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Hkang/summarize_sft-test_lm-EleutherAI_pythia-1b_seed-42_numex-250_lr3e8_3K-BON_32 | Hkang | 2025-05-03T10:18:42Z | 8 | 0 | [
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zhengbang0707/REFUEL_it2_mask1_v2_30k_CUDA | zhengbang0707 | 2025-05-03T09:46:49Z | 0 | 0 | [
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Jianshu001/reasoning-data-collections | Jianshu001 | 2025-05-03T09:39:36Z | 0 | 0 | [
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hettc/polkadot-elections | hettc | 2025-05-03T09:37:18Z | 236 | 0 | [
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license: apache-2.0
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findableai/phenology | findableai | 2025-05-03T09:29:28Z | 0 | 0 | [
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license: mit
---
# Dataset Card for Phenology
<!-- Provide a quick summary of the dataset. -->
## Summary
This dataset contains data necessary for running the code in the GitHub repository https://github.com/findable-no/phenology-data
The file tables.tgz contains raw scans of the pages in the article
> A. Lauscher, F. Lauscher, and H. Printz, *Die Phänologie Norwegens, Teil II. Phänologischce Mittelwerte für 260 Orte*, Skr. Det Norske Videnskaps-Akademi Oslo. I. Mat.-Naturv. Kl. No.l 1959, 1-176, 1959
that contain data tables. Unpack this file in the folder ./data/raw in the repository and use the notebook __phenology_preprocess_1.ipynb__ to generate the final processed data.
## Professor Printz and his phenology data
Phenology is, according to [Wikipedia](https://en.wikipedia.org/wiki/Phenology):
> the study of periodic events in biological life cycles and how these are influenced by seasonal and interannual variations in climate, as well as habitat factors (such as elevation)
It is arguably one of the oldest of mans sciences since the very survival of our species depended (and depends) on this knowledge. Knowing for instance about migratory patterns of animals allowed for effective hunting, observations about plants allowed for effective sowing and harvesting and thereby planning of food supply etc. A lot is known, of course, about phenology, but there have been relatively few systematic and official efforts dedicated to collection of such information.
In Norway, a large effort at collecting phenological data concerning plants, birds and agricultural phenomena was undertaken in 1928 by a botanist at the University of Oslo, Henrik Printz. He established an extensive network of observers all over Norway and tasked them with observing a large number of different phenological phases such as flowering and budburst of different plants, arrival of certain migratory birds etc.
In the 1959 publication
> A. Lauscher, F. Lauscher, and H. Printz, *Die Phänologie Norwegens, Teil II. Phänologischce Mittelwerte für 260 Orte*, Skr. Det Norske Videnskaps-Akademi Oslo. I. Mat.-Naturv. Kl. No.l 1959, 1-176, 1959
he published observations from 278 observation stations for the years 1928 to 1952. The article, 182 pages long and written in German, contains the data from each observation station in the form of handwritten tables looking like this:
For instance, the cell indexed as row i and column 1 (shown in blue in the figure above) represents the Julian date of the first flowering of a tiny plant called [Coltsfoot](https://en.wikipedia.org/wiki/Tussilago) (in Latin, *tussilago farfara*). [Julian dates](https://en.wikipedia.org/wiki/Julian_day) , as used in this article, are just the day number after the 1st of January, so in a non-leap year the Julian date 137 corresponds to the 17th of May.
The coltsfoot is interesting since it is a so-called phenologically plastic plant. It will basically start its lifecycle when local climatic conditions permit. If spring is early a year it will blossom early, if spring is cold and late it will blossom later. So you can think of these tiny plants a climatic laboratories spread all over Norway.
So why should we care about these old data? Well, the observation period coincides with a period when the human contribution of potential greenhouse gases was much lower than today (see [here](https://www.climate.gov/media/14596) for instance for an overview of the evolution of CO2 in the athmosphere since 1750). In this sense, Henrik Printz’s data represents a *time capsule* of indirect climatic observations that is soon to be a hundred years old. It serves as a very simple baseline against which we can compare todays conditions and verify if indeed natures phenological phases have been influenced by a possible climatic change.
In this repository you will find:
1) A Jupyter notebook to extract, from every table, a specific cell as an image and code to use a visual large language model (vLLM) to perform an initial reading of these cells
2) A Jupyter notebook that shows how to finetune the vLLM to read the data more precisely.
3) Pandas data frames containing the observations in digital format.
4) A Jupyter notebook containing some examples of how this data can be used to make geoplots of the variability of the data in Norway.
At [Findable AS](https://www.findable.ai/) we do document understanding for the building industry. This current project is a “labor of love” that we release to the public domain as a token of appreciation for all the different open source tools that we use in our daily work. We hope this will be of interest to at least some people, and we believe (and fear) that the data will show that nature, in Norway, has not gone uninfluenced by climatic changes. We welcome suggestions for improvements and additions and hope people will surprise us with their own uses of this data. If you continue work on this data we demand that you kindly maintain the reference to the original article by Henrik Printz cited above. Likewise, a kind mention of Findable and the work we put into digitising this would be appreciated 😄.
A few observations about the data:
1. There are 278 tables corresponding to the 278 observation locations.
2. Every table contains 4 metadata fields (show in yellow below) and 292 data fields (shown in blue):
3. There are 83.956 fields all in all, but as can be observed above not all observation were made in every location.
4. There are a total of 33.905 cells that are not blank.
5. You will find a comprehensive list of all the observations and how to interpret them in the file xxx in the data folder.
6. Finally, you will find a pandas data frame and an excel sheet containing the resulting data after *manual verification* by real human beings.
## Disclaimer
We present this data *in good faith and as is* without any claims, either direct nor implied, as to their usability for any purpose whatsoever.
Findable AS nor the authors make any claim as to the correctness of the data and shall not be held liable for any consequences, either direct or indirect, of the use or interpretation of this data.
Any conclusions or inferences drawn from this data are at the sole discretion of the entities setting forth such conclusions or inferences.
- **Curated by:** Lars Aurdal, Eivind Kjosbakken, Findable AS (https://www.findable.ai)
- **Language(s) (NLP):** English, Norwegian, German.
- **License:** MIT
## Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/findable-no/phenology-data
## Dataset Card Authors [optional]
Lars Aurdal, Findable AS (https://www.findable.ai)
## Dataset Card Contact
Lars Aurdal, Findable AS (https://www.findable.ai) |
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HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_1_for_gen_13 | HungVu2003 | 2025-05-03T08:45:39Z | 0 | 0 | [
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fakeemailqwe/emoji-vae-dataset | fakeemailqwe | 2025-05-03T08:29:59Z | 0 | 0 | [
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justaszie/emoji-vae-dataset | justaszie | 2025-05-03T08:24:52Z | 0 | 0 | [
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shylee/eval_DP_cube_downDims1_cropNo224_freeze1_32_32_ema0_1e-4_ckpt030000 | shylee | 2025-05-03T07:10:57Z | 0 | 0 | [
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---
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
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
shylee/eval_DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4_ckpt180000 | shylee | 2025-05-03T07:01:45Z | 0 | 0 | [
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] | [
"robotics"
] | 2025-05-03T07:01:39Z | 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
{
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}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
lambara/tool_shuffle_small | lambara | 2025-05-03T07:00:05Z | 214 | 0 | [
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] | [] | 2025-03-11T09:38:39Z | null | ---
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|
junieg/validation-dataset | junieg | 2025-05-03T06:47:40Z | 0 | 0 | [
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chiyuanhsiao/text_L2-regular-ASR_spoken-web-questions-score | chiyuanhsiao | 2025-05-03T06:45:18Z | 0 | 0 | [
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|
chiyuanhsiao/text_L2-regular-15_trivia_qa-audio-score | chiyuanhsiao | 2025-05-03T06:43:01Z | 0 | 0 | [
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|
chiyuanhsiao/text_L2-regular_trivia_qa-audio-score | chiyuanhsiao | 2025-05-03T06:42:27Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_1_for_gen_8 | HungVu2003 | 2025-05-03T06:41:22Z | 0 | 0 | [
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|
mlfoundations-dev/hero_run_2_with_domain | mlfoundations-dev | 2025-05-03T06:40:33Z | 0 | 0 | [
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|
shylee/eval_DP_cube_downDims1_cropNo_freeze1_64_64_ema0_1e-4_ckpt420000 | shylee | 2025-05-03T06:40:24Z | 0 | 0 | [
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] | [
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] | 2025-05-03T06:40:08Z | null | ---
license: apache-2.0
task_categories:
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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
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
cchoi1/kodcode-complete_1000_gpt-4o_qwen7b_att_iter0_att10_sol5_relabeled_grpo_20000 | cchoi1 | 2025-05-03T06:34:44Z | 0 | 0 | [
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shylee/eval_DP_cube_downDims1_cropNo_freeze1_64_64_ema0_1e-4_ckpt480000 | shylee | 2025-05-03T06:33:10Z | 0 | 0 | [
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] | [
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] | 2025-05-03T06:33:00Z | null | ---
license: apache-2.0
task_categories:
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tags:
- LeRobot
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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
{
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
kmrasmussen/intercebd-sft-proj-af06b3e1-b92a-40ae-93fe-a92b01f5b81c-20250503061257 | kmrasmussen | 2025-05-03T06:13:00Z | 0 | 0 | [
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HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_2_for_gen_7 | HungVu2003 | 2025-05-03T05:57:25Z | 0 | 0 | [
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mlfoundations-dev/d1_science_load_in_qwen3 | mlfoundations-dev | 2025-05-03T05:48:22Z | 0 | 0 | [
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|
test-gen/mbpp_mbpp-qwen-coder-7b-instruct-from-sft_t1.0_n8_generated_tests | test-gen | 2025-05-03T05:44:37Z | 0 | 0 | [
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movefast/math_gen_writing_20k_v2 | movefast | 2025-05-03T04:55:14Z | 9 | 0 | [
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NONHUMAN-RESEARCH/tic-tac-toe-v2-three | NONHUMAN-RESEARCH | 2025-05-03T04:46:25Z | 0 | 0 | [
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license: apache-2.0
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---
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):
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```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
zhengbang0707/REFUEL_it2_mask2_v2_60k | zhengbang0707 | 2025-05-03T04:17:38Z | 0 | 0 | [
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---
# Dataset Card for "REFUEL_it2_mask2_v2_60k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cchoi1/kodcode-complete_1000_qwen7b_sol_iter0_att10_sol5_lr5e5_10ep_dedup_dpo_6000 | cchoi1 | 2025-05-03T04:05:14Z | 0 | 0 | [
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abehandlerorg/olmobypublisherdev | abehandlerorg | 2025-05-03T03:53:16Z | 0 | 0 | [
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theprint/Coach-1.2k | theprint | 2025-05-03T03:52:45Z | 0 | 0 | [
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license: apache-2.0
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HungVu2003/opt-350m_beta_0.5_alpha_0.6_num-company_3_dataset_0_for_gen_6 | HungVu2003 | 2025-05-03T03:47:06Z | 0 | 0 | [
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|
mothnaZl/s1-Qwen2.5-7B-Instruct-6-best_of_n-VLLM-Skywork-o1-Open-PRM-Qwen-2.5-7B-completions | mothnaZl | 2025-05-03T03:03:00Z | 0 | 0 | [
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dataset_info:
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|
osama24sy/llama3.2-3b-it-countdown-game-7k-qwq-r64-results-20250503-17462399256186 | osama24sy | 2025-05-03T02:53:51Z | 0 | 0 | [
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|
jaeyong2/Math-Qwen3-14B-Ko | jaeyong2 | 2025-05-03T02:25:21Z | 17 | 0 | [
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|
bismarck91/frA-enA-tokenised-qwen-synthetic_16khz | bismarck91 | 2025-05-03T01:44:27Z | 0 | 0 | [
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|
hanaearg/MyMultiLabelEmotionsNew | hanaearg | 2025-05-03T01:43:44Z | 0 | 0 | [
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|
CohenQu/HintGenerator.10.02 | CohenQu | 2025-05-03T01:12:12Z | 0 | 0 | [
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Aravindh25/test_depth_2 | Aravindh25 | 2025-05-03T01:00:37Z | 0 | 0 | [
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license: apache-2.0
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tags:
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configs:
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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
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}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
cfpark00/toy-multistep-v2-nn_20-na_10-nab_40-test | cfpark00 | 2025-05-03T00:43:48Z | 0 | 0 | [
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|
VGraf/mt_dependent_user_2_turns | VGraf | 2025-05-03T00:35:28Z | 41 | 0 | [
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] | [] | 2025-04-09T15:30:17Z | null | ---
dataset_info:
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data_files:
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---
|
pacscilab/VoxCommunis | pacscilab | 2025-05-03T00:33:01Z | 102 | 2 | [
"language:ab",
"language:am",
"language:ba",
"language:be",
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"size_categories:n<1K",
"region:us",
"Phonetics",
"Linguistics",
"Corpus"
] | [] | 2024-11-06T22:33:01Z | null | ---
language:
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- am
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- be
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tags:
- Phonetics
- Linguistics
- Corpus
size_categories:
- n<1K
pretty_name: VXC
license: cc0-1.0
---
The VoxCommunis Corpus contains acoustic models, lexicons, and force-aligned TextGrids with phone- and word-level segmentations derived from the Mozilla Common Voice Corpus. The Mozilla Common Voice Corpus and derivative VoxCommunis Corpus stored here are free to download and use under a CC0 license.
The lexicons are developed using Epitran, the XPF Corpus, Charsiu, and some custom dictionaries. Some manual correction has been applied, and we hope to continue improving these. Any updates from the community are welcome.
The acoustic models have been trained using the Montreal Forced Aligner, and the force-aligned TextGrids are obtained directly from those alignments. These acoustic models can be downloaded and re-used with the Montreal Forced Aligner for new data.
The TextGrids contain phone- and word-level alignments of the validated set of the Common Voice data.
The filename has the structure: Common Voice language code, G2P system, Common Voice version (validated), VoxCommunis acoustic model.
mk_xpf_textgrids19_acoustic19 corresponds to: alignments from the validated portion of the Macedonian Common Voice 19 Corpus using a lexicon generated with the XPF Corpus, aligned using an acoustic model trained on the validated portion of the Macedonian Common Voice 19 Corpus.
The spkr_files contain a mapping from the original client_id to a simplified spkr_id.
The corresponding Github repository can be found here: https://github.com/pacscilab/voxcommunis |
Aravindh25/test_depth_1 | Aravindh25 | 2025-05-03T00:26:10Z | 0 | 0 | [
"task_categories:robotics",
"license:apache-2.0",
"region:us",
"LeRobot",
"tutorial"
] | [
"robotics"
] | 2025-05-03T00:24:41Z | null | ---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- tutorial
configs:
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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
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}
}
```
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |
chiyuanhsiao/text_L2-regular-ties_llama-questions | chiyuanhsiao | 2025-05-03T00:24:09Z | 0 | 0 | [
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|
ymroddi/langa_chat | ymroddi | 2025-05-03T00:17:19Z | 0 | 0 | [
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|
AlignmentResearch/StrongREJECT | AlignmentResearch | 2025-05-02T23:59:46Z | 1,452 | 0 | [
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|
Asap7772/s1K-1.1-gemini-qwensft | Asap7772 | 2025-05-02T23:56:43Z | 0 | 0 | [
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] | [] | 2025-05-02T23:54:37Z | null | ---
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|
jaeyong2/Math-Qwen3-14B-vi | jaeyong2 | 2025-05-02T23:44:31Z | 7 | 0 | [
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|
chiyuanhsiao/text_L2-regular-14_trivia_qa-audio | chiyuanhsiao | 2025-05-02T23:42:51Z | 0 | 0 | [
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|
chiyuanhsiao/text_L2-regular-linear_trivia_qa-audio | chiyuanhsiao | 2025-05-02T23:34:03Z | 0 | 0 | [
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|
ieuniversity/group_1_submission | ieuniversity | 2025-05-02T23:32:50Z | 375 | 0 | [
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|
cchoi1/kodcode-complete_1000_gpt-4o_qwen7b_att_iter0_att10_sol5_relabeled_grpo_10000 | cchoi1 | 2025-05-02T23:31:36Z | 0 | 0 | [
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|
slava-medvedev/zelensky-speeches | slava-medvedev | 2025-05-02T23:30:13Z | 324 | 3 | [
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"zelensky",
"ukraine",
"politics"
] | [
"summarization",
"text-classification"
] | 2023-11-14T18:43:21Z | null | ---
annotations_creators:
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- found
language:
- uk
- en
license: cc-by-4.0
multilinguality:
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size_categories:
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task_categories:
- summarization
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pretty_name: 'Speeches given by the president of Ukraine Volodymyr Zelensky
Language: Ukrainian
Source: https://www.president.gov.ua/news/speeches'
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dtype: int64
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tags:
- zelensky
- ukraine
- politics
---
# Dataset Card for "zelenskiy-speeches"
Speeches given by the president of Ukraine Volodymyr Zelensky
Languages: Ukrainian, English
Source: [president.gov.ua](https://www.president.gov.ua/news/speeches)
Auto-updated daily by Github Actions of [zelensky-speech-fetcher](https://github.com/medvedev/zelensky-speech-fetcher)
License: [CC BY-NC-ND 4.0 Deed](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) |
marrowdust/ev_charging | marrowdust | 2025-05-02T22:57:59Z | 0 | 0 | [
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] | [] | 2025-05-02T22:54:53Z | null | ---
license: apache-2.0
---
|
tphage/beam_dataset_0502 | tphage | 2025-05-02T22:57:11Z | 0 | 0 | [
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|
HungVu2003/opt-350m_beta_1.0_alpha_0.6_num-company_3_dataset_0_for_gen_10 | HungVu2003 | 2025-05-02T22:54:44Z | 0 | 0 | [
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|
mlfoundations-dev/d1_code_long_paragraphs_10k | mlfoundations-dev | 2025-05-02T22:31:34Z | 0 | 0 | [
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|
chiyuanhsiao/text_L2-regular-SQA-14_IFEval | chiyuanhsiao | 2025-05-02T22:12:22Z | 0 | 0 | [
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|
SynthData/TER-Token_Efficient_Reasoning | SynthData | 2025-05-02T21:41:12Z | 168 | 3 | [
"language:en",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-03-17T16:56:51Z | null | ---
license: apache-2.0
language:
- en
---
# Token Efficient Reasoning
The Token Efficient Reasoning dataset contains high-quality, expert-level reasoning demonstrations structured to capture how domain experts actually think through complex problems. Unlike traditional reasoning traces, TER features information-dense, concise reasoning paths that maintain full logical integrity without veering into "cryptic" shorthand territory.
## Dataset Details
TER consists of high-quality Question/Answers from pretraining corpora DCLM, FineMath, etc. in addition to synthetically-generated variations on questions from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. We use the reference final answer from the original document from the pretraining corpora when possible, or extract it from a mixture of models + n-sampling agent flow if there is wide consensus.
Each Q/A example follows a consistent six-tier reasoning framework that progresses from problem reformulation through essential reasoning steps to solution confirmation. The dataset spans multiple domains including mathematics, physics, computer science, engineering, and social sciences, with each entry demonstrating how to solve complex problems with minimal token usage while preserving deep domain expertise. For robustness, each Q/A pair contains 3 separate reasoning trails, generated under the TER framework with slightly varied prompting styles, by DeepSeek-R1 and QwQ-32b.
Notably, we've observed that when reasoning models are provided with enough relevant context tangential to possible solution paths - before entering the reasoning phase - the final outputs maintain high quality (human-verified) and low hallucination rates across domains.
TER was developed to address the challenges of a) inefficient reasoning in language models, where verbosity often masks core insights; and b) a lack of training data for teaching models how to think through a framework similar to that of domain experts. By training on this dataset, models can learn to generate more precise, logically sound reasoning that better reflects how experts actually approach difficult problems: recognizing patterns instantly, avoiding reasoning pitfalls, verifying solutions - all while using conceptual concision and focusing on critical decision points.
## Contact
[email protected] |
HPAI-BSC/MRI-MCQA | HPAI-BSC | 2025-05-02T21:36:34Z | 5 | 1 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:en",
"license:cc-by-nc-4.0",
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"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"medical"
] | [
"multiple-choice",
"question-answering"
] | 2025-04-30T15:17:09Z | null | ---
license: cc-by-nc-4.0
language:
- en
tags:
- medical
task_categories:
- multiple-choice
- question-answering
pretty_name: MRI-MCQA
size_categories:
- n<1K
---
# MRI-MCQA
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6620f941eba5274b5c12f83d/DlPW2CN-qErpC3QJqdNho.png" width="400" alt="HPAI"/>
</div>
<hr style="margin: 15px">
<div align="center" style="line-height: 1;">
<a href="https://hpai.bsc.es/" target="_blank" style="margin: 1px;">
<img alt="Web" src="https://img.shields.io/badge/Website-HPAI-8A2BE2" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/HPAI-BSC" target="_blank" style="margin: 1px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-HPAI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/HPAI-BSC" target="_blank" style="margin: 1px;">
<img alt="GitHub" src="https://img.shields.io/badge/GitHub-HPAI-%23121011.svg?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://www.linkedin.com/company/hpai" target="_blank" style="margin: 1px;">
<img alt="Linkedin" src="https://img.shields.io/badge/Linkedin-HPAI-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://bsky.app/profile/hpai.bsky.social" target="_blank" style="margin: 1px;">
<img alt="BlueSky" src="https://img.shields.io/badge/Bluesky-HPAI-0285FF?logo=bluesky&logoColor=fff" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://linktr.ee/hpai_bsc" target="_blank" style="margin: 1px;">
<img alt="LinkTree" src="https://img.shields.io/badge/Linktree-HPAI-43E55E?style=flat&logo=linktree&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Dataset Description
MRI-MCQA is a benchmark composed by multiple-choice questions related to Magnetic Resonance Imaging (MRI). We use this dataset to evaluate the level of knowledge of various LLMs about the MRI field.
- **Curated by:** [Oscar Molina Sedano](https://huggingface.co/OscarMolina)
- **Language(s) (NLP):** English
## License
This dataset is licensed under [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
## Disclaimer
Courtesy of Allen D. Elster, [MRIquestions.com](http://mriquestions.com). |
MBZUAI-IFM/OpenO1-SFT_final | MBZUAI-IFM | 2025-05-02T21:31:26Z | 0 | 1 | [
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mlfoundations-dev/no_pipeline_science_300k | mlfoundations-dev | 2025-05-02T21:30:11Z | 0 | 0 | [
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mlfoundations-dev/no_pipeline_science_1k | mlfoundations-dev | 2025-05-02T21:26:45Z | 0 | 0 | [
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mlfoundations-dev/no_pipeline_code_100k | mlfoundations-dev | 2025-05-02T21:22:10Z | 0 | 0 | [
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mlfoundations-dev/no_pipeline_code_0.3k | mlfoundations-dev | 2025-05-02T21:20:16Z | 0 | 0 | [
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|
felixZzz/math_eval_suite-math | felixZzz | 2025-05-02T21:16:11Z | 0 | 0 | [
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] | [] | 2025-05-02T21:16:10Z | null | ---
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|
AdoCleanCode/VGG_sound_distorted_v1_025 | AdoCleanCode | 2025-05-02T21:08:41Z | 0 | 0 | [
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] | [] | 2025-05-02T17:25:54Z | null | ---
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---
|
chiyuanhsiao/text_L2-regular-ties_spoken-web-questions | chiyuanhsiao | 2025-05-02T21:07:18Z | 0 | 0 | [
"size_categories:1K<n<10K",
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] | [] | 2025-05-02T21:07:13Z | null | ---
dataset_info:
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---
|
ysn-rfd/alpaca_fibonacci_dataset_v7 | ysn-rfd | 2025-05-02T20:50:17Z | 0 | 1 | [
"license:apache-2.0",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-02T20:49:31Z | null | ---
license: apache-2.0
---
|
ai4m3/example_dataset | ai4m3 | 2025-05-02T20:49:06Z | 0 | 0 | [
"task_categories:robotics",
"region:us",
"phosphobot",
"so100",
"phospho-dk"
] | [
"robotics"
] | 2025-05-02T20:49:04Z | null |
---
tags:
- phosphobot
- so100
- phospho-dk
task_categories:
- robotics
---
# example_dataset
**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.
|
leeroy-jankins/Appropriations | leeroy-jankins | 2025-05-02T20:31:55Z | 97 | 0 | [
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"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | [] | 2025-04-26T14:03:49Z | null | ---
license: mit
pretty_name: U.S. Appropriations Dataset
---
# 💵 U.S. Appropriations Dataset (1996–2025)
This dataset links enacted U.S. Public Laws with their corresponding Explanatory Statements and Appropriations Titles,
covering the major federal appropriations acts from FY1996 through FY2025.
---
## 📊 Structure
Each entry includes:
- `public_law`: Official citation of the enacted appropriations law (e.g. P.L. 117-328)
- `explanatory_statement`: House or Senate report number accompanying the law (e.g. H. Rpt. 117-328)
- `appropriation_title`: Full name of the Appropriations Act or Continuing Resolution
---
## 🗂️ Sample Entries
| Public Law | Explanatory Statement | Appropriation Title |
|---------------|------------------------|--------------------------------------------------------------------------------------|
| P.L. 104-134 | H. Rpt. 104-537 | Omnibus Consolidated Rescissions and Appropriations Act |
| P.L. 104-208 | H. Rpt. 104-863 | Omnibus Consolidated Appropriations Act, 1997 |
| P.L. 105-277 | H. Rpt. 105-825 | Omnibus Consolidated and Emergency Supplemental Appropriations Act |
| P.L. 105-277 | H. Rpt. 106-110 | Omnibus Consolidated and Emergency Supplemental Appropriations Act |
| P.L. 106-113 | H. Rpt. 106-479 | Consolidated Appropriations Act, 2000 |
| P.L. 106-79 | H. Rpt. 106-371 | Department of Defense Appropriations Act, 2000 |
| P.L. 106-554 | H. Rpt. 106-1033 | Consolidated Appropriations Act, 2001 |
| P.L. 106-259 | S. Rpt. 106-298 | Department of Defense Appropriations Act, 2001 |
| P.L. 107-117 | H. Rpt. 107-350 | Department of Defense and Emergency Supplemental Appropriations |
| P.L. 107-206 | H. Rpt. 107-593 | Supplemental Appropriations Act, 2002 |
| P.L. 108-7 | H. Rpt. 108-10 | Consolidated Appropriations Resolution, 2003 |
| P.L. 108-199 | H. Rpt. 108-401 | Consolidated Appropriations Act, 2004 |
| P.L. 108-11 | H. Rpt. 108-55 | Emergency Supplemental Appropriations Act for Defense |
| P.L. 108-447 | H. Rpt. 108-792 | Consolidated Appropriations Act, 2005 |
| P.L. 109-13 | H. Rpt. 109-72 | Emergency Supplemental Appropriations Act for Defense, Global War on Terror, Tsunami Relief |
| P.L. 109-108 | H. Rpt. 109-272 | Science, State, Justice, Commerce Appropriations Act |
| P.L. 109-148 | S. Rpt. 109-141 | Department of Defense Appropriations Act, 2006 |
| P.L. 110-5 | H. Rpt. 110-5 | Revised Continuing Appropriations Resolution, 2007 |
| P.L. 110-161 | H. Rpt. 110-497 | Consolidated Appropriations Act, 2008 |
| P.L. 110-252 | H. Rpt. 110-656 | Supplemental Appropriations Act, 2008 |
| P.L. 111-8 | H. Rpt. 111-8 | Omnibus Appropriations Act, 2009 |
| P.L. 111-32 | H. Rpt. 111-105 | Supplemental Appropriations Act, 2009 |
| P.L. 111-117 | H. Rpt. 111-366 | Consolidated Appropriations Act, 2010 |
| P.L. 112-10 | H. Rpt. 112-331 | Department of Defense and Full-Year Continuing Appropriations Act, 2011 |
| P.L. 112-74 | H. Rpt. 112-331 | Consolidated Appropriations Act, 2012 |
| P.L. 113-6 | H. Rpt. 113-6 | Consolidated and Further Continuing Appropriations Act, 2013 |
| P.L. 113-76 | H. Rpt. 113-76 | Consolidated Appropriations Act, 2014 |
| P.L. 113-235 | H. Rpt. 113-235 | Consolidated and Further Continuing Appropriations Act, 2015 |
| P.L. 114-113 | H. Rpt. 114-113 | Consolidated Appropriations Act, 2016 |
| P.L. 115-31 | H. Rpt. 115-31 | Consolidated Appropriations Act, 2017 |
| P.L. 115-141 | H. Rpt. 115-141 | Consolidated Appropriations Act, 2018 |
| P.L. 116-6 | H. Rpt. 116-6 | Consolidated Appropriations Act, 2019 |
| P.L. 116-93 | H. Rpt. 116-93 | Further Consolidated Appropriations Act, 2020 |
| P.L. 116-260 | H. Rpt. 116-260 | Consolidated Appropriations Act, 2021 |
| P.L. 117-103 | H. Rpt. 117-103 | Consolidated Appropriations Act, 2022 |
| P.L. 117-328 | H. Rpt. 117-328 | Consolidated Appropriations Act, 2023 |
| P.L. 118-42 | H. Rpt. 118-42 | Continuing Appropriations Act, 2024 |
| P.L. 118-83 | H. Rpt. 118-83 | Continuing Appropriations Act, 2025
---
## 🔍 Use Cases
- 🧠 Train NLP models for legislative reference extraction
- 🧾 Link Appropriations Acts to their respective explanatory documents
- 🗃️ Construct longitudinal appropriations histories for federal program analysis
- 📜 Support research on continuing resolutions and omnibus legislation
---
## 📚 Related Concepts
- Omnibus and Consolidated Appropriations
- Explanatory Statements (House/Senate Reports)
- Continuing Resolutions
- Title-by-Title Budget Authority
---
## 🧠 Example Usage (Python)
```python
from datasets import load_dataset
ds = load_dataset("leeroy-jankins/Regulations", split="train")
for item in ds:
print(f"{item['public_law']} — {item['appropriation_title']}") |
Hamzah-Asadullah/TA-WQS-8k | Hamzah-Asadullah | 2025-05-02T20:30:28Z | 55 | 1 | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:text-generation",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"conversational",
"synthetic"
] | [
"question-answering",
"text2text-generation",
"text-generation"
] | 2025-04-27T15:31:59Z | null | ---
license: mit
task_categories:
- question-answering
- text2text-generation
- text-generation
language:
- en
tags:
- conversational
- synthetic
---
> [!NOTE]
> Consider supporting me [here](https://ko-fi.com/hamzahasadullah) 🎉
> Try out my assistant for free [here](https://xetute.github.io/)
TinyAlpaca WQS (Weird Question Specific) 8k is part of the TA (Tiny Alpaca) series. This dataset is generated using [the SyntheticAlpaca script](https://github.com/Hamzah-Asadullah/SyntheticAlpaca) as a pipeline and [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) as a generator.
## Loading through HuggingFace Datasets
First, install the library: `pip install --upgrade datasets`
Then, load this dataset:
```py
from datasets import load_dataset
dataset = load_dataset("Hamzah-Asadullah/TA-WQS-8k")
print(dataset["train"][0])
```
**Happy coding**
<div style="display: flex; flex-direction: column; justify-content: center; align-items: left; font-size: 1rem; padding: 20px;">
<div style="display: flex; flex-direction: row; align-items: center; margin: 10px; margin-left: 0; padding: 0;">
<img src="https://xetute.github.io/favicon.ico" style="margin: 0; border-radius: 50%; height: 2rem;"/>
<h2 style="margin: 0; margin-left: 10px;">XeTute Technologies</h2>
</div>
<div style="display: flex; flex-direction: row; gap: 5px; margin: 0; max-width: 500px;">
XeTute Technologies is an unofficial Pakistani organisation created by <a href="https://huggingface.co/Hamzah-Asadullah">Hamzah Asadullah.</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Links</h2>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://huggingface.co/XeTute">HuggingFace</a>
<a href="https://github.com/XeTute">GitHub</a>
</div>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://ko-fi.com/hamzahasadullah">Buy me a Coffee</a>
<a href="https://xetute.github.io">Apex Webpage</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Pakistan</h2>
Pakistan is a country in South-Asia known for its rich culture despite the British, its stunning landscape, and PAF (Pakistan Armed Forces), its military. Long live the Islamic Republic of Pakistan.<br>
<img src="https://upload.wikimedia.org/wikipedia/commons/3/32/Flag_of_Pakistan.svg" style="width: 85%; max-width: 512px; border-radius: 25px;"/>
</div> |
Hamzah-Asadullah/TA-4k | Hamzah-Asadullah | 2025-05-02T20:27:47Z | 411 | 1 | [
"task_categories:question-answering",
"task_categories:translation",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:summarization",
"language:en",
"language:de",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us",
"alpaca",
"reasoning",
"synthetic"
] | [
"question-answering",
"translation",
"text-generation",
"text2text-generation",
"summarization"
] | 2025-03-26T15:30:46Z | null | ---
license: mit
task_categories:
- question-answering
- translation
- text-generation
- text2text-generation
- summarization
language:
- en
- de
tags:
- alpaca
- reasoning
- synthetic
size_categories:
- 1K<n<10K
---
**TL;DR**: *TA*, short for *Tiny-Alpaca*, is a reasoning-included dataset with **4,096 samples** synthetically generated using [Gemma3:4b](https://huggingface.co/Google/Gemma-3-4b-it) and [Synthetic-Alpaca](https://github.com/Hamzah-Asadullah/SyntheticAlpaca). It took ~one day to generate this dataset on a RTX4060 8GB.
> [!NOTE]
> Took quite some nerves to generate and motiviate Gemma3 to produce something publishable, [you can support me here](https://ko-fi.com/hamzahasadullah).
---
## Gemma3:4b generated this dataset
> [!NOTE]
> Following description is copy pasted from [the original Gemma3 release](https://huggingface.co/google/gemma-3-4b-it).
_These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:_
- _Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages._
- _Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions._
- _Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries._
- _Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks._
_The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats._
---
You can find the script used to generate this dataset [here (GitHub)](https://github.com/Hamzah-Asadullah/SyntheticAlpaca) and [here (raw)](https://huggingface.co/datasets/Hamzah-Asadullah/TA-4k/resolve/main/main.py).
If you're only using this dataset or only adding samples which are ~5MB and under 2,048 samples, you should note that Gemma3 (or at least the 4b version) loved to ask itself all of following around 1k times, but re-phrased:
- Explaining Quantum Entanglement & Blockchain Technology to minors
- Imagining it's a travel blogger & therefore describing Kyoto, Japan (Kyoto is beautiful, but I don't need 815 samples of that Google)
- Analying extremely exaggerated customer reviews which all sound the same, basically
- Instead of saying "Summarize this article: A long article comes here" (and actually putting in an article), it will literally do "Summarize this article: {Generate an article here}."
The dataset **is deduped** if that's a concern for you, but some questions are **still very similar** even though the temperature was set to `1`, which is already quite high. Happens when **[big companies](https://hf.co/google/)** overfit their model on benchmarks or datasets optimized for those.
That said, the dataset isn't too bad, around an average of what you'd expect from something you can add to an instruction-tuning dataset. You can filter out too similar topics by just searching for "Kyoto" or other relevant keys and removing 50% of the rows containing these; you should still end up with a good amount of 'good' samples. This doesn't mean Gemma3 didn't memorize its training data though.
A larger version of this dataset with higher-quality samples (using LLaMA3.1 8B) at more quantity is available on [here](https://huggingface.co/datasets/XeTute/TA-8k).
---
<div style="display: flex; flex-direction: column; justify-content: center; align-items: left; font-size: 1rem; padding: 20px;">
<div style="display: flex; flex-direction: row; align-items: center; margin: 10px; margin-left: 0; padding: 0;">
<img src="https://xetute.github.io/favicon.ico" style="margin: 0; border-radius: 50%; height: 2rem;"/>
<h2 style="margin: 0; margin-left: 10px;">XeTute Technologies</h2>
</div>
<div style="display: flex; flex-direction: row; gap: 5px; margin: 0; max-width: 500px;">
XeTute Technologies is an unofficial Pakistani organisation created by <a href="https://huggingface.co/Hamzah-Asadullah">Hamzah Asadullah.</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Links</h2>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://huggingface.co/XeTute">HuggingFace</a>
<a href="https://github.com/XeTute">GitHub</a>
</div>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://ko-fi.com/hamzahasadullah">Buy me a Coffee</a>
<a href="https://xetute.github.io">Apex Webpage</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Pakistan</h2>
Pakistan is a country in South-Asia known for its rich culture despite the British, its stunning landscape, and PAF (Pakistan Armed Forces), its military. Long live the Islamic Republic of Pakistan.<br>
<img src="https://upload.wikimedia.org/wikipedia/commons/3/32/Flag_of_Pakistan.svg" style="width: 85%; max-width: 512px; border-radius: 25px;"/>
</div> |
magnifi/parser_user_v39d | magnifi | 2025-05-02T20:18:16Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-02T20:18:12Z | null | ---
dataset_info:
features:
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dtype: int64
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download_size: 213107
dataset_size: 667853
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
IDEALLab/photonics_2d_MIT_Workshop_May2nd | IDEALLab | 2025-05-02T20:12:27Z | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-02T20:12:22Z | null | ---
dataset_info:
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num_examples: 124
download_size: 3226379
dataset_size: 73550400.0
configs:
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data_files:
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path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
alchemistyzz/MMIU_TEST | alchemistyzz | 2025-05-02T19:54:46Z | 0 | 0 | [
"license:apache-2.0",
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-05-02T10:34:13Z | null | ---
license: apache-2.0
---
|
TheRealPilot638/Llama-3.2-1B-dvts_16_H200 | TheRealPilot638 | 2025-05-02T19:40:24Z | 4 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [] | 2025-04-30T23:41:49Z | null | ---
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|
TheRealPilot638/Llama-3.2-1B-best_of_16_H200 | TheRealPilot638 | 2025-05-02T19:16:20Z | 3 | 0 | [
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] | [] | 2025-04-30T03:49:38Z | null | ---
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|
tcapelle/train_ds_triton | tcapelle | 2025-05-02T19:15:54Z | 301 | 0 | [
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|
zhengbang0707/REFUEL_it2_mask1_v2_90k | zhengbang0707 | 2025-05-02T19:13:40Z | 0 | 0 | [
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] | [] | 2025-05-02T19:01:30Z | null | ---
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# Dataset Card for "REFUEL_it2_mask1_v2_90k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SpeedyFrostfish/llama-3.2-3b-Cooking-ChatBot | SpeedyFrostfish | 2025-05-02T18:51:19Z | 49 | 0 | [
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] | [] | 2025-04-23T14:46:37Z | null | ---
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|
ryzax/train_v1 | ryzax | 2025-05-02T18:46:32Z | 113 | 0 | [
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] | [] | 2025-03-28T00:34:04Z | null | ---
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---
|
winterkitsune/elka-pl-news | winterkitsune | 2025-05-02T18:46:03Z | 0 | 0 | [
"task_categories:summarization",
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"library:mlcroissant",
"library:polars",
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] | [
"summarization",
"text-classification"
] | 2025-05-02T18:28:21Z | null | ---
license: apache-2.0
task_categories:
- summarization
- text-classification
language:
- pl
pretty_name: elka.pl news
size_categories:
- 10K<n<100K
---
# Elka.pl news
This dataset contains scraped news articles from polish, regional news site [https://elka.pl](elka.pl)
## Dataset Details
This dataset contains articles about news, events from over 20 years in Leszno, Kościan, Gostyń, Góra, Rawicz, Wschowa cities. The CSV contains the following fields: `id, url, title, subtitle, lead, author, date, content`.
At first, i didn't create that dataset with AI processing in mind, but mostly as a way to preserve history of region and be able to better search across articles.
### Source Data
#### Data Collection and Processing
As the website is using incremental IDs for articles, it was pretty trivial to scrape the data. As the articles spans across over near 20 years, and various cms updates with questionable html practices it was not that easy to establish a stable way to get all the data, but it should be mostly there.
To scrape the articles, i used a [https://github.com/gocolly/colly](colly) golang framework.
As to respect the small team, and not overwhelm the server, I set hard limit at 1 request per second, the scrape took about 3 days. |
VGraf/context_switch_alpacaeval_3lt | VGraf | 2025-05-02T18:25:39Z | 62 | 0 | [
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|
flagrantia/character_select_stand_alone_app | flagrantia | 2025-05-02T18:10:08Z | 7,907 | 1 | [
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"size_categories:10K<n<100K",
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] | [] | 2025-03-07T07:48:49Z | null | ---
license: mit
configs:
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path: "wai_character_md5.csv"
- config_name: default2
data_files:
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path: "wai_character_thumbs.json"
size_categories:
- n<6K
---
https://github.com/mirabarukaso/character_select_stand_alone_app |
tacab/Asr_agri_somalii | tacab | 2025-05-02T17:59:34Z | 0 | 0 | [
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] | [] | 2025-05-02T17:55:53Z | null | ---
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---
|
Misraj/SadeedDiac-25 | Misraj | 2025-05-02T17:57:58Z | 33 | 2 | [
"task_categories:text-generation",
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"library:mlcroissant",
"library:polars",
"arxiv:2504.21635",
"region:us"
] | [
"text-generation"
] | 2025-04-27T14:03:31Z | null | ---
language:
- ar
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---
# SadeedDiac-25: A Benchmark for Arabic Diacritization
[Paper](https://huggingface.co/papers/2504.21635)
**SadeedDiac-25** is a comprehensive and linguistically diverse benchmark specifically designed for evaluating Arabic diacritization models. It unifies Modern Standard Arabic (MSA) and Classical Arabic (CA) in a single dataset, addressing key limitations in existing benchmarks.
## Overview
Existing Arabic diacritization benchmarks tend to focus on either Classical Arabic (e.g., Fadel, Abbad) or Modern Standard Arabic (e.g., CATT, WikiNews), with limited domain diversity and quality inconsistencies. SadeedDiac-25 addresses these issues by:
- Combining MSA and CA in one dataset
- Covering diverse domains (e.g., news, religion, politics, sports, culinary arts)
- Ensuring high annotation quality through a multi-stage expert review process
- Avoiding contamination from large-scale pretraining corpora
## Dataset Composition
SadeedDiac-25 consists of 1,200 paragraphs:
- **📘 50% Modern Standard Arabic (MSA)**
- 454 paragraphs of curated original MSA content
- 146 paragraphs from WikiNews
- Length: 40–50 words per paragraph
- **📗 50% Classical Arabic (CA)**
- 📖 600 paragraphs from the Fadel test set
## Evaluation Results
We evaluated several models on SadeedDiac-25, including proprietary LLMs and open-source Arabic models. Evaluation metrics include Diacritic Error Rate (DER), Word Error Rate (WER), and hallucination rates.
The evaluation code for this dataset is available at: https://github.com/misraj-ai/Sadeed
### Evaluation Table
| Model | DER (CE) | WER (CE) | DER (w/o CE) | WER (w/o CE) | Hallucinations |
| ------------------------ | ---------- | ---------- | ------------ | ------------ | -------------- |
| Claude-3-7-Sonnet-Latest | **1.3941** | **4.6718** | **0.7693** | **2.3098** | **0.821** |
| GPT-4 | 3.8645 | 5.2719 | 3.8645 | 10.9274 | 1.0242 |
| Gemini-Flash-2.0 | 3.1926 | 7.9942 | 2.3783 | 5.5044 | 1.1713 |
| *Sadeed* | *7.2915* | *13.7425* | *5.2625* | *9.9245* | *7.1946* |
| Aya-23-8B | 25.6274 | 47.4908 | 19.7584 | 40.2478 | 5.7793 |
| ALLaM-7B-Instruct | 50.3586 | 70.3369 | 39.4100 | 67.0920 | 36.5092 |
| Yehia-7B | 50.8801 | 70.2323 | 39.7677 | 67.1520 | 43.1113 |
| Jais-13B | 78.6820 | 99.7541 | 60.7271 | 99.5702 | 61.0803 |
| Gemma-2-9B | 78.8560 | 99.7928 | 60.9188 | 99.5895 | 86.8771 |
| SILMA-9B-Instruct-v1.0 | 78.6567 | 99.7367 | 60.7106 | 99.5586 | 93.6515 |
> **Note**: CE = Case Ending
## Citation
If you use SadeedDiac-25 in your work, please cite:
## Citation
If you use this dataset, please cite:
```bibtex
@misc{aldallal2025sadeedadvancingarabicdiacritization,
title={Sadeed: Advancing Arabic Diacritization Through Small Language Model},
author={Zeina Aldallal and Sara Chrouf and Khalil Hennara and Mohamed Motaism Hamed and Muhammad Hreden and Safwan AlModhayan},
year={2025},
eprint={2504.21635},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.21635},
}
```
## License
📄 This dataset is released under the CC BY-NC-SA 4.0 License.
## Contact
📬 For questions, contact [Misraj-AI](https://misraj.ai/) on Hugging Face. |
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ieuniversity/group_4_submission | ieuniversity | 2025-05-02T17:46:00Z | 148 | 0 | [
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rayonlabs/wmt19-gu-en | rayonlabs | 2025-05-02T17:11:48Z | 0 | 0 | [
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IABD11/DatasetEmocionesIABD11 | IABD11 | 2025-05-02T17:11:04Z | 0 | 0 | [
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SmilingWolf/wdtagger-v3-seed | SmilingWolf | 2025-05-02T17:10:53Z | 309 | 0 | [
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