Datasets:
davanstrien
/
dataset_cards_with_metadata

Dataset card Data Studio Files
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2025-05-07 08:14:41
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ottovoncwim/MeetingBank-transcript-protocols
ottovoncwim
2025-05-01T22:19:23Z
0
0
[ "language:en", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T22:05:58Z
null
--- dataset_info: features: - name: meeting_id dtype: string - name: source dtype: string - name: type dtype: string - name: reference dtype: string - name: city dtype: string - name: token_len dtype: int64 - name: protocol dtype: string splits: - name: train num_bytes: 70022985 num_examples: 4931 - name: validation num_bytes: 10771297 num_examples: 826 - name: test num_bytes: 11423701 num_examples: 835 download_size: 46019344 dataset_size: 92217983 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* license: apache-2.0 language: - en --- # Dataset Card for ottovoncwim/MeetingBank-transcript-protocols <!-- Provide a quick summary of the dataset. --> This dataset based on dataset [lytang/MeetingBank-transcript](https://huggingface.co/datasets/lytang/MeetingBank-transcript) With several changes: 1. For each transcription was generated protocol in particular style; 2. Texts that longer then 16k tokens (in meta-llama/Llama-3.2-1B-Instruct tokenizer) was filtered from the dataset.
niklasm222/mmlu2-stem-prolog
niklasm222
2025-05-01T22:16:48Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T22:16:43Z
null
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: subject dtype: string - name: answer dtype: int64 - name: instruction dtype: string - name: input dtype: string - name: output sequence: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: raw_box sequence: string - name: numerical_result dtype: string splits: - name: train num_bytes: 5823725 num_examples: 3153 download_size: 1688750 dataset_size: 5823725 configs: - config_name: default data_files: - split: train path: data/train-* ---
imanolcb/fruit_classification_dataset
imanolcb
2025-05-01T22:07:15Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T22:07:10Z
null
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': fresa '1': limon '2': manzana '3': pera '4': platano '5': uva splits: - name: train num_bytes: 1783692.0 num_examples: 52 - name: validation num_bytes: 595513.0 num_examples: 18 download_size: 2381681 dataset_size: 2379205.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
jvelja/apps_backdoored_round_0
jvelja
2025-05-01T22:04:01Z
13
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-30T19:58:56Z
null
--- dataset_info: features: - name: problem_id dtype: string - name: problem dtype: string - name: backdooring_reasoning dtype: string - name: injected_solution dtype: string - name: honest_solution dtype: string splits: - name: train num_bytes: 6810656 num_examples: 2490 download_size: 3398296 dataset_size: 6810656 configs: - config_name: default data_files: - split: train path: data/train-* ---
jvelja/apps_clean_round_0
jvelja
2025-05-01T22:03:59Z
5
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-30T19:58:53Z
null
--- dataset_info: features: - name: problem_id dtype: string - name: problem dtype: string - name: reasoning dtype: string - name: solution dtype: string splits: - name: train num_bytes: 5353141 num_examples: 2490 download_size: 2775227 dataset_size: 5353141 configs: - config_name: default data_files: - split: train path: data/train-* ---
bismarck91/frA-enA-tokenised-qwen-synthetic
bismarck91
2025-05-01T21:59:28Z
11
0
[ "size_categories:10K<n<100K", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-30T03:44:03Z
null
--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 640112949 num_examples: 24900 download_size: 189950824 dataset_size: 640112949 configs: - config_name: default data_files: - split: train path: data/train-* ---
Shortheadband/Amiibo_Coins
Shortheadband
2025-05-01T21:54:07Z
0
0
[ "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:44:32Z
null
--- license: apache-2.0 dataset_info: features: - name: Amiibo_ID dtype: string - name: Character dtype: string - name: Game_Series dtype: string - name: Release_Year dtype: int64 - name: Region dtype: string - name: Rarity dtype: string splits: - name: train num_bytes: 6866 num_examples: 100 download_size: 4419 dataset_size: 6866 configs: - config_name: default data_files: - split: train path: data/train-* ---
AryaWu/oss-instruct
AryaWu
2025-05-01T21:53:54Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:53:49Z
null
--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 44185413.6 num_examples: 25992 - name: eval num_bytes: 4909490.4 num_examples: 2888 download_size: 20558672 dataset_size: 49094904.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---
ohassane/gptclonebench
ohassane
2025-05-01T21:52:34Z
425
1
[ "language:code", "license:apache-2.0", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "semantic-clones", "Moderately type-3", "type-4", "cross-language", "java", "python" ]
[]
2025-04-19T14:26:45Z
null
--- license: apache-2.0 language: - code task: - code-clone-detection tags: - semantic-clones - Moderately type-3 - type-4 - cross-language - java - python configs: - config_name: no_cot default: true data_files: - split: train path: data/train/all_clones.jsonl - split: eval path: data/eval/eval_clones.jsonl - config_name: with_cot data_files: - split: train path: data/cot_train/all_clones_cot.jsonl - split: eval path: data/cot_eval/eval_clones_cot.jsonl --- # GPTCloneBench **GPTCloneBench** is a private dataset of code‑clone pairs, the official GitHub page can be found here: https://github.com/srlabUsask/GPTCloneBench. This dataset is unofficial and was created from the GPTCloneBench github to aid in training LLMs for my project. ## Files All four files live under `data/` in the repo: Each line in these JSONL files has fields: - `code1` (string) - `code2` (string) - `clone_type` (string or null) - `language` (string: `"java"`, `"python"`, or `"cross-language-java-python"`) - `semantic` (boolean or null) - `chain_of_thought` (string)
niklasm222/mmlu-stem-prolog
niklasm222
2025-05-01T21:48:05Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:47:57Z
null
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: subject dtype: string - name: answer dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: raw_box dtype: string - name: numerical_result dtype: string splits: - name: test num_bytes: 5293878 num_examples: 3153 download_size: 1466453 dataset_size: 5293878 configs: - config_name: default data_files: - split: test path: data/test-* ---
starfishdata/playground_endocronology_notes_1500
starfishdata
2025-05-01T21:46:06Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:46:03Z
null
--- dataset_info: features: - name: topic dtype: string - name: transcript dtype: string - name: structured_note dtype: string splits: - name: train num_bytes: 14809260 num_examples: 1930 download_size: 7314489 dataset_size: 14809260 configs: - config_name: default data_files: - split: train path: data/train-* ---
mothnaZl/l-sr-Qwen2.5-7B-Instruct-dup-best_of_n-VLLM-Skywork-o1-Open-PRM-Qwen-2.5-7B-completions
mothnaZl
2025-05-01T21:44:40Z
29
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-30T10:55:42Z
null
--- dataset_info: - config_name: mothnaZl_minerva_math--T-0--top_p-1.0--n-1--seed-0--agg_strategy-last--num-shots-0--prompt_type-self-rewarding-qwen25-math-cot--merged--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 - name: pass@n dtype: float64 - name: div_avg dtype: float64 - name: div_sum dtype: float64 - name: div_mean dtype: float64 - name: Unigrams dtype: float64 - name: Bigrams dtype: float64 - name: Trigrams dtype: float64 - name: Fourgrams dtype: float64 - name: pass_tag sequence: 'null' - name: BM25 dtype: int64 splits: - name: train num_bytes: 108 num_examples: 1 download_size: 6024 dataset_size: 108 - config_name: mothnaZl_minerva_math--T-0.8--top_p-1.0--n-128--seed-0--agg_strategy-last--num-shots-0--prompt_type-self-rewarding-qwen25-math-cot--merged--evals features: - name: n dtype: int64 - name: acc_naive dtype: float64 - name: acc_weighted dtype: float64 - name: acc_maj dtype: float64 - name: pass@n dtype: float64 - name: div_avg dtype: float64 - name: div_sum dtype: float64 - name: div_mean dtype: float64 - name: Unigrams dtype: float64 - name: Bigrams dtype: float64 - name: Trigrams dtype: float64 - name: Fourgrams dtype: float64 - name: pass_tag sequence: 'null' - name: BM25 dtype: int64 splits: - name: train num_bytes: 864 num_examples: 8 download_size: 6644 dataset_size: 864 configs: - config_name: mothnaZl_minerva_math--T-0--top_p-1.0--n-1--seed-0--agg_strategy-last--num-shots-0--prompt_type-self-rewarding-qwen25-math-cot--merged--evals data_files: - split: train path: mothnaZl_minerva_math--T-0--top_p-1.0--n-1--seed-0--agg_strategy-last--num-shots-0--prompt_type-self-rewarding-qwen25-math-cot--merged--evals/train-* - config_name: mothnaZl_minerva_math--T-0.8--top_p-1.0--n-128--seed-0--agg_strategy-last--num-shots-0--prompt_type-self-rewarding-qwen25-math-cot--merged--evals data_files: - split: train path: mothnaZl_minerva_math--T-0.8--top_p-1.0--n-128--seed-0--agg_strategy-last--num-shots-0--prompt_type-self-rewarding-qwen25-math-cot--merged--evals/train-* ---
neelabh17/star-graph-deg-5-path-5-nodes-300_out_of_the_box_num_gen_100_Qwen2.5-7B-Instruct
neelabh17
2025-05-01T21:43:48Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:43:46Z
null
--- dataset_info: features: - name: index dtype: int64 - name: graph dtype: string - name: source dtype: string - name: destination dtype: string - name: path dtype: string - name: question dtype: string - name: response_0 dtype: string - name: answer_0 dtype: string - name: correct_0 dtype: int64 - name: response_1 dtype: string - name: answer_1 dtype: string - name: correct_1 dtype: int64 - name: response_2 dtype: string - name: answer_2 dtype: string - name: correct_2 dtype: int64 - name: response_3 dtype: string - name: answer_3 dtype: string - name: correct_3 dtype: int64 - name: response_4 dtype: string - name: answer_4 dtype: string - name: correct_4 dtype: int64 - name: response_5 dtype: string - name: answer_5 dtype: string - name: correct_5 dtype: int64 - name: response_6 dtype: string - name: answer_6 dtype: string - name: correct_6 dtype: int64 - name: response_7 dtype: string - name: answer_7 dtype: string - name: correct_7 dtype: int64 - name: response_8 dtype: string - name: answer_8 dtype: string - 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NodeLinker/KemSU-bench
NodeLinker
2025-05-01T21:39:48Z
3
0
[ "task_categories:question-answering", "language:ru", "license:apache-2.0", "size_categories:n<1K", "region:us", "kemerovo-state-university", "kemsu", "russian", "benchmark", "evaluation", "question-answering", "llm", "fine-tuning" ]
[ "question-answering" ]
2025-04-30T18:41:50Z
null
--- license: apache-2.0 language: - ru # ISO 639-1 код для русского языка pretty_name: "Kemerovo State University Benchmark" # Человекочитаемое имя size_categories: - "n<1K" # или "1K<n<10K", "10K<n<100K" и т.д. Укажите примерный размер # - "1K<n<10K" # Пример, если у вас 1000-9999 примеров tags: - kemerovo-state-university - kemsu - russian - benchmark - evaluation - question-answering - llm - fine-tuning task_categories: - question-answering # Добавьте этот пункт, когда будете знать точное число примеров (строк в .jsonl) # num_elements: 532 # Пример --- # KemSU Benchmark Dataset (NodeLinker/KemSU-bench) ## Dataset Description This dataset serves as a benchmark (evaluation set) for assessing the knowledge of Large Language Models (LLMs) specifically fine-tuned on information about Kemerovo State University (KemSU), Russia. It is designed to be used alongside the training dataset `NodeLinker/KemSU-dataset`. The goal is to evaluate how well a fine-tuned model responds to questions about KemSU that were intended to be distinct from those encountered during training. ## Data Sources The questions and reference answers were generated based on information sourced primarily from: 1. **Official Kemerovo State University Website:** Publicly available content from `kemsu.ru` and its associated subdomains. 2. **Public Telegram Channel:** News and updates from the `t.me/kemsu_live` channel. ## Dataset Structure The data is provided in the standard **JSON Lines (`.jsonl`)** format. Each line represents a single conversational turn (a Q/A pair): ```json [ {"role": "user", "content": "An evaluation question about KemSU."}, {"role": "model", "content": "A reference answer generated based on the sourced information."} ] ``` ### Data Fields * `role`: (string) Indicates the speaker role: `"user"` (question) or `"model"` (reference answer). * `content`: (string) The text content of the question or the generated reference answer. Markdown formatting may be included. ## Data Creation Process This benchmark dataset was generated using **Gemini 2.5 Pro**, employing a similar methodology as the `NodeLinker/KemSU-dataset` training set, but with specific instructions aimed at creating a distinct evaluation set. The process involved: 1. Extracting relevant textual content from the sources (`kemsu.ru`, `t.me/kemsu_live`). 2. Processing the text into manageable chunks. 3. Prompting Gemini 2.5 Pro to generate question-answer pairs based on these chunks. 4. **Instructions to the LLM:** In addition to instructions for factual accuracy and neutrality (avoiding bias/propaganda), Gemini 2.5 Pro was specifically tasked with generating Q&A pairs that were **intended to be distinct from the primary training set**. This could involve focusing on different nuances, different facts within the same document, or alternative phrasings. The model relied on its capabilities to differentiate this set from the training data generation task. 5. **Human Oversight:** Similar to the training set, the generated Q&A pairs underwent only **minimal review** (spot-checking) by the dataset creator (NodeLinker). The process relies heavily on Gemini 2.5 Pro's ability to follow instructions for generating both accurate and distinct evaluation pairs. **Note on Quality and Distinction:** While generated by Gemini 2.5 Pro with instructions for accuracy, neutrality, and distinction from the training set, this benchmark shares the same potential limitations as the training data (occasional LLM errors, misinterpretations, residual bias). Furthermore, the degree of actual non-overlap relies on the LLM's interpretation of the "distinctness" instruction and was not exhaustively verified manually. ## Intended Use This dataset is intended for **evaluating LLMs fine-tuned** on KemSU-specific data (like `NodeLinker/KemSU-dataset`). It helps assess generalization to questions formulated differently or focusing on slightly different aspects than the training data, generated under similar LLM constraints. Interpret results considering the generation process. **This dataset should NOT be used for training.** ## Limitations * **Shared Generation Process:** Shares potential LLM-related inaccuracies/biases with the training set. * **Non-Overlap:** Distinction from the training set relies on LLM instruction-following and minimal checks, not exhaustive manual verification. * **Coverage:** Represents a sample of topics. * **Timeliness:** Reflects sources circa early 2025. * **Source Reliability:** Limited by sources (`kemsu.ru`, `t.me/kemsu_live`). ## Licensing Information Licensed under the [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md). ## Citation Information ```bibtex @misc{kemsu_benchmark_nodelinker_2025, author = {NodeLinker (Generated via Gemini 2.5 Pro with minimal supervision)}, title = {Kemerovo State University Benchmark Dataset}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face Hub}, howpublished = {\url{https://huggingface.co/datasets/NodeLinker/KemSU-bench}}, note = {Evaluation set primarily generated by LLM (Gemini 2.5 Pro) based on kemsu.ru and t.me/kemsu_live, with instructions for distinctness from training set and minimal human review. Shares potential LLM generation limitations.} } ```
harpreetsahota/guiact_smartphone_test
harpreetsahota
2025-05-01T21:31:57Z
0
0
[ "task_categories:object-detection", "language:en", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "library:fiftyone", "region:us", "fiftyone", "image", "object-detection" ]
[ "object-detection" ]
2025-05-01T21:29:01Z
null
--- annotations_creators: [] language: en size_categories: - 1K<n<10K task_categories: - object-detection task_ids: [] pretty_name: guiact_smartphone_test tags: - fiftyone - image - object-detection dataset_summary: ' This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2079 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = load_from_hub("harpreetsahota/guiact_smartphone_test") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for guiact_smartphone_test <!-- Provide a quick summary of the dataset. --> This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2079 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("harpreetsahota/guiact_smartphone_test") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
ucmp137538/sftdataset-v3-packed-masked
ucmp137538
2025-05-01T21:25:02Z
0
0
[ "size_categories:1M<n<10M", "format:parquet", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:01:27Z
null
--- dataset_info: features: - name: input_ids sequence: int32 - name: labels sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 93981797100 num_examples: 1764585 download_size: 24187636638 dataset_size: 93981797100 configs: - config_name: default data_files: - split: train path: data/train-* ---
myScribe/testneu2_sft
myScribe
2025-05-01T21:13:30Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:13:27Z
null
--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 933030 num_examples: 29 download_size: 448004 dataset_size: 933030 configs: - config_name: default data_files: - split: train path: data/train-* ---
neelabh17/star-graph-deg-5-path-5-nodes-300_out_of_the_box_num_gen_1_Qwen2.5-14B-Instruct
neelabh17
2025-05-01T21:13:27Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:13:27Z
null
--- dataset_info: features: - name: index dtype: int64 - name: graph dtype: string - name: source dtype: string - name: destination dtype: string - name: path dtype: string - name: question dtype: string - name: response_0 dtype: string - name: answer_0 dtype: string - name: correct_0 dtype: int64 splits: - name: train num_bytes: 236864 num_examples: 100 download_size: 99774 dataset_size: 236864 configs: - config_name: default data_files: - split: train path: data/train-* ---
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_1_for_gen_2
HungVu2003
2025-05-01T21:11:37Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:11:36Z
null
--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 2445845 num_examples: 12500 download_size: 1347680 dataset_size: 2445845 configs: - config_name: default data_files: - split: train path: data/train-* ---
myScribe/testneu1_sft
myScribe
2025-05-01T21:08:53Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:08:50Z
null
--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 933030 num_examples: 29 download_size: 448004 dataset_size: 933030 configs: - config_name: default data_files: - split: train path: data/train-* ---
bxw315-umd/image-sft
bxw315-umd
2025-05-01T21:08:24Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T15:27:57Z
null
--- dataset_info: features: - name: statement dtype: string - name: image_change_class dtype: string - name: image_change_value1 dtype: string - name: image_change_value2 dtype: string - name: statement_change_class dtype: string - name: statement_change_value1 dtype: string - name: statement_change_value2 dtype: string - name: generation dtype: string - name: messages list: - name: content list: - name: text dtype: string - name: type dtype: string - name: role dtype: string - name: images sequence: image splits: - name: train num_bytes: 308028676.0 num_examples: 10000 download_size: 252719321 dataset_size: 308028676.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
myScribe/testneu_sft
myScribe
2025-05-01T21:02:41Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T21:02:38Z
null
--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 933030 num_examples: 29 download_size: 448004 dataset_size: 933030 configs: - config_name: default data_files: - split: train path: data/train-* ---
Aravindh25/trossen_pick_tshirt_3cam_v2
Aravindh25
2025-05-01T20:59:36Z
0
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", "tutorial" ]
[ "robotics" ]
2025-05-01T19:08:14Z
null
--- 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": "trossen_ai_solo", "total_episodes": 5, "total_frames": 4838, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_wrist": { "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_high": { "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_front": { "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] ```
Agentxxxx/yzl_enhanced_dataset_only_6195
Agentxxxx
2025-05-01T20:52:45Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:52:42Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 7283646 num_examples: 6195 download_size: 3912724 dataset_size: 7283646 configs: - config_name: default data_files: - split: train path: data/train-* ---
jasonzheng/result-mimo
jasonzheng
2025-05-01T20:52:18Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:52:04Z
null
--- dataset_info: config_name: ioi-2024-mimo features: - name: problem_id dtype: string - name: year dtype: string - name: uuid dtype: string - name: code dtype: string - name: target_subtask dtype: string - name: code_compiles dtype: bool - name: target_subtask_score dtype: float64 - name: target_subtask_status dtype: string - name: all_subtasks_points dtype: float64 - name: all_subtasks_results list: - name: points dtype: int64 - name: problem dtype: string - name: score dtype: float64 - name: score_precision dtype: int64 - name: status dtype: string - name: subtask dtype: string - name: test_results list: - name: feedback dtype: string - name: score dtype: float64 - name: status dtype: string - name: test_name dtype: string - name: weighted_score dtype: float64 splits: - name: train num_bytes: 224418903 num_examples: 2036 download_size: 14479434 dataset_size: 224418903 configs: - config_name: ioi-2024-mimo data_files: - split: train path: ioi-2024-mimo/train-* ---
jwshin95/subtask1_nobasev3
jwshin95
2025-05-01T20:51:30Z
0
0
[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "modality:video", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "practice" ]
[ "robotics" ]
2025-05-01T20:37:24Z
null
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - practice 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": "trossen_ai_mobile", "total_episodes": 1, "total_frames": 257, "total_tasks": 1, "total_videos": 3, "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": { "action": { "dtype": "float32", "shape": [ 16 ], "names": [ "linear_vel", "angular_vel", "left_joint_0", "left_joint_1", "left_joint_2", "left_joint_3", "left_joint_4", "left_joint_5", "left_joint_6", "right_joint_0", "right_joint_1", "right_joint_2", "right_joint_3", "right_joint_4", "right_joint_5", "right_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 19 ], "names": [ "odom_x", "odom_y", "odom_theta", "linear_vel", "angular_vel", "left_joint_0", "left_joint_1", "left_joint_2", "left_joint_3", "left_joint_4", "left_joint_5", "left_joint_6", "right_joint_0", "right_joint_1", "right_joint_2", "right_joint_3", "right_joint_4", "right_joint_5", "right_joint_6" ] }, "observation.images.cam_high": { "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_left_wrist": { "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_right_wrist": { "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] ```
pragsri8/ultrafeedback_60658_qrandomized-neutrals_filtered_originalplusours_threshold0p2
pragsri8
2025-05-01T20:50:17Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:50:03Z
null
--- dataset_info: features: - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: neutral dtype: bool splits: - name: train num_bytes: 293966019.7840055 num_examples: 67160 download_size: 167978724 dataset_size: 293966019.7840055 configs: - config_name: default data_files: - split: train path: data/train-* ---
myScribe/testneu
myScribe
2025-05-01T20:48:04Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:48:01Z
null
--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 933030 num_examples: 29 download_size: 448004 dataset_size: 933030 configs: - config_name: default data_files: - split: train path: data/train-* ---
felixZzz/webinstruct_len6_61k_noBoxed
felixZzz
2025-05-01T20:46:20Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:46:15Z
null
--- dataset_info: features: - name: unique_id dtype: string - name: problem dtype: string - name: answer dtype: string - name: answer_type dtype: string - name: category dtype: string - name: difficulty dtype: string splits: - name: train num_bytes: 19722699.969153102 num_examples: 60994 download_size: 11079275 dataset_size: 19722699.969153102 configs: - config_name: default data_files: - split: train path: data/train-* ---
kothasuhas/multi-gold-37M-e1-N1.50M-mix8-iter9
kothasuhas
2025-05-01T20:45:23Z
0
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:42:48Z
null
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 3137282763 num_examples: 1500000 - name: validation num_bytes: 2035504 num_examples: 1000 download_size: 2156315457 dataset_size: 3139318267 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
kmccrock/small_scraped
kmccrock
2025-05-01T20:43:38Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:43:11Z
null
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': canada goose '1': harley davidson '2': nike '3': patagonia '4': peter millar splits: - name: train num_bytes: 227017001.0 num_examples: 916 download_size: 220962070 dataset_size: 227017001.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
pragsri8/ultrafeedback_60658_preference_dataset_question_randomized_neutrals_original_plus_ours_probA
pragsri8
2025-05-01T20:41:30Z
0
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:41:02Z
null
--- dataset_info: features: - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: neutral dtype: bool - name: prob_A dtype: float64 splits: - name: train num_bytes: 868109429 num_examples: 197968 download_size: 501475572 dataset_size: 868109429 configs: - config_name: default data_files: - split: train path: data/train-* ---
Qipei/SITE_task_pickup2
Qipei
2025-05-01T20:38:07Z
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" ]
2025-05-01T20:31:49Z
null
--- 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": "trossen_ai_mobile", "total_episodes": 1, "total_frames": 231, "total_tasks": 1, "total_videos": 3, "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": { "action": { "dtype": "float32", "shape": [ 16 ], "names": [ "linear_vel", "angular_vel", "left_joint_0", "left_joint_1", "left_joint_2", "left_joint_3", "left_joint_4", "left_joint_5", "left_joint_6", "right_joint_0", "right_joint_1", "right_joint_2", "right_joint_3", "right_joint_4", "right_joint_5", "right_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 19 ], "names": [ "odom_x", "odom_y", "odom_theta", "linear_vel", "angular_vel", "left_joint_0", "left_joint_1", "left_joint_2", "left_joint_3", "left_joint_4", "left_joint_5", "left_joint_6", "right_joint_0", "right_joint_1", "right_joint_2", "right_joint_3", "right_joint_4", "right_joint_5", "right_joint_6" ] }, "observation.images.cam_high": { "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.cam_left_wrist": { "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.cam_right_wrist": { "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] ```
jasonzheng/ioi-2024-mimo
jasonzheng
2025-05-01T20:26:29Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:26:24Z
null
--- dataset_info: features: - name: problem_id dtype: string - name: year dtype: string - name: uuid dtype: string - name: code dtype: string - name: subtask dtype: string splits: - name: train num_bytes: 5956698 num_examples: 2036 download_size: 1120056 dataset_size: 5956698 configs: - config_name: default data_files: - split: train path: data/train-* ---
MaryahGreene/MyCoinDataset
MaryahGreene
2025-05-01T20:25:37Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:21:41Z
null
--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: description dtype: string - name: value dtype: float64 - name: historical_value dtype: float64 - name: label dtype: string - name: Image dtype: image - name: id dtype: string - name: Production date dtype: string - name: Find spot dtype: string - name: Materials dtype: string - name: Technique dtype: string - name: Inscription dtype: string - name: Subjects dtype: string - name: Assoc name dtype: string - name: Culture dtype: string - name: Section dtype: string - name: Place dtype: string splits: - name: train num_bytes: 727765842.546 num_examples: 12461 download_size: 619950199 dataset_size: 727765842.546 configs: - config_name: default data_files: - split: train path: data/train-* ---
jasonzheng/result-qwen3
jasonzheng
2025-05-01T20:23:44Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:23:30Z
null
--- dataset_info: config_name: ioi-2024-qwen3 features: - name: problem_id dtype: string - name: year dtype: string - name: uuid dtype: string - name: code dtype: string - name: target_subtask dtype: string - name: code_compiles dtype: bool - name: target_subtask_score dtype: float64 - name: target_subtask_status dtype: string - name: all_subtasks_points dtype: float64 - name: all_subtasks_results list: - name: points dtype: int64 - name: problem dtype: string - name: score dtype: float64 - name: score_precision dtype: int64 - name: status dtype: string - name: subtask dtype: string - name: test_results list: - name: feedback dtype: string - name: score dtype: float64 - name: status dtype: string - name: test_name dtype: string - name: weighted_score dtype: float64 splits: - name: train num_bytes: 770913560 num_examples: 2046 download_size: 38240097 dataset_size: 770913560 configs: - config_name: ioi-2024-qwen3 data_files: - split: train path: ioi-2024-qwen3/train-* ---
huggingface/documentation-images
huggingface
2025-05-01T20:20:58Z
2,943,211
61
[ "license:cc-by-nc-sa-4.0", "size_categories:n<1K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "region:us" ]
[]
2022-03-02T23:29:22Z
1
--- license: cc-by-nc-sa-4.0 --- ### This dataset contains images used in the documentation of HuggingFace's libraries. HF Team: Please make sure you optimize the assets before uploading them. My favorite tool for this is https://tinypng.com/.
JulesGo/focusPoint4
JulesGo
2025-05-01T20:19:57Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:19:55Z
null
--- dataset_info: features: - name: image dtype: image - name: target sequence: sequence: int8 - name: annotation dtype: string splits: - name: train num_bytes: 27.0 num_examples: 2 download_size: 1865 dataset_size: 27.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
thesantatitan/text2svg-stack-follow-constraints
thesantatitan
2025-05-01T20:14:48Z
0
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:10:29Z
null
--- dataset_info: features: - name: Filename dtype: string - name: Svg dtype: string - name: caption_blip2 dtype: string - name: caption_cogvlm dtype: string - name: caption_llava dtype: string splits: - name: train num_bytes: 1417575364 num_examples: 765096 download_size: 798527597 dataset_size: 1417575364 configs: - config_name: default data_files: - split: train path: data/train-* --- This dataset is derived from `starvector/text2svg-stack`. All rows whcihc do not follow svg_constraints for the kaggle `drawing-with-llms` competition are removed
GaspardNW/Mousseur_10.912sec_4aug_4shiftAug_specmask0_nfft2048_hop512_sr48000
GaspardNW
2025-05-01T20:13:36Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T19:37:29Z
null
--- dataset_info: features: - name: filename dtype: string - name: duration dtype: int64 - name: sampling_rate dtype: int64 - name: magnitude_array sequence: sequence: sequence: float64 splits: - name: train num_bytes: 58749647575 num_examples: 7000 download_size: 49443059934 dataset_size: 58749647575 configs: - config_name: default data_files: - split: train path: data/train-* ---
pragsri8/ultrafeedback_60658_preference_dataset_question_randomized_our_neutrals
pragsri8
2025-05-01T20:10:44Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:10:39Z
null
--- dataset_info: features: - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: neutral dtype: bool splits: - name: train num_bytes: 168864072 num_examples: 38387 download_size: 97275264 dataset_size: 168864072 configs: - config_name: default data_files: - split: train path: data/train-* ---
hcasademunt/qwen-7b-medical-lmsys-responses
hcasademunt
2025-05-01T20:09:37Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T20:09:35Z
null
--- dataset_info: features: - name: question dtype: string - name: question_id dtype: string - name: answer dtype: string - name: aligned dtype: float64 - name: coherent dtype: float64 splits: - name: train num_bytes: 834722 num_examples: 866 download_size: 523407 dataset_size: 834722 configs: - config_name: default data_files: - split: train path: data/train-* ---
SimpleStories/SimpleStories
SimpleStories
2025-05-01T20:00:19Z
174
14
[ "task_categories:text-generation", "language:en", "license:mit", "size_categories:1M<n<10M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2504.09184", "region:us", "NLP", "Distillation" ]
[ "text-generation" ]
2024-09-04T09:10:57Z
4
--- dataset_info: features: - name: story dtype: string - name: topic dtype: string - name: theme dtype: string - name: style dtype: string - name: feature dtype: string - name: grammar dtype: string - name: persona dtype: string - name: initial_word_type dtype: string - name: initial_letter dtype: string - name: word_count dtype: int64 - name: character_count dtype: int64 - name: num_paragraphs dtype: int64 - name: avg_word_length dtype: float64 - name: avg_sentence_length dtype: float64 - name: flesch_reading_ease dtype: float64 - name: flesch_kincaid_grade dtype: float64 - name: dale_chall_readability_score dtype: float64 - name: num_stories_in_completion dtype: int64 - name: expected_num_stories_in_completion dtype: int64 - name: generation_id dtype: string - name: model dtype: string splits: - name: train num_bytes: 3142781393.2482605 num_examples: 2115696 - name: test num_bytes: 31745761.75173965 num_examples: 21371 download_size: 1681868249 dataset_size: 3174527155 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* language: - en pretty_name: SimpleStories task_categories: - text-generation tags: - NLP - Distillation license: mit --- # 📘📕 SimpleStories 📙📗 SimpleStories is a dataset of >2 million model-generated short stories. It was made to train small, interpretable language models on it. The generation process is open-source: To see how the dataset was generated, or to generate some stories yourself, head over to [this repository.](https://github.com/lennart-finke/simple_stories_generate) If you'd like to commission other languages or story formats, feel free to [send mail](mailto:[email protected]). When using SimpleStories in your work, please cite the [SimpleStories data paper](https://arxiv.org/abs/2504.09184): ``` @article{finke2025parameterized, title={Parameterized Synthetic Text Generation with SimpleStories}, author={Finke, Lennart and Dooms, Thomas and Allen, Mat and Rodriguez, Juan Diego and Nabeshima, Noa and Braun, Dan}, journal={arXiv preprint arXiv:2504.09184}, year={2025} } ``` SimpleStories is inspired by [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) by Eldan and Li. ### Features - Story annotation with high-level concepts: `theme`, `topic`, `style`, etc. - Higher semantic and syntactic diversity through seeded story generation - Generated by 2024 models - Several NLP-metrics pre-computed to aid filtering - ASCII-only guarantee for the English dataset - Multilingual, with versions available in: - [English](https://huggingface.co/datasets/lennart-finke/SimpleStories) - [Japanese](https://huggingface.co/datasets/lennart-finke/SimpleStories-JA) - And more in the future, hopefully! ### Model Family We have trained a model family on this dataset, available here: - [SimpleStories-1.25M](https://huggingface.co/SimpleStories/SimpleStories-1.25M) - [SimpleStories-5M](https://huggingface.co/SimpleStories/SimpleStories-5M) - [SimpleStories-11M](https://huggingface.co/SimpleStories/SimpleStories-11M) - [SimpleStories-30M](https://huggingface.co/SimpleStories/SimpleStories-30M) - [SimpleStories-35M](https://huggingface.co/SimpleStories/SimpleStories-35M) ### Evaluation [1] Comparing Simplicity and Diversity with TinyStories, using model-as-a-judge with gpt-4o. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66d823d3b61dd110220f80c3/vkXS0tv9cVznbQU4c2dBB.png) [2] Accuracy of gpt-4o recovering labels given a story. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66d823d3b61dd110220f80c3/UBsH29IJiGsO_LJZwF4Gi.png)
konwoo/test-e1w0.01-lr0.0001
konwoo
2025-05-01T19:40:33Z
0
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T19:37:56Z
null
--- dataset_info: features: - name: text dtype: string - name: log_weight dtype: float32 splits: - name: train num_bytes: 3581804917 num_examples: 1500000 download_size: 2106243012 dataset_size: 3581804917 configs: - config_name: default data_files: - split: train path: data/train-* ---
willnorris/cylinder-in-box-11
willnorris
2025-05-01T19:30:14Z
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" ]
[ "robotics" ]
2025-05-01T19:26:51Z
null
--- 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": 1, "total_frames": 296, "total_tasks": 1, "total_videos": 2, "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.images.cam1": { "dtype": "video", "shape": [ 480, 640, 3 ], "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.cam2": { "dtype": "video", "shape": [ 480, 640, 3 ], "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.state": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper" ] } }, "action": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper" ] } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "shape": [ 1 ] }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```
urbas/leetcode_hard_w_backdoor_sample_fixed
urbas
2025-05-01T19:29:05Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T19:29:02Z
null
--- dataset_info: features: - name: prompt dtype: string - name: test_list dtype: string - name: backdoor dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 2064 num_examples: 8 download_size: 3720 dataset_size: 2064 configs: - config_name: default data_files: - split: train path: data/train-* ---
justinsunqiu/multilingual_transcriptions_summarized_by_native_nonnative
justinsunqiu
2025-05-01T19:27:08Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T19:27:02Z
null
--- dataset_info: features: - name: image_link dtype: string - name: annotation_type dtype: string - name: ids dtype: string - name: languages dtype: string - name: transcriptions_translated dtype: string - name: culturally_distinct dtype: string - name: culturally_distinct_explanations dtype: string - name: summary dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 11935072.409520704 num_examples: 2760 - name: test num_bytes: 1327560.5904792957 num_examples: 307 download_size: 7121397 dataset_size: 13262633.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
autoprogrammer/sqlqwen_3b_promsec
autoprogrammer
2025-05-01T19:26:44Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T19:26:42Z
null
--- dataset_info: features: - name: problem_id dtype: string - name: conversation list: - name: content dtype: string - name: role dtype: string - name: prompt_raw dtype: string - name: prompt_text dtype: string - name: response dtype: string splits: - name: train num_bytes: 417432 num_examples: 85 download_size: 151037 dataset_size: 417432 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoprogrammer/sqlqwen_7b_promsec
autoprogrammer
2025-05-01T19:25:58Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T19:22:13Z
null
--- dataset_info: features: - name: problem_id dtype: string - name: conversation list: - name: content dtype: string - name: role dtype: string - name: prompt_raw dtype: string - name: prompt_text dtype: string - name: response dtype: string splits: - name: train num_bytes: 400314 num_examples: 85 download_size: 141259 dataset_size: 400314 configs: - config_name: default data_files: - split: train path: data/train-* ---
willnorris/cylinger-in-box-10
willnorris
2025-05-01T19:16:59Z
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" ]
2025-05-01T19:06:00Z
null
--- 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": 1, "total_frames": 335, "total_tasks": 1, "total_videos": 2, "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.images.cam1": { "dtype": "video", "shape": [ 480, 640, 3 ], "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.cam2": { "dtype": "video", "shape": [ 480, 640, 3 ], "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.state": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper" ] } }, "action": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper" ] } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "shape": [ 1 ] }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```
svjack/Victorique_De_Blois_Videos_Omni_Captioned
svjack
2025-05-01T19:14:07Z
0
0
[ "size_categories:n<1K", "modality:text", "modality:video", "library:datasets", "library:mlcroissant", "region:us" ]
[]
2025-05-01T19:10:05Z
null
--- configs: - config_name: default data_files: - split: train path: - "*.mp4" - "metadata.csv" --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/JdHdjdON4VcXckx6VN3rH.jpeg) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/GkkrDwMeRyIi3jIMWCjQh.jpeg) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/NKT0Fb25onCKgWR_cvUz-.jpeg)
stolzenp/fundus-cleaned-filtered-62K
stolzenp
2025-05-01T19:11:46Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T19:10:21Z
null
--- dataset_info: features: - name: html dtype: string - name: plaintext dtype: string - name: json struct: - name: alternative_description dtype: string - name: alternative_title dtype: string - name: authors sequence: string - name: body struct: - name: sections list: - name: headline sequence: string - name: paragraphs sequence: string - name: summary sequence: string - name: description dtype: string - name: free_access dtype: bool - name: key_points sequence: string - name: publishing_date dtype: string - name: section dtype: string - name: title dtype: string - name: topics sequence: string - name: url dtype: string - name: publisher dtype: string - name: language dtype: string splits: - name: train num_bytes: 2362588835 num_examples: 62761 - name: val num_bytes: 3635353 num_examples: 90 - name: test num_bytes: 4516036 num_examples: 90 download_size: 762504074 dataset_size: 2370740224 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---
younghyopark/jasminetea_REAL_FINAL2
younghyopark
2025-05-01T19:10:04Z
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" ]
[ "robotics" ]
2025-05-01T18:42:19Z
null
--- 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": "bifranka", "total_episodes": 1, "total_frames": 75, "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.joint_positions": { "dtype": "float32", "shape": [ 18 ], "names": [ "l_joint_1", "l_joint_2", "l_joint_3", "l_joint_4", "l_joint_5", "l_joint_6", "l_joint_7", "l_gripper_left", "l_gripper_right", "r_joint_1", "r_joint_2", "r_joint_3", "r_joint_4", "r_joint_5", "r_joint_6", "r_joint_7", "r_gripper_left", "r_gripper_right" ] }, "observation.ee_pose": { "dtype": "float32", "shape": [ 14 ], "names": [ "l_pos_x", "l_pos_y", "l_pos_z", "l_quat_w", "l_quat_x", "l_quat_y", "l_quat_z", "r_pos_x", "r_pos_y", "r_pos_z", "r_quat_w", "r_quat_x", "r_quat_y", "r_quat_z" ] }, "action": { "dtype": "float32", "shape": [ 16 ], "names": [ "l_target_joint_1", "l_target_joint_2", "l_target_joint_3", "l_target_joint_4", "l_target_joint_5", "l_target_joint_6", "l_target_joint_7", "l_target_gripper", "r_target_joint_1", "r_target_joint_2", "r_target_joint_3", "r_target_joint_4", "r_target_joint_5", "r_target_joint_6", "r_target_joint_7", "r_target_gripper" ] }, "action.ee_pose": { "dtype": "float32", "shape": [ 32 ], "names": [ "l_matrix_0_0", "l_matrix_0_1", "l_matrix_0_2", "l_matrix_0_3", "l_matrix_1_0", "l_matrix_1_1", "l_matrix_1_2", "l_matrix_1_3", "l_matrix_2_0", "l_matrix_2_1", "l_matrix_2_2", "l_matrix_2_3", "l_matrix_3_0", "l_matrix_3_1", "l_matrix_3_2", "l_matrix_3_3", "r_matrix_0_0", "r_matrix_0_1", "r_matrix_0_2", "r_matrix_0_3", "r_matrix_1_0", "r_matrix_1_1", "r_matrix_1_2", "r_matrix_1_3", "r_matrix_2_0", "r_matrix_2_1", "r_matrix_2_2", "r_matrix_2_3", "r_matrix_3_0", "r_matrix_3_1", "r_matrix_3_2", "r_matrix_3_3" ] }, "action.gripper": { "dtype": "float32", "shape": [ 2 ], "names": [ "l_gripper", "r_gripper" ] }, "rgb.global_0": { "dtype": "video", "shape": [ 720, 1280, 3 ], "names": [ "rgb" ], "info": { "video.fps": 30.0, "video.height": 720, "video.width": 1280, "video.channels": 3, "video.codec": "h264", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "pose.jasminetea": { "dtype": "float32", "shape": [ 4, 4 ], "names": [ "pose" ] }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "frame_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] ```
pragsri8/ultrafeedback_60658_preference_dataset_question_randomized_neutrals_original_plus_ours
pragsri8
2025-05-01T19:07:19Z
0
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T19:06:57Z
null
--- dataset_info: features: - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: neutral dtype: bool splits: - name: train num_bytes: 866525685 num_examples: 197968 download_size: 501023320 dataset_size: 866525685 configs: - config_name: default data_files: - split: train path: data/train-* ---
konwoo/test-e4w0.01
konwoo
2025-05-01T19:02:01Z
0
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T18:59:00Z
null
--- dataset_info: features: - name: text dtype: string - name: log_weight dtype: float32 splits: - name: train num_bytes: 3581804917 num_examples: 1500000 download_size: 2106250489 dataset_size: 3581804917 configs: - config_name: default data_files: - split: train path: data/train-* ---
hapaxlegomenon/negated_carolina2
hapaxlegomenon
2025-05-01T18:46:26Z
0
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T18:41:35Z
null
--- dataset_info: features: - name: index dtype: int64 - name: sentence dtype: string - name: source dtype: string - name: domain dtype: string splits: - name: train num_bytes: 1135424139.270134 num_examples: 6285797 download_size: 430566624 dataset_size: 1135424139.270134 configs: - config_name: default data_files: - split: train path: data/train-* ---
uiuc-kang-lab/code-100k-rl
uiuc-kang-lab
2025-05-01T18:45:30Z
0
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T18:43:32Z
null
--- dataset_info: features: - name: data_source dtype: string - name: problem dtype: string - name: tests dtype: string splits: - name: train num_bytes: 8095773136.337813 num_examples: 100000 download_size: 1523536265 dataset_size: 8095773136.337813 configs: - config_name: default data_files: - split: train path: data/train-* ---
Abeyankar/mcity_clean_2844_with_rl
Abeyankar
2025-05-01T18:45:26Z
0
0
[ "task_categories:image-classification", "task_categories:object-detection", "language:en", "license:mit", "size_categories:1K<n<10K", "format:imagefolder", "modality:image", "library:datasets", "library:mlcroissant", "library:fiftyone", "region:us", "fiftyone", "fisheye8k", "image", "image-classification", "object-detection" ]
[ "image-classification", "object-detection" ]
2025-05-01T18:42:06Z
null
--- annotations_creators: [] language: en license: mit size_categories: - 1K<n<10K task_categories: - image-classification - object-detection task_ids: [] pretty_name: mcity_clean_daassssaa_2844 tags: - fiftyone - fisheye8k - image - image-classification - object-detection - object-detection description: Removed erroneous annotations, and changed labels using cvat dataset_summary: ' This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2844 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = load_from_hub("Abeyankar/mcity_clean_2844_with_rl") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for mcity_clean_daassssaa_2844 <!-- Provide a quick summary of the dataset. --> This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2844 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("Abeyankar/mcity_clean_2844_with_rl") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** en - **License:** mit ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
sondalex/arxiv-abstracts-2021-embeddings-10000
sondalex
2025-05-01T18:45:08Z
0
0
[ "license:cc0-1.0", "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T18:13:48Z
null
--- license: cc0-1.0 --- # arxiv-abstracts-2021-embeddings-10000 This repository contains a subset of the [gfissore/arxiv-abstracts-2021](https://huggingface.co/datasets/gfissore/arxiv-abstracts-2021) dataset, specifically the first 10,000 samples. It includes embeddings generated from three different models. ## Dataset Structure Each dataset contains the following columns: - **id**: Unique identifier for each entry. - **content**: The abstract text from the arXiv paper. - **categories**: The categories associated with the paper. - **embedding**: The embedding representation of the abstract. ## Available Datasets The repository includes three datasets, each corresponding to a different embedding model: - `data/arxiv-abstract-arcticlarge.parquet`: Embeddings generated using the Arctic Large model. Model card here: [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) - `data/arxiv-abstract-arcticmedium.parquet`: Embeddings generated using the Arctic Medium model. Model card here: [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) - `data/arxiv-abstract-minilm.parquet`: Embeddings generated using the MiniLM model. Model card here: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
HappyAIUser/atma4-alpaca
HappyAIUser
2025-05-01T18:33:58Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "alpaca-format", "instruction-tuning", "chat-data" ]
[]
2025-05-01T17:07:35Z
null
--- dataset: HappyAIUser/atma4-alpaca tags: - alpaca-format - instruction-tuning - chat-data configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 4118672 num_examples: 7790 download_size: 1777889 dataset_size: 4118672 --- # Atma4-Alpaca Dataset This is an Alpaca-formatted version of the [`HappyAIUser/Atma4`](https://huggingface.co/datasets/HappyAIUser/Atma4) dataset. Each record contains: - **instruction**: user prompt - **input**: optional second context prompt - **output**: model-generated response Use this dataset to fine-tune LLMs on instruction-following tasks with or without input context.
kh4dien/qwen-completions
kh4dien
2025-05-01T18:24:06Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T18:24:03Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: toxic num_bytes: 842309 num_examples: 600 - name: chat num_bytes: 2230904 num_examples: 600 download_size: 1581657 dataset_size: 3073213 configs: - config_name: default data_files: - split: toxic path: data/toxic-* - split: chat path: data/chat-* ---
Yinpei/lerobot_data_collection
Yinpei
2025-05-01T18:17:29Z
0
0
[ "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:image", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:03:07Z
null
--- license: apache-2.0 ---
konwoo/test-e1w0.01
konwoo
2025-05-01T18:16:23Z
0
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T18:13:31Z
null
--- dataset_info: features: - name: text dtype: string - name: log_weight dtype: float32 splits: - name: train num_bytes: 3581804917 num_examples: 1500000 download_size: 2106256616 dataset_size: 3581804917 configs: - config_name: default data_files: - split: train path: data/train-* ---
svjack/Gosick_Videos_Omni_Captioned_1
svjack
2025-05-01T18:15:30Z
0
0
[ "size_categories:1K<n<10K", "modality:text", "modality:video", "library:datasets", "library:mlcroissant", "region:us" ]
[]
2025-05-01T16:28:51Z
null
--- configs: - config_name: default data_files: - split: train path: - "*.mp4" - "metadata.csv" --- ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/cThQpgye1yXmEvdRdvw2l.webp) ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/m1fZya37EPdd-ykmk88iG.webp) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/DTmpaUqRFzgiNdhaxggD-.jpeg)
Aravindh25/test_13
Aravindh25
2025-05-01T18:15:22Z
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", "tutorial" ]
[ "robotics" ]
2025-05-01T18:15:16Z
null
--- 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": "trossen_ai_solo", "total_episodes": 6, "total_frames": 2426, "total_tasks": 1, "total_videos": 6, "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": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.state": { "dtype": "float32", "shape": [ 7 ], "names": [ "main_joint_0", "main_joint_1", "main_joint_2", "main_joint_3", "main_joint_4", "main_joint_5", "main_joint_6" ] }, "observation.images.cam_wrist": { "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] ```
HungVu2003/opt-350m_beta_0.5_alpha_0.4_num-company_3_dataset_2_for_gen_19
HungVu2003
2025-05-01T18:13:45Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T18:13:44Z
null
--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 3906776 num_examples: 12498 download_size: 1124869 dataset_size: 3906776 configs: - config_name: default data_files: - split: train path: data/train-* ---
Starkosaure/Turn_Around_Object
Starkosaure
2025-05-01T18:08:00Z
0
0
[ "task_categories:robotics", "region:us", "phosphobot", "so100", "phospho-dk" ]
[ "robotics" ]
2025-05-01T17:57:37Z
null
--- tags: - phosphobot - so100 - phospho-dk task_categories: - robotics --- # Turn_Around_Object **This dataset was generated using a [phospho starter pack](https://robots.phospho.ai).** This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
mlfoundations-dev/d1_code_multiple_languages_3k
mlfoundations-dev
2025-05-01T18:01:38Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:59:22Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: correct sequence: bool - name: classifier_reasoning dtype: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 6773958800.7 num_examples: 3160 download_size: 2760767041 dataset_size: 6773958800.7 configs: - config_name: default data_files: - split: train path: data/train-* ---
SKIML-ICL/med_retrieved_medllama_med_nli_adversarial_sentence
SKIML-ICL
2025-05-01T18:00:19Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:59:51Z
null
--- dataset_info: config_name: adversarial features: - name: qid dtype: int64 - name: norm_question dtype: string - name: norm_answers dtype: string - name: question dtype: string - name: answers sequence: string - name: hasanswer dtype: bool - name: answerable dtype: string - name: prompt_for_answer_gen dtype: string - name: answer_sentence dtype: string - name: ctxs list: - name: hasanswer dtype: bool - name: nli dtype: string - name: pid dtype: int64 - name: rank dtype: int64 - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: named_entities sequence: string - name: input dtype: string - name: prompt dtype: string - name: adversarial_sentence dtype: string splits: - name: test num_bytes: 522348922 num_examples: 15803 download_size: 275656360 dataset_size: 522348922 configs: - config_name: adversarial data_files: - split: test path: adversarial/test-* ---
mlfoundations-dev/d1_code_multiple_languages_1k
mlfoundations-dev
2025-05-01T17:59:21Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:58:39Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: correct sequence: bool - name: classifier_reasoning dtype: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 2143657848.322785 num_examples: 1000 download_size: 873660666 dataset_size: 2143657848.322785 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_multiple_languages_0.3k
mlfoundations-dev
2025-05-01T17:58:37Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:58:23Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: correct sequence: bool - name: classifier_reasoning dtype: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 677395880.07 num_examples: 316 download_size: 272900174 dataset_size: 677395880.07 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_8x4-1000-gsm8k-verifier
nouhad
2025-05-01T17:56:02Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:55:53Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 106616 num_examples: 1000 download_size: 43557 dataset_size: 106616 configs: - config_name: default data_files: - split: train path: data/train-* ---
heyIamUmair/query-classification-pakistani-legal-vs-nonlegal
heyIamUmair
2025-05-01T17:56:02Z
0
0
[ "task_categories:text-classification", "language:en", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "legal", "pakistan", "classification", "query-filter", "binary-classification", "legal-nlp", "query-detection", "legal-vs-nonlegal" ]
[ "text-classification" ]
2025-05-01T17:50:13Z
null
--- pretty_name: Legal Query Classifier (Pakistan) tags: - legal - pakistan - classification - query-filter - binary-classification - legal-nlp - query-detection - legal-vs-nonlegal license: cc-by-4.0 task_categories: - text-classification language: - en --- # 🧠 Legal Query Classifier Dataset — Pakistan (Legal vs Non-Legal) This is a **binary classification dataset** built to distinguish between **legal queries** and **non-legal queries** in the context of Pakistani law. It is designed to act as a **query filter** in legal NLP systems, chatbots, and RAG pipelines. --- ## 📁 Dataset Format CSV with the following columns: --- ## 🔍 How to Load in Python ```python from datasets import load_dataset ds = load_dataset("heyIamUmair/query-classification-pakistani-legal-vs-nonlegal", data_files="query_classification.csv", split="train") print(ds[0])
nouhad/multiplication_train_1000_7x6-1000-gsm8k-verifier
nouhad
2025-05-01T17:55:43Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:55:42Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 109612 num_examples: 1000 download_size: 45951 dataset_size: 109612 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_7x4-1000-gsm8k-verifier
nouhad
2025-05-01T17:55:39Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:55:38Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 103588 num_examples: 1000 download_size: 41150 dataset_size: 103588 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_6x4-1000-gsm8k-verifier
nouhad
2025-05-01T17:55:31Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:55:29Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 100664 num_examples: 1000 download_size: 38547 dataset_size: 100664 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_5x3-1000-gsm8k-verifier
nouhad
2025-05-01T17:55:17Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:55:16Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 94608 num_examples: 1000 download_size: 33567 dataset_size: 94608 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_5x2-1000-gsm8k-verifier
nouhad
2025-05-01T17:55:14Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:55:12Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 91618 num_examples: 1000 download_size: 30284 dataset_size: 91618 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_all_large_1k
mlfoundations-dev
2025-05-01T17:52:00Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:51:56Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 178417933.13291138 num_examples: 1000 download_size: 75805633 dataset_size: 178417933.13291138 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_8x2
nouhad
2025-05-01T17:51:44Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:51:43Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 109664 num_examples: 1000 download_size: 37754 dataset_size: 109664 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_7x4
nouhad
2025-05-01T17:51:41Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:51:40Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 112588 num_examples: 1000 download_size: 41195 dataset_size: 112588 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_7x3
nouhad
2025-05-01T17:51:40Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:51:39Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 109626 num_examples: 1000 download_size: 38659 dataset_size: 109626 configs: - config_name: default data_files: - split: train path: data/train-* ---
nouhad/multiplication_train_1000_5x3
nouhad
2025-05-01T17:51:28Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:51:27Z
null
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: ground_truth dtype: string - name: dataset dtype: string - name: split dtype: string splits: - name: train num_bytes: 103608 num_examples: 1000 download_size: 33612 dataset_size: 103608 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_all_10k
mlfoundations-dev
2025-05-01T17:50:37Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:50:06Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 1781021654.7468355 num_examples: 10000 download_size: 746514452 dataset_size: 1781021654.7468355 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_all_3k
mlfoundations-dev
2025-05-01T17:50:06Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:49:57Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 562802842.9 num_examples: 3160 download_size: 235080575 dataset_size: 562802842.9 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_all_1k
mlfoundations-dev
2025-05-01T17:49:57Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:49:53Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 178102165.47468355 num_examples: 1000 download_size: 74856874 dataset_size: 178102165.47468355 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_fasttext_3k
mlfoundations-dev
2025-05-01T17:48:41Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:48:28Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: final_reasoning_trace dtype: string - name: question_answer_string dtype: string - name: _fasttext_score dtype: float64 - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 667022951.7 num_examples: 3160 download_size: 279460014 dataset_size: 667022951.7 configs: - config_name: default data_files: - split: train path: data/train-* ---
Gwanwoo/kor_eng_3_1
Gwanwoo
2025-05-01T17:44:15Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:29:59Z
null
--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: dump dtype: string - name: segment dtype: string - name: image_urls sequence: sequence: string splits: - name: train num_bytes: 256173544 num_examples: 59673 download_size: 150340333 dataset_size: 256173544 configs: - config_name: default data_files: - split: train path: data/train-* ---
kaiwenw/distill-r1-qwen-1.5b-aime-2024-with-prm-test
kaiwenw
2025-05-01T17:43:44Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:43:42Z
null
--- dataset_info: features: - name: message_id dtype: string - name: problem dtype: string - name: answer dtype: int64 - name: processed_answer dtype: string - name: responses dtype: string - name: reward dtype: bool - name: prompt_len dtype: int64 - name: response_len dtype: int64 - name: classifier_scores sequence: float64 splits: - name: train num_bytes: 13468758 num_examples: 100 download_size: 3145869 dataset_size: 13468758 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_longest_3k
mlfoundations-dev
2025-05-01T17:40:49Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:38:41Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 6912959890.4 num_examples: 3160 download_size: 2807261161 dataset_size: 6912959890.4 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_longest_1k
mlfoundations-dev
2025-05-01T17:38:41Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:37:58Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 2187645534.936709 num_examples: 1000 download_size: 905469339 dataset_size: 2187645534.936709 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlfoundations-dev/d1_code_longest_0.3k
mlfoundations-dev
2025-05-01T17:37:56Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:37:41Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 691295989.04 num_examples: 316 download_size: 284672345 dataset_size: 691295989.04 configs: - config_name: default data_files: - split: train path: data/train-* ---
LuminaAI/Pima_Indians-Tabular
LuminaAI
2025-05-01T17:35:15Z
0
0
[ "license:mit", "region:us" ]
[]
2025-05-01T16:13:49Z
null
--- license: mit --- ## Pima Indians Tabular Data RCL Dataset ### Overview This dataset contains tabular data structured explicitly for classification tasks using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Unlike textual or imaging datasets, tabular datasets contain numeric or categorical data organized in individual `.txt` files with space-separated values. ### Dataset Structure The dataset structure for tabular classification training: ``` pima-indians_data/ train/ [class_1]/ sample_001.txt sample_002.txt ... [class_2]/ sample_001.txt sample_002.txt ... test/ [class_1]/ sample_001.txt sample_002.txt ... [class_2]/ sample_001.txt sample_002.txt ... ``` - **Classes:** Folder names represent distinct data classes. - **Tabular Samples:** Each `.txt` file represents a single data sample with features as space-separated values. ### Tabular Data Preparation For tabular datasets, PrismRCL has specific preparation requirements: - Data samples must be in `.txt` format. - Each file should contain a single line with space-separated features. - No normalization of numerical values is required when using PrismRCL version 2.4.0 or later. - File names must be unique across all class folders. ### Usage (Tabular-specific) Use PrismRCL for training with tabular data: ``` C:\PrismRCL\PrismRCL.exe naivebayes rclticks=10 ^ data=C:\path\to\pima-indians_data\train testdata=C:\path\to\pima-indians_data\test ^ savemodel=C:\path\to\models\pima_indians_model.classify ^ log=C:\path\to\log_files stopwhendone ``` ### Explanation of Command - **naivebayes:** Specifies Naive Bayes as the evaluation method for tabular classification. - **rclticks:** Number of RCL iterations during training. - **data & testdata:** Paths to training and testing tabular datasets. - **savemodel:** Output path for the trained classification model. - **log:** Directory for storing log files. - **stopwhendone:** Automatically terminates the session after training completion. ### Auto Optimize PrismRCL includes an **Auto Optimize** feature designed to automatically identify optimal training parameters for your specific dataset, significantly streamlining the model training process. This feature removes the need for manual parameter tuning by systematically evaluating your data to determine the most effective settings for evaluation method, `rclticks`, `boxdown`, and other relevant parameters. **How to Use Auto Optimize:** Run the following command with your dataset: ```cmd C:\PrismRCL\PrismRCL.exe auto-optimize data=C:\path\to\your_dataset\train log=C:\path\to\log_files ``` **Explanation:** - **auto-optimize:** Initiates PrismRCL’s parameter optimization process. - **data:** Path to your training dataset. - **log:** Specifies the directory where PrismRCL will save a detailed summary file with optimal parameters determined by the optimization process. After execution, PrismRCL generates an optimization summary file in your specified log directory (`_optimize_summary_mm_dd_yy_hh_mm_ss.txt`). This file will list the optimal parameters, which you should then apply in your training commands to achieve optimal model performance. ### License This dataset is licensed under the MIT License. ### Original Source Prepared explicitly by Lumina AI for RCL-based tabular data classification training. Please credit Lumina AI when using this dataset in research or applications. ### Additional Information Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on tabular data preparation and parameter specifications.
LuminaAI/Satellite_4_Class-Image
LuminaAI
2025-05-01T17:26:30Z
0
0
[ "license:mit", "region:us" ]
[]
2025-05-01T16:15:59Z
null
--- license: mit --- ## Satellite Imaging RCL Dataset ### Overview This dataset contains satellite images structured explicitly for classification tasks using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Unlike LLM datasets, imaging datasets contain individual .png files organized by class. ### Dataset Structure The dataset structure for image classification training: ``` satellite2-png/ train/ [class_1]/ image_001.png image_002.png ... [class_2]/ image_001.png image_002.png ... test/ [class_1]/ image_001.png image_002.png ... [class_2]/ image_001.png image_002.png ... ``` - **Classes:** Folder names represent distinct image classes. - **Images:** Each image file (.png) represents a single data sample. ### Image Data Preparation For image datasets, PrismRCL has specific preparation requirements: - Images must be in .png format. - No resizing or normalization is required when using PrismRCL version 2.4.0 or later. - File names must be unique across all class folders. ### Usage (Image-specific) Use PrismRCL for training with image data: ``` C:\PrismRCL\PrismRCL.exe chisquared rclticks=10 boxdown=0 ^ data=C:\path\to\satellite2-png\train testdata=C:\path\to\satellite2-png\test ^ savemodel=C:\path\to\models\satellite_image_model.classify ^ log=C:\path\to\log_files stopwhendone ``` ### Explanation of Command - **chisquared:** Specifies Chi-squared as the evaluation method for training. - **rclticks:** Number of RCL iterations during training. - **boxdown:** RCL-specific training parameter. - **data & testdata:** Paths to training and testing image datasets. - **savemodel:** Output path for the trained classification model. - **log:** Directory for storing log files. - **stopwhendone:** Automatically terminates the session after training completion. ### License This dataset is licensed under the MIT License. ### Original Source Prepared explicitly by Lumina AI for RCL-based image classification training. Please credit Lumina AI when using this dataset in research or applications. ### Additional Information Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on imaging data preparation and parameter specifications.
LuminaAI/A_Christmas_Carol-LLM
LuminaAI
2025-05-01T17:22:19Z
0
0
[ "license:mit", "region:us" ]
[]
2025-05-01T16:02:12Z
null
--- license: mit --- ## A Christmas Carol RCL LLM Dataset ### Overview This dataset is explicitly structured for training Large Language Models (LLMs) using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Unlike standard classification datasets, LLM datasets require textual data formatted into input sequences and corresponding target tokens. ### Dataset Structure For LLM training, the dataset structure differs significantly from traditional classification datasets: ``` a-christmas-carol-rcl-mm/ train/ [class_token_1]/ values.txt [class_token_2]/ values.txt ... test/ [class_token_1]/ values.txt [class_token_2]/ values.txt ... ``` - **Class tokens:** Folder names represent the target token for sequences. - **values.txt:** Each line within `values.txt` files represents an individual input sequence mapping to the target token of its containing folder. ### LLM Data Preparation PrismRCL requires LLM datasets to follow specific formatting distinct from classification tasks: - Clean raw text data (removing overly long or non-printable characters). - Create input sequences with a sliding-window method. For instance, a 4-token input sequence predicts the 5th token. - Each input sequence is stored as a single line within the class-specific `values.txt` files. **Example:**\ Original text: "Marley was dead: to begin with." - Input: "Marley was dead: to" → Target: "begin" - Input: "was dead: to begin" → Target: "with" ### Usage (LLM-specific) Use PrismRCL's `llm` parameter for LLM-specific training: ``` C:\PrismRCL\PrismRCL.exe llm naivebayes directional rclticks=67 readtextbyline ^ data=C:\path\to\a-christmas-carol-rcl-mm\train testdata=C:\path\to\a-christmas-carol-rcl-mm\test ^ savemodel=C:\path\to\models\christmas_carol_llm.classify ^ log=C:\path\to\log_files stopwhendone ``` ### Explanation of Command - **llm:** Specifies the dataset as an LLM training dataset. - **naivebayes:** Evaluation method suitable for LLM data. - **directional:** Maintains token order, essential for language modeling. - **rclticks:** Sets RCL discretization granularity. - **readtextbyline:** Treats each line in the text files as separate data samples. - **data & testdata:** Paths to training and testing datasets. - **savemodel:** Output path for the trained LLM model. - **log:** Directory for storing log files. - **stopwhendone:** Automatically terminates the session after training completion. ### License This dataset is licensed under the MIT License. ### Original Source Prepared explicitly by Lumina AI for RCL-based LLM training. Please credit Lumina AI when using this dataset in research or applications. ### Additional Information Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on LLM data preparation and parameter specifications.
mlfoundations-dev/d1_code_shortest_0.3k
mlfoundations-dev
2025-05-01T17:22:12Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:21:58Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 650328556.24 num_examples: 316 download_size: 256251526 dataset_size: 650328556.24 configs: - config_name: default data_files: - split: train path: data/train-* ---
LuminaAI/Doctrina_Christiana-LLM
LuminaAI
2025-05-01T17:20:53Z
0
0
[ "license:mit", "region:us" ]
[]
2025-05-01T16:02:54Z
null
--- license: mit --- ## Doctrina Christiana RCL LLM Dataset ### Overview This dataset is explicitly structured for training Large Language Models (LLMs) using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Unlike standard classification datasets, LLM datasets require textual data formatted into input sequences and corresponding target tokens. ### Dataset Structure For LLM training, the dataset structure differs significantly from traditional classification datasets: ``` doctrina-christiana-rcl-mm/ train/ [class_token_1]/ values.txt [class_token_2]/ values.txt ... test/ [class_token_1]/ values.txt [class_token_2]/ values.txt ... ``` - **Class tokens:** Folder names represent the target token for sequences. - **values.txt:** Each line within `values.txt` files represents an individual input sequence mapping to the target token of its containing folder. ### LLM Data Preparation PrismRCL requires LLM datasets to follow specific formatting distinct from classification tasks: - Clean raw text data (removing overly long or non-printable characters). - Create input sequences with a sliding-window method. For instance, a 4-token input sequence predicts the 5th token. - Each input sequence is stored as a single line within the class-specific `values.txt` files. **Example:**\ Original text: "Ama a tu prójimo como a ti mismo." - Input: "Ama a tu prójimo" → Target: "como" - Input: "a tu prójimo como" → Target: "a" ### Usage (LLM-specific) Use PrismRCL's `llm` parameter for LLM-specific training: ``` C:\PrismRCL\PrismRCL.exe llm naivebayes directional rclticks=67 readtextbyline ^ data=C:\path\to\doctrina-christiana-rcl-mm\train testdata=C:\path\to\doctrina-christiana-rcl-mm\test ^ savemodel=C:\path\to\models\doctrina_christiana_llm.classify ^ log=C:\path\to\log_files stopwhendone ``` ### Explanation of Command - **llm:** Specifies the dataset as an LLM training dataset. - **naivebayes:** Evaluation method suitable for LLM data. - **directional:** Maintains token order, essential for language modeling. - **rclticks:** Sets RCL discretization granularity. - **readtextbyline:** Treats each line in the text files as separate data samples. - **data & testdata:** Paths to training and testing datasets. - **savemodel:** Output path for the trained LLM model. - **log:** Directory for storing log files. - **stopwhendone:** Automatically terminates the session after training completion. ### License This dataset is licensed under the MIT License. ### Original Source Prepared explicitly by Lumina AI for RCL-based LLM training. Please credit Lumina AI when using this dataset in research or applications. ### Additional Information Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on LLM data preparation and parameter specifications.
osama24sy/llama3.2-3b-it-10k-qwen-singleturn-onesolution-64-results-20250501-17461194036674
osama24sy
2025-05-01T17:17:39Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:17:37Z
null
--- dataset_info: features: - name: index dtype: int64 - name: numbers sequence: int64 - name: operations sequence: sequence: string - name: response dtype: string - name: token_count dtype: int64 splits: - name: train num_bytes: 249314 num_examples: 150 download_size: 106742 dataset_size: 249314 configs: - config_name: default data_files: - split: train path: data/train-* ---
LuminaAI/Pride_and_Prejudice-LLM
LuminaAI
2025-05-01T17:16:14Z
0
0
[ "license:mit", "region:us" ]
[]
2025-05-01T16:04:46Z
null
--- license: mit --- ## Pride and Prejudice RCL LLM Dataset ### Overview This dataset is explicitly structured for training Large Language Models (LLMs) using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Unlike standard classification datasets, LLM datasets require textual data formatted into input sequences and corresponding target tokens. ### Dataset Structure For LLM training, the dataset structure differs significantly from traditional classification datasets: ``` pride-and-prejudice-rcl-mm/ train/ [class_token_1]/ values.txt [class_token_2]/ values.txt ... test/ [class_token_1]/ values.txt [class_token_2]/ values.txt ... ``` - **Class tokens:** Folder names represent the target token for sequences. - **values.txt:** Each line within `values.txt` files represents an individual input sequence mapping to the target token of its containing folder. ### LLM Data Preparation PrismRCL requires LLM datasets to follow specific formatting distinct from classification tasks: - Clean raw text data (removing overly long or non-printable characters). - Create input sequences with a sliding-window method. For instance, a 4-token input sequence predicts the 5th token. - Each input sequence is stored as a single line within the class-specific `values.txt` files. **Example:**\ Original text: "It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife." - Input: "It is a truth universally" → Target: "acknowledged" - Input: "is a truth universally acknowledged," → Target: "that" ### Usage (LLM-specific) Use PrismRCL's `llm` parameter for LLM-specific training: ``` C:\PrismRCL\PrismRCL.exe llm naivebayes directional rclticks=67 readtextbyline ^ data=C:\path\to\pride-and-prejudice-rcl-mm\train testdata=C:\path\to\pride-and-prejudice-rcl-mm\test ^ savemodel=C:\path\to\models\pride_prejudice_llm.classify ^ log=C:\path\to\log_files stopwhendone ``` ### Explanation of Command - **llm:** Specifies the dataset as an LLM training dataset. - **naivebayes:** Evaluation method suitable for LLM data. - **directional:** Maintains token order, essential for language modeling. - **rclticks:** Sets RCL discretization granularity. - **readtextbyline:** Treats each line in the text files as separate data samples. - **data & testdata:** Paths to training and testing datasets. - **savemodel:** Output path for the trained LLM model. - **log:** Directory for storing log files. - **stopwhendone:** Automatically terminates the session after training completion. ### License This dataset is licensed under the MIT License. ### Original Source Prepared explicitly by Lumina AI for RCL-based LLM training. Please credit Lumina AI when using this dataset in research or applications. ### Additional Information Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on LLM data preparation and parameter specifications.
mlfoundations-dev/d1_code_gpt_10k
mlfoundations-dev
2025-05-01T17:14:46Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-01T17:08:15Z
null
--- dataset_info: features: - name: id dtype: string - name: instruction_seed dtype: string - name: output dtype: string - name: source dtype: string - name: license dtype: string - name: dataset dtype: string - name: split dtype: string - name: difficulty dtype: int64 - name: solution dtype: string - name: index dtype: string - name: _source dtype: string - name: difficulty_reasoning dtype: string - name: __original_row_idx dtype: int64 - name: ms_id dtype: int64 - name: reasoning sequence: string - name: deepseek_solution sequence: string - name: final_reasoning_trace sequence: string - name: correct sequence: bool - name: classifier_reasoning dtype: string - name: _majority_responses sequence: string - name: verified_final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 21030101607.27848 num_examples: 10000 download_size: 8567374107 dataset_size: 21030101607.27848 configs: - config_name: default data_files: - split: train path: data/train-* ---