nvidia Canary 1b Modell genutzt. ### Commonvoice 16.1 Common Voice ist ein öffentlich verfügbarer Sprachdatensatz, der durch Stimmen freiwilliger Mitwirkender auf der ganzen Welt erstellt wird. Der Datensatz enthält Aufnahmen von Sätzen in verschiedenen Sprachen, einschließlich Deutsch. ### Voxpopuli Die Rohdaten für diesen Teil des Datensatzes stammen aus den Aufzeichnungen von Veranstaltungen des Europäischen Parlaments von 2009 bis 2020. Wir danken dem Europäischen Parlament dafür, dass es diese Materialien erstellt und geteilt hat. ### Multilingual librispeech Der Multilingual LibriSpeech (MLS) Datensatz ist ein umfangreicher mehrsprachiger Korpus, der sich für die Sprachforschung eignet. Der Datensatz basiert auf vorgelesenen Hörbüchern von LibriVox und enthält auch deutschsprachige Aufnahmen. ## Datenverarbeitungsschritte Um einen qualitativ hochwertigen deutschen Sprachdatensatz zu erstellen, wurden folgende Schritte durchgeführt: 1. Filterung: Es wurden nur die deutschen Sätze aus den jeweiligen Quelldatensätzen extrahiert. 2. Normalisierung: Die Texte wurden auf eine einheitliche Form gebracht, um Inkonsistenzen zu beseitigen. 3. Grammatikkorrektur: Fehlerhafte Grammatik wurde korrigiert, um die Qualität der Sätze zu verbessern. ## Verwendungszweck Dieser kombinierte deutsche Sprachdatensatz kann für verschiedene Zwecke verwendet werden: - ASR (Automatic Speech Recognition) Modelltraining - NLP (Natural Language Processing) Forschung - Text-to-Speech Anwendungen Bitte beachten Sie jedoch bei der Verwendung dieses Datensatzes die Lizenzbedingungen der einzelnen Quellen sowie etwaige Einschränkungen oder Richtlinien bezüglich des Datenschutzes oder Urheberrechts.
chiyuanhsiao/llama-questions-ASR_GT-score	chiyuanhsiao	2025-01-01T13:58:17Z	15	0	[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-31T08:39:20Z	0	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: audio dtype: audio - name: question_unit sequence: int64 - name: response_interleaf dtype: string - name: response_text dtype: string - name: response_speech dtype: audio - name: response_asr dtype: string - name: speech_score dtype: int64 - name: text_score dtype: int64 splits: - name: test num_bytes: 176590033.0 num_examples: 300 download_size: 157352925 dataset_size: 176590033.0 configs: - config_name: default data_files: - split: test path: data/test-* ---
jasren/lerobot-test-10	jasren	2025-04-20T03:26:05Z	24	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]	[ "robotics" ]	2025-04-20T03:26:01Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 6, "total_frames": 3302, "total_tasks": 1, "total_videos": 12, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:6" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
nhorner/record-test	nhorner	2025-06-15T19:52:26Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]	[ "robotics" ]	2025-06-15T19:52:16Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 2, "total_frames": 3557, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ] }, "observation.images.front": { "dtype": "video", "shape": [ 1080, 1920, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 1080, "video.width": 1920, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
psg777/so100test1	psg777	2025-05-28T17:55:53Z	0	0	[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so101", "tutorial" ]	[ "robotics" ]	2025-05-28T17:55:47Z	0	--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - tutorial configs: - config_name: default data_files: data//.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - Homepage: [More Information Needed] - Paper: [More Information Needed] - License: apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so101", "total_episodes": 5, "total_frames": 3457, "total_tasks": 1, "total_videos": 15, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:5" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.base": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.gripper": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.bird": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation BibTeX: ```bibtex [More Information Needed] ```
THU-KEG/LongWriter-Zero-RLData	THU-KEG	2025-06-24T03:12:49Z	72	2	[ "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2506.18841", "arxiv:2506.01234", "region:us" ]	[]	2025-06-18T09:11:45Z	2	--- license: apache-2.0 --- # LongWriter-Zero RL Data <p align="center"> 🤗 <a href="https://huggingface.co/THU-KEG/LongWriter-Zero-32B" target="_blank">[Model]</a> • 📃 <a href="https://arxiv.org/abs/2506.18841" target="_blank">[Paper]</a> • 💾 <a href="https://huggingface.co/datasets/THU-KEG/LongWriter-Zero-RLData" target="_blank">[Dataset Card]</a> </p> LongWriter-Zero RL Data is designed for ultra-long text generation via reinforcement learning. The dataset consists of conversational queries paired with length-range tags, which specify the desired output span (measured in words or Chinese characters). These annotations are used to train the LongWriter-Zero model, enabling it to consistently generate passages exceeding 10,000 words. ## Dataset at a Glance \| Field \| Type \| Description \| \|---------\|--------\|---------------------------------------------------------------------------------------\| \| `idx` \| `int` \| Unique example identifier \| \| `query` \| string \| User instruction / prompt (English or Chinese) \| \| `label` \| object \| JSON dict `{"range": [low, high]}` denoting the target word‑count interval \| <!-- --- <!-- ## Citation If you find LongWriter‑zero RLData useful, please cite: ```bibtex @article{wu2025longwriterzero, title = {LongWriter-zero: Length-Controlled Reinforcement Learning for 10,000-Word Generation}, author = {Yuhao Wu and Zhiqiang Hu and Yushi Bai and Jie Tang}, journal = {arXiv preprint arXiv:2506.01234}, year = {2025} } ``` --> Happy long-form writing!
kobybar/tokenized_roots_dataset_mbert	kobybar	2024-11-23T19:43:53Z	54	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-11-23T19:39:00Z	0	--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 13672989742 num_examples: 7044 download_size: 6246959869 dataset_size: 13672989742 configs: - config_name: default data_files: - split: train path: data/train-* ---
IntMeGroup/env	IntMeGroup	2025-06-04T06:44:11Z	54	0	[ "license:apache-2.0", "region:us" ]	[]	2025-05-07T12:45:21Z	0	--- license: apache-2.0 ---
zenless-lab/jmmlu	zenless-lab	2024-12-26T00:51:34Z	19	0	[ "language:ja", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2024-12-18T10:14:10Z	0	--- language: - ja dataset_info: features: - name: question dtype: large_string - name: choice0 dtype: large_string - name: choice1 dtype: large_string - name: choice2 dtype: large_string - name: choice3 dtype: large_string - name: split dtype: large_string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' splits: - name: train num_bytes: 2325309.4389178525 num_examples: 5677 - name: test num_bytes: 581634.5610821474 num_examples: 1420 download_size: 1751049 dataset_size: 2906944.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
s-nlp/paradetox	s-nlp	2025-04-02T15:20:04Z	853	9	[ "task_categories:text-generation", "language:en", "license:openrail++", "size_categories:10K<n<100K", "format:csv", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "detoxification" ]	[ "text-generation" ]	2022-05-19T17:12:06Z	0	--- license: openrail++ task_categories: - text-generation language: - en tags: - detoxification size_categories: - 10K<n<100K --- # ParaDetox: Text Detoxification with Parallel Data (English) This repository contains information about ParaDetox dataset -- the first parallel corpus for the detoxification task -- as well as models and evaluation methodology for the detoxification of English texts. The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference. 📰 Updates [2025] !!!NOW OPEN!!! TextDetox CLEF2025 shared task: for even more -- 15 languages! [website](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) 🤗[Starter Kit](https://huggingface.co/collections/textdetox/) [2025] COLNG2025: Daryna Dementieva, Nikolay Babakov, Amit Ronen, Abinew Ali Ayele, Naquee Rizwan, Florian Schneider, Xintong Wang, Seid Muhie Yimam, Daniil Alekhseevich Moskovskiy, Elisei Stakovskii, Eran Kaufman, Ashraf Elnagar, Animesh Mukherjee, and Alexander Panchenko. 2025. *Multilingual and Explainable Text Detoxification with Parallel Corpora. In Proceedings of the 31st International Conference on Computational Linguistics, pages 7998–8025, Abu Dhabi, UAE. Association for Computational Linguistics. [pdf](https://aclanthology.org/2025.coling-main.535/) [2024]* We have also created versions of ParaDetox in more languages. You can checkout a [RuParaDetox](https://huggingface.co/datasets/s-nlp/ru_paradetox) dataset as well as a [Multilingual TextDetox](https://huggingface.co/textdetox) project that includes 9 languages. Corresponding papers: * [MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages](https://aclanthology.org/2024.naacl-short.12/) (NAACL 2024) * [Overview of the multilingual text detoxification task at pan 2024](https://ceur-ws.org/Vol-3740/paper-223.pdf) (CLEF Shared Task 2024) ## ParaDetox Collection Pipeline <img alt="Collection Pipeline" src="generation_pipeline_blue-1.png"> The ParaDetox Dataset collection was done via [Toloka.ai](https://toloka.ai) crowdsource platform. The collection was done in three steps: * Task 1: Generation of Paraphrases: The first crowdsourcing task asks users to eliminate toxicity in a given sentence while keeping the content. * Task 2: Content Preservation Check: We show users the generated paraphrases along with their original variants and ask them to indicate if they have close meanings. * Task 3: Toxicity Check: Finally, we check if the workers succeeded in removing toxicity. All these steps were done to ensure high quality of the data and make the process of collection automated. For more details please refer to the original paper. ## ParaDetox Dataset As a result, we get paraphrases for 11,939 toxic sentences (on average 1.66 paraphrases per sentence), 19,766 paraphrases total. In addition to all ParaDetox dataset, we also make public [samples](https://huggingface.co/datasets/s-nlp/en_non_detoxified) that were marked by annotators as "cannot rewrite" in Task 1 of crowdsource pipeline. # Detoxification evaluation The automatic evaluation of the model were produced based on three parameters: * style transfer accuracy (STA): percentage of nontoxic outputs identified by a style classifier. We pretrained [toxicity classifier](https://huggingface.co/s-nlp/roberta_toxicity_classifier) on Jigsaw data and put it online in HuggingFace🤗 [repo](https://huggingface.co/s-nlp/roberta_toxicity_classifier). * content preservation (SIM): cosine similarity between the embeddings of the original text and the output computed with the model of [Wieting et al. (2019)](https://aclanthology.org/P19-1427/). * fluency (FL): percentage of fluent sentences identified by a RoBERTa-based classifier of linguistic acceptability trained on the [CoLA dataset](https://nyu-mll.github.io/CoLA/). All code used for our experiments to evluate different detoxifcation models can be run via Colab notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xTqbx7IPF8bVL2bDCfQSDarA43mIPefE?usp=sharing) ## Detoxification model The first seq2seq SOTA for the text detoxification task in English -- BART (base) model trained on ParaDetox dataset -- we release online in HuggingFace🤗 [repo](https://huggingface.co/s-nlp/bart-base-detox). You can also check out our [web-demo](https://detoxifier.nlp.zhores.net/junction/). ## Citation ``` @inproceedings{logacheva-etal-2022-paradetox, title = "{P}ara{D}etox: Detoxification with Parallel Data", author = "Logacheva, Varvara and Dementieva, Daryna and Ustyantsev, Sergey and Moskovskiy, Daniil and Dale, David and Krotova, Irina and Semenov, Nikita and Panchenko, Alexander", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.469", pages = "6804--6818", abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.", } ``` and ``` @inproceedings{dementieva2021crowdsourcing, title = "Crowdsourcing of Parallel Corpora: the Case of Style Transfer for Detoxification", author = {Dementieva, Daryna and Ustyantsev, Sergey and Dale, David and Kozlova, Olga and Semenov, Nikita and Panchenko, Alexander and Logacheva, Varvara}, booktitle = "Proceedings of the 2nd Crowd Science Workshop: Trust, Ethics, and Excellence in Crowdsourced Data Management at Scale co-located with 47th International Conference on Very Large Data Bases (VLDB 2021 (https://vldb.org/2021/))", year = "2021", address = "Copenhagen, Denmark", publisher = "CEUR Workshop Proceedings", pages = "35--49", url={http://ceur-ws.org/Vol-2932/paper2.pdf} } ``` ## Contacts If you find some issue, do not hesitate to add it to [Github Issues](https://github.com/s-nlp/paradetox/issues). For any questions and get the TEST SET, please, contact: Daryna Dementieva ([email protected]), Daniil Moskovskiy ([email protected]), or Alexander Panchenko ([email protected]) Dataset Card and Paper corresponding contact: [Daryna Dementieva](https://huggingface.co/dardem)
rajesh-lm/data_feed	rajesh-lm	2025-02-01T06:58:29Z	15	0	[ "license:apache-2.0", "region:us" ]	[]	2025-02-01T06:58:29Z	0	--- license: apache-2.0 ---
kothasuhas/llama-3b-gold-15M-1.5MSNIS-iter1-4-26-generations_SNIS_2048_iter2-426-init-i1_baseN1.50M_N1.50M	kothasuhas	2025-04-27T05:20:21Z	33	0	[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-27T05:19:04Z	0	--- dataset_info: features: - name: text dtype: string - name: log_weight dtype: float32 - name: sampling_p_scaled dtype: float64 - name: sampling_p_temperature_scaled dtype: float64 splits: - name: train num_bytes: 2446360037 num_examples: 1500000 - name: validation num_bytes: 2444848 num_examples: 1000 download_size: 1311908664 dataset_size: 2448804885 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
mlfoundations-dev/get_question_answer_codeforces	mlfoundations-dev	2025-03-14T18:44:27Z	28	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-03-14T18:23:27Z	0	--- dataset_info: features: - name: question_answer_string dtype: string splits: - name: train num_bytes: 2408636233 num_examples: 47780 download_size: 969021289 dataset_size: 2408636233 configs: - config_name: default data_files: - split: train path: data/train-* ---
vamshi0317/team4-888_CodeforcesProblems_ts_cleaned_summarized_v2	vamshi0317	2025-04-22T22:41:01Z	22	0	[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-04-22T22:33:43Z	0	--- dataset_info: features: - name: Problem Description dtype: string - name: Tag dtype: string - name: math dtype: bool - name: greedy dtype: bool - name: implementation dtype: bool - name: dp dtype: bool - name: data structures dtype: bool - name: constructive algorithms dtype: bool - name: brute force dtype: bool - name: binary search dtype: bool - name: sortings dtype: bool - name: graphs dtype: bool splits: - name: train num_bytes: 20516156 num_examples: 9285 - name: validation num_bytes: 2610352 num_examples: 1161 - name: test num_bytes: 2581096 num_examples: 1161 download_size: 11177161 dataset_size: 25707604 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
reasoning-proj/c_dfiltered_Llama-3_1-Nemotron-Nano-8B-v1_madversarial_continue_unrelated_t10	reasoning-proj	2025-05-09T07:06:27Z	0	0	[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-09T01:28:54Z	0	--- dataset_info: features: - name: question dtype: string - name: answer_content dtype: string - name: reference_answer dtype: string - name: id dtype: string - name: metadata struct: - name: question_license dtype: string - name: question_source dtype: string - name: model_name dtype: string - name: verifier_score dtype: int64 - name: mutated_answer_content dtype: string - name: continuation_1 dtype: string - name: complete_answer_1 dtype: string - name: continuation_2 dtype: string - name: complete_answer_2 dtype: string - name: continuation_3 dtype: string - name: complete_answer_3 dtype: string - name: continuation_4 dtype: string - name: complete_answer_4 dtype: string - name: continuation_5 dtype: string - name: complete_answer_5 dtype: string - name: continuation_6 dtype: string - name: complete_answer_6 dtype: string - name: continuation_7 dtype: string - name: complete_answer_7 dtype: string - name: continuation_8 dtype: string - name: complete_answer_8 dtype: string - name: continuation_model dtype: string splits: - name: train num_bytes: 125443699 num_examples: 600 download_size: 46970431 dataset_size: 125443699 configs: - config_name: default data_files: - split: train path: data/train-* ---
Ktzoras/shipping_llm_results_3k_sample	Ktzoras	2025-05-29T08:32:30Z	38	0	[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	[]	2025-05-29T08:32:12Z	0	--- dataset_info: features: - name: link dtype: string - name: date dtype: timestamp[ns] - name: title dtype: string - name: content dtype: string - name: vectors_L6_v2 sequence: float64 - name: vectors_distil sequence: sequence: int64 - name: classes_distil dtype: string - name: Scale_fe dtype: string - name: Type of Vessel_fe dtype: string - name: Vessel Size_fe dtype: string - name: Sea Route_fe dtype: string - name: Duration of Positive Impact_fe dtype: string - name: Impact_fe dtype: string - name: Hire Rate Impact_fe dtype: string - name: Scale_rag dtype: string - name: Type of Vessel_rag dtype: string - name: Vessel Size_rag dtype: string - name: Sea Route_rag dtype: string - name: Duration of Positive Impact_rag dtype: string - name: Impact_rag dtype: string - name: Hire Rate Impact_rag dtype: string - name: Scale_idfe dtype: string - name: Type of Vessel_idfe dtype: string - name: Vessel Size_idfe dtype: string - name: Sea Route_idfe dtype: string - name: Duration of Positive Impact_idfe dtype: string - name: Impact_idfe dtype: string - name: Hire Rate Impact_idfe dtype: string - name: Scale_idrbfe dtype: string - name: Type of Vessel_idrbfe dtype: string - name: Vessel Size_idrbfe dtype: string - name: Sea Route_idrbfe dtype: string - name: Duration of Positive Impact_idrbfe dtype: string - name: Impact_idrbfe dtype: string - name: Hire Rate Impact_idrbfe dtype: string - name: Hire rate impact_rag dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 38557055 num_examples: 3000 download_size: 18290021 dataset_size: 38557055 configs: - config_name: default data_files: - split: train path: data/train-* ---

abhinav302019/olympiad_data_320

abhinav302019

2025-03-05T16:53:53Z

14

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-05T16:53:50Z

0

--- dataset_info: features: - name: problem dtype: string - name: Known_Solution dtype: string - name: Known_Answer dtype: string - name: Generated_Solution dtype: string - name: Generated_Answer dtype: string - name: Judge_Evaluation dtype: string - name: Judge_Rating dtype: string - name: Judge_Justification dtype: string splits: - name: train num_bytes: 46504 num_examples: 10 download_size: 46114 dataset_size: 46504 configs: - config_name: default data_files: - split: train path: data/train-* ---

RLHF-And-Friends/tldr-thematic

RLHF-And-Friends

2025-05-19T23:44:12Z

351

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-28T18:38:31Z

0

--- dataset_info: - config_name: Advice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 3248777.2880519526 num_examples: 2088 - name: validation num_bytes: 361482.16246153845 num_examples: 232 download_size: 2218375 dataset_size: 3610259.450513491 - config_name: AskDocs features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 440327.5730453556 num_examples: 283 - name: validation num_bytes: 62324.51076923077 num_examples: 40 download_size: 312880 dataset_size: 502652.0838145864 - config_name: AskReddit features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 24023525.540001027 num_examples: 15440 - name: validation num_bytes: 2779673.1803076924 num_examples: 1784 download_size: 15633065 dataset_size: 26803198.72030872 - config_name: BreakUps features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1303867.513116636 num_examples: 838 - name: validation num_bytes: 168276.1790769231 num_examples: 108 download_size: 883490 dataset_size: 1472143.6921935591 - config_name: Cooking features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 177375.77147410085 num_examples: 114 - name: validation num_bytes: 10906.789384615384 num_examples: 7 download_size: 110570 dataset_size: 188282.56085871623 - config_name: Dogtraining features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 563245.8708212676 num_examples: 362 - name: validation num_bytes: 65440.73630769231 num_examples: 42 download_size: 397400 dataset_size: 628686.6071289598 - config_name: GetMotivated features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 262951.8015712548 num_examples: 169 - name: validation num_bytes: 37394.70646153846 num_examples: 24 download_size: 196817 dataset_size: 300346.50803279324 - config_name: Parenting features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 676828.60167749 num_examples: 435 - name: validation num_bytes: 74789.41292307692 num_examples: 48 download_size: 480742 dataset_size: 751618.0146005669 - config_name: Pets features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 569469.5821010607 num_examples: 366 - name: validation num_bytes: 68556.96184615385 num_examples: 44 download_size: 394199 dataset_size: 638026.5439472145 - config_name: askwomenadvice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1070478.3401243982 num_examples: 688 - name: validation num_bytes: 115300.34492307692 num_examples: 74 download_size: 686910 dataset_size: 1185778.6850474752 - config_name: books features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 250504.37901166876 num_examples: 161 - name: validation num_bytes: 34278.480923076924 num_examples: 22 download_size: 173603 dataset_size: 284782.8599347457 - config_name: cats features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 504120.613663234 num_examples: 324 - name: validation num_bytes: 62324.51076923077 num_examples: 40 download_size: 338338 dataset_size: 566445.1244324647 - config_name: college features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 410764.94446633884 num_examples: 264 - name: validation num_bytes: 70115.07461538461 num_examples: 45 download_size: 275869 dataset_size: 480880.01908172347 - config_name: dating_advice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 4432838.359032573 num_examples: 2849 - name: validation num_bytes: 500154.1989230769 num_examples: 321 download_size: 2859130 dataset_size: 4932992.55795565 - config_name: dogs features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 992681.9491269855 num_examples: 638 - name: validation num_bytes: 104393.55553846154 num_examples: 67 download_size: 686858 dataset_size: 1097075.504665447 - config_name: jobs features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1686625.7568239064 num_examples: 1084 - name: validation num_bytes: 166718.0663076923 num_examples: 107 download_size: 1115205 dataset_size: 1853343.8231315988 - config_name: legaladvice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 3107187.8564366614 num_examples: 1997 - name: validation num_bytes: 316296.89215384616 num_examples: 203 download_size: 2148216 dataset_size: 3423484.7485905075 - config_name: loseit features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 2259207.1945648636 num_examples: 1452 - name: validation num_bytes: 267995.3963076923 num_examples: 172 download_size: 1502741 dataset_size: 2527202.590872556 - config_name: needadvice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 821529.8889326777 num_examples: 528 - name: validation num_bytes: 81021.864 num_examples: 52 download_size: 566990 dataset_size: 902551.7529326777 - config_name: offmychest features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 2461477.8111581365 num_examples: 1582 - name: validation num_bytes: 232158.80261538463 num_examples: 149 download_size: 1752857 dataset_size: 2693636.613773521 - config_name: personalfinance features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 3597305.119720361 num_examples: 2312 - name: validation num_bytes: 422248.56046153844 num_examples: 271 download_size: 2364890 dataset_size: 4019553.6801818996 - config_name: pettyrevenge features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 852648.4453316428 num_examples: 548 - name: validation num_bytes: 96602.9916923077 num_examples: 62 download_size: 639836 dataset_size: 949251.4370239505 - config_name: relationship_advice features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 13522568.683170268 num_examples: 8691 - name: validation num_bytes: 1480207.1307692307 num_examples: 950 download_size: 9038096 dataset_size: 15002775.813939499 - config_name: relationships features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 98527573.27040318 num_examples: 63324 - name: validation num_bytes: 10908347.497384615 num_examples: 7001 download_size: 65934652 dataset_size: 109435920.76778778 - config_name: running features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 882211.0739106595 num_examples: 567 - name: validation num_bytes: 110626.00661538461 num_examples: 71 download_size: 542869 dataset_size: 992837.0805260441 - config_name: self features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 1630612.3553057692 num_examples: 1048 - name: validation num_bytes: 204112.77276923077 num_examples: 131 download_size: 1156745 dataset_size: 1834725.128075 - config_name: tifu features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 11957305.296302326 num_examples: 7685 - name: validation num_bytes: 1305698.5006153847 num_examples: 838 download_size: 8213592 dataset_size: 13263003.79691771 - config_name: travel features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 703279.3746166104 num_examples: 452 - name: validation num_bytes: 81021.864 num_examples: 52 download_size: 450229 dataset_size: 784301.2386166104 - config_name: weddingplanning features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 673716.7460375936 num_examples: 433 - name: validation num_bytes: 66998.84907692307 num_examples: 43 download_size: 471160 dataset_size: 740715.5951145167 configs: - config_name: Advice data_files: - split: train path: Advice/train-* - split: validation path: Advice/validation-* - config_name: AskDocs data_files: - split: train path: AskDocs/train-* - split: validation path: AskDocs/validation-* - config_name: AskReddit data_files: - split: train path: AskReddit/train-* - split: validation path: AskReddit/validation-* - config_name: BreakUps data_files: - split: train path: BreakUps/train-* - split: validation path: BreakUps/validation-* - config_name: Cooking data_files: - split: train path: Cooking/train-* - split: validation path: Cooking/validation-* - config_name: Dogtraining data_files: - split: train path: Dogtraining/train-* - split: validation path: Dogtraining/validation-* - config_name: GetMotivated data_files: - split: train path: GetMotivated/train-* - split: validation path: GetMotivated/validation-* - config_name: Parenting data_files: - split: train path: Parenting/train-* - split: validation path: Parenting/validation-* - config_name: Pets data_files: - split: train path: Pets/train-* - split: validation path: Pets/validation-* - config_name: askwomenadvice data_files: - split: train path: askwomenadvice/train-* - split: validation path: askwomenadvice/validation-* - config_name: books data_files: - split: train path: books/train-* - split: validation path: books/validation-* - config_name: cats data_files: - split: train path: cats/train-* - split: validation path: cats/validation-* - config_name: college data_files: - split: train path: college/train-* - split: validation path: college/validation-* - config_name: dating_advice data_files: - split: train path: dating_advice/train-* - split: validation path: dating_advice/validation-* - config_name: dogs data_files: - split: train path: dogs/train-* - split: validation path: dogs/validation-* - config_name: jobs data_files: - split: train path: jobs/train-* - split: validation path: jobs/validation-* - config_name: legaladvice data_files: - split: train path: legaladvice/train-* - split: validation path: legaladvice/validation-* - config_name: loseit data_files: - split: train path: loseit/train-* - split: validation path: loseit/validation-* - config_name: needadvice data_files: - split: train path: needadvice/train-* - split: validation path: needadvice/validation-* - config_name: offmychest data_files: - split: train path: offmychest/train-* - split: validation path: offmychest/validation-* - config_name: personalfinance data_files: - split: train path: personalfinance/train-* - split: validation path: personalfinance/validation-* - config_name: pettyrevenge data_files: - split: train path: pettyrevenge/train-* - split: validation path: pettyrevenge/validation-* - config_name: relationship_advice data_files: - split: train path: relationship_advice/train-* - split: validation path: relationship_advice/validation-* - config_name: relationships data_files: - split: train path: relationships/train-* - split: validation path: relationships/validation-* - config_name: running data_files: - split: train path: running/train-* - split: validation path: running/validation-* - config_name: self data_files: - split: train path: self/train-* - split: validation path: self/validation-* - config_name: tifu data_files: - split: train path: tifu/train-* - split: validation path: tifu/validation-* - config_name: travel data_files: - split: train path: travel/train-* - split: validation path: travel/validation-* - config_name: weddingplanning data_files: - split: train path: weddingplanning/train-* - split: validation path: weddingplanning/validation-* ---

ziyu3141/rf_newtrain_1_48

ziyu3141

2025-02-07T03:57:05Z

14

0

[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-07T03:57:03Z

0

--- dataset_info: features: - name: Filename dtype: string - name: Aesthetics score dtype: float64 - name: Artifact score dtype: float64 - name: Misalignment score dtype: float64 - name: Overall score dtype: float64 - name: Artifact heatmap sequence: sequence: sequence: int64 - name: Misalignment heatmap sequence: sequence: sequence: int64 - name: Misalignment token label dtype: string - name: is_uneven dtype: bool - name: preferred_image dtype: binary - name: unpreferred_image dtype: binary - name: revised_image dtype: binary - name: unrevised_id dtype: string - name: is_preferred dtype: bool splits: - name: train num_bytes: 135379387 num_examples: 20 download_size: 9833035 dataset_size: 135379387 configs: - config_name: default data_files: - split: train path: data/train-* ---

Hennara/Recap-DataComp-1B_split_4

Hennara

2024-11-28T20:08:01Z

46

0

[ "size_categories:100M<n<1B", "format:parquet", "modality:image", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-11-28T19:00:44Z

0

--- dataset_info: features: - name: url dtype: string - name: re_caption dtype: string - name: org_caption dtype: string - name: sha256 dtype: string - name: key dtype: string - name: re_clip_score dtype: float64 - name: org_clip_score dtype: float64 - name: re_length dtype: int64 - name: org_length dtype: int64 - name: re_gpt4v_score dtype: int64 - name: org_gpt4v_score dtype: int64 splits: - name: train num_bytes: 67854510316 num_examples: 117611282 download_size: 41259522279 dataset_size: 67854510316 configs: - config_name: default data_files: - split: train path: data/train-* ---

fdschmidt93/synthetic-llama-8b

fdschmidt93

2025-03-21T18:55:21Z

17

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-21T18:55:10Z

0

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 657960740 num_examples: 478247 download_size: 353896912 dataset_size: 657960740 configs: - config_name: default data_files: - split: train path: data/train-* ---

ChaosAiVision/Medical_reasoning

ChaosAiVision

2025-06-22T13:04:27Z

31

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-20T11:02:46Z

0

--- dataset_info: features: - name: question dtype: string - name: anwser dtype: string - name: chain_of_though dtype: string splits: - name: train num_bytes: 6323164 num_examples: 2144 download_size: 2322394 dataset_size: 6323164 configs: - config_name: default data_files: - split: train path: data/train-* ---

sucharush/rag_mcqa_with_doc

sucharush

2025-06-08T03:03:07Z

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-08T02:43:40Z

0

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 240267317 num_examples: 122868 download_size: 122928694 dataset_size: 240267317 configs: - config_name: default data_files: - split: train path: data/train-* ---

philschmid/open-orca-10k-guidellm

philschmid

2024-10-09T11:45:44Z

33

1

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-10-09T11:45:42Z

0

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 55855617.39201845 num_examples: 10000 download_size: 34695180 dataset_size: 55855617.39201845 configs: - config_name: default data_files: - split: train path: data/train-* ---

french-datasets/bismarck91_frA-enA-tokenised-qwen-part1

french-datasets

2025-06-21T14:19:03Z

0

[ "task_categories:audio-to-audio", "language:fra", "language:eng", "region:us" ]

[ "audio-to-audio" ]

2025-06-21T14:18:28Z

0

--- language: - fra - eng viewer: false task_categories: - audio-to-audio --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [bismarck91/frA-enA-tokenised-qwen-part1](https://huggingface.co/datasets/bismarck91/frA-enA-tokenised-qwen-part1).

mlfoundations-dev/multiple_samples_sympy_numina_aime

mlfoundations-dev

2025-02-04T05:33:24Z

14

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-03T23:22:34Z

0

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string - name: r1_distill_70b_response sequence: string - name: r1_distill_70b_extracted_answer sequence: string - name: sympy_code dtype: string - name: correct dtype: bool - name: execution_output dtype: string splits: - name: train num_bytes: 177622 num_examples: 3 download_size: 101520 dataset_size: 177622 configs: - config_name: default data_files: - split: train path: data/train-* ---

woshityj/xsum_dataset

woshityj

2024-12-11T08:49:23Z

15

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-11T08:49:14Z

0

--- dataset_info: features: - name: document dtype: string - name: summary dtype: string - name: id dtype: string splits: - name: train num_bytes: 69746054 num_examples: 30000 - name: test num_bytes: 8864589 num_examples: 3750 - name: validation num_bytes: 8637471 num_examples: 3750 download_size: 55382887 dataset_size: 87248114 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

Tobius/9e77d18e-3e9a-46af-ac54-7342711a98b6

Tobius

2024-11-29T11:14:06Z

13

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-11-29T11:14:05Z

0

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 108838.4 num_examples: 800 - name: test num_bytes: 27209.6 num_examples: 200 download_size: 11524 dataset_size: 136048.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

lighteval/RULER-131072-Lamma3-Instruct

lighteval

2025-06-18T12:10:44Z

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-18T12:03:29Z

0

--- dataset_info: features: - name: index dtype: int64 - name: input dtype: string - name: outputs sequence: string - name: length dtype: int64 splits: - name: vt num_bytes: 245243500 num_examples: 500 - name: fwe num_bytes: 160767200 num_examples: 500 - name: niah_single_1 num_bytes: 245447719 num_examples: 500 - name: qa_2 num_bytes: 278549005 num_examples: 500 - name: niah_multikey_1 num_bytes: 306810494 num_examples: 500 - name: niah_multivalue num_bytes: 306833986 num_examples: 500 - name: niah_multikey_3 num_bytes: 128503000 num_examples: 500 - name: niah_single_3 num_bytes: 306735516 num_examples: 500 - name: niah_single_2 num_bytes: 306711618 num_examples: 500 - name: qa_1 num_bytes: 312918473 num_examples: 500 - name: niah_multikey_2 num_bytes: 242897246 num_examples: 500 - name: niah_multiquery num_bytes: 306890263 num_examples: 500 - name: cwe num_bytes: 177649224 num_examples: 500 download_size: 1617169541 dataset_size: 3325957244 configs: - config_name: default data_files: - split: vt path: data/vt-* - split: fwe path: data/fwe-* - split: niah_single_1 path: data/niah_single_1-* - split: qa_2 path: data/qa_2-* - split: niah_multikey_1 path: data/niah_multikey_1-* - split: niah_multivalue path: data/niah_multivalue-* - split: niah_multikey_3 path: data/niah_multikey_3-* - split: niah_single_3 path: data/niah_single_3-* - split: niah_single_2 path: data/niah_single_2-* - split: qa_1 path: data/qa_1-* - split: niah_multikey_2 path: data/niah_multikey_2-* - split: niah_multiquery path: data/niah_multiquery-* - split: cwe path: data/cwe-* ---

RyanYr/reflect_collegemath-test_t4

RyanYr

2025-01-19T04:11:17Z

15

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-01-17T17:09:46Z

0

--- dataset_info: features: - name: data_source dtype: string - name: question_number dtype: string - name: problem dtype: string - name: answer dtype: string - name: license dtype: string - name: data_topic dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@3 sequence: string - name: response@4 sequence: string - name: response@5 sequence: string - name: response@6 sequence: string - name: response@7 sequence: string - name: response@8 sequence: string splits: - name: train num_bytes: 46205198 num_examples: 2818 download_size: 16907418 dataset_size: 46205198 configs: - config_name: default data_files: - split: train path: data/train-* ---

nvidia/Scoring-Verifiers

nvidia

2025-04-01T16:35:02Z

33

6

[ "task_categories:text-ranking", "license:other", "size_categories:1K<n<10K", "arxiv:2502.13820", "region:us", "code", "synthetic", "nvidia", "reasoning", "llms", "verifiers" ]

[ "text-ranking" ]

2025-04-01T15:55:46Z

0

--- license: other license_name: nsclv1 license_link: https://github.com/aleksficek/Scoring-Verifiers/blob/main/LICENSE task_categories: - text-ranking tags: - code - synthetic - nvidia - reasoning - llms - verifiers pretty_name: Scoring Verifiers size_categories: - 1K<n<10K --- # Scoring Verifiers Scoring Verifiers is a set of 4 benchmarks that evaluate the scoring and ranking capabilities of synthetic verifiers such as test case generation and reward modelling. You can find our paper [Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning](https://www.arxiv.org/abs/2502.13820) which explains in more detail our methodology, benchmark details and findings. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b8b0096c3182e96bf6cea1/HPZXg6QOjpI1h9NvujHQr.png) ## Datasets In this repository, we include 4 benchmarks that are code scoring and ranking versions of HumanEval and MBPP: - HE-R - HE-R+ - MBPP-R - MBPP-R+ Each dataset sample contains a question from HumanEval or MBPP following by several `gpt-4o` solutions and their rankings based on pre-defined test case execution scores. Alongside the keys found in the original benchmarks each sample contains the following keys: - `task_id` - `prompt` - `canonical_solution` - `all_solutions` (each solution contains the following) - `rank` - `average_test_score` - `average_time_taken` - `solution` For example, the following is a distribution of the test case scores for all solutions in HE-R+ and MBPP-R+ respectively. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b8b0096c3182e96bf6cea1/ZHYrzGVdirzifWoBJT518.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b8b0096c3182e96bf6cea1/LxcCczOl_BbG2UdFTthxy.png) ## Paper Overall our paper's contributions can be summarized as follows: 1. We provide a recipe to transform any coding benchmark with predefined test cases into a code scoring and ranking benchmark. 2. We certify our recipe by creating code scoring and ranking versions of HumanEval and MBPP datasets: HE-R, HE-R+, MBPP-R, MBPP-R+. 3. We use our benchmark to evaluate synthetic verification methods such as test case generation in standard, reward and reasoning LLM’s. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67b8b0096c3182e96bf6cea1/s-mRwk11u_BYXvaeN9lq7.png) We also open-source the [code used to generate these benchmarks](https://github.com/aleksficek/Scoring-Verifiers). ## Citation ``` @misc{ficek2025scoringverifiersevaluatingsynthetic, title={Scoring Verifiers: Evaluating Synthetic Verification in Code and Reasoning}, author={Aleksander Ficek and Somshubra Majumdar and Vahid Noroozi and Boris Ginsburg}, year={2025}, eprint={2502.13820}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2502.13820}, } ```

ssktora/train-jqara-for-tevatron-1-all

ssktora

2025-04-16T06:17:45Z

16

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-16T06:17:41Z

0

--- dataset_info: features: - name: query_id dtype: string - name: query dtype: string - name: positive_passages list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string - name: negative_passages list: - name: docid dtype: string - name: text dtype: string - name: title dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 643740 num_examples: 18 download_size: 406196 dataset_size: 643740 configs: - config_name: default data_files: - split: train path: data/train-* ---

french-datasets/ProfessorBob-text-embedding-dataset

french-datasets

2025-03-30T17:00:19Z

16

0

[ "language:fra", "region:us" ]

[]

2025-03-29T20:42:45Z

0

--- language: "fra" viewer: false --- Ce répertoire est vide, il a été créé pour améliorer le référencement du jeu de données [ProfessorBob/text-embedding-dataset](huggingface.co/datasets/ProfessorBob/text-embedding-dataset).

jed351/Chinese-Common-Crawl-Filtered

jed351

2025-06-02T05:32:00Z

264

15

[ "language:zh", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2023-07-20T21:23:06Z

0

--- language: - zh --- # Traditional Chinese C4 ### Dataset Summary Data obtained from 2025-18 and 2025-13 Common Crawl. Downloaded and processed using [code](https://github.com/jedcheng/c4-dataset-script) based on another [project](https://github.com/shjwudp/c4-dataset-script) attempting to recreate the C4 dataset. The resultant dataset contains both simplified and traditional Chinese. It was then filtered using a [modified list](https://github.com/jedcheng/c4-dataset-script/blob/master/SC_filter/SC_list.txt) of simplified Chinese characters to obtain [another traditional Chinese dataset](https://huggingface.co/datasets/jed351/Traditional-Chinese-Common-Crawl-Filtered). I am still ironning out the process of filtering. The 2025-13 dataset was deduplicated without splitting into shards resulting in a smaller dataset than the 2025-18 dataset. Unfortunately, this process takes 2 CPU nodes for 4 hours. In addition, other people would have done a better job at cleaning the common crawl. To preserver time, money and resources, I will not work on this dataset anymore but instead shift my focus to the [Traditional Chinese Common Crawl dataset](https://huggingface.co/datasets/jed351/Traditional-Chinese-Common-Crawl).

sergiov2000/eval_wm_act_yellow3

sergiov2000

2025-05-30T10:17:50Z

114

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]

[ "robotics" ]

2025-05-30T10:17:34Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 2, "total_frames": 1728, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.above": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.side": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

mhr2004/NevIR-val

mhr2004

2025-04-25T05:29:58Z

30

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-25T05:29:56Z

0

--- dataset_info: features: - name: id dtype: string - name: version dtype: string - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 689464 num_examples: 450 download_size: 125405 dataset_size: 689464 configs: - config_name: default data_files: - split: train path: data/train-* ---

michsethowusu/dyula-kamba_sentence-pairs

michsethowusu

2025-04-02T13:14:12Z

11

0

[ "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-02T13:14:08Z

0

--- dataset_info: features: - name: score dtype: float32 - name: Dyula dtype: string - name: Kamba dtype: string splits: - name: train num_bytes: 2691554 num_examples: 22901 download_size: 2691554 dataset_size: 2691554 configs: - config_name: default data_files: - split: train path: Dyula-Kamba_Sentence-Pairs.csv --- # Dyula-Kamba_Sentence-Pairs Dataset This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks. This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php) ## Metadata - **File Name**: Dyula-Kamba_Sentence-Pairs - **Number of Rows**: 22901 - **Number of Columns**: 3 - **Columns**: score, Dyula, Kamba ## Dataset Description The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns: 1. `score`: The similarity score between the two sentences (range from 0 to 1). 2. `Dyula`: The first sentence in the pair (language 1). 3. `Kamba`: The second sentence in the pair (language 2). This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning. ## References Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications: [1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017 [2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018. [3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018 [4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018. [5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018. [6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018. [7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019. [8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB [9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761. [10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages

SciFy/annotation_demo

SciFy

2025-03-10T16:39:57Z

17

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-10T16:39:55Z

0

--- dataset_info: features: - name: id dtype: string - name: metadata struct: - name: ocr_model dtype: string - name: python_version dtype: string splits: - name: annotations num_bytes: 89 num_examples: 2 download_size: 1735 dataset_size: 89 configs: - config_name: default data_files: - split: annotations path: data/annotations-* ---

taeyoon12321421/final_datasets_gorani

taeyoon12321421

2025-02-12T03:01:45Z

14

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-12T03:01:22Z

0

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: target_language dtype: string - name: metadata struct: - name: ENG dtype: string - name: JPN dtype: string - name: KO dtype: string splits: - name: train num_bytes: 10250793 num_examples: 21662 - name: test num_bytes: 2558493 num_examples: 5416 download_size: 4724881 dataset_size: 12809286 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sl-revised_NoQuant_32_32_0.05_64_BestF1

ferrazzipietro

2024-11-25T14:10:59Z

20

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-11-25T11:24:26Z

0

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 149854 num_examples: 101 - name: test num_bytes: 1063090 num_examples: 654 download_size: 241896 dataset_size: 1212944 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

severo/fix-401

severo

2022-06-24T11:45:48Z

12

0

[ "region:us" ]

[]

2022-06-20T16:04:10Z

0

--- viewer: false --- # Try to include an iframe from observable: <iframe width="100%" height="635" frameborder="0" src="https://observablehq.com/embed/@d3/sortable-bar-chart?cell=viewof+order&cell=chart"></iframe> from an HF space: <iframe src="https://hf.space/embed/YoannLemesle/CLIPictionary/+?__theme=system" data-src="https://hf.space/embed/YoannLemesle/CLIPictionary/+" data-sdk="gradio" title="Gradio app" class="container p-0 flex-grow overflow-hidden space-iframe" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking" sandbox="allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads" scrolling="no" id="iFrameResizer0" style="overflow: hidden; height: 725px;"></iframe>

yunjae-won/mp_gemma9b_sft_ogd_rms_epoch4_multisample_2.5k

yunjae-won

2025-05-11T20:41:37Z

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-11T20:41:32Z

0

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: output_logps dtype: float64 splits: - name: train num_bytes: 58064068 num_examples: 20000 download_size: 20871352 dataset_size: 58064068 configs: - config_name: default data_files: - split: train path: data/train-* ---

a1031737/flickr8k_geometry3k_style

a1031737

2025-05-09T10:12:01Z

0

[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-09T07:31:56Z

0

--- dataset_info: features: - name: images sequence: image - name: problem dtype: string - name: answer dtype: string splits: - name: train num_bytes: 12950265.6 num_examples: 135 - name: validation num_bytes: 671495.2533333333 num_examples: 7 - name: test num_bytes: 767423.1466666666 num_examples: 8 download_size: 14477291 dataset_size: 14389184.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Rapidata/text-2-image-Rich-Human-Feedback-32k

Rapidata

2025-04-29T11:28:30Z

159

12

[ "language:en", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2312.10240", "region:us", "heatmap", "t2i", "human", "feedback", "rich", "annotation", "open-image-preferences" ]

[]

2025-04-24T15:55:37Z

12

--- dataset_info: features: - name: image_name dtype: image - name: sentence dtype: string - name: word_scores dtype: string - name: alignment_score_norm dtype: float32 - name: coherence_score_norm dtype: float32 - name: style_score_norm dtype: float32 - name: alignment_heatmap dtype: array2_d: shape: - 1024 - 1024 dtype: float32 - name: coherence_heatmap dtype: array2_d: shape: - 1024 - 1024 dtype: float32 - name: alignment_score dtype: float32 - name: coherence_score dtype: float32 - name: style_score dtype: float32 splits: - name: train num_bytes: 116617124714.976 num_examples: 32528 download_size: 91216762385 dataset_size: 116617124714.976 configs: - config_name: default data_files: - split: train path: data/train-* tags: - heatmap - t2i - human - feedback - rich - annotation - open-image-preferences license: apache-2.0 language: - en pretty_name: Text to image - Rich Annotation size_categories: - 10K<n<100K --- <a href="https://www.rapidata.ai"> <img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/jfxR79bOztqaC6_yNNnGU.jpeg" width="250" alt="Rapidata Logo"> </a> Building upon Google's research [Rich Human Feedback for Text-to-Image Generation](https://arxiv.org/abs/2312.10240), and the [smaller, previous version of this dataset](https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback), we have collected over 3.7 million responses from 307'415 individual humans for the [open-image-preference-v1](https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1) dataset using Rapidata via the [Python API](https://docs.rapidata.ai/). Collection took less than 2 weeks. If you get value from this dataset and would like to see more in the future, please consider liking it ♥️ # Overview We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were asked to identify specific problematic areas. Additionally, for all images, participants identified words from the prompts that were not accurately represented in the generated images. If you want to replicate the annotation setup, the steps are outlined at the [bottom](#replicating-the-annotation-setup). This dataset and the annotation process is described in further detail in our blog post [Beyond Image Preferences](https://huggingface.co/blog/RapidataAI/beyond-image-preferences). # Usage Examples Accessing this data is easy with the Huggingface `dataset` library. For quick demos or previews, we recommend setting `streaming=True` as downloading the whole dataset can take a while. ```python from datasets import load_dataset ds = load_dataset("Rapidata/text-2-image-Rich-Human-Feedback-32k", split="train", streaming=True) ``` As an example, below we show how to replicate the figures below. <details> <summary>Click to expand Select Words example</summary> The methods below can be used to produce figures similar to the ones shownn below. Note however that the figures below were created using `matplotlib`, however we opt to use `opencv` here as it makes calculating the text spacing much easier. **Methods** ```python from PIL import Image from datasets import load_dataset import cv2 import numpy as np def get_colors(words): colors = [] for item in words: intensity = item / max(words) value = np.uint8((1 - intensity) * 255) color = tuple(map(int, cv2.applyColorMap(np.array([[value]]), cv2.COLORMAP_AUTUMN)[0][0])) colors.append(color) return colors def get_wrapped_text(text_color_pairs, font, font_scale, thickness, word_spacing, max_width): wrapped_text_color_pairs, current_line, line_width = [], [], 0 for text, color in text_color_pairs: text_size = cv2.getTextSize(text, font, font_scale, thickness)[0] if line_width + text_size[0] > max_width: wrapped_text_color_pairs.append(current_line) current_line, line_width = [], 0 current_line.append((text, color, text_size)) line_width += text_size[0] + word_spacing wrapped_text_color_pairs.append(current_line) return wrapped_text_color_pairs def add_multicolor_text(input, text_color_pairs, font_scale=1, thickness=2, word_spacing=20): image = cv2.cvtColor(np.array(input), cv2.COLOR_RGB2BGR) image_height, image_width, _ = image.shape font = cv2.FONT_HERSHEY_SIMPLEX wrapped_text = get_wrapped_text(text_color_pairs, font, font_scale, thickness, word_spacing, int(image_width*0.95)) position = (int(0.025*image_width), int(word_spacing*2)) overlay = image.copy() cv2.rectangle(overlay, (0, 0), (image_width, int((len(wrapped_text)+1)*word_spacing*2)), (100,100,100), -1) out_img = cv2.addWeighted(overlay, 0.75, image, 0.25, 0) for idx, text_line in enumerate(wrapped_text): current_x, current_y = position[0], position[1] + int(idx*word_spacing*2) for text, color, text_size in text_line: cv2.putText(out_img, text, (current_x, current_y), font, font_scale, color, thickness) current_x += text_size[0] + word_spacing return Image.fromarray(cv2.cvtColor(out_img, cv2.COLOR_BGR2RGB)) ``` **Create figures** ```python ds_words = ds.select_columns(["image","prompt", "word_scores"]) for example in ds_words.take(5): image = example["image"] prompt = example["prompt"] word_scores = [s[1] for s in eval(example["word_scores"])] words = [s[0] for s in eval(example["word_scores"])] colors = get_colors(word_scores) display(add_multicolor_text(image, list(zip(words, colors)), font_scale=1, thickness=2, word_spacing=20)) ``` </details> <details> <summary>Click to expand Heatmap example</summary> **Methods** ```python import cv2 import numpy as np from PIL import Image def overlay_heatmap(image, heatmap, alpha=0.3): cv2_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) heatmap_normalized = ((heatmap - heatmap.min()) / (heatmap.max() - heatmap.min())) heatmap_normalized = np.uint8(255 * (heatmap_normalized)) heatmap_colored = cv2.applyColorMap(heatmap_normalized, cv2.COLORMAP_HOT) overlaid_image = cv2.addWeighted(cv2_image, 1 - alpha, heatmap_colored, alpha, 0) return Image.fromarray(cv2.cvtColor(overlaid_image, cv2.COLOR_BGR2RGB)) ``` **Create figures** ```python ds_heatmap = ds.select_columns(["image","prompt", "alignment_heatmap"]) for example in ds_heatmap.take(5): image = example["image"] heatmap = example["alignment_heatmap"] if heatmap: display(overlay_heatmap(image, np.asarray(heatmap))) ``` </details> </br> # Data Summary ## Word Scores Users identified words from the prompts that were NOT accurately depicted in the generated images. Higher word scores indicate poorer representation in the image. Participants also had the option to select "[No_mistakes]" for prompts where all elements were accurately depicted. ### Examples Results: | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/lzlWHmLKBvBJhjGWP8xZZ.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/b38uskYWaGEgfeJQtKiaO.png" width="500"> | |---|---| | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/4uWKVjZBA5aX2YDUYNpdV.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/f9JIuwDoNohy7EkDYILFm.png" width="500"> | ## Coherence The coherence score measures whether the generated image is logically consistent and free from artifacts or visual glitches. Without seeing the original prompt, users were asked: "Look closely, does this image have weird errors, like senseless or malformed objects, incomprehensible details, or visual glitches?" Each image received at least 21 responses indicating the level of coherence on a scale of 1-5, which were then averaged to produce the final scores where 5 indicates the highest coherence. Images scoring below 3.5 in coherence were further evaluated, with participants marking specific errors in the image. ### Example Results: | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/sc-4ls9X0yO-hGN0VCDSX.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/J77EmYp4oyRRakkcRnaF9.png" width="500"> | |---|---| | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/mRDdoQdc4_iy2JcLhdI7J.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/2N2KJyz4YOGT6N6tuUX8M.png" width="500"> | ## Alignment The alignment score quantifies how well an image matches its prompt. Users were asked: "How well does the image match the description?". Again, each image received at least 21 responses indicating the level of alignment on a scale of 1-5 (5 being the highest), which were then averaged. For images with an alignment score below 3.2, additional users were asked to highlight areas where the image did not align with the prompt. These responses were then compiled into a heatmap. As mentioned in the google paper, aligment is harder to annotate consistently, if e.g. an object is missing, it is unclear to the annotators what they need to highlight. ### Example Results: <style> .example-results-grid { display: grid; grid-template-columns: repeat(2, 450px); gap: 20px; margin: 20px 0; justify-content: left; } .result-card { background-color: #fff; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); padding: 15px; width: 450px; } .prompt { margin-bottom: 10px; font-size: 18px; line-height: 1.4; color: #333; background-color: #f8f8f8; padding: 10px; border-radius: 5px; } .image-container img { width: 450px; height: auto; border-radius: 4px; } @media (max-width: 1050px) { .example-results-grid { grid-template-columns: 450px; } } </style> <div class="example-results-grid"> <div class="result-card"> <div class="prompt"> <strong>Prompt:</strong> Three cats and one dog sitting on the grass. </div> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/qCNWVSNjPsp8XQ3zliLcp.png" alt="Three cats and one dog"> </div> </div> <div class="result-card"> <div class="prompt"> <strong>Prompt:</strong> A brown toilet with a white wooden seat. </div> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/M3buzP-5k4pRCxOi_ijxM.png" alt="Brown toilet"> </div> </div> <div class="result-card"> <div class="prompt"> <strong>Prompt:</strong> Photograph of a pale Asian woman, wearing an oriental costume, sitting in a luxurious white chair. Her head is floating off the chair, with the chin on the table and chin on her knees, her chin on her knees. Closeup </div> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/ggYXUEbGppiTeL84pG-DP.png" alt="Asian woman in costume"> </div> </div> <div class="result-card"> <div class="prompt"> <strong>Prompt:</strong> A tennis racket underneath a traffic light. </div> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/mT7sAbnO-w6ySXaeEqEki.png" alt="Racket under traffic light"> </div> </div> </div> ## Style The style score reflects how visually appealing participants found each image, independent of the prompt. Users were asked: "How much do you like the way this image looks?" Each image received 21 responses grading on a scale of 1-5, which were then averaged. In contrast to other prefrence collection methods, such as the huggingface image arena, the preferences were collected from humans from around the world (156 different countries) from all walks of life, creating a more representative score. # About Rapidata Rapidata's technology makes collecting human feedback at scale faster and more accessible than ever before. Visit [rapidata.ai](https://www.rapidata.ai/) to learn more about how we're revolutionizing human feedback collection for AI development. # Other Datasets We run a benchmark of the major image generation models, the results can be found on our [website](https://www.rapidata.ai/leaderboard/image-models). We rank the models according to their coherence/plausiblity, their aligment with the given prompt and style prefernce. The underlying 2M+ annotations can be found here: - Link to the [Coherence dataset](https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset) - Link to the [Text-2-Image Alignment dataset](https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Alignment_Dataset) - Link to the [Preference dataset](https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3) We have also started to run a [video generation benchmark](https://www.rapidata.ai/leaderboard/video-models), it is still a work in progress and currently only covers 2 models. They are also analysed in coherence/plausiblity, alignment and style preference. # Replicating the Annotation Setup For researchers interested in producing their own rich preference dataset, you can directly use the Rapidata API through python. The code snippets below show how to replicate the modalities used in the dataset. Additional information is available through the [documentation](https://docs.rapidata.ai/) <details> <summary>Creating the Rapidata Client and Downloading the Dataset</summary> First install the rapidata package, then create the RapidataClient() this will be used create and launch the annotation setup ```bash pip install rapidata ``` ```python from rapidata import RapidataClient, LabelingSelection, ValidationSelection client = RapidataClient() ``` As example data we will just use images from the dataset. Make sure to set `streaming=True` as downloading the whole dataset might take a significant amount of time. ```python from datasets import load_dataset ds = load_dataset("Rapidata/text-2-image-Rich-Human-Feedback-32k", split="train", streaming=True) ds = ds.select_columns(["image","prompt"]) ``` Since we use streaming, we can extract the prompts and download the images we need like this: ```python import os tmp_folder = "demo_images" # make folder if it doesn't exist if not os.path.exists(tmp_folder): os.makedirs(tmp_folder) prompts = [] image_paths = [] for i, row in enumerate(ds.take(10)): prompts.append(row["prompt"]) # save image to disk save_path = os.path.join(tmp_folder, f"{i}.jpg") row["image"].save(save_path) image_paths.append(save_path) ``` </details> <details> <summary>Likert Scale Alignment Score</summary> To launch a likert scale annotation order, we make use of the classification annotation modality. Below we show the setup for the alignment criteria. The structure is the same for style and coherence, however arguments have to be adjusted of course. I.e. different instructions, options and validation set. ```python # Alignment Example instruction = "How well does the image match the description?" answer_options = [ "1: Not at all", "2: A little", "3: Moderately", "4: Very well", "5: Perfectly" ] order = client.order.create_classification_order( name="Alignment Example", instruction=instruction, answer_options=answer_options, datapoints=image_paths, contexts=prompts, # for alignment, prompts are required as context for the annotators. responses_per_datapoint=10, selections=[ValidationSelection("676199a5ef7af86285630ea6"), LabelingSelection(1)] # here we use a pre-defined validation set. See https://docs.rapidata.ai/improve_order_quality/ for details ) order.run() # This starts the order. Follow the printed link to see progress. ``` </details> <details> <summary>Alignment Heatmap</summary> To produce heatmaps, we use the locate annotation modality. Below is the setup used for creating the alignment heatmaps. ```python # alignment heatmap # Note that the selected images may not actually have severely misaligned elements, but this is just for demonstration purposes. order = client.order.create_locate_order( name="Alignment Heatmap Example", instruction="What part of the image does not match with the description? Tap to select.", datapoints=image_paths, contexts=prompts, # for alignment, prompts are required as context for the annotators. responses_per_datapoint=10, selections=[ValidationSelection("67689e58026456ec851f51f8"), LabelingSelection(1)] # here we use a pre-defined validation set for alignment. See https://docs.rapidata.ai/improve_order_quality/ for details ) order.run() # This starts the order. Follow the printed link to see progress. ``` </details> <details> <summary>Select Misaligned Words</summary> To launch the annotation setup for selection of misaligned words, we used the following setup ```python # Select words example from rapidata import LanguageFilter select_words_prompts = [p + " [No_Mistake]" for p in prompts] order = client.order.create_select_words_order( name="Select Words Example", instruction = "The image is based on the text below. Select mistakes, i.e., words that are not aligned with the image.", datapoints=image_paths, sentences=select_words_prompts, responses_per_datapoint=10, filters=[LanguageFilter(["en"])], # here we add a filter to ensure only english speaking annotators are selected selections=[ValidationSelection("6761a86eef7af86285630ea8"), LabelingSelection(1)] # here we use a pre-defined validation set. See https://docs.rapidata.ai/improve_order_quality/ for details ) order.run() ``` </details>

shreyas231219/new_dataset_1

shreyas231219

2025-02-09T03:06:24Z

17

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-09T03:06:20Z

0

--- dataset_info: features: - name: What dtype: string - name: When dtype: string - name: Where dtype: string - name: Who dtype: string - name: Category dtype: string splits: - name: train num_bytes: 118713 num_examples: 1500 download_size: 35429 dataset_size: 118713 configs: - config_name: default data_files: - split: train path: data/train-* ---

selfcorrexp2/llama31_first_wrong_and_20kfirst_corr_regular_norr_20k

selfcorrexp2

2024-12-25T21:08:39Z

13

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-25T21:08:10Z

0

--- dataset_info: features: - name: idx dtype: int64 - name: prompt dtype: string - name: answers sequence: string - name: first_round dtype: bool - name: gt dtype: string - name: rewards sequence: bool - name: my_solu sequence: string - name: flag dtype: bool - name: turn dtype: int64 - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 338968859.6649252 num_examples: 20000 download_size: 152955034 dataset_size: 338968859.6649252 configs: - config_name: default data_files: - split: train path: data/train-* ---

RobotisSW/eval_ffw_test_pc_8

RobotisSW

2025-04-28T14:37:43Z

28

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "tutorial" ]

[ "robotics" ]

2025-04-28T14:37:36Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "ffw", "total_episodes": 2, "total_frames": 327, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 16 ], "names": [ "arm_right_waist", "arm_right_shoulder", "arm_right_shoulder_shadow", "arm_right_elbow", "arm_right_elbow_shadow", "arm_right_forearm_roll", "arm_right_wrist_angle", "arm_right_gripper", "arm_left_waist", "arm_left_shoulder", "arm_left_shoulder_shadow", "arm_left_elbow", "arm_left_elbow_shadow", "arm_left_forearm_roll", "arm_left_wrist_angle", "arm_left_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 16 ], "names": [ "arm_right_waist", "arm_right_shoulder", "arm_right_shoulder_shadow", "arm_right_elbow", "arm_right_elbow_shadow", "arm_right_forearm_roll", "arm_right_wrist_angle", "arm_right_gripper", "arm_left_waist", "arm_left_shoulder", "arm_left_shoulder_shadow", "arm_left_elbow", "arm_left_elbow_shadow", "arm_left_forearm_roll", "arm_left_wrist_angle", "arm_left_gripper" ] }, "observation.images.cam_head": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_wrist_1": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.cam_wrist_2": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

UniDataPro/portuguese-speech-recognition-dataset

UniDataPro

2025-05-22T09:06:31Z

75

0

[ "license:cc-by-nc-nd-4.0", "size_categories:n<1K", "format:audiofolder", "modality:audio", "modality:text", "library:datasets", "library:mlcroissant", "region:us", "speech recognition", "machine learning", "NLP", "ASR", "audio", "speech", "portuguese" ]

[]

2025-03-17T21:47:11Z

0

--- license: cc-by-nc-nd-4.0 tags: - speech recognition - machine learning - NLP - ASR - audio - speech - portuguese size_categories: - 1K<n<10K --- # Portuguese Speech Dataset for recognition task Dataset comprises **406** hours of telephone dialogues in Portuguese, collected from **590** native speakers across various topics and domains. This dataset boasts an impressive **98%** word accuracy rate, making it a valuable resource for advancing **speech recognition technology**. By utilizing this dataset, researchers and developers can advance their understanding and capabilities in **automatic speech recognition** (ASR) systems, **transcribing audio**, and **natural language processing** (NLP). - **[Get the data](https://unidata.pro/datasets/portuguese-speech-recognition-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=portuguese-speech-recognition-dataset)** The dataset includes high-quality audio recordings with text transcriptions, making it ideal for training and evaluating speech recognition models. # 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/portuguese-speech-recognition-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=portuguese-speech-recognition-dataset) to discuss your requirements and pricing options. ## Metadata for the dataset ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fa3f375fb273dcad3fe17403bdfccb63b%2Fssssssssss.PNG?generation=1739884059328284&alt=media) - **Audio files:** High-quality recordings in **WAV** format - **Text transcriptions:** Accurate and detailed **transcripts** for each audio segment - **Speaker information:** Metadata on **native speakers**, including **gender** and etc - **Topics:** Diverse domains such as **general conversations**, **business** and etc This dataset is a valuable resource for researchers and developers working on speech recognition, language models, and speech technology. # 🌐 [UniData](https://unidata.pro/datasets/portuguese-speech-recognition-dataset/?utm_source=huggingface&utm_medium=referral&utm_campaign=portuguese-speech-recognition-dataset) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

r2e-edits/14b_swebv_temp08_10_patch_verifier

r2e-edits

2025-02-26T03:54:00Z

19

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-26T03:53:54Z

0

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: docker_images dtype: string - name: rewards dtype: float64 splits: - name: train num_bytes: 356479621 num_examples: 4801 download_size: 106826836 dataset_size: 356479621 configs: - config_name: default data_files: - split: train path: data/train-* ---

justus27/lcbv5-test

justus27

2025-06-25T00:22:26Z

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-23T23:42:25Z

0

--- dataset_info: features: - name: problem_id dtype: string - name: task_type dtype: string - name: prompt dtype: string - name: verification_info dtype: string - name: responses sequence: string - name: response_lens sequence: int64 - name: avg_reward dtype: float64 splits: - name: train num_bytes: 3921596209 num_examples: 279 download_size: 2415217496 dataset_size: 3921596209 configs: - config_name: default data_files: - split: train path: data/train-* ---

Iess/chinese_modern_poetry

Iess

2023-06-25T16:39:13Z

281

25

[ "language:zh", "license:mit", "size_categories:100K<n<1M", "format:json", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us", "poetry", "chinese poetry", "modern poetry", "chinese modern poetry" ]

[]

2023-06-25T15:59:49Z

1

--- license: mit language: - zh tags: - poetry - chinese poetry - modern poetry - chinese modern poetry --- ### 简介 1. 数据集包括了近现代的中国诗人及外国诗人（中译版）作品，所有作品著作权归原作者所有，侵删请联系[email protected] 2. chinese_poems.jsonl为原数据，training_imagery2-5_maxlen256.json 分别是根据2-5个关键意象生成诗歌的相关数据集 3. 数据来源于网络，包括但不限于 + https://github.com/sheepzh/poetry + https://bedtimepoem.com/ + https://poemwiki.org/ + baidu、google、zhihu等 ### 一些作品使用此数据集训练ChatGLM、LLaMA7b模型生成的诗歌，更多诗歌查看poems目录 ![alt 云](https://huggingface.co/datasets/Iess/chinese_modern_poetry/resolve/main/poems/%E4%BA%91.jpg) ![alt 桃花潭](https://huggingface.co/datasets/Iess/chinese_modern_poetry/resolve/main/poems/%E6%A1%83%E8%8A%B1%E6%BD%AD.jpg) ![alt 荒原](https://huggingface.co/datasets/Iess/chinese_modern_poetry/resolve/main/poems/%E8%8D%92%E5%8E%9F.jpg)

test-gen/code_humaneval_qwen2.5-3b_t0.1_n8_tests_humaneval_qwen3-8b_t0.7_n1

test-gen

2025-05-16T15:32:56Z

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-16T15:32:55Z

0

--- dataset_info: features: - name: task_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: test dtype: string - name: entry_point dtype: string - name: generated_code sequence: string - name: gt_rewards sequence: float64 - name: rewards sequence: float64 - name: verification_info struct: - name: language dtype: string - name: test_cases sequence: string splits: - name: test num_bytes: 2299064 num_examples: 164 download_size: 396095 dataset_size: 2299064 configs: - config_name: default data_files: - split: test path: data/test-* ---

chiyuanhsiao/text_L2-regular-ASR_trivia_qa-audio

chiyuanhsiao

2025-04-28T16:02:06Z

13

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-28T15:57:00Z

0

--- dataset_info: features: - name: question dtype: string - name: question_id dtype: string - name: question_source dtype: string - name: entity_pages sequence: - name: doc_source dtype: string - name: filename dtype: string - name: title dtype: string - name: wiki_context dtype: string - name: search_results sequence: - name: description dtype: string - name: filename dtype: string - name: rank dtype: int32 - name: title dtype: string - name: url dtype: string - name: search_context dtype: string - name: answer struct: - name: aliases sequence: string - name: normalized_aliases sequence: string - name: matched_wiki_entity_name dtype: string - name: normalized_matched_wiki_entity_name dtype: string - name: normalized_value dtype: string - name: type dtype: string - name: value dtype: string - name: my_prediction_text dtype: string splits: - name: validation num_bytes: 51583519 num_examples: 1000 download_size: 30132540 dataset_size: 51583519 configs: - config_name: default data_files: - split: validation path: data/validation-* ---

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_41d6cd9b-6ceb-4990-a7dc-c6f7e62b549a

argilla-internal-testing

2024-12-18T10:23:25Z

16

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-18T10:23:24Z

0

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

alea-institute/kl3m-filter-data-dotgov-www.usa.gov

alea-institute

2025-02-04T21:20:16Z

15

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-04T21:20:15Z

0

--- dataset_info: features: - name: identifier dtype: string - name: dataset dtype: string - name: mime_type dtype: string - name: score dtype: float64 - name: tokens sequence: int64 splits: - name: train num_bytes: 4093672 num_examples: 615 download_size: 602873 dataset_size: 4093672 configs: - config_name: default data_files: - split: train path: data/train-* ---

BoooomNing/Faces_Dataset

BoooomNing

2025-01-09T12:12:34Z

29

1

[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-01-09T12:12:16Z

0

--- dataset_info: features: - name: face_images dtype: image - name: target_images dtype: image - name: captions dtype: string splits: - name: train num_bytes: 144501219.0 num_examples: 470 download_size: 106181649 dataset_size: 144501219.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

serEzioAuditore/turkceVeriset3

serEzioAuditore

2025-03-16T19:51:48Z

15

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-16T19:51:34Z

0

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 245519628.3 num_examples: 90000 - name: validation num_bytes: 27279958.7 num_examples: 10000 download_size: 160223582 dataset_size: 272799587.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

JavisGPT-dev/InstUnd-Audio

JavisGPT-dev

2025-03-17T08:42:18Z

152

1

[ "size_categories:1M<n<10M", "format:json", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "region:us" ]

[]

2025-02-24T02:55:19Z

0

--- size_categories: - 100K<n<1M --- - After downloading the dataset, you need to concat the files and extract the files. ```shell cat data_part_* > data.tar.gz tar -xzvf data.tar.gz ```

ZhengGuangze/TAP-Vid

ZhengGuangze

2025-05-28T01:32:13Z

0

[ "license:apache-2.0", "size_categories:1K<n<10K", "format:text", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]

[]

2025-05-28T01:03:50Z

0

--- license: apache-2.0 ---

thainq107/c4-small

thainq107

2025-05-23T02:32:41Z

0

[ "region:us" ]

[]

2025-05-23T02:32:24Z

0

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 194484214.8 num_examples: 90000 - name: test num_bytes: 21609357.2 num_examples: 10000 download_size: 131631260 dataset_size: 216093572.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

eliasfiz/emilia-snac-with-spk-emb-DE

eliasfiz

2025-05-06T23:08:35Z

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-06T22:57:00Z

0

--- dataset_info: features: - name: codes_list sequence: int64 - name: speaker_embedding sequence: float32 - name: text dtype: string splits: - name: part_82 num_bytes: 3004706 num_examples: 378 - name: part_33 num_bytes: 26900546 num_examples: 3853 - name: part_72 num_bytes: 11162071 num_examples: 1474 - name: part_73 num_bytes: 11241726 num_examples: 1485 - name: part_69 num_bytes: 16655292 num_examples: 2172 - name: part_36 num_bytes: 32814113 num_examples: 4402 - name: part_66 num_bytes: 16919071 num_examples: 2222 - name: part_77 num_bytes: 11260986 num_examples: 1461 - name: part_50 num_bytes: 25353469 num_examples: 3319 - name: part_59 num_bytes: 23748838 num_examples: 3179 - name: part_55 num_bytes: 24708418 num_examples: 3278 - name: part_47 num_bytes: 37466034 num_examples: 5054 - name: part_54 num_bytes: 24892316 num_examples: 3332 - name: part_57 num_bytes: 24722307 num_examples: 3336 - name: part_11 num_bytes: 86811637 num_examples: 14069 - name: part_12 num_bytes: 86629026 num_examples: 13529 - name: part_14 num_bytes: 89289674 num_examples: 14227 - name: part_8 num_bytes: 104772590 num_examples: 15144 - name: part_27 num_bytes: 92785970 num_examples: 15702 - name: part_16 num_bytes: 104976420 num_examples: 16303 - name: part_21 num_bytes: 103246091 num_examples: 16788 - name: part_13 num_bytes: 111827449 num_examples: 17857 - name: part_20 num_bytes: 103331829 num_examples: 16560 - name: part_5 num_bytes: 128579654 num_examples: 18538 download_size: 541210774 dataset_size: 1303100233 configs: - config_name: default data_files: - split: part_82 path: data/part_82-* - split: part_33 path: data/part_33-* - split: part_72 path: data/part_72-* - split: part_73 path: data/part_73-* - split: part_69 path: data/part_69-* - split: part_36 path: data/part_36-* - split: part_66 path: data/part_66-* - split: part_77 path: data/part_77-* - split: part_50 path: data/part_50-* - split: part_59 path: data/part_59-* - split: part_55 path: data/part_55-* - split: part_47 path: data/part_47-* - split: part_54 path: data/part_54-* - split: part_57 path: data/part_57-* - split: part_11 path: data/part_11-* - split: part_12 path: data/part_12-* - split: part_14 path: data/part_14-* - split: part_8 path: data/part_8-* - split: part_27 path: data/part_27-* - split: part_16 path: data/part_16-* - split: part_21 path: data/part_21-* - split: part_13 path: data/part_13-* - split: part_20 path: data/part_20-* - split: part_5 path: data/part_5-* ---

tthoma909/math-basics

tthoma909

2025-02-15T21:44:07Z

17

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-15T21:35:12Z

0

--- dataset_info: features: - name: nums sequence: int64 - name: expression dtype: string - name: target dtype: int64 splits: - name: train num_bytes: 5246324 num_examples: 100000 download_size: 2559611 dataset_size: 5246324 configs: - config_name: default data_files: - split: train path: data/train-* ---

junnystateofmind/conversational_ai_5_turns_only_ckp_3

junnystateofmind

2024-11-22T17:06:21Z

15

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-11-22T16:02:54Z

0

--- dataset_info: features: - name: narrative dtype: string - name: trajectory list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 7488 num_examples: 20 download_size: 7108 dataset_size: 7488 configs: - config_name: default data_files: - split: train path: data/train-* ---

laochengzi/stanford_df_rectified

laochengzi

2024-11-05T06:37:00Z

18

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-11-05T06:36:47Z

0

--- dataset_info: features: - name: Image_name dtype: int64 - name: Paragraph dtype: string - name: train dtype: bool - name: test dtype: bool - name: url dtype: string - name: val dtype: bool splits: - name: train num_bytes: 7510209 num_examples: 19561 download_size: 3505452 dataset_size: 7510209 configs: - config_name: default data_files: - split: train path: data/train-* ---

Ahmed-ibn-Harun/BrainHermorrhage

Ahmed-ibn-Harun

2024-10-23T11:12:58Z

18

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-10-23T11:03:46Z

0

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 0_no_hermorrhage '1': 1_hermorrhage splits: - name: train num_bytes: 286881574.338 num_examples: 7298 - name: validation num_bytes: 72656887.733 num_examples: 2029 - name: test num_bytes: 35044806.0 num_examples: 811 download_size: 433700277 dataset_size: 394583268.071 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

YYT-t/code_opencoder_edu-deepseek-coder-6.7b-instruct-iter1_sample_4000_tp

YYT-t

2025-04-24T13:23:47Z

19

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-24T13:23:46Z

0

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: rational_answer dtype: string splits: - name: train num_bytes: 5920468 num_examples: 4000 download_size: 2067106 dataset_size: 5920468 configs: - config_name: default data_files: - split: train path: data/train-* ---

magnifi/Phi3_intent_v56_2_w_unknown_remove_68_intents_upper_lower

magnifi

2025-03-10T01:02:42Z

14

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-10T01:02:40Z

0

--- dataset_info: features: - name: Query dtype: string - name: true_intent dtype: string splits: - name: train num_bytes: 1505486 num_examples: 20664 - name: validation num_bytes: 8109 num_examples: 113 download_size: 430246 dataset_size: 1513595 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

andreuka18/DeepSeek-R1-Distill-Llama-8B-OpenThoughts-114k-tokenized

andreuka18

2025-03-06T12:21:55Z

111

0

[ "size_categories:100K<n<1M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-06T12:20:29Z

0

--- dataset_info: features: - name: tokens sequence: int64 splits: - name: train num_bytes: 6166867104.0 num_examples: 752424 download_size: 1238722898 dataset_size: 6166867104.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

MisterEScholar/s50K_part_24

MisterEScholar

2025-02-09T06:07:00Z

16

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-09T06:06:56Z

0

--- dataset_info: features: - name: solution dtype: string - name: question dtype: string - name: cot_type dtype: string - name: source_type dtype: string - name: metadata dtype: string - name: cot dtype: 'null' splits: - name: train num_bytes: 1639387 num_examples: 1000 download_size: 894444 dataset_size: 1639387 configs: - config_name: default data_files: - split: train path: data/train-* ---

griffinpinney/Sort-4to6

griffinpinney

2025-02-24T03:53:42Z

15

0

[ "size_categories:100K<n<1M", "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-23T01:22:14Z

0

--- dataset_info: features: - name: target sequence: int64 - name: original sequence: int64 splits: - name: train num_bytes: 44009696 num_examples: 500000 download_size: 4058605 dataset_size: 44009696 configs: - config_name: default data_files: - split: train path: data/train-* ---

eagle0504/MedQuad-MedicalQnADataset-1024-synth-aug-1024-synth-aug-1024-synth-aug

eagle0504

2025-04-29T09:10:07Z

27

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-29T09:10:06Z

0

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string splits: - name: train num_bytes: 165392 num_examples: 85 download_size: 82738 dataset_size: 165392 configs: - config_name: default data_files: - split: train path: data/train-* ---

WaltonFuture/GEOQA_R1V_Train_8K

WaltonFuture

2025-04-24T12:01:25Z

39

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-24T12:01:11Z

0

--- dataset_info: features: - name: images sequence: image - name: problem dtype: string - name: answer dtype: string splits: - name: train num_bytes: 28815465.18 num_examples: 8030 download_size: 35537104 dataset_size: 28815465.18 configs: - config_name: default data_files: - split: train path: data/train-* ---

AFZAL0008/mal-eng-translation

AFZAL0008

2024-12-20T05:08:01Z

70

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-20T04:22:38Z

0

--- dataset_info: features: - name: 'Unnamed: 0.1' dtype: int64 - name: 'Unnamed: 0' dtype: int64 - name: English dtype: string - name: Malayalam dtype: string splits: - name: train num_bytes: 84825396 num_examples: 361847 download_size: 33788639 dataset_size: 84825396 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/s1_ablation_diversity_sampling_1k

mlfoundations-dev

2025-02-12T16:02:43Z

16

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-02-10T15:52:35Z

0

--- dataset_info: features: - name: problem dtype: string - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: response dtype: string - name: math_class dtype: string - name: system dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 40463615 num_examples: 1000 download_size: 17218343 dataset_size: 40463615 configs: - config_name: default data_files: - split: train path: data/train-* ---

gaotang/sky_v02_processed_qwen

gaotang

2025-03-20T16:11:04Z

16

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-20T16:10:56Z

0

--- dataset_info: features: - name: context_messages list: - name: content dtype: string - name: role dtype: string - name: winner dtype: string splits: - name: train num_bytes: 503664912 num_examples: 77016 download_size: 192509566 dataset_size: 503664912 configs: - config_name: default data_files: - split: train path: data/train-* ---

Alex-xu/secalign-dbg-haiku-javascript-all

Alex-xu

2025-01-22T03:44:18Z

16

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-01-22T03:44:17Z

0

--- dataset_info: features: - name: lang dtype: string - name: cwe dtype: string - name: original_instruction dtype: string - name: original_code dtype: string - name: empty dtype: string - name: fixed_code dtype: string - name: benign dtype: bool splits: - name: train num_bytes: 60992724 num_examples: 17892 download_size: 27563981 dataset_size: 60992724 configs: - config_name: default data_files: - split: train path: data/train-* ---

chocckaka/CriticTool-Dataset

chocckaka

2025-06-24T02:53:49Z

0

[ "license:apache-2.0", "region:us" ]

[]

2025-06-24T02:49:53Z

0

--- license: apache-2.0 ---

DanqingZ/tic_tac_toe_5_raw_2

DanqingZ

2025-06-15T07:40:19Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2025-06-15T07:24:55Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 2, "total_frames": 715, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.on_robot": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.side_view": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

Pisethan/plp-ai-dataset

Pisethan

2025-05-26T09:08:01Z

0

[ "license:apache-2.0", "size_categories:n<1K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-26T07:49:28Z

0

--- license: apache-2.0 ---

qrk-labs/QRK-Islam-Basic-Weak

qrk-labs

2025-05-12T14:12:43Z

73

0

[ "license:mit", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-09T21:42:54Z

0

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1334778 num_examples: 7099 download_size: 475144 dataset_size: 1334778 ---

Asap7772/omnimath-hint-v6-r1distill15b-respgen__1534_1705

Asap7772

2025-04-07T00:12:53Z

7

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-07T00:12:06Z

0

--- dataset_info: features: - name: hint_chosen dtype: string - name: hint_completion sequence: string - name: hint_completion_answer sequence: string - name: hint_completion_correct sequence: bool - name: hint_completion_succ_rate dtype: float64 - name: domain dtype: string - name: difficulty dtype: float64 - name: problem dtype: string - name: solution dtype: string - name: answer dtype: string - name: source dtype: string - name: completion sequence: string - name: completion_answer sequence: string - name: completion_correct sequence: bool - name: completion_succ_rate dtype: float64 - name: context dtype: string - name: hint1 dtype: string - name: hint2 dtype: string - name: hint3 dtype: string - name: hint4 dtype: string - name: hint5 dtype: string splits: - name: train num_bytes: 1311018061 num_examples: 1026 download_size: 466969384 dataset_size: 1311018061 configs: - config_name: default data_files: - split: train path: data/train-* ---

datasets-CNRS/CIENSFO

datasets-CNRS

2025-03-29T21:41:48Z

22

0

[ "language:fra", "license:cc-by-4.0", "region:us" ]

[]

2024-10-22T20:23:17Z

0

--- language: - fra viewer: false license: cc-by-4.0 --- > [!NOTE] > Dataset origin: https://www.ortolang.fr/market/corpora/ciensfo > # CIENSFO Corpus of Non-Standard Spoken French Subordinated Interrogatives (Corpus d'Interrogatives Enchâssées Non-Standards du Français Oral) ## Corpus content This corpus contains transcriptions of spoken French sentences which exhibit non-standard subordinated interrogatives. ex. ma façon de voir les choses c'est de faire le bilan de [pause] c'est quoi notre expertise More precisely, five types of subordinated clauses are present: 1. Interrogatives **dependent on a noun** * ex. moi je pouvais observer les différences de comment on était éduqués 2. Interrogatives dependent on a verb and **introduced by a preposition** * ex. ça a pratiquement tout de suite reposé sur qu'est-ce qu'on va inventer comme femme [...] 3. Interrogatives being **verbal adjunct** * ex. [...] ça peut être paronyme ou homonyme suivant comment vous le prononcez 4. Subordinated interrogatives using a **non-standard form** (ex. a marker unexpected in subordination) * ex. avant ça je me pose jamais la question de est-ce que j'aime faire ça 5. Interrogatives used **in a nominal context** * ex. avant de s'attaquer au à quoi ça sert commençons par le à quoi ça ne sert pas **TW:** Some sources mention sensitive questions (sex, sexism, etc.). Thus, some sentences may use explicit words. ## Corpus elaboration This corpus is constituted with transcribed sentences personally observed by the authors. There are two kinds of sources. 1. heard in a **personnal conversations**: sentences uttered by someone else, sometimes the author being an interlocutor, sometimes not 2. from an **online** (often free) **material** (podcast, YouTube video, series, etc.) 3. saw written on an online forum or in a text message 4. sentences taken from the CEFC (see `cefc.tsv`) Sentences of type 2. are provided with a complete description of their source (title, author, publisher, URL, time code), so that it is possible to check the transcription and obtain the actual prosody. Sentences of type 1. were transcribed on the fly from a spontaneous conversation. There was no recording. Therefore, it is not possible to check the accuracy of the transcription nor to obtain the actual prosody. As a consequence, sentences of type 1. and 3. are little trustworthy. Please take that into account in your analyses. Type 1. and 3. sentences were taken from persons of various age (but mostly French young adults) bewteen June 2022 and November 2023. Type 2 sentences are mostly extracted from materials put online between 2018 and November 2023 by persons of various age (but mostly French adults). ## Transcription choices Sentence segmentation is based on locutory units. A token `NAME` was substituted to proper names present in personal conversations. Transcription is performed using standard lexical spelling forms. In particular, silent `e`'s are not removed (ex. `tu as` instead of `t'as` for `/ta/`). However, "missing words" are not added back (ex. `y a` instead of `y'a` or `il y a` for `/ja/`). Punctuation and capital letters starting sentences are not considered. The transcription follows the 1990 French spelling reform. A `[pause]` symbol is added when there is a long enough pause between the predicate and the interrogative (only for type 2 sentences). Similarly, `euh`'s are not transcribed, except the ones close to the interrogative boarders. ex. [...] un de mes objectifs c'est de partager avec vous mes réflexions sur [pause] comment vous pouvez vous créer votre propre mindset A lot a extracts mentioned a list of interrogations following a first embedded interrogative. To avoid getting cumbersome lines, most of these additional interrogations were omitted. ## Structure The main document `ciensfo.json` is a json file. It contains a list of records. Each record contains fields: * **id** (mandatory): unique sentence identifier * **source** (mandatory): json description of the source of the sentence * **time** (mandatory, except for type 1. and 2. sentences): time code of the online material `(hours:)minutes:seconds` * **text** (mandatory): transcription * **subtitles** (optional): official subtitles given by the publisher / author(s), they may differ from the transcription * **modality** (optional): `written` or `spoken`, when absent, `spoken` is default * **note** (optional): e.g. `ungrammatical, humoristic`, `maybe subrodinated exclamative`, `maybe free relative`, `maybe reported` * **variant** (optional): variant of French, e.g. `Québec`, `Belgium`, when absent, default is European French The source field has the following fields: * **id** (mandatory): unique material identifier * **title** (mandatory) * **type** (mandatory): among: * type 1.: `conversation`, `scientific_conference` * type 2.: `online_podcast`, `series_epidose`, `radio_programme`, `recorded_speech`, `comedy_video`, `interview_video`, `newspaper_video`, `popularization_video`, `position_video`, `documentary_video`, `music`, `FAQ_video`, `tv_programme` * type 3.: `online_forum`, `comics`, `text_message` * **date** (mandatory): online publication date, vector date format ``[[year:month:day]]`` (may be underspecified) * **duration** (mandatory): `(hours:)minutes:seconds` * **publisher** (optional) * **catalog** (optional) * **authors** (mandatory): the authors on the list may be identified by their given name, family name, literal name (ex. YouTube channel) or a mix of them * **URL** (optional) * **accessed** (mandatory, except for written): vector date format * **page** (optional) * **pages** (optional) * **booktitle** (optional) * **series** (optional) * **volume** (optional) * **ISSN** (optional) Type 1. and 3. sentences only have source fields `id` and `type`. Note that the person saying the extracted sentence may not always be one of the authors, but e.g. an interviewed person. When some sentences are extracted from the same material, the source field of subsequent sentences may only contain the `id` field. Therefore, the couple "source" "id" + "time" (or just "source" "id" for type 1 sentences) constitute another possible unique sentence identifier. ## Annotation The file `classification_ciensfo.csv` contains, for each occurrence of a non-standard subordinated interrogatve, annotations about syntactic features. Theses labels have been added by hand. Columns: 1. **sentence id**, if a sentence has several of such patterns, we add a dot and a second id. (e.g `14.1`, `14.2`). The id of CEFC sentences begins with a `c` 2. **sentence type**: 1, 2 or 3 3. **dependent on a noun**: if so, the field contains the lemma of the noun 4. **dependent on an adjective**: if so, the field contains the lemma of the adjective 5. **dependent on a verb (includes semi-fixed verbal expressions)**: if so, the field contains the lemma of the predicate (or `CONJ` if it is conjuncted with the previous interrogative in the same sentence) 6. **negated**: if dependent on a verb or attribute adjective, 1 if the predicate is negated or 0 is not 7. **adverbial adjunct clause**: if the interrogative is an adverbial modifier clause, the field contains the preposition(al locution) introducing it 8. **introducing preposition**: ("/" if no preposition) 9. **graft**: if the interrogative is a graft, the field contains the preceding word, typicaly a preposition or a determiner 10. **non-standard type**: if applicable: * `qecq`: occurrence of *qu'est-ce que/qui* * `ecq`: *est-ce que* instead of *si* or *WH + est-ce que* other than *qu'est-ce que/qui* * `in-situ`: e.g *c'est quoi* * `spp`: suffixed personal pronoun (aka. subject -verb inversion) 11. **WH**: interrogative word lemma 12. **WH 2**: second interrogative word lemma, if applicable 13. **marker**: interrogative marker 14. **marker**: additional morphosyntactic phenomenon which can hint at interrogativeness, e.g. *oui ou non*, *ou pas*, *ou non* 15. **class**: class according to [Coveney 2011] 16. **additional note**, e.g. `ungrammatical, humoristic`, `maybe subrodinated exclamative`, `maybe free relative`, `maybe reported` Note: columns 3, 4, 5 and 7 may contain the token `CONJ` to indicate that the interrogative is conjuncted with the previous line, under the same governor. ### Extended Coveney classification The classification `type` (direct interrogatives only) is based on: > Aidan Coveney. 2011. L’interrogation directe. Travaux de linguistique, 63(2):112–145. De Boeck Supérieur. We extend it to account for infinitival interrogatives, subordinated interrogatives, nominal and elliptical interrogatives. **Note:** Contrary to [Coveney 2011], `stats.py` considers expression *qu'est-ce que/qui* as an interrogative word, and not as `Q` + *est-ce que* pattern. The list of categories is: * yes-no interrogatives: * `ESV`: 'est-ce que', e.g. *Est-ce que les autres / ils sont partis ?* * `V-CL`: clitic inversion, e.g. *Sont-ils partis ?* * `GN V-CL`: complex inversion, e.g. *Les autres sont-ils partis ?* * `SV-ti`: '-ti' marker, *C'est-ti pas fini ?* * constituent (fr. partielle): * `SVQ`: in situ, e.g. *Ils sont partis où ?* * `QSV`: fronting (fr. antéposition), e.g. *Où ils sont partis ?* * `QV-CL`: qu + clitic inversion, e.g. *Où sont-ils partis ?* * `Q GN V-CL`: qu + complex inversion, e.g. *Où les autres sont-ils partis ?* * `QV GN`: qu + stylistic inversion, e.g. *Où sont partis les autres ?* * `seQkSV`: cleft, e.g. *C’est où qu’ils sont partis ?* * `QESV`: qu + 'est-ce que', e.g. *Où est-ce qu’ils sont partis ?* * `QsekSV`: qu + cleft variant, e.g. *Où c’est qu’ils sont partis ?* * `QkSV`: qu + complementizer, e.g. *Où qu’ils sont partis ?* * `Q=S V`: subject qu, e.g. *Lesquels sont partis ?* * hybrid (non-standard) * `QEV GN`: qu+ 'est-ce que' + stylistic inversion, e.g. *Avec qui est-ce que travaille nicole Dupont ?* * `Q=S V-CL`: subject qu + clitic inversion, e.g. *De ces fillettes, lesquelles sont-elles les tiennes ?* * `E GN V-CL`: 'est-ce que' + complex inversion, e.g. *Est-ce que demain les sauveteurs pourront-ils s’approcher des alpinistes en détresse ?* * `QE GN V-CL`: qu + 'est-ce que' + complex inversion e.g. *Qu’est-ce que le rédacteur de la rubrique des chats écrasés entend-il par un pachyderme ?* Our extension includes: * other case * `Q=S sekV`: subject qu + cleft *c'est qui* + verb, e.g. *Qui c'est qui diffuse ça ?* * infinitival * `QVinf`: qu + infinitival verb, e.g. *Où partir ?* * `Vinf Q`: infinitival verb + in-situ qu, e.g. *Pour partir où ?* * `QsekVinf`: qu + cleft variant + infinitival verb, e.g. *Qu'est-ce que c'est qu'être une fille ?* * multiple qu-words * `Q=S VQ`: double qu-interrogative with one qu subject, e.g. *Qui veut intervenir dans quoi ?* * `QSVQ`: double qu-interrogative, e.g. *Combien d'infanteries tu envoies sur quelle planète ?* * `QVinf QQ`: triple qu-interrogative + infinitival verb, e.g. *Qui inviter à quel endroit sur quel sujet ?* * Nominal or elliptical * `Q GN`: qu + noun phrase, e.g. *Pourquoi Angiox ?* * `Qsek GN`: qu + cleft variant + noun phrase, e.g. *Qu'est-ce que c'est que l'énergie ?* * `Q`: elliptical qu (interrogative phrase alone), e.g. *Où ?* * embedded yes-no * `si SV`: 'si', e.g. *Je sais s'ils sont partis.* * other regional variants * `SV-tu`: '-tu' marker, *C'est-tu vraiment si pire que ça ?* ## Corpus searches The pattern created to search for interrogative adverbial modifier clauses use [Grew](https://grew.fr/) (v. >= 1.14). Request files and found sentences are in the `searches` folder. * `fib_comp_prep.req` used on the [FIB](https://github.com/Valentin-D-Richard/UD_French-FIB) (on the enriched version) * `orfeo_prep_int.req` used on the [CEFC corpus](https://orfeo.grew.fr/?corpus=cefc-gold) The raw results can be found in the json files for the FIB and in the `CEFC_found` folder for the CEFC. The files with `_0` ending contain the pattern with the preposition immediately preceding the QU-word or morphosyntactic marking. The files with `_1` ending, where there is one word in between. ## License The data is collected under the Right to quote. It is distributed under the Creative Common [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. ## Publication Please cite this publication to mention CIENSFO. Richard, V. D. (2024). “selon coment vous vous positionnez” : Étude des circonstancielles à interrogative. to appear in 9e Congrès Mondial de Linguistique Française, Lausanne. ## Citation ``` @misc{11403/ciensfo/v1, title = {CIENSFO (Corpus d'Interrogatives Enchâssées Non-Standards du Fran\c{c}ais Oral)}, author = {Valentin D. Richard}, url = {https://hdl.handle.net/11403/ciensfo/v1}, note = {{ORTOLANG} ({Open} {Resources} {and} {TOols} {for} {LANGuage}) \textendash www.ortolang.fr}, copyright = {Licence Creative Commons - Attribution 4.0 International}, year = {2024} } ```

pragsri8/WildBenchGenv2-hard-matched

pragsri8

2025-05-11T00:11:53Z

0

[ "region:us" ]

[]

2025-05-10T23:56:08Z

0

--- dataset_info: - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390600 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390600 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390600 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390600 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 744190 num_examples: 256 download_size: 399797 dataset_size: 744190 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390148 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390184 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390184 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390184 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390184 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390848 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390848 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390848 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 390848 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_CARMA-no_neutrals_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 389845 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 389845 dataset_size: 726930 - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RRM_pairpm_wildbench features: - name: id dtype: string - name: session_id dtype: string - name: conversation_input list: - name: content dtype: string - name: language dtype: string - name: redacted dtype: bool - name: role dtype: string - name: timestamp dtype: 'null' - name: toxic dtype: bool - name: length dtype: int64 - name: checklist sequence: string - name: intent dtype: string - name: primary_tag dtype: string - name: secondary_tags sequence: string - name: avg_score dtype: float64 - name: var_score dtype: float64 splits: - name: train num_bytes: 726930 num_examples: 254 download_size: 389845 dataset_size: 726930 configs: - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K16_reward_eval16._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K1_reward_eval1._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA-pragyas_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K2_reward_eval2._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K32_reward_eval32._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_CARMA_qrandomized_neutrals_our_improve_degrade_data_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K4_reward_eval4._bon_sampling_RRM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_CARMA-no_neutrals_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_CARMA-no_neutrals_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RM_pairpm_wildbench/train-* - config_name: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RRM_pairpm_wildbench data_files: - split: train path: Test_Gemma9B_SFT_BoN_K8_reward_eval8._bon_sampling_RRM_pairpm_wildbench/train-* ---

vlm-reasoning-cot/ciphers

vlm-reasoning-cot

2025-05-15T18:11:15Z

128

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-13T04:58:14Z

0

--- dataset_info: - config_name: chunk_0001 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 707261228.0 num_examples: 1000 download_size: 578886758 dataset_size: 707261228.0 - config_name: chunk_0002 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 714895362.0 num_examples: 1000 download_size: 586241826 dataset_size: 714895362.0 - config_name: chunk_0003 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 718190062.0 num_examples: 1000 download_size: 589335104 dataset_size: 718190062.0 - config_name: chunk_0004 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 704665360.0 num_examples: 1000 download_size: 577484472 dataset_size: 704665360.0 - config_name: chunk_0005 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 716389846.0 num_examples: 1000 download_size: 593459805 dataset_size: 716389846.0 - config_name: chunk_0006 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 705398322.0 num_examples: 1000 download_size: 583947568 dataset_size: 705398322.0 - config_name: chunk_0007 features: - name: question dtype: string - name: reasoning dtype: string - name: answer dtype: string - name: source_folder dtype: string - name: problem_image_1 dtype: image - name: problem_image_1_base64 dtype: string - name: reasoning_image_1 dtype: image - name: reasoning_image_1_base64 dtype: string - name: reasoning_image_10 dtype: image - name: reasoning_image_10_base64 dtype: string - name: reasoning_image_11 dtype: image - name: reasoning_image_11_base64 dtype: string - name: reasoning_image_12 dtype: image - name: reasoning_image_12_base64 dtype: string - name: reasoning_image_2 dtype: image - name: reasoning_image_2_base64 dtype: string - name: reasoning_image_3 dtype: image - name: reasoning_image_3_base64 dtype: string - name: reasoning_image_4 dtype: image - name: reasoning_image_4_base64 dtype: string - name: reasoning_image_5 dtype: image - name: reasoning_image_5_base64 dtype: string - name: reasoning_image_6 dtype: image - name: reasoning_image_6_base64 dtype: string - name: reasoning_image_7 dtype: image - name: reasoning_image_7_base64 dtype: string - name: reasoning_image_8 dtype: image - name: reasoning_image_8_base64 dtype: string - name: reasoning_image_9 dtype: image - name: reasoning_image_9_base64 dtype: string splits: - name: train num_bytes: 415705968.0 num_examples: 593 download_size: 340850442 dataset_size: 415705968.0 configs: - config_name: chunk_0001 data_files: - split: train path: chunk_0001/train-* - config_name: chunk_0002 data_files: - split: train path: chunk_0002/train-* - config_name: chunk_0003 data_files: - split: train path: chunk_0003/train-* - config_name: chunk_0004 data_files: - split: train path: chunk_0004/train-* - config_name: chunk_0005 data_files: - split: train path: chunk_0005/train-* - config_name: chunk_0006 data_files: - split: train path: chunk_0006/train-* - config_name: chunk_0007 data_files: - split: train path: chunk_0007/train-* ---

SarraChab/MNLP_M2_mcqa_dataset

SarraChab

2025-05-26T09:39:13Z

85

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-18T09:37:53Z

0

--- dataset_info: features: - name: id dtype: string - name: dataset dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string splits: - name: train num_bytes: 2268293 num_examples: 10687 download_size: 1254741 dataset_size: 2268293 configs: - config_name: default data_files: - split: train path: data/train-* --- This dataset was constructed as part of the EPFL Modern NLP (MNLP) course project to train and evaluate large language models on **multiple-choice question answering (MCQA)** tasks focused on scientific reasoning. It aggregates and reformats **10,687 unique examples** from five high-quality academic and biomedical QA datasets, applying consistent structure, question normalization, and cross-source deduplication. ### 📊 Dataset Composition | Source Dataset | Link | Questions Used | Description | |----------------|------|----------------|-------------| | ARC-Challenge | [ai2_arc](https://huggingface.co/datasets/ai2_arc) | 1,119 | Harder science exam questions requiring multi-step reasoning | | ARC-Easy | [ai2_arc](https://huggingface.co/datasets/ai2_arc) | 2,251 | Simpler science questions at the elementary/middle school level | | QASC | [qasc](https://huggingface.co/datasets/qasc) | 3,000 (subset) | A filtered and deduplicated subset of the QASC dataset, which was originally larger (~8,000+ examples). Only 3,000 unique and diverse questions were selected for balance | | OpenBookQA | [openbookqa](https://huggingface.co/datasets/openbookqa) | 3,317 | 4-option science questions, filtered to keep `humanScore ≥ 1` | | PubMedQA | [pubmed_qa](https://huggingface.co/datasets/pubmed_qa) | 1,000 | Biomedical questions with Yes/No/Maybe answers based on PubMed abstracts | ### 🧪 Preprocessing Pipeline - **Normalization**: All questions were lowercased and stripped of whitespace for consistency. - **Deduplication**: Each question was hashed (`md5(lowercase question)`) to detect and eliminate duplicates across datasets. - **Filtering**: - OpenBookQA was filtered to retain only questions with `humanScore ≥ 1`. - PubMedQA was filtered to retain only labeled questions with answers in {yes, no, maybe}. - QASC was **sampled and capped** at 3,000 unique questions to ensure dataset balance. - **Unified formatting**: All entries follow the same JSON schema across sources. ### 📦 Format Each sample follows this structure: ```json { "id": "qasc_481", "dataset": "qasc", "question": "What do bees use to make honey?", "options": ["nectar", "pollen", "water", "leaves"], "answer": "A" }

extralit-dev/test_import_dataset_from_hub_with_classlabel_fc75f806-a0ee-4fad-a969-29e01309ac96

extralit-dev

2025-06-19T05:05:44Z

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-06-19T05:05:44Z

0

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1264 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---

TianHongZXY/MATH-test-Tulu-3-8B-SFT-beam_search-completions-temp_0.8-range_3800_to_3900

TianHongZXY

2025-01-19T21:15:18Z

7

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-01-18T00:46:18Z

0

--- dataset_info: config_name: TianHongZXY_MATH--T-0.8--top_p-1.0--n-32--m-4--iters-20--look-1--seed-42--agg_strategy--last features: - name: problem dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string - name: completions sequence: string - name: pred dtype: string - name: completion_tokens sequence: int64 - name: scores sequence: sequence: float64 - name: agg_scores sequence: float64 - name: pred_weighted@1 dtype: string - name: pred_maj@1 dtype: string - name: pred_naive@1 dtype: string - name: pred_weighted@2 dtype: string - name: pred_maj@2 dtype: string - name: pred_naive@2 dtype: string - name: pred_weighted@4 dtype: string - name: pred_maj@4 dtype: string - name: pred_naive@4 dtype: string - name: pred_weighted@8 dtype: string - name: pred_maj@8 dtype: string - name: pred_naive@8 dtype: string - name: pred_weighted@16 dtype: string - name: pred_maj@16 dtype: string - name: pred_naive@16 dtype: string - name: pred_weighted@32 dtype: string - name: pred_maj@32 dtype: string - name: pred_naive@32 dtype: string splits: - name: train num_bytes: 2963248 num_examples: 100 download_size: 381984 dataset_size: 2963248 configs: - config_name: TianHongZXY_MATH--T-0.8--top_p-1.0--n-32--m-4--iters-20--look-1--seed-42--agg_strategy--last data_files: - split: train path: TianHongZXY_MATH--T-0.8--top_p-1.0--n-32--m-4--iters-20--look-1--seed-42--agg_strategy--last/train-* ---

fernandabufon/ukr_to_pt_json_gpt

fernandabufon

2025-01-15T13:43:17Z

18

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-01-15T13:43:15Z

0

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: translation dtype: string - name: anger dtype: int64 - name: disgust dtype: int64 - name: fear dtype: int64 - name: joy dtype: int64 - name: sadness dtype: int64 - name: surprise dtype: int64 - name: inference_time dtype: float64 - name: inference_total_time dtype: float64 - name: inference_average_time dtype: float64 splits: - name: train num_bytes: 1016468 num_examples: 2466 download_size: 514168 dataset_size: 1016468 configs: - config_name: default data_files: - split: train path: data/train-* ---

zuozhuan/so100_close

zuozhuan

2024-12-16T02:08:01Z

26

0

[ "task_categories:robotics", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2024-12-16T02:03:20Z

0

--- task_categories: - robotics tags: - LeRobot - so100 - tutorial --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

pbvr/so101_test011

pbvr

2025-05-26T07:42:36Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so101", "tutorial" ]

[ "robotics" ]

2025-05-26T07:42:18Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so101", "total_episodes": 2, "total_frames": 1788, "total_tasks": 1, "total_videos": 6, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.handeye": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.side": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.topdown": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

dylanebert/iso3d

dylanebert

2024-06-10T19:38:43Z

112

9

[ "license:mit", "size_categories:n<1K", "format:imagefolder", "modality:image", "modality:text", "library:datasets", "library:mlcroissant", "region:us" ]

[]

2024-06-07T21:02:18Z

1

--- license: mit --- # iso3d - Isolated Synthetic Objects 3D A dataset of isolated object images for evaluating [image-to-3D](https://huggingface.co/models?pipeline_tag=image-to-3d) models. ## Leaderboard Vote and view results at [3d-arena](https://huggingface.co/spaces/dylanebert/3d-arena). ## Curation Images are created using [dreamshaper-xl](https://huggingface.co/Lykon/dreamshaper-xl-v2-turbo) and [white background lora](https://civitai.com/models/119388/white-background) on [karlo-v1](https://huggingface.co/datasets/diffusers-parti-prompts/karlo-v1) prompts. 1. Each [karlo-v1](https://huggingface.co/datasets/diffusers-parti-prompts/karlo-v1) prompt is extended with `{prompt}, isolated object render, with a white background` and negative prompt `text, watermark, shadow, background`. 2. Images are generated using [ComfyUI](https://github.com/comfyanonymous/ComfyUI) with [dreamshaper-xl](https://huggingface.co/Lykon/dreamshaper-xl-v2-turbo) and [white background lora](https://civitai.com/models/119388/white-background). 3. Backgrounds are removed using [rembg](https://github.com/danielgatis/rembg). 4. 100 images are hand-selected from the 1.63k generated images. ## Contributing The leaderboard is automatically populated by the [3d-arena dataset](https://huggingface.co/datasets/dylanebert/3d-arena). To submit your model, [open a PR](https://huggingface.co/docs/hub/en/repositories-pull-requests-discussions) on the dataset. ## Citation ``` @misc{3d-arena, author = {Dylan Ebert} title = {3D Arena} year = {2024} publisher = {Hugging Face} howpublished = \url{https://huggingface.co/spaces/dylanebert/3d-arena} } ```

ganker5/so100_toy_20250402

ganker5

2025-04-02T03:33:53Z

56

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so100", "tutorial" ]

[ "robotics" ]

2025-04-02T02:36:36Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 10, "total_frames": 4946, "total_tasks": 1, "total_videos": 20, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:10" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

PhilSad/SCP-Wiki-Dataset

PhilSad

2024-12-14T15:09:58Z

20

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-14T15:09:52Z

0

--- dataset_info: features: - name: description dtype: string - name: content dtype: string splits: - name: train num_bytes: 17570353.015576325 num_examples: 2311 - name: test num_bytes: 1953950.984423676 num_examples: 257 download_size: 11421492 dataset_size: 19524304.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

RyanYr/reflect_mmlumathpro_nonmrkv-test_t4_crtc

RyanYr

2025-01-27T21:11:22Z

20

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-01-27T16:40:52Z

0

--- dataset_info: features: - name: question_id dtype: int64 - name: original_question dtype: string - name: options sequence: string - name: answer dtype: string - name: answer_index dtype: int64 - name: cot_content dtype: string - name: category dtype: string - name: src dtype: string - name: problem dtype: string - name: alt_answer dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@3 sequence: string - name: response@4 sequence: string - name: response@5 sequence: string - name: response@6 sequence: string - name: response@7 sequence: string splits: - name: train num_bytes: 21694922 num_examples: 1351 download_size: 8184506 dataset_size: 21694922 configs: - config_name: default data_files: - split: train path: data/train-* ---

MInference/SCBench

MInference

2024-12-13T07:06:57Z

104

0

[ "license:mit", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-12T18:43:55Z

0

--- license: mit dataset_info: - config_name: multi_turn_choice_eng features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: options sequence: string - name: id dtype: int64 splits: - name: train num_bytes: 46482955 num_examples: 58 download_size: 28590613 dataset_size: 46482955 - config_name: multi_turn_kv features: - name: id dtype: int64 - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string splits: - name: train num_bytes: 20071200 num_examples: 100 download_size: 18278186 dataset_size: 20071200 - config_name: multi_turn_many_shot features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: id dtype: int64 - name: task dtype: string splits: - name: train num_bytes: 4734315 num_examples: 54 download_size: 99406 dataset_size: 4734315 - config_name: multi_turn_mf features: - name: id dtype: int64 - name: context sequence: int64 - name: multi_turns list: - name: answer dtype: int64 - name: input dtype: string splits: - name: train num_bytes: 24065100 num_examples: 100 download_size: 3766479 dataset_size: 24065100 - config_name: multi_turn_prefix_suffix features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string splits: - name: train num_bytes: 17498600 num_examples: 100 download_size: 16417345 dataset_size: 17498600 - config_name: multi_turn_qa_chn features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180437341 num_examples: 35 download_size: 115936454 dataset_size: 180437341 - config_name: multi_turn_qa_eng features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 58359967 num_examples: 69 download_size: 35648660 dataset_size: 58359967 - config_name: multi_turn_repoqa features: - name: context dtype: string - name: id dtype: int64 - name: multi_turns list: - name: answer dtype: string - name: code_ratio dtype: float64 - name: description dtype: string - name: end_byte dtype: int64 - name: end_line dtype: int64 - name: func dtype: string - name: global_end_byte dtype: int64 - name: global_end_line dtype: int64 - name: global_start_byte dtype: int64 - name: global_start_line dtype: int64 - name: input dtype: string - name: name dtype: string - name: path dtype: string - name: start_byte dtype: int64 - name: start_line dtype: int64 - name: lang dtype: string - name: repo dtype: string splits: - name: train num_bytes: 24847710 num_examples: 88 download_size: 4427455 dataset_size: 24847710 - config_name: multi_turn_repoqa_and_kv features: - name: context dtype: string - name: id dtype: int64 - name: multi_turns list: - name: answer dtype: string - name: code_ratio dtype: float64 - name: description dtype: string - name: end_byte dtype: int64 - name: end_line dtype: int64 - name: func dtype: string - name: global_end_byte dtype: int64 - name: global_end_line dtype: int64 - name: global_start_byte dtype: int64 - name: global_start_line dtype: int64 - name: input dtype: string - name: name dtype: string - name: path dtype: string - name: start_byte dtype: int64 - name: start_line dtype: int64 - name: task dtype: string - name: lang dtype: string - name: repo dtype: string splits: - name: train num_bytes: 25019328 num_examples: 88 download_size: 8583611 dataset_size: 25019328 - config_name: multi_turn_summary features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 28622955 num_examples: 70 download_size: 14245669 dataset_size: 28622955 - config_name: multi_turn_summary_with_needles features: - name: context dtype: string - name: multi_turns list: - name: answer dtype: string - name: input dtype: string - name: task dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 28629718 num_examples: 70 download_size: 14233712 dataset_size: 28629718 - config_name: multi_turn_vt features: - name: index dtype: int64 - name: input dtype: string - name: length dtype: int64 - name: multi_turns list: - name: answer sequence: string - name: input dtype: string splits: - name: train num_bytes: 42549030 num_examples: 90 download_size: 2160077 dataset_size: 42549030 configs: - config_name: multi_turn_choice_eng data_files: - split: train path: multi_turn_choice_eng/train-* - config_name: multi_turn_kv data_files: - split: train path: multi_turn_kv/train-* - config_name: multi_turn_many_shot data_files: - split: train path: multi_turn_many_shot/train-* - config_name: multi_turn_mf data_files: - split: train path: multi_turn_mf/train-* - config_name: multi_turn_prefix_suffix data_files: - split: train path: multi_turn_prefix_suffix/train-* - config_name: multi_turn_qa_chn data_files: - split: train path: multi_turn_qa_chn/train-* - config_name: multi_turn_qa_eng data_files: - split: train path: multi_turn_qa_eng/train-* - config_name: multi_turn_repoqa data_files: - split: train path: multi_turn_repoqa/train-* - config_name: multi_turn_repoqa_and_kv data_files: - split: train path: multi_turn_repoqa_and_kv/train-* - config_name: multi_turn_summary data_files: - split: train path: multi_turn_summary/train-* - config_name: multi_turn_summary_with_needles data_files: - split: train path: multi_turn_summary_with_needles/train-* - config_name: multi_turn_vt data_files: - split: train path: multi_turn_vt/train-* --- # SCBench [[Paper]](https://drive.google.com/file/d/1_DFu11V7HbktvEMRqMUAWGm7DTkVXlOR/view?usp=drive_link) [[Code]](https://github.com/microsoft/MInference/SCBench) ![SCBench](./data/framework.png) SCBench (SharedContextBench) is a comprehensive benchmark to evaluate efficient long-context methods in a KV cache-centric perspective, analyzing their performance across **the full KV cache lifecycle (generation, compression, retrieval, and loading)** in real-world scenarios where context memory (KV cache) is shared and reused across multiple requests. ## Dataset ![SCBench](./data/overview.png) SCBench covers 12 diverse tasks that test four key long-context capabilities: string retrieval, semantic retrieval, global information processing, and multi-tasking. ### String Retrieval - **Retr.KV**: Tests key-value lookup in large JSON objects with random, incompressible content - **Retr.Prefix-Suffix**: Evaluates finding strings with specific prefix and suffix patterns - **Retr.MultiHop**: Assesses multi-hop variable tracing capabilities in long inputs ### Semantic Retrieval - **Code.RepoQA**: Function retrieval from large codebases based on natural language descriptions - **Language QA**: Includes English QA, Chinese QA, and multi-choice questions on long texts - Requires semantic understanding on length inputs ### Global Information Processing - **Many-shot ICL**: Tests in-context learning with hundreds of examples - **Math.Find**: Statistical tasks on large arrays - **En.Sum**: Summarization of documents - Requires global information processing or aggregation ### Multi-Tasking - **Mix.Sum+NIAH**: Combines summarization with needle-in-haystack search - **Mix.RepoQA+KV**: Integrates code function retrieval with key-value lookup - Requires multi-tasking or multi-step reasoning ## Two Shared Context Modes The benchmark evaluates these tasks across two shared context modes: - **Multi-turn Mode**: Caches context within single sessions - **Multi-request Mode**: Shares context across multiple sessions ## Compared to previous long-context benchmarks ![SCBench](./data/comparison.png) Our SCBench is the first long-context benchmark that covers single-turn, multi-turn, and multi-request scenarios. In addition, our impelmentation also involves KV cache reuse techniques, thereby providing a more comprehensive analysis on the full KV cache lifecycle of efficient long-context methods. ## Results and Findings ![SCBench](./data/results.png) Our SCBench reveals that the following key insights: ### Finding 1: Sub-O(n) Memory is Problematic in Multi-Request/Multi-Turn Decoding - Sparse decoding methods with sub-O(n) memory perform well on first queries but lose accuracy in subsequent requests - Methods maintaining O(n) memory with sub-O(n²) computation during pre-filling can better approximate full attention accuracy across multiple queries ### Finding 2: Task Performance Shows Varying Decline Patterns - Sparse KV cache methods excel in tasks requiring global information processing - O(n) memory is essential for tasks involving exact match retrieval ### Finding 3: Performance vs Compression Rate - All methods show performance degradation as compression rates increase - Sub-O(n) memory methods exhibit significant drop at 1/4 compression rate - Methods like RetrievalAttention and KIVI that maintain O(n) memory with sparse decoding show better resilience at higher compression rates ### Finding 4: Issues with Long-Generation Scenarios - Attention distribution shifts significantly as generation length and number of rounds increase - This out-of-distribution (OOD) issue impacts performance even for O(n) memory methods ### Finding 5: Dynamic vs Static Patterns - Dynamic sparse patterns generally outperform static patterns ## Citation ```bibtex @article{li2024scbench, title={SCBench: A KV cache-centric analysis of long-context methods}, author={Li, Yucheng and Jiang, Huiqiang and Wu, Qianhui and Luo, Xufang and Ahn, Surin and Zhang, Chengruidong and Abdi, Amir H and Li, Dongsheng and Gao, Jianfeng and Yang, Yuqing and Qiu, Lili}, journal={arXiv preprint arXiv:2412.}, year={2024} } ```

dgambettaphd/D_gen1_run2_llama2-7b_wiki_doc1000_real32_synt96_vuw

dgambettaphd

2024-12-17T10:01:08Z

17

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-17T10:01:05Z

0

--- dataset_info: features: - name: id dtype: int64 - name: doc dtype: string splits: - name: train num_bytes: 373990 num_examples: 1000 download_size: 211688 dataset_size: 373990 configs: - config_name: default data_files: - split: train path: data/train-* ---

dcml0714/StyleSet

dcml0714

2025-06-24T11:21:32Z

37

1

[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2506.05984", "region:us" ]

[]

2025-06-09T03:21:45Z

0

--- dataset_info: - config_name: role_playing features: - name: ID dtype: int64 - name: text_0 dtype: string - name: text_1 dtype: string - name: audio_0 dtype: audio: sampling_rate: 16000 - name: audio_1 dtype: audio: sampling_rate: 16000 - name: source dtype: string - name: speaker1 dtype: string - name: speaker2 dtype: string splits: - name: test num_bytes: 182310504.0 num_examples: 20 download_size: 148908359 dataset_size: 182310504.0 - config_name: voice_instruction_following features: - name: ID dtype: int64 - name: text_1 dtype: string - name: text_2 dtype: string - name: audio_1 dtype: audio: sampling_rate: 16000 - name: audio_2 dtype: audio: sampling_rate: 16000 splits: - name: test num_bytes: 36665909.0 num_examples: 20 download_size: 35109899 dataset_size: 36665909.0 configs: - config_name: role_playing data_files: - split: test path: role_playing/test-* - config_name: voice_instruction_following data_files: - split: test path: voice_instruction_following/test-* --- # StyleSet **WARNING**: This dataset contains some profane words. **A spoken language benchmark for evaluating speaking-style-related speech generation** Released in our paper, [Audio-Aware Large Language Models as Judges for Speaking Styles](https://arxiv.org/abs/2506.05984) This dataset is released by NTU Speech Lab under the MIT license. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/622326ae0129f2097d69a3e2/Q8Os1g5vfy22Y9myvSc7X.png) --- ## Tasks 1. **Voice Style Instruction Following** - Reproduce a given sentence verbatim. - Match specified prosodic styles (emotion, volume, pace, emphasis, pitch, non-verbal cues). 2. **Role Playing** - Continue a two-turn dialogue prompt in character. - Generate the next utterance with appropriate prosody and style. - The dataset is modified from IEMOCAP with the consent of the authors. Please refer to [IEMOCAP](https://sail.usc.edu/iemocap/) for details and the original data of IEMOCAP. We do not redistribute the data here. --- ## Evaluation We use ALLM-as-a-judge for evaluation. Currently, we found that `gemini-2.5-pro-0506` reaches the best agreement with human evaluators. The complete evaluation prompt and evaluation pipelines can be found in Table 3 to Table 5 in our paper. ## Citation If you use StyleSet or find ALLM-as-a-judge useful, please cite our paper by ``` @misc{chiang2025audioawarelargelanguagemodels, title={Audio-Aware Large Language Models as Judges for Speaking Styles}, author={Cheng-Han Chiang and Xiaofei Wang and Chung-Ching Lin and Kevin Lin and Linjie Li and Radu Kopetz and Yao Qian and Zhendong Wang and Zhengyuan Yang and Hung-yi Lee and Lijuan Wang}, year={2025}, eprint={2506.05984}, archivePrefix={arXiv}, primaryClass={eess.AS}, url={https://arxiv.org/abs/2506.05984}, } ```

YounesHouhou/filter

YounesHouhou

2025-05-22T12:43:35Z

0

[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot" ]

[ "robotics" ]

2025-05-22T12:43:32Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": null, "total_episodes": 1, "total_frames": 600, "total_tasks": 1, "total_videos": 1, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.state": { "dtype": "float32", "shape": [ 9 ], "names": { "motors": [ "Measurement_RIGHT_ARM_SHOULDER_PITCH", "Measurement_RIGHT_ARM_SHOULDER_ROLL", "Measurement_RIGHT_ARM_BICEP_YAW", "Measurement_RIGHT_ARM_ELBOW_PITCH", "Measurement_RIGHT_ARM_WRIST_YAW", "Measurement_RIGHT_ARM_WRIST_PITCH", "Measurement_RIGHT_ARM_WRIST_ROLL", "Measurement_RIGHT_ARM_THUMB", "Measurement_RIGHT_ARM_FINGERS" ] } }, "action": { "dtype": "float32", "shape": [ 9 ], "names": { "motors": [ "MpcInput_RIGHT_ARM_SHOULDER_PITCH", "MpcInput_RIGHT_ARM_SHOULDER_ROLL", "MpcInput_RIGHT_ARM_BICEP_YAW", "MpcInput_RIGHT_ARM_ELBOW_PITCH", "MpcInput_RIGHT_ARM_WRIST_YAW", "MpcInput_RIGHT_ARM_WRIST_PITCH", "MpcInput_RIGHT_ARM_WRIST_ROLL", "Target_RIGHT_ARM_THUMB", "Target_RIGHT_ARM_FINGERS" ] } }, "next.done": { "dtype": "bool", "shape": [ 1 ] }, "observation.images.camera_head": { "dtype": "video", "shape": [ 3, 480, 640 ], "names": [ "channels", "height", "width" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

test-gen/livecodebench_qwen-0.5b-random_t0.0_n1_generated_tests_updated

test-gen

2025-05-23T02:45:12Z

24

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-15T02:30:33Z

0

--- dataset_info: features: - name: question_title dtype: string - name: question_content dtype: string - name: question_id dtype: string - name: contest_id dtype: string - name: test_id dtype: int64 - name: contest_date dtype: timestamp[us] - name: starter_code dtype: string - name: function_name dtype: string - name: difficulty dtype: string - name: test dtype: string - name: verification_info struct: - name: language dtype: string - name: test_cases sequence: string - name: new_verification_info struct: - name: language dtype: string - name: test_cases sequence: string splits: - name: test num_bytes: 183440 num_examples: 182 download_size: 77951 dataset_size: 183440 configs: - config_name: default data_files: - split: test path: data/test-* ---

DuongTrongChi/vov_crawler

DuongTrongChi

2024-10-25T15:22:36Z

29

0

[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-10-25T15:22:13Z

0

--- dataset_info: features: - name: title dtype: string - name: content sequence: string - name: metadata struct: - name: date dtype: string - name: url dtype: string splits: - name: train num_bytes: 1085141731 num_examples: 372528 download_size: 510136321 dataset_size: 1085141731 configs: - config_name: default data_files: - split: train path: data/train-* ---

flozi00/german-canary-asr-0324

flozi00

2024-03-19T10:48:50Z

270

6

[ "task_categories:automatic-speech-recognition", "language:de", "size_categories:100K<n<1M", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[ "automatic-speech-recognition" ]

2024-03-16T10:46:35Z

1

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 decode: false - name: transkription dtype: string - name: source dtype: string splits: - name: train num_bytes: 41511776468.673 num_examples: 985257 download_size: 142197574339 dataset_size: 41511776468.673 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - automatic-speech-recognition language: - de pretty_name: German Canary ASR --- # Dataset Beschreibung ## Allgemeine Informationen Dieser Datensatz ist eine Kombination aus drei verschiedenen Quellen für die deutsche Sprache: Commonvoice 16.1, Voxpopuli und Multilingual librispeech. Die Daten wurden gefiltert, normalisiert und grammatikalisch korrigiert. Die drei Datensätze wurden erneut transkribiert und mit den entsprechenden Audio-Daten abgeglichen, um genaue Transkriptionen zu erhalten. Anschließend wurde ein Abgleich mit den Originaltranskripten durchgeführt, um fehlerhafte Transkriptionen zu korrigieren oder zu entfernen, sofern dies möglich war. Für diese Aufgabe wurde das Nvidia Canary 1b Modell genutzt. ### Commonvoice 16.1 Common Voice ist ein öffentlich verfügbarer Sprachdatensatz, der durch Stimmen freiwilliger Mitwirkender auf der ganzen Welt erstellt wird. Der Datensatz enthält Aufnahmen von Sätzen in verschiedenen Sprachen, einschließlich Deutsch. ### Voxpopuli Die Rohdaten für diesen Teil des Datensatzes stammen aus den Aufzeichnungen von Veranstaltungen des Europäischen Parlaments von 2009 bis 2020. Wir danken dem Europäischen Parlament dafür, dass es diese Materialien erstellt und geteilt hat. ### Multilingual librispeech Der Multilingual LibriSpeech (MLS) Datensatz ist ein umfangreicher mehrsprachiger Korpus, der sich für die Sprachforschung eignet. Der Datensatz basiert auf vorgelesenen Hörbüchern von LibriVox und enthält auch deutschsprachige Aufnahmen. ## Datenverarbeitungsschritte Um einen qualitativ hochwertigen deutschen Sprachdatensatz zu erstellen, wurden folgende Schritte durchgeführt: 1. Filterung: Es wurden nur die deutschen Sätze aus den jeweiligen Quelldatensätzen extrahiert. 2. Normalisierung: Die Texte wurden auf eine einheitliche Form gebracht, um Inkonsistenzen zu beseitigen. 3. Grammatikkorrektur: Fehlerhafte Grammatik wurde korrigiert, um die Qualität der Sätze zu verbessern. ## Verwendungszweck Dieser kombinierte deutsche Sprachdatensatz kann für verschiedene Zwecke verwendet werden: - ASR (Automatic Speech Recognition) Modelltraining - NLP (Natural Language Processing) Forschung - Text-to-Speech Anwendungen Bitte beachten Sie jedoch bei der Verwendung dieses Datensatzes die Lizenzbedingungen der einzelnen Quellen sowie etwaige Einschränkungen oder Richtlinien bezüglich des Datenschutzes oder Urheberrechts.

chiyuanhsiao/llama-questions-ASR_GT-score

chiyuanhsiao

2025-01-01T13:58:17Z

15

0

[ "size_categories:n<1K", "format:parquet", "modality:audio", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-31T08:39:20Z

0

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: audio dtype: audio - name: question_unit sequence: int64 - name: response_interleaf dtype: string - name: response_text dtype: string - name: response_speech dtype: audio - name: response_asr dtype: string - name: speech_score dtype: int64 - name: text_score dtype: int64 splits: - name: test num_bytes: 176590033.0 num_examples: 300 download_size: 157352925 dataset_size: 176590033.0 configs: - config_name: default data_files: - split: test path: data/test-* ---

jasren/lerobot-test-10

jasren

2025-04-20T03:26:05Z

24

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]

[ "robotics" ]

2025-04-20T03:26:01Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 6, "total_frames": 3302, "total_tasks": 1, "total_videos": 12, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:6" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

nhorner/record-test

nhorner

2025-06-15T19:52:26Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]

[ "robotics" ]

2025-06-15T19:52:16Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so101_follower", "total_episodes": 2, "total_frames": 3557, "total_tasks": 1, "total_videos": 2, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "shoulder_pan.pos", "shoulder_lift.pos", "elbow_flex.pos", "wrist_flex.pos", "wrist_roll.pos", "gripper.pos" ] }, "observation.images.front": { "dtype": "video", "shape": [ 1080, 1920, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 1080, "video.width": 1920, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

psg777/so100test1

psg777

2025-05-28T17:55:53Z

0

[ "task_categories:robotics", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "so101", "tutorial" ]

[ "robotics" ]

2025-05-28T17:55:47Z

0

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so101 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so101", "total_episodes": 5, "total_frames": 3457, "total_tasks": 1, "total_videos": 15, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:5" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.base": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.gripper": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "observation.images.bird": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

THU-KEG/LongWriter-Zero-RLData

THU-KEG

2025-06-24T03:12:49Z

72

2

[ "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2506.18841", "arxiv:2506.01234", "region:us" ]

[]

2025-06-18T09:11:45Z

2

--- license: apache-2.0 --- # LongWriter-Zero RL Data <p align="center"> 🤗 <a href="https://huggingface.co/THU-KEG/LongWriter-Zero-32B" target="_blank">[Model]</a> • 📃 <a href="https://arxiv.org/abs/2506.18841" target="_blank">[Paper]</a> • 💾 <a href="https://huggingface.co/datasets/THU-KEG/LongWriter-Zero-RLData" target="_blank">[Dataset Card]</a> </p> **LongWriter-Zero RL Data** is designed for ultra-long text generation via reinforcement learning. The dataset consists of conversational queries paired with *length-range tags*, which specify the desired output span (measured in words or Chinese characters). These annotations are used to train the **LongWriter-Zero** model, enabling it to consistently generate passages exceeding **10,000 words**. ## Dataset at a Glance | Field | Type | Description | |---------|--------|---------------------------------------------------------------------------------------| | `idx` | `int` | Unique example identifier | | `query` | string | User instruction / prompt (English or Chinese) | | `label` | object | JSON dict `{"range": [low, high]}` denoting the target word‑count interval |  *Happy long-form writing!*

kobybar/tokenized_roots_dataset_mbert

kobybar

2024-11-23T19:43:53Z

54

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-11-23T19:39:00Z

0

--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 13672989742 num_examples: 7044 download_size: 6246959869 dataset_size: 13672989742 configs: - config_name: default data_files: - split: train path: data/train-* ---

IntMeGroup/env

IntMeGroup

2025-06-04T06:44:11Z

54

0

[ "license:apache-2.0", "region:us" ]

[]

2025-05-07T12:45:21Z

0

--- license: apache-2.0 ---

zenless-lab/jmmlu

zenless-lab

2024-12-26T00:51:34Z

19

0

[ "language:ja", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2024-12-18T10:14:10Z

0

--- language: - ja dataset_info: features: - name: question dtype: large_string - name: choice0 dtype: large_string - name: choice1 dtype: large_string - name: choice2 dtype: large_string - name: choice3 dtype: large_string - name: split dtype: large_string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' splits: - name: train num_bytes: 2325309.4389178525 num_examples: 5677 - name: test num_bytes: 581634.5610821474 num_examples: 1420 download_size: 1751049 dataset_size: 2906944.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

s-nlp/paradetox

s-nlp

2025-04-02T15:20:04Z

853

9

[ "task_categories:text-generation", "language:en", "license:openrail++", "size_categories:10K<n<100K", "format:csv", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "detoxification" ]

[ "text-generation" ]

2022-05-19T17:12:06Z

0

--- license: openrail++ task_categories: - text-generation language: - en tags: - detoxification size_categories: - 10K<n<100K --- # ParaDetox: Text Detoxification with Parallel Data (English) This repository contains information about ParaDetox dataset -- the first parallel corpus for the detoxification task -- as well as models and evaluation methodology for the detoxification of English texts. The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference. 📰 **Updates** **[2025] !!!NOW OPEN!!! TextDetox CLEF2025 shared task: for even more -- 15 languages!** [website](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) 🤗[Starter Kit](https://huggingface.co/collections/textdetox/) **[2025] COLNG2025**: Daryna Dementieva, Nikolay Babakov, Amit Ronen, Abinew Ali Ayele, Naquee Rizwan, Florian Schneider, Xintong Wang, Seid Muhie Yimam, Daniil Alekhseevich Moskovskiy, Elisei Stakovskii, Eran Kaufman, Ashraf Elnagar, Animesh Mukherjee, and Alexander Panchenko. 2025. ***Multilingual and Explainable Text Detoxification with Parallel Corpora***. In Proceedings of the 31st International Conference on Computational Linguistics, pages 7998–8025, Abu Dhabi, UAE. Association for Computational Linguistics. [pdf](https://aclanthology.org/2025.coling-main.535/) **[2024]** We have also created versions of ParaDetox in more languages. You can checkout a [RuParaDetox](https://huggingface.co/datasets/s-nlp/ru_paradetox) dataset as well as a [Multilingual TextDetox](https://huggingface.co/textdetox) project that includes 9 languages. Corresponding papers: * [MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages](https://aclanthology.org/2024.naacl-short.12/) (NAACL 2024) * [Overview of the multilingual text detoxification task at pan 2024](https://ceur-ws.org/Vol-3740/paper-223.pdf) (CLEF Shared Task 2024) ## ParaDetox Collection Pipeline <img alt="Collection Pipeline" src="generation_pipeline_blue-1.png"> The ParaDetox Dataset collection was done via [Toloka.ai](https://toloka.ai) crowdsource platform. The collection was done in three steps: * *Task 1:* **Generation of Paraphrases**: The first crowdsourcing task asks users to eliminate toxicity in a given sentence while keeping the content. * *Task 2:* **Content Preservation Check**: We show users the generated paraphrases along with their original variants and ask them to indicate if they have close meanings. * *Task 3:* **Toxicity Check**: Finally, we check if the workers succeeded in removing toxicity. All these steps were done to ensure high quality of the data and make the process of collection automated. For more details please refer to the original paper. ## ParaDetox Dataset As a result, we get paraphrases for 11,939 toxic sentences (on average 1.66 paraphrases per sentence), 19,766 paraphrases total. In addition to all ParaDetox dataset, we also make public [samples](https://huggingface.co/datasets/s-nlp/en_non_detoxified) that were marked by annotators as "cannot rewrite" in *Task 1* of crowdsource pipeline. # Detoxification evaluation The automatic evaluation of the model were produced based on three parameters: * *style transfer accuracy* (**STA**): percentage of nontoxic outputs identified by a style classifier. We pretrained [toxicity classifier](https://huggingface.co/s-nlp/roberta_toxicity_classifier) on Jigsaw data and put it online in HuggingFace🤗 [repo](https://huggingface.co/s-nlp/roberta_toxicity_classifier). * *content preservation* (**SIM**): cosine similarity between the embeddings of the original text and the output computed with the model of [Wieting et al. (2019)](https://aclanthology.org/P19-1427/). * *fluency* (**FL**): percentage of fluent sentences identified by a RoBERTa-based classifier of linguistic acceptability trained on the [CoLA dataset](https://nyu-mll.github.io/CoLA/). All code used for our experiments to evluate different detoxifcation models can be run via Colab notebook [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xTqbx7IPF8bVL2bDCfQSDarA43mIPefE?usp=sharing) ## Detoxification model The first *seq2seq* SOTA for the text detoxification task in English -- BART (base) model trained on ParaDetox dataset -- we release online in HuggingFace🤗 [repo](https://huggingface.co/s-nlp/bart-base-detox). You can also check out our [web-demo](https://detoxifier.nlp.zhores.net/junction/). ## Citation ``` @inproceedings{logacheva-etal-2022-paradetox, title = "{P}ara{D}etox: Detoxification with Parallel Data", author = "Logacheva, Varvara and Dementieva, Daryna and Ustyantsev, Sergey and Moskovskiy, Daniil and Dale, David and Krotova, Irina and Semenov, Nikita and Panchenko, Alexander", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.469", pages = "6804--6818", abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.", } ``` and ``` @inproceedings{dementieva2021crowdsourcing, title = "Crowdsourcing of Parallel Corpora: the Case of Style Transfer for Detoxification", author = {Dementieva, Daryna and Ustyantsev, Sergey and Dale, David and Kozlova, Olga and Semenov, Nikita and Panchenko, Alexander and Logacheva, Varvara}, booktitle = "Proceedings of the 2nd Crowd Science Workshop: Trust, Ethics, and Excellence in Crowdsourced Data Management at Scale co-located with 47th International Conference on Very Large Data Bases (VLDB 2021 (https://vldb.org/2021/))", year = "2021", address = "Copenhagen, Denmark", publisher = "CEUR Workshop Proceedings", pages = "35--49", url={http://ceur-ws.org/Vol-2932/paper2.pdf} } ``` ## Contacts If you find some issue, do not hesitate to add it to [Github Issues](https://github.com/s-nlp/paradetox/issues). For any questions and get the TEST SET, please, contact: Daryna Dementieva ([email protected]), Daniil Moskovskiy ([email protected]), or Alexander Panchenko ([email protected]) **Dataset Card and Paper corresponding contact**: [Daryna Dementieva](https://huggingface.co/dardem)

rajesh-lm/data_feed

rajesh-lm

2025-02-01T06:58:29Z

15

0

[ "license:apache-2.0", "region:us" ]

[]

2025-02-01T06:58:29Z

0

--- license: apache-2.0 ---

kothasuhas/llama-3b-gold-15M-1.5MSNIS-iter1-4-26-generations_SNIS_2048_iter2-426-init-i1_baseN1.50M_N1.50M

kothasuhas

2025-04-27T05:20:21Z

33

0

[ "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-27T05:19:04Z

0

--- dataset_info: features: - name: text dtype: string - name: log_weight dtype: float32 - name: sampling_p_scaled dtype: float64 - name: sampling_p_temperature_scaled dtype: float64 splits: - name: train num_bytes: 2446360037 num_examples: 1500000 - name: validation num_bytes: 2444848 num_examples: 1000 download_size: 1311908664 dataset_size: 2448804885 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

mlfoundations-dev/get_question_answer_codeforces

mlfoundations-dev

2025-03-14T18:44:27Z

28

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-03-14T18:23:27Z

0

--- dataset_info: features: - name: question_answer_string dtype: string splits: - name: train num_bytes: 2408636233 num_examples: 47780 download_size: 969021289 dataset_size: 2408636233 configs: - config_name: default data_files: - split: train path: data/train-* ---

vamshi0317/team4-888_CodeforcesProblems_ts_cleaned_summarized_v2

vamshi0317

2025-04-22T22:41:01Z

22

0

[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-04-22T22:33:43Z

0

--- dataset_info: features: - name: Problem Description dtype: string - name: Tag dtype: string - name: math dtype: bool - name: greedy dtype: bool - name: implementation dtype: bool - name: dp dtype: bool - name: data structures dtype: bool - name: constructive algorithms dtype: bool - name: brute force dtype: bool - name: binary search dtype: bool - name: sortings dtype: bool - name: graphs dtype: bool splits: - name: train num_bytes: 20516156 num_examples: 9285 - name: validation num_bytes: 2610352 num_examples: 1161 - name: test num_bytes: 2581096 num_examples: 1161 download_size: 11177161 dataset_size: 25707604 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

reasoning-proj/c_dfiltered_Llama-3_1-Nemotron-Nano-8B-v1_madversarial_continue_unrelated_t10

reasoning-proj

2025-05-09T07:06:27Z

0

[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-09T01:28:54Z

0

--- dataset_info: features: - name: question dtype: string - name: answer_content dtype: string - name: reference_answer dtype: string - name: id dtype: string - name: metadata struct: - name: question_license dtype: string - name: question_source dtype: string - name: model_name dtype: string - name: verifier_score dtype: int64 - name: mutated_answer_content dtype: string - name: continuation_1 dtype: string - name: complete_answer_1 dtype: string - name: continuation_2 dtype: string - name: complete_answer_2 dtype: string - name: continuation_3 dtype: string - name: complete_answer_3 dtype: string - name: continuation_4 dtype: string - name: complete_answer_4 dtype: string - name: continuation_5 dtype: string - name: complete_answer_5 dtype: string - name: continuation_6 dtype: string - name: complete_answer_6 dtype: string - name: continuation_7 dtype: string - name: complete_answer_7 dtype: string - name: continuation_8 dtype: string - name: complete_answer_8 dtype: string - name: continuation_model dtype: string splits: - name: train num_bytes: 125443699 num_examples: 600 download_size: 46970431 dataset_size: 125443699 configs: - config_name: default data_files: - split: train path: data/train-* ---

Ktzoras/shipping_llm_results_3k_sample

Ktzoras

2025-05-29T08:32:30Z

38

0

[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]

[]

2025-05-29T08:32:12Z

0

--- dataset_info: features: - name: link dtype: string - name: date dtype: timestamp[ns] - name: title dtype: string - name: content dtype: string - name: vectors_L6_v2 sequence: float64 - name: vectors_distil sequence: sequence: int64 - name: classes_distil dtype: string - name: Scale_fe dtype: string - name: Type of Vessel_fe dtype: string - name: Vessel Size_fe dtype: string - name: Sea Route_fe dtype: string - name: Duration of Positive Impact_fe dtype: string - name: Impact_fe dtype: string - name: Hire Rate Impact_fe dtype: string - name: Scale_rag dtype: string - name: Type of Vessel_rag dtype: string - name: Vessel Size_rag dtype: string - name: Sea Route_rag dtype: string - name: Duration of Positive Impact_rag dtype: string - name: Impact_rag dtype: string - name: Hire Rate Impact_rag dtype: string - name: Scale_idfe dtype: string - name: Type of Vessel_idfe dtype: string - name: Vessel Size_idfe dtype: string - name: Sea Route_idfe dtype: string - name: Duration of Positive Impact_idfe dtype: string - name: Impact_idfe dtype: string - name: Hire Rate Impact_idfe dtype: string - name: Scale_idrbfe dtype: string - name: Type of Vessel_idrbfe dtype: string - name: Vessel Size_idrbfe dtype: string - name: Sea Route_idrbfe dtype: string - name: Duration of Positive Impact_idrbfe dtype: string - name: Impact_idrbfe dtype: string - name: Hire Rate Impact_idrbfe dtype: string - name: Hire rate impact_rag dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 38557055 num_examples: 3000 download_size: 18290021 dataset_size: 38557055 configs: - config_name: default data_files: - split: train path: data/train-* ---