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longjae/klue-mrc-bge-m3
longjae
2025-05-10T15:46:29Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-10T15:46:17Z
0
--- dataset_info: features: - name: title dtype: string - name: news_category dtype: string - name: source dtype: string - name: context dtype: string - name: question dtype: string - name: question_type dtype: int64 - name: is_impossible dtype: bool - name: answer_text dtype: string - name: answer_start dtype: int64 - name: negative_samples sequence: string splits: - name: train num_bytes: 130307018 num_examples: 10434 download_size: 76987383 dataset_size: 130307018 configs: - config_name: default data_files: - split: train path: data/train-* ---
Alexator26/query2doc-ru-24
Alexator26
2025-04-27T20:12:37Z
20
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-27T20:12:34Z
0
--- dataset_info: features: - name: query dtype: string - name: positive dtype: string splits: - name: train num_bytes: 9320061 num_examples: 15000 download_size: 4781856 dataset_size: 9320061 configs: - config_name: default data_files: - split: train path: data/train-* ---
TAUR-dev/SIE_EVAL__SIEXP_concat_until_correct_lm2d__sft__samples__bf_evaluated
TAUR-dev
2025-06-09T02:42:34Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-09T02:42:31Z
0
--- dataset_info: features: - name: doc_id dtype: int64 - name: doc dtype: string - name: target dtype: string - name: arguments dtype: string - name: exact_match dtype: int64 - name: extracted_answers dtype: string - name: source_file dtype: string - name: info dtype: string - name: evaluation_api_cost dtype: string - name: eval_type dtype: string - name: response_to_evaluate dtype: string - name: row_idx dtype: int64 - name: gen_idx dtype: int64 - name: eval_extracted_answer dtype: string - name: answer_extraction_llm_prompt dtype: string - name: answer_extraction_reasoning dtype: string - name: answer_idx dtype: int64 - name: answer_is_correct dtype: bool - name: answer_judgement_reasoning dtype: string - name: answer_judgement_llm_prompt dtype: string - name: internal_answers_per_gen sequence: sequence: string - name: internal_answers_is_correct_per_gen sequence: sequence: bool - name: internal_answers_judgement_reasoning_per_gen sequence: sequence: string - name: internal_answers_judgement_llm_prompt_per_gen sequence: sequence: string - name: responses_to_evaluate sequence: string - name: eval_extracted_answers sequence: string - name: answer_is_corrects sequence: bool - name: mock_budget_force_convo list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 174206961 num_examples: 3656 download_size: 31106371 dataset_size: 174206961 configs: - config_name: default data_files: - split: train path: data/train-* ---
sameearif/imdb-urdu
sameearif
2025-04-07T03:29:43Z
45
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-07T03:29:39Z
0
--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 39966979 num_examples: 40000 - name: validation num_bytes: 5008891 num_examples: 5000 - name: test num_bytes: 4995525 num_examples: 5000 download_size: 15744626 dataset_size: 49971395 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
nurettin2615/Search_Engine_Optimization_1
nurettin2615
2024-12-28T22:36:56Z
17
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-28T22:22:23Z
0
--- dataset_info: features: - name: Instruction dtype: string - name: Input dtype: string - name: Response dtype: string splits: - name: train num_bytes: 4271.8 num_examples: 13 - name: test num_bytes: 657.2 num_examples: 2 download_size: 10580 dataset_size: 4929.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
gremlin97/dust_devil_detection
gremlin97
2025-05-12T02:44:08Z
0
0
[ "task_categories:object-detection", "task_ids:instance-segmentation", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[ "object-detection" ]
2025-05-12T02:43:28Z
0
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - object-detection task_ids: - instance-segmentation pretty_name: dust_devil_detection --- # dust_devil_detection Dataset An object detection dataset in YOLO format containing 3 splits: train, val, test. ## Dataset Metadata * **License:** CC-BY-4.0 (Creative Commons Attribution 4.0 International) * **Version:** 1.0 * **Date Published:** 2025-05-11 * **Cite As:** TBD ## Dataset Details - Format: YOLO - Splits: train, val, test - Classes: dustdevil ## Additional Formats - Includes COCO format annotations - Includes Pascal VOC format annotations ## Usage ```python from datasets import load_dataset dataset = load_dataset("gremlin97/dust_devil_detection") ```
ferrazzipietro/LS_Llama-3.1-8B_e3c-sentences-sk-unrevised_NoQuant_16_32_0.01_64_BestF1_sk
ferrazzipietro
2024-12-02T17:59:58Z
17
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-02T17:59:55Z
0
--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 148050 num_examples: 102 - name: test num_bytes: 1034730 num_examples: 653 download_size: 248164 dataset_size: 1182780 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---
kothasuhas/philosophy-textbooks-9
kothasuhas
2024-11-14T23:12:53Z
16
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-11-14T23:12:50Z
0
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 32652469 num_examples: 7908 download_size: 19386911 dataset_size: 32652469 configs: - config_name: default data_files: - split: train path: data/train-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_d1e32cc4-6531-4a5a-944e-578f6388248b
argilla-internal-testing
2024-12-12T10:24:47Z
13
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-12-12T10:24:46Z
0
--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1256 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---
wanhin/new_cad_2
wanhin
2025-06-20T09:44:11Z
0
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-20T09:43:11Z
0
--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: range_0_1000 num_bytes: 485474701 num_examples: 213489 - name: range_1000_2000 num_bytes: 432378750 num_examples: 89014 - name: range_2000_3500 num_bytes: 73046950 num_examples: 9063 download_size: 236810696 dataset_size: 990900401 configs: - config_name: default data_files: - split: range_0_1000 path: data/range_0_1000-* - split: range_1000_2000 path: data/range_1000_2000-* - split: range_2000_3500 path: data/range_2000_3500-* ---
jordinia/netpro-finetune
jordinia
2025-05-14T16:18:23Z
73
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-29T14:13:10Z
0
--- dataset_info: - config_name: chatml_thought_25k features: - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 524399437 num_examples: 25008 - name: validation num_bytes: 1248094 num_examples: 60 download_size: 142398784 dataset_size: 525647531 - config_name: chatml_thought_33k features: - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 692838511.9417955 num_examples: 33128 - name: test num_bytes: 2802474.0582045577 num_examples: 134 download_size: 209642745 dataset_size: 695640986.0 - config_name: chatml_thought_7k features: - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 152111743 num_examples: 7245 - name: validation num_bytes: 1253112 num_examples: 60 download_size: 40973096 dataset_size: 153364855 - config_name: full features: - name: Domain dtype: string - name: Content dtype: string - name: Label dtype: int64 - name: Classification dtype: string - name: Reason dtype: string - name: Confidence dtype: int64 - name: Thought dtype: string splits: - name: train num_bytes: 536555898 num_examples: 77461 - name: validation num_bytes: 133561964 num_examples: 19367 download_size: 343767552 dataset_size: 670117862 - config_name: raw_7k features: - name: Domain dtype: string - name: Content dtype: string - name: Label dtype: int64 - name: Classification dtype: string - name: Reason dtype: string - name: Confidence dtype: int64 - name: Thought dtype: string splits: - name: train num_bytes: 51549369 num_examples: 7245 - name: validation num_bytes: 420299 num_examples: 60 download_size: 26238304 dataset_size: 51969668 configs: - config_name: chatml_thought_25k data_files: - split: train path: chatml_thought_25k/train-* - split: validation path: chatml_thought_25k/validation-* - config_name: chatml_thought_33k data_files: - split: train path: chatml_thought_33k/train-* - split: test path: chatml_thought_33k/test-* - config_name: chatml_thought_7k data_files: - split: train path: chatml_thought_7k/train-* - split: validation path: chatml_thought_7k/validation-* - config_name: full data_files: - split: train path: full/train-* - split: validation path: full/validation-* - config_name: raw_7k data_files: - split: train path: raw_7k/train-* - split: validation path: raw_7k/validation-* ---
JanGoeran/chess-fens-dataset
JanGoeran
2025-03-26T10:27:41Z
16
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-26T10:27:36Z
0
--- dataset_info: features: - name: fen dtype: string - name: top_moves list: - name: Centipawn dtype: int64 - name: Mate dtype: int64 - name: Move dtype: string - name: top_moves_list sequence: string splits: - name: train num_bytes: 221067 num_examples: 1000 download_size: 82557 dataset_size: 221067 configs: - config_name: default data_files: - split: train path: data/train-* ---
juliadollis/teste_sim4
juliadollis
2025-02-12T21:17:25Z
16
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-12T21:15:54Z
0
--- dataset_info: features: - name: text_id_1 dtype: string - name: text_id_2 dtype: string - name: similarity dtype: float64 splits: - name: train num_bytes: 480 num_examples: 15 download_size: 1665 dataset_size: 480 configs: - config_name: default data_files: - split: train path: data/train-* ---
argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_b7a60f80-0952-4c57-8de8-561cdda7376e
argilla-internal-testing
2024-10-16T12:58:22Z
18
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-16T12:58:21Z
0
--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train num_bytes: 111 num_examples: 3 download_size: 1454 dataset_size: 111 configs: - config_name: default data_files: - split: train path: data/train-* ---
neelabh17/new_news_exploded_prompt_n_75_d_perc_60_num_gen_10_Qwen2.5-7B-Instruct
neelabh17
2025-05-15T16:51:22Z
0
0
[ "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-15T16:51:22Z
0
--- dataset_info: features: - name: id dtype: string - name: name dtype: string - name: topic dtype: string - name: news dtype: string - name: category dtype: string - name: question dtype: string - name: option sequence: string - name: prompt 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 - name: correct_8 dtype: int64 - name: response_9 dtype: string - name: answer_9 dtype: string - name: correct_9 dtype: int64 splits: - name: train num_bytes: 8330869 num_examples: 375 download_size: 2324377 dataset_size: 8330869 configs: - config_name: default data_files: - split: train path: data/train-* ---
nguyentranAI2/Volality15000-20000
nguyentranAI2
2025-04-15T15:43:00Z
17
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-15T03:58:25Z
0
--- dataset_info: features: - name: report dtype: string - name: labels dtype: float64 splits: - name: train num_bytes: 2700876 num_examples: 4996 download_size: 745220 dataset_size: 2700876 configs: - config_name: default data_files: - split: train path: data/train-* ---
NeutrinoPit/OpenSubtitles2024-en-ar-batch42
NeutrinoPit
2025-03-04T03:19:46Z
16
0
[ "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-04T03:19:42Z
0
--- dataset_info: features: - name: en dtype: string - name: ar dtype: string splits: - name: train num_bytes: 104563267 num_examples: 1000000 download_size: 63668057 dataset_size: 104563267 configs: - config_name: default data_files: - split: train path: data/train-* ---
AnnetteDUBUS/Polyvor_test_Annette
AnnetteDUBUS
2025-02-21T15:44:14Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-21T15:44:13Z
0
--- dataset_info: features: - name: set_name dtype: string - name: item_index dtype: int64 - name: item_name dtype: string - name: item_price dtype: float64 - name: item_likes dtype: int64 - name: item_image dtype: string - name: item_categoryid dtype: int64 - name: views dtype: int64 - name: likes_set dtype: int64 - name: date dtype: string - name: set_id dtype: int64 - name: set_desc dtype: string - name: categoryid dtype: int64 - name: category_name dtype: string - name: item_name.1 dtype: string - name: image_exists dtype: bool - name: dominant_color_name dtype: string - name: image_bytes dtype: image splits: - name: train num_bytes: 96544.0 num_examples: 6 download_size: 104893 dataset_size: 96544.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
jkazdan/gemma-2-9b-it-baseline-5000-HeX-PHI-hard_no
jkazdan
2025-03-28T00:05:13Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-28T00:05:11Z
0
--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 752889 num_examples: 300 download_size: 352487 dataset_size: 752889 configs: - config_name: default data_files: - split: train path: data/train-* ---
michsethowusu/fulah-kimbundu_sentence-pairs
michsethowusu
2025-04-02T11:34:56Z
9
0
[ "size_categories:10K<n<100K", "format:csv", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-02T11:34:41Z
0
--- dataset_info: features: - name: score dtype: float32 - name: Fulah dtype: string - name: Kimbundu dtype: string splits: - name: train num_bytes: 2305203 num_examples: 19095 download_size: 2305203 dataset_size: 2305203 configs: - config_name: default data_files: - split: train path: Fulah-Kimbundu_Sentence-Pairs.csv --- # Fulah-Kimbundu_Sentence-Pairs Dataset This dataset contains sentence pairs for African languages along with similarity scores. It can be used for machine translation, sentence alignment, or other natural language processing tasks. This dataset is based on the NLLBv1 dataset, published on OPUS under an open-source initiative led by META. You can find more information here: [OPUS - NLLB-v1](https://opus.nlpl.eu/legacy/NLLB-v1.php) ## Metadata - **File Name**: Fulah-Kimbundu_Sentence-Pairs - **Number of Rows**: 19095 - **Number of Columns**: 3 - **Columns**: score, Fulah, Kimbundu ## Dataset Description The dataset contains sentence pairs in African languages with an associated similarity score. Each row consists of three columns: 1. `score`: The similarity score between the two sentences (range from 0 to 1). 2. `Fulah`: The first sentence in the pair (language 1). 3. `Kimbundu`: The second sentence in the pair (language 2). This dataset is intended for use in training and evaluating machine learning models for tasks like translation, sentence similarity, and cross-lingual transfer learning. ## References Below are papers related to how the data was collected and used in various multilingual and cross-lingual applications: [1] Holger Schwenk and Matthijs Douze, Learning Joint Multilingual Sentence Representations with Neural Machine Translation, ACL workshop on Representation Learning for NLP, 2017 [2] Holger Schwenk and Xian Li, A Corpus for Multilingual Document Classification in Eight Languages, LREC, pages 3548-3551, 2018. [3] Holger Schwenk, Filtering and Mining Parallel Data in a Joint Multilingual Space ACL, July 2018 [4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov, XNLI: Cross-lingual Sentence Understanding through Inference, EMNLP, 2018. [5] Mikel Artetxe and Holger Schwenk, Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings arXiv, Nov 3 2018. [6] Mikel Artetxe and Holger Schwenk, Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond arXiv, Dec 26 2018. [7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman, WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia arXiv, July 11 2019. [8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB [9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk, Multimodal and Multilingual Embeddings for Large-Scale Speech Mining, NeurIPS 2021, pages 15748-15761. [10] Kevin Heffernan, Onur Celebi, and Holger Schwenk, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
svjack/Nino_Videos_Captioned
svjack
2025-04-29T00:16:12Z
492
0
[ "size_categories:n<1K", "modality:text", "modality:video", "library:datasets", "library:mlcroissant", "region:us" ]
[]
2025-04-29T00:08:32Z
0
--- configs: - config_name: default data_files: - split: train path: - "*.mp4" - "metadata.csv" --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/plwPHNfHowmUXXFS5nok_.png) ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/4-IvBz-OHmLHIYhbHRJNU.jpeg) ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/634dffc49b777beec3bc6448/BDcMYFiwxzSVL0gJakt6M.webp)
mlfoundations-dev/stackexchange_physics_seed_science_20K
mlfoundations-dev
2025-03-18T21:18:27Z
36
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-18T21:18:07Z
0
--- dataset_info: features: - name: problem dtype: string - name: reasoning dtype: string - name: deepseek_solution dtype: string - name: __original_row_idx dtype: int64 - name: final_reasoning_trace dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 902936485 num_examples: 20000 download_size: 404868374 dataset_size: 902936485 configs: - config_name: default data_files: - split: train path: data/train-* ---
yleo/moss_cube_stacking
yleo
2024-10-27T13:55:12Z
26
0
[ "task_categories:robotics", "region:us", "LeRobot", "tutorial" ]
[ "robotics" ]
2024-10-27T13:54:38Z
0
--- task_categories: - robotics tags: - LeRobot - tutorial --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
giskardai/phare
giskardai
2025-06-06T10:07:00Z
472
9
[ "task_categories:text-generation", "language:fr", "language:en", "language:es", "license:cc-by-4.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2505.11365", "region:us" ]
[ "text-generation" ]
2025-03-25T16:05:29Z
0
--- language: - fr - en - es license: cc-by-4.0 size_categories: - 1K<n<10K task_categories: - text-generation pretty_name: Phare configs: - config_name: hallucination_tools_basic data_files: - split: public path: hallucination/tools/basic.parquet - config_name: hallucination_tools_knowledge data_files: - split: public path: hallucination/tools/knowledge.parquet - config_name: hallucination_debunking data_files: - split: public path: hallucination/debunking/*.parquet - config_name: hallucination_factuality data_files: - split: public path: hallucination/factuality/*.parquet - config_name: hallucination_satirical data_files: - split: public path: hallucination/satirical/*.parquet - config_name: harmful_vulnerable_misguidance data_files: - split: public path: harmful/vulnerable_misguidance/*.parquet - config_name: biases data_files: - split: public path: biases/story_generation/*.parquet --- <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6596ca5cce76219628b8eab4/d8DzaI1j6B9GyKFg6DAyg.png" alt="Phare Logo" width="75%"/> </p> # [Phare Benchmark](https://huggingface.co/papers/2505.11365) Phare is a multilingual benchmark that measures LLM Safety across multiple categories of vulnerabilities, including hallucination, biases & stereotypes, harmful content, and prompt injection. ## Dataset Details ### Dataset Description This dataset contains the public set of samples of Phare Benchmark. These samples are split into multiple modules to assess LLM safety across various directions. Each module is responsible for detecting vulnerabilities in the LLM response: - **Hallucination**: evaluates the factuality and the level of misinformation spread by the models in a question-answer setting. Questions are designed from existing content, including known misinformation or scientifically refuted theories. - **Biases & stereotypes**: assess the presence of biases in the LLM generations for creative tasks. - **Harmful content**: measure the dangerous behavior endorsement and misguidance rate of LLM with vulnerable people. - **Prompt injection**: (not yet included in the benchmark) Each module is split into several submodules. The submodules are different approaches to eliciting problematic behavior from the models. For instance, the hallucination modules has several submodules: - **Debunking**: questions about scientifically refuted facts or theories with various levels of bias - **Satirical**: questions derived from misinformation and satirical sources - **Factuality**: questions about generic facts - **Tools**: questions that can be answered with the use of a tool available for the model, to measure hallucination in tool parameters and correct tool usage. ### Extra information - **Author:** Giskard AI - **Language(s):** English, French, Spanish - **License:** CC BY 4.0 ## Dataset Structure The dataset is split into a **public** (available in this repository) and a **private** sets. Giskard reserves the private set to run the [Phare Benchmark](http://phare.giskard.ai/) and keep the leaderboard up-to-date. Each submodule is a set of `.jsonl` files containing the samples. Each sample in these files has the following structure: ``` { "id": # unique id of the sample, "messages": # the list of messages to be sent to the LLM "metadata": { "task_name": # the name of the task, typically differs from one file to another or from one submodule to another "language": # the language of the question "evaluation_data": # a dictionary with additional elements required for the automatic evaluation of the response (include context about the question, expected answers, etc.), changes from between submodules. } ``` ## Dataset Creation ### Curation Rationale Most safety evaluation datasets lack comprehensiveness and multicultural support. Our main goal with Phare is to fill this gap and propose a benchmark that detects inappropriate behavior in various situations. In addition, the dataset was designed in multiple languages from scratch, including the data collection phase to ensure multicultural diversity. ### Source Data Data sources are diverse and change for each module: - **Hallucinations**: news articles, wikipedia articles, satirical articles, forum threads, etc. - **Harmful content**: examples of AI incident from https://incidentdatabase.ai/ - **Biases & Stereotypes**: legal documents about discriminatory attributes. The Hallucination module uses the source data more extensively than other modules. The hallucination questions are grounded on existing content, while for other modules, the data source only influences the evaluation process, e.g. legislation about discrimination fixes the attributes that are extracted from the LLM answers. #### Data Collection and Processing Data collection and filtering were done semi-automatically by the Giskard team. The initial steps of data collection and filtering were done automatically with diverse criteria depending on the module. Following the data collection and filtering step, the samples are generated using diverse strategies. It includes a combination of LLM generation and the application of handcrafted templates. All details about the generation process are available in our [technical report](https://arxiv.org/abs/2505.11365). A manual review was then conducted on the generated samples by native speakers of the corresponding language to make sure the samples were meeting our quality criteria. #### 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. --> The dataset contains samples that can be sensitive or misleading. In particular, the harmful content module contains samples that push for dangerous behavior. Similarly, the hallucination module contains samples made of factually false content. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> - Some content was generated with the help of LLMs and can be imperfect. - Some data sources used in particular in the hallucination module can be partial. - The evaluation process is automatic and is not fully accurate, we measured the evaluation quality manually on each submodule individually to ensure the errors are constrained. Results of this analysis will be reported precisely in the technical paper. - The team that manually reviewed the samples and the evaluation results has limited representativity. - Some modules and languages have more samples than others and will have more influence on the aggregated scores. - Private and public splits representativity differs across modules. ## Dataset Card Contact - Matteo Dora -- @mattbit -- [email protected] - Pierre Le Jeune -- @pierlj -- [email protected]
zhaoraning/eval_Orbbec111
zhaoraning
2025-05-15T14:43:31Z
0
0
[ "task_categories:robotics", "license:apache-2.0", "region:us", "LeRobot", "tutorial" ]
[ "robotics" ]
2025-05-15T14:42:22Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "so100", "total_episodes": 10, "total_frames": 8914, "total_tasks": 1, "total_videos": 10, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:10" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 880, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 880, "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] ```
FloatFrank/TULU3TIPA
FloatFrank
2025-04-05T06:04:24Z
70
1
[ "license:apache-2.0", "size_categories:1M<n<10M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-04-05T05:32:02Z
0
--- license: apache-2.0 ---
hyunjinlee/sample-data
hyunjinlee
2025-02-19T15:08:40Z
53
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-02-19T15:07:17Z
0
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 9036 num_examples: 41 download_size: 7661 dataset_size: 9036 configs: - config_name: default data_files: - split: train path: data/train-* ---
CasperLD/cartoons_with_blip_captions_512_max_500
CasperLD
2025-01-12T00:49:33Z
17
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-12T00:48:45Z
0
--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 313734571.0 num_examples: 6000 download_size: 299119632 dataset_size: 313734571.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cartoons_with_blip_captions_512_max_500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pmdlt/MNLP_M3_rag_dataset
pmdlt
2025-06-04T15:19:33Z
0
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-04T12:28:28Z
0
--- dataset_info: features: - name: text dtype: string - name: source dtype: string splits: - name: train num_bytes: 45680052.74761919 num_examples: 18495 download_size: 207191707 dataset_size: 45680052.74761919 configs: - config_name: default data_files: - split: train path: data/train-* ---
TAUR-dev/solution-trees__short-and-wide_p3_batch9
TAUR-dev
2025-03-13T14:01:06Z
16
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-13T14:00:59Z
0
--- dataset_info: features: - name: solution dtype: string - name: question dtype: string - name: cot_type dtype: string - name: source_type dtype: string - name: metadata dtype: string - name: gemini_thinking_trajectory dtype: string - name: gemini_attempt dtype: string - name: deepseek_thinking_trajectory dtype: string - name: deepseek_attempt dtype: string - name: gemini_grade dtype: string - name: gemini_grade_reason dtype: string - name: deepseek_grade dtype: string - name: deepseek_grade_reason dtype: string - name: trees dtype: string splits: - name: train num_bytes: 203046053 num_examples: 1000 download_size: 49779349 dataset_size: 203046053 configs: - config_name: default data_files: - split: train path: data/train-* ---
DavidYeh0709/so100_david_0610_2
DavidYeh0709
2025-06-10T02:01:22Z
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", "so100", "tutorial" ]
[ "robotics" ]
2025-06-10T02:01:07Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": "so100", "total_episodes": 5, "total_frames": 2566, "total_tasks": 1, "total_videos": 5, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:5" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.height": 480, "video.width": 640, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "video.fps": 30, "video.channels": 3, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```
Heaplax/ARMAP-RM-Game24
Heaplax
2025-02-19T20:22:26Z
17
0
[ "license:apache-2.0", "region:us" ]
[]
2025-02-19T20:22:11Z
0
--- license: apache-2.0 ---
villekuosmanen/agilex_place_cube_in_the_center
villekuosmanen
2025-02-11T18:56:05Z
24
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" ]
[ "robotics" ]
2025-02-11T18:55:48Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "arx5_bimanual", "total_episodes": 20, "total_frames": 10737, "total_tasks": 1, "total_videos": 60, "total_chunks": 1, "chunks_size": 1000, "fps": 25, "splits": { "train": "0:20" }, "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": [ 14 ] }, "observation.state": { "dtype": "float32", "shape": [ 14 ] }, "observation.effort": { "dtype": "float32", "shape": [ 14 ] }, "observation.images.cam_high": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 25.0, "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": 25.0, "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": 25.0, "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] ```
OALL/details_FreedomIntelligence__AceGPT-v2-70B-Chat
OALL
2025-01-30T19:57:48Z
8
0
[ "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-30T19:57:37Z
0
--- pretty_name: Evaluation run of FreedomIntelligence/AceGPT-v2-70B-Chat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FreedomIntelligence/AceGPT-v2-70B-Chat](https://huggingface.co/FreedomIntelligence/AceGPT-v2-70B-Chat).\n\ \nThe dataset is composed of 136 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\"OALL/details_FreedomIntelligence__AceGPT-v2-70B-Chat\"\ ,\n\t\"lighteval_xstory_cloze_ar_0_2025_01_30T19_54_59_135980_parquet\",\n\tsplit=\"\ train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2025-01-30T19:54:59.135980](https://huggingface.co/datasets/OALL/details_FreedomIntelligence__AceGPT-v2-70B-Chat/blob/main/results_2025-01-30T19-54-59.135980.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc_norm\": 0.49008307379293065,\n\ \ \"acc_norm_stderr\": 0.03742700461947868,\n \"acc\": 0.7253474520185308,\n\ \ \"acc_stderr\": 0.01148620035471171\n },\n \"community|acva:Algeria|0\"\ : {\n \"acc_norm\": 0.5230769230769231,\n \"acc_norm_stderr\": 0.0358596530894741\n\ \ },\n \"community|acva:Ancient_Egypt|0\": {\n \"acc_norm\": 0.05396825396825397,\n\ \ \"acc_norm_stderr\": 0.012751380783465839\n },\n \"community|acva:Arab_Empire|0\"\ : {\n \"acc_norm\": 0.30943396226415093,\n \"acc_norm_stderr\": 0.028450154794118627\n\ \ },\n \"community|acva:Arabic_Architecture|0\": {\n \"acc_norm\":\ \ 0.4564102564102564,\n \"acc_norm_stderr\": 0.035761230969912135\n },\n\ \ \"community|acva:Arabic_Art|0\": {\n \"acc_norm\": 0.3641025641025641,\n\ \ \"acc_norm_stderr\": 0.03454653867786389\n },\n \"community|acva:Arabic_Astronomy|0\"\ : {\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.03581804596782233\n\ \ },\n \"community|acva:Arabic_Calligraphy|0\": {\n \"acc_norm\": 0.47843137254901963,\n\ \ \"acc_norm_stderr\": 0.0313435870640056\n },\n \"community|acva:Arabic_Ceremony|0\"\ : {\n \"acc_norm\": 0.518918918918919,\n \"acc_norm_stderr\": 0.036834092970087065\n\ \ },\n \"community|acva:Arabic_Clothing|0\": {\n \"acc_norm\": 0.5128205128205128,\n\ \ \"acc_norm_stderr\": 0.03588610523192215\n },\n \"community|acva:Arabic_Culture|0\"\ : {\n \"acc_norm\": 0.23076923076923078,\n \"acc_norm_stderr\": 0.0302493752938313\n\ \ },\n \"community|acva:Arabic_Food|0\": {\n \"acc_norm\": 0.441025641025641,\n\ \ \"acc_norm_stderr\": 0.0356473293185358\n },\n \"community|acva:Arabic_Funeral|0\"\ : {\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.050529115263991134\n\ \ },\n \"community|acva:Arabic_Geography|0\": {\n \"acc_norm\": 0.6068965517241379,\n\ \ \"acc_norm_stderr\": 0.040703290137070705\n },\n \"community|acva:Arabic_History|0\"\ : {\n \"acc_norm\": 0.30256410256410254,\n \"acc_norm_stderr\": 0.03298070870085619\n\ \ },\n \"community|acva:Arabic_Language_Origin|0\": {\n \"acc_norm\"\ : 0.5473684210526316,\n \"acc_norm_stderr\": 0.051339113773544845\n },\n\ \ \"community|acva:Arabic_Literature|0\": {\n \"acc_norm\": 0.4689655172413793,\n\ \ \"acc_norm_stderr\": 0.04158632762097828\n },\n \"community|acva:Arabic_Math|0\"\ : {\n \"acc_norm\": 0.30256410256410254,\n \"acc_norm_stderr\": 0.03298070870085618\n\ \ },\n \"community|acva:Arabic_Medicine|0\": {\n \"acc_norm\": 0.46206896551724136,\n\ \ \"acc_norm_stderr\": 0.041546596717075474\n },\n \"community|acva:Arabic_Music|0\"\ : {\n \"acc_norm\": 0.23741007194244604,\n \"acc_norm_stderr\": 0.036220593237998276\n\ \ },\n \"community|acva:Arabic_Ornament|0\": {\n \"acc_norm\": 0.4717948717948718,\n\ \ \"acc_norm_stderr\": 0.035840746749208334\n },\n \"community|acva:Arabic_Philosophy|0\"\ : {\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"community|acva:Arabic_Physics_and_Chemistry|0\": {\n \"acc_norm\"\ : 0.5333333333333333,\n \"acc_norm_stderr\": 0.03581804596782232\n },\n\ \ \"community|acva:Arabic_Wedding|0\": {\n \"acc_norm\": 0.41025641025641024,\n\ \ \"acc_norm_stderr\": 0.03531493712326671\n },\n \"community|acva:Bahrain|0\"\ : {\n \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.06979205927323111\n\ \ },\n \"community|acva:Comoros|0\": {\n \"acc_norm\": 0.37777777777777777,\n\ \ \"acc_norm_stderr\": 0.07309112127323451\n },\n \"community|acva:Egypt_modern|0\"\ : {\n \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.04794350420740798\n\ \ },\n \"community|acva:InfluenceFromAncientEgypt|0\": {\n \"acc_norm\"\ : 0.6051282051282051,\n \"acc_norm_stderr\": 0.03509545602262038\n },\n\ \ \"community|acva:InfluenceFromByzantium|0\": {\n \"acc_norm\": 0.7172413793103448,\n\ \ \"acc_norm_stderr\": 0.03752833958003337\n },\n \"community|acva:InfluenceFromChina|0\"\ : {\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.0317493043641267\n\ \ },\n \"community|acva:InfluenceFromGreece|0\": {\n \"acc_norm\":\ \ 0.6307692307692307,\n \"acc_norm_stderr\": 0.034648411418637566\n },\n\ \ \"community|acva:InfluenceFromIslam|0\": {\n \"acc_norm\": 0.296551724137931,\n\ \ \"acc_norm_stderr\": 0.03806142687309993\n },\n \"community|acva:InfluenceFromPersia|0\"\ : {\n \"acc_norm\": 0.7028571428571428,\n \"acc_norm_stderr\": 0.03464507889884372\n\ \ },\n \"community|acva:InfluenceFromRome|0\": {\n \"acc_norm\": 0.5743589743589743,\n\ \ \"acc_norm_stderr\": 0.03549871080367708\n },\n \"community|acva:Iraq|0\"\ : {\n \"acc_norm\": 0.5058823529411764,\n \"acc_norm_stderr\": 0.05455069703232772\n\ \ },\n \"community|acva:Islam_Education|0\": {\n \"acc_norm\": 0.4512820512820513,\n\ \ \"acc_norm_stderr\": 0.03572709860318392\n },\n \"community|acva:Islam_branches_and_schools|0\"\ : {\n \"acc_norm\": 0.4342857142857143,\n \"acc_norm_stderr\": 0.037576101528126626\n\ \ },\n \"community|acva:Islamic_law_system|0\": {\n \"acc_norm\": 0.4256410256410256,\n\ \ \"acc_norm_stderr\": 0.035498710803677086\n },\n \"community|acva:Jordan|0\"\ : {\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.07106690545187012\n\ \ },\n \"community|acva:Kuwait|0\": {\n \"acc_norm\": 0.26666666666666666,\n\ \ \"acc_norm_stderr\": 0.06666666666666667\n },\n \"community|acva:Lebanon|0\"\ : {\n \"acc_norm\": 0.17777777777777778,\n \"acc_norm_stderr\": 0.05763774795025094\n\ \ },\n \"community|acva:Libya|0\": {\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.07491109582924914\n },\n \"community|acva:Mauritania|0\"\ : {\n \"acc_norm\": 0.4222222222222222,\n \"acc_norm_stderr\": 0.07446027270295805\n\ \ },\n \"community|acva:Mesopotamia_civilization|0\": {\n \"acc_norm\"\ : 0.5225806451612903,\n \"acc_norm_stderr\": 0.0402500394824441\n },\n\ \ \"community|acva:Morocco|0\": {\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.06267511942419628\n },\n \"community|acva:Oman|0\"\ : {\n \"acc_norm\": 0.17777777777777778,\n \"acc_norm_stderr\": 0.05763774795025094\n\ \ },\n \"community|acva:Palestine|0\": {\n \"acc_norm\": 0.24705882352941178,\n\ \ \"acc_norm_stderr\": 0.047058823529411785\n },\n \"community|acva:Qatar|0\"\ : {\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.07385489458759964\n\ \ },\n \"community|acva:Saudi_Arabia|0\": {\n \"acc_norm\": 0.3282051282051282,\n\ \ \"acc_norm_stderr\": 0.03371243782413707\n },\n \"community|acva:Somalia|0\"\ : {\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.07216392363431012\n\ \ },\n \"community|acva:Sudan|0\": {\n \"acc_norm\": 0.35555555555555557,\n\ \ \"acc_norm_stderr\": 0.07216392363431012\n },\n \"community|acva:Syria|0\"\ : {\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.07106690545187012\n\ \ },\n \"community|acva:Tunisia|0\": {\n \"acc_norm\": 0.3111111111111111,\n\ \ \"acc_norm_stderr\": 0.06979205927323111\n },\n \"community|acva:United_Arab_Emirates|0\"\ : {\n \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.04628210543937907\n\ \ },\n \"community|acva:Yemen|0\": {\n \"acc_norm\": 0.2,\n \ \ \"acc_norm_stderr\": 0.13333333333333333\n },\n \"community|acva:communication|0\"\ : {\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025974025974025955\n\ \ },\n \"community|acva:computer_and_phone|0\": {\n \"acc_norm\": 0.45084745762711864,\n\ \ \"acc_norm_stderr\": 0.02901934773187137\n },\n \"community|acva:daily_life|0\"\ : {\n \"acc_norm\": 0.18694362017804153,\n \"acc_norm_stderr\": 0.021268948348414647\n\ \ },\n \"community|acva:entertainment|0\": {\n \"acc_norm\": 0.23389830508474577,\n\ \ \"acc_norm_stderr\": 0.024687839412166384\n },\n \"community|alghafa:mcq_exams_test_ar|0\"\ : {\n \"acc_norm\": 0.3608617594254937,\n \"acc_norm_stderr\": 0.020367158199199216\n\ \ },\n \"community|alghafa:meta_ar_dialects|0\": {\n \"acc_norm\":\ \ 0.4220574606116775,\n \"acc_norm_stderr\": 0.0067246959144321005\n },\n\ \ \"community|alghafa:meta_ar_msa|0\": {\n \"acc_norm\": 0.4759776536312849,\n\ \ \"acc_norm_stderr\": 0.01670319018930019\n },\n \"community|alghafa:multiple_choice_facts_truefalse_balanced_task|0\"\ : {\n \"acc_norm\": 0.8933333333333333,\n \"acc_norm_stderr\": 0.03588436550487813\n\ \ },\n \"community|alghafa:multiple_choice_grounded_statement_soqal_task|0\"\ : {\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.03932313218491396\n\ \ },\n \"community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0\"\ : {\n \"acc_norm\": 0.5466666666666666,\n \"acc_norm_stderr\": 0.04078279527880808\n\ \ },\n \"community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0\"\ : {\n \"acc_norm\": 0.8341463414634146,\n \"acc_norm_stderr\": 0.004160079026167048\n\ \ },\n \"community|alghafa:multiple_choice_rating_sentiment_task|0\": {\n\ \ \"acc_norm\": 0.6010008340283569,\n \"acc_norm_stderr\": 0.00632506746203751\n\ \ },\n \"community|alghafa:multiple_choice_sentiment_task|0\": {\n \ \ \"acc_norm\": 0.40813953488372096,\n \"acc_norm_stderr\": 0.011854303984148063\n\ \ },\n \"community|arabic_exams|0\": {\n \"acc_norm\": 0.5512104283054003,\n\ \ \"acc_norm_stderr\": 0.02148313691486752\n },\n \"community|arabic_mmlu:abstract_algebra|0\"\ : {\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n\ \ },\n \"community|arabic_mmlu:anatomy|0\": {\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"community|arabic_mmlu:astronomy|0\"\ : {\n \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952929\n\ \ },\n \"community|arabic_mmlu:business_ethics|0\": {\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"community|arabic_mmlu:clinical_knowledge|0\"\ : {\n \"acc_norm\": 0.6452830188679245,\n \"acc_norm_stderr\": 0.02944517532819959\n\ \ },\n \"community|arabic_mmlu:college_biology|0\": {\n \"acc_norm\"\ : 0.6527777777777778,\n \"acc_norm_stderr\": 0.03981240543717861\n },\n\ \ \"community|arabic_mmlu:college_chemistry|0\": {\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"community|arabic_mmlu:college_computer_science|0\"\ : {\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n\ \ },\n \"community|arabic_mmlu:college_mathematics|0\": {\n \"acc_norm\"\ : 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"community|arabic_mmlu:college_medicine|0\"\ : {\n \"acc_norm\": 0.5317919075144508,\n \"acc_norm_stderr\": 0.03804749744364763\n\ \ },\n \"community|arabic_mmlu:college_physics|0\": {\n \"acc_norm\"\ : 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n\ \ \"community|arabic_mmlu:computer_security|0\": {\n \"acc_norm\": 0.66,\n\ \ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"community|arabic_mmlu:conceptual_physics|0\"\ : {\n \"acc_norm\": 0.6553191489361702,\n \"acc_norm_stderr\": 0.03106898596312215\n\ \ },\n \"community|arabic_mmlu:econometrics|0\": {\n \"acc_norm\":\ \ 0.45614035087719296,\n \"acc_norm_stderr\": 0.04685473041907789\n },\n\ \ \"community|arabic_mmlu:electrical_engineering|0\": {\n \"acc_norm\"\ : 0.4827586206896552,\n \"acc_norm_stderr\": 0.04164188720169377\n },\n\ \ \"community|arabic_mmlu:elementary_mathematics|0\": {\n \"acc_norm\"\ : 0.5052910052910053,\n \"acc_norm_stderr\": 0.02574986828855657\n },\n\ \ \"community|arabic_mmlu:formal_logic|0\": {\n \"acc_norm\": 0.5317460317460317,\n\ \ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"community|arabic_mmlu:global_facts|0\"\ : {\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n\ \ },\n \"community|arabic_mmlu:high_school_biology|0\": {\n \"acc_norm\"\ : 0.6193548387096774,\n \"acc_norm_stderr\": 0.02762171783290703\n },\n\ \ \"community|arabic_mmlu:high_school_chemistry|0\": {\n \"acc_norm\"\ : 0.4975369458128079,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n\ \ \"community|arabic_mmlu:high_school_computer_science|0\": {\n \"acc_norm\"\ : 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"community|arabic_mmlu:high_school_european_history|0\"\ : {\n \"acc_norm\": 0.23636363636363636,\n \"acc_norm_stderr\": 0.033175059300091805\n\ \ },\n \"community|arabic_mmlu:high_school_geography|0\": {\n \"acc_norm\"\ : 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932026\n },\n\ \ \"community|arabic_mmlu:high_school_government_and_politics|0\": {\n \ \ \"acc_norm\": 0.7668393782383419,\n \"acc_norm_stderr\": 0.03051611137147601\n\ \ },\n \"community|arabic_mmlu:high_school_macroeconomics|0\": {\n \ \ \"acc_norm\": 0.6538461538461539,\n \"acc_norm_stderr\": 0.024121125416941197\n\ \ },\n \"community|arabic_mmlu:high_school_mathematics|0\": {\n \"\ acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131137\n\ \ },\n \"community|arabic_mmlu:high_school_microeconomics|0\": {\n \ \ \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.031499305777849054\n\ \ },\n \"community|arabic_mmlu:high_school_physics|0\": {\n \"acc_norm\"\ : 0.3973509933774834,\n \"acc_norm_stderr\": 0.0399552400768168\n },\n\ \ \"community|arabic_mmlu:high_school_psychology|0\": {\n \"acc_norm\"\ : 0.6935779816513762,\n \"acc_norm_stderr\": 0.019765517220458523\n },\n\ \ \"community|arabic_mmlu:high_school_statistics|0\": {\n \"acc_norm\"\ : 0.47685185185185186,\n \"acc_norm_stderr\": 0.034063153607115086\n },\n\ \ \"community|arabic_mmlu:high_school_us_history|0\": {\n \"acc_norm\"\ : 0.3480392156862745,\n \"acc_norm_stderr\": 0.03343311240488418\n },\n\ \ \"community|arabic_mmlu:high_school_world_history|0\": {\n \"acc_norm\"\ : 0.37130801687763715,\n \"acc_norm_stderr\": 0.03145068600744859\n },\n\ \ \"community|arabic_mmlu:human_aging|0\": {\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575498\n },\n \"community|arabic_mmlu:human_sexuality|0\"\ : {\n \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834278\n\ \ },\n \"community|arabic_mmlu:international_law|0\": {\n \"acc_norm\"\ : 0.7933884297520661,\n \"acc_norm_stderr\": 0.036959801280988226\n },\n\ \ \"community|arabic_mmlu:jurisprudence|0\": {\n \"acc_norm\": 0.6388888888888888,\n\ \ \"acc_norm_stderr\": 0.04643454608906275\n },\n \"community|arabic_mmlu:logical_fallacies|0\"\ : {\n \"acc_norm\": 0.5398773006134969,\n \"acc_norm_stderr\": 0.039158572914369714\n\ \ },\n \"community|arabic_mmlu:machine_learning|0\": {\n \"acc_norm\"\ : 0.5267857142857143,\n \"acc_norm_stderr\": 0.047389751192741546\n },\n\ \ \"community|arabic_mmlu:management|0\": {\n \"acc_norm\": 0.6407766990291263,\n\ \ \"acc_norm_stderr\": 0.04750458399041696\n },\n \"community|arabic_mmlu:marketing|0\"\ : {\n \"acc_norm\": 0.7991452991452992,\n \"acc_norm_stderr\": 0.02624677294689049\n\ \ },\n \"community|arabic_mmlu:medical_genetics|0\": {\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.04793724854411021\n },\n \"community|arabic_mmlu:miscellaneous|0\"\ : {\n \"acc_norm\": 0.7279693486590039,\n \"acc_norm_stderr\": 0.015913367447500517\n\ \ },\n \"community|arabic_mmlu:moral_disputes|0\": {\n \"acc_norm\"\ : 0.6502890173410405,\n \"acc_norm_stderr\": 0.02567428145653102\n },\n\ \ \"community|arabic_mmlu:moral_scenarios|0\": {\n \"acc_norm\": 0.39776536312849164,\n\ \ \"acc_norm_stderr\": 0.016369204971262995\n },\n \"community|arabic_mmlu:nutrition|0\"\ : {\n \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.02573885479781874\n\ \ },\n \"community|arabic_mmlu:philosophy|0\": {\n \"acc_norm\": 0.6237942122186495,\n\ \ \"acc_norm_stderr\": 0.027513925683549434\n },\n \"community|arabic_mmlu:prehistory|0\"\ : {\n \"acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.027002521034516464\n\ \ },\n \"community|arabic_mmlu:professional_accounting|0\": {\n \"\ acc_norm\": 0.4148936170212766,\n \"acc_norm_stderr\": 0.0293922365846125\n\ \ },\n \"community|arabic_mmlu:professional_law|0\": {\n \"acc_norm\"\ : 0.3813559322033898,\n \"acc_norm_stderr\": 0.012405509401888119\n },\n\ \ \"community|arabic_mmlu:professional_medicine|0\": {\n \"acc_norm\"\ : 0.3088235294117647,\n \"acc_norm_stderr\": 0.028064998167040094\n },\n\ \ \"community|arabic_mmlu:professional_psychology|0\": {\n \"acc_norm\"\ : 0.5882352941176471,\n \"acc_norm_stderr\": 0.01991037746310594\n },\n\ \ \"community|arabic_mmlu:public_relations|0\": {\n \"acc_norm\": 0.6181818181818182,\n\ \ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"community|arabic_mmlu:security_studies|0\"\ : {\n \"acc_norm\": 0.5877551020408164,\n \"acc_norm_stderr\": 0.03151236044674268\n\ \ },\n \"community|arabic_mmlu:sociology|0\": {\n \"acc_norm\": 0.6915422885572139,\n\ \ \"acc_norm_stderr\": 0.03265819588512697\n },\n \"community|arabic_mmlu:us_foreign_policy|0\"\ : {\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n\ \ },\n \"community|arabic_mmlu:virology|0\": {\n \"acc_norm\": 0.4819277108433735,\n\ \ \"acc_norm_stderr\": 0.038899512528272166\n },\n \"community|arabic_mmlu:world_religions|0\"\ : {\n \"acc_norm\": 0.7485380116959064,\n \"acc_norm_stderr\": 0.033275044238468436\n\ \ },\n \"community|arc_challenge_okapi_ar|0\": {\n \"acc_norm\": 0.48017241379310344,\n\ \ \"acc_norm_stderr\": 0.014675285076749806\n },\n \"community|arc_easy_ar|0\"\ : {\n \"acc_norm\": 0.4703891708967851,\n \"acc_norm_stderr\": 0.010267748561209244\n\ \ },\n \"community|boolq_ar|0\": {\n \"acc_norm\": 0.6236196319018404,\n\ \ \"acc_norm_stderr\": 0.008486550314509648\n },\n \"community|copa_ext_ar|0\"\ : {\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.05235473399540656\n\ \ },\n \"community|hellaswag_okapi_ar|0\": {\n \"acc_norm\": 0.3273361683567768,\n\ \ \"acc_norm_stderr\": 0.004900172489811871\n },\n \"community|openbook_qa_ext_ar|0\"\ : {\n \"acc_norm\": 0.4868686868686869,\n \"acc_norm_stderr\": 0.02248830414033472\n\ \ },\n \"community|piqa_ar|0\": {\n \"acc_norm\": 0.6950354609929078,\n\ \ \"acc_norm_stderr\": 0.01075636221184271\n },\n \"community|race_ar|0\"\ : {\n \"acc_norm\": 0.5005072022722662,\n \"acc_norm_stderr\": 0.007122532364122539\n\ \ },\n \"community|sciq_ar|0\": {\n \"acc_norm\": 0.6572864321608041,\n\ \ \"acc_norm_stderr\": 0.015053926480256236\n },\n \"community|toxigen_ar|0\"\ : {\n \"acc_norm\": 0.4320855614973262,\n \"acc_norm_stderr\": 0.01620887578524445\n\ \ },\n \"lighteval|xstory_cloze:ar|0\": {\n \"acc\": 0.7253474520185308,\n\ \ \"acc_stderr\": 0.01148620035471171\n },\n \"community|acva:_average|0\"\ : {\n \"acc_norm\": 0.3952913681266578,\n \"acc_norm_stderr\": 0.045797189866892664\n\ \ },\n \"community|alghafa:_average|0\": {\n \"acc_norm\": 0.575798176004883,\n\ \ \"acc_norm_stderr\": 0.020236087527098254\n },\n \"community|arabic_mmlu:_average|0\"\ : {\n \"acc_norm\": 0.5657867209093309,\n \"acc_norm_stderr\": 0.03562256482932646\n\ \ }\n}\n```" repo_url: https://huggingface.co/FreedomIntelligence/AceGPT-v2-70B-Chat configs: - config_name: community_acva_Algeria_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Algeria|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Algeria|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Ancient_Egypt_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Ancient_Egypt|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Ancient_Egypt|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arab_Empire_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arab_Empire|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arab_Empire|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Architecture_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Architecture|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Architecture|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Art_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Art|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Art|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Astronomy_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Astronomy|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Astronomy|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Calligraphy_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Calligraphy|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Calligraphy|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Ceremony_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Ceremony|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Ceremony|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Clothing_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Clothing|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Clothing|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Culture_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Culture|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Culture|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Food_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Food|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Food|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Funeral_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Funeral|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Funeral|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Geography_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Geography|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Geography|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_History_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_History|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_History|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Language_Origin_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Language_Origin|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Language_Origin|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Literature_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Literature|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Literature|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Math_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Math|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Math|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Medicine_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Medicine|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Medicine|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Music_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Music|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Music|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Ornament_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Ornament|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Ornament|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Philosophy_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Philosophy|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Philosophy|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Physics_and_Chemistry_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Physics_and_Chemistry|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Physics_and_Chemistry|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Arabic_Wedding_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Arabic_Wedding|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Arabic_Wedding|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Bahrain_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Bahrain|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Bahrain|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Comoros_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Comoros|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Comoros|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Egypt_modern_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Egypt_modern|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Egypt_modern|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_InfluenceFromAncientEgypt_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:InfluenceFromAncientEgypt|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromAncientEgypt|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_InfluenceFromByzantium_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:InfluenceFromByzantium|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromByzantium|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_InfluenceFromChina_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:InfluenceFromChina|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromChina|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_InfluenceFromGreece_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:InfluenceFromGreece|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromGreece|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_InfluenceFromIslam_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:InfluenceFromIslam|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromIslam|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_InfluenceFromPersia_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:InfluenceFromPersia|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromPersia|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_InfluenceFromRome_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:InfluenceFromRome|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromRome|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Iraq_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Iraq|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Iraq|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Islam_Education_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Islam_Education|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Islam_Education|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Islam_branches_and_schools_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Islam_branches_and_schools|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Islam_branches_and_schools|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Islamic_law_system_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Islamic_law_system|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Islamic_law_system|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Jordan_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Jordan|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Jordan|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Kuwait_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Kuwait|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Kuwait|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Lebanon_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Lebanon|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Lebanon|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Libya_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Libya|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Libya|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Mauritania_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Mauritania|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Mauritania|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Mesopotamia_civilization_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Mesopotamia_civilization|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Mesopotamia_civilization|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Morocco_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Morocco|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Morocco|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Oman_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Oman|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Oman|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Palestine_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Palestine|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Palestine|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Qatar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Qatar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Qatar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Saudi_Arabia_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Saudi_Arabia|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Saudi_Arabia|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Somalia_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Somalia|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Somalia|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Sudan_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Sudan|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Sudan|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Syria_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Syria|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Syria|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Tunisia_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Tunisia|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Tunisia|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_United_Arab_Emirates_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:United_Arab_Emirates|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:United_Arab_Emirates|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_Yemen_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:Yemen|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:Yemen|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_communication_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:communication|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:communication|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_computer_and_phone_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:computer_and_phone|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:computer_and_phone|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_daily_life_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:daily_life|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:daily_life|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_acva_entertainment_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|acva:entertainment|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|acva:entertainment|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_mcq_exams_test_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:mcq_exams_test_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:mcq_exams_test_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_meta_ar_dialects_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:meta_ar_dialects|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:meta_ar_dialects|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_meta_ar_msa_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:meta_ar_msa|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:meta_ar_msa|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_multiple_choice_facts_truefalse_balanced_task_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:multiple_choice_facts_truefalse_balanced_task|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_facts_truefalse_balanced_task|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_multiple_choice_grounded_statement_soqal_task_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:multiple_choice_grounded_statement_soqal_task|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_grounded_statement_soqal_task|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_multiple_choice_grounded_statement_xglue_mlqa_task_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_multiple_choice_rating_sentiment_no_neutral_task_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_multiple_choice_rating_sentiment_task_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_task|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_task|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_alghafa_multiple_choice_sentiment_task_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|alghafa:multiple_choice_sentiment_task|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_sentiment_task|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_exams_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_exams|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_exams|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_abstract_algebra_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:abstract_algebra|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:abstract_algebra|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_anatomy_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:anatomy|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:anatomy|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_astronomy_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:astronomy|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:astronomy|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_business_ethics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:business_ethics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:business_ethics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_clinical_knowledge_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:clinical_knowledge|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:clinical_knowledge|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_college_biology_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:college_biology|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_biology|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_college_chemistry_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:college_chemistry|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_chemistry|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_college_computer_science_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:college_computer_science|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_computer_science|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_college_mathematics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:college_mathematics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_mathematics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_college_medicine_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:college_medicine|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_medicine|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_college_physics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:college_physics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_physics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_computer_security_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:computer_security|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:computer_security|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_conceptual_physics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:conceptual_physics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:conceptual_physics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_econometrics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:econometrics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:econometrics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_electrical_engineering_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:electrical_engineering|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:electrical_engineering|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_elementary_mathematics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:elementary_mathematics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:elementary_mathematics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_formal_logic_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:formal_logic|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:formal_logic|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_global_facts_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:global_facts|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:global_facts|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_biology_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_biology|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_biology|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_chemistry_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_chemistry|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_chemistry|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_computer_science_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_computer_science|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_computer_science|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_european_history_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_european_history|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_european_history|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_geography_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_geography|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_geography|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_government_and_politics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_government_and_politics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_government_and_politics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_macroeconomics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_macroeconomics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_macroeconomics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_mathematics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_mathematics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_mathematics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_microeconomics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_microeconomics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_microeconomics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_physics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_physics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_physics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_psychology_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_psychology|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_psychology|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_statistics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_statistics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_statistics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_us_history_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_us_history|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_us_history|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_high_school_world_history_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:high_school_world_history|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_world_history|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_human_aging_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:human_aging|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:human_aging|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_human_sexuality_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:human_sexuality|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:human_sexuality|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_international_law_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:international_law|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:international_law|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_jurisprudence_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:jurisprudence|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:jurisprudence|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_logical_fallacies_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:logical_fallacies|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:logical_fallacies|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_machine_learning_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:machine_learning|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:machine_learning|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_management_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:management|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:management|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_marketing_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:marketing|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:marketing|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_medical_genetics_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:medical_genetics|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:medical_genetics|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_miscellaneous_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:miscellaneous|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:miscellaneous|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_moral_disputes_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:moral_disputes|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:moral_disputes|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_moral_scenarios_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:moral_scenarios|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:moral_scenarios|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_nutrition_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:nutrition|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:nutrition|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_philosophy_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:philosophy|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:philosophy|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_prehistory_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:prehistory|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:prehistory|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_professional_accounting_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:professional_accounting|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_accounting|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_professional_law_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:professional_law|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_law|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_professional_medicine_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:professional_medicine|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_medicine|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_professional_psychology_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:professional_psychology|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_psychology|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_public_relations_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:public_relations|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:public_relations|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_security_studies_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:security_studies|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:security_studies|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_sociology_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:sociology|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:sociology|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_us_foreign_policy_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:us_foreign_policy|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:us_foreign_policy|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_virology_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:virology|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:virology|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arabic_mmlu_world_religions_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arabic_mmlu:world_religions|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arabic_mmlu:world_religions|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arc_challenge_okapi_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arc_challenge_okapi_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arc_challenge_okapi_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_arc_easy_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|arc_easy_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|arc_easy_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_boolq_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|boolq_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|boolq_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_copa_ext_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|copa_ext_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|copa_ext_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_hellaswag_okapi_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|hellaswag_okapi_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|hellaswag_okapi_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_openbook_qa_ext_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|openbook_qa_ext_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|openbook_qa_ext_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_piqa_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|piqa_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|piqa_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_race_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|race_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|race_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_sciq_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|sciq_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|sciq_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: community_toxigen_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_community|toxigen_ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_community|toxigen_ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: lighteval_xstory_cloze_ar_0_2025_01_30T19_54_59_135980_parquet data_files: - split: 2025_01_30T19_54_59.135980 path: - '**/details_lighteval|xstory_cloze:ar|0_2025-01-30T19-54-59.135980.parquet' - split: latest path: - '**/details_lighteval|xstory_cloze:ar|0_2025-01-30T19-54-59.135980.parquet' - config_name: results data_files: - split: 2025_01_30T19_54_59.135980 path: - results_2025-01-30T19-54-59.135980.parquet - split: latest path: - results_2025-01-30T19-54-59.135980.parquet --- # Dataset Card for Evaluation run of FreedomIntelligence/AceGPT-v2-70B-Chat <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [FreedomIntelligence/AceGPT-v2-70B-Chat](https://huggingface.co/FreedomIntelligence/AceGPT-v2-70B-Chat). The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("OALL/details_FreedomIntelligence__AceGPT-v2-70B-Chat", "lighteval_xstory_cloze_ar_0_2025_01_30T19_54_59_135980_parquet", split="train") ``` ## Latest results These are the [latest results from run 2025-01-30T19:54:59.135980](https://huggingface.co/datasets/OALL/details_FreedomIntelligence__AceGPT-v2-70B-Chat/blob/main/results_2025-01-30T19-54-59.135980.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc_norm": 0.49008307379293065, "acc_norm_stderr": 0.03742700461947868, "acc": 0.7253474520185308, "acc_stderr": 0.01148620035471171 }, "community|acva:Algeria|0": { "acc_norm": 0.5230769230769231, "acc_norm_stderr": 0.0358596530894741 }, "community|acva:Ancient_Egypt|0": { "acc_norm": 0.05396825396825397, "acc_norm_stderr": 0.012751380783465839 }, "community|acva:Arab_Empire|0": { "acc_norm": 0.30943396226415093, "acc_norm_stderr": 0.028450154794118627 }, "community|acva:Arabic_Architecture|0": { "acc_norm": 0.4564102564102564, "acc_norm_stderr": 0.035761230969912135 }, "community|acva:Arabic_Art|0": { "acc_norm": 0.3641025641025641, "acc_norm_stderr": 0.03454653867786389 }, "community|acva:Arabic_Astronomy|0": { "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.03581804596782233 }, "community|acva:Arabic_Calligraphy|0": { "acc_norm": 0.47843137254901963, "acc_norm_stderr": 0.0313435870640056 }, "community|acva:Arabic_Ceremony|0": { "acc_norm": 0.518918918918919, "acc_norm_stderr": 0.036834092970087065 }, "community|acva:Arabic_Clothing|0": { "acc_norm": 0.5128205128205128, "acc_norm_stderr": 0.03588610523192215 }, "community|acva:Arabic_Culture|0": { "acc_norm": 0.23076923076923078, "acc_norm_stderr": 0.0302493752938313 }, "community|acva:Arabic_Food|0": { "acc_norm": 0.441025641025641, "acc_norm_stderr": 0.0356473293185358 }, "community|acva:Arabic_Funeral|0": { "acc_norm": 0.4, "acc_norm_stderr": 0.050529115263991134 }, "community|acva:Arabic_Geography|0": { "acc_norm": 0.6068965517241379, "acc_norm_stderr": 0.040703290137070705 }, "community|acva:Arabic_History|0": { "acc_norm": 0.30256410256410254, "acc_norm_stderr": 0.03298070870085619 }, "community|acva:Arabic_Language_Origin|0": { "acc_norm": 0.5473684210526316, "acc_norm_stderr": 0.051339113773544845 }, "community|acva:Arabic_Literature|0": { "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "community|acva:Arabic_Math|0": { "acc_norm": 0.30256410256410254, "acc_norm_stderr": 0.03298070870085618 }, "community|acva:Arabic_Medicine|0": { "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "community|acva:Arabic_Music|0": { "acc_norm": 0.23741007194244604, "acc_norm_stderr": 0.036220593237998276 }, "community|acva:Arabic_Ornament|0": { "acc_norm": 0.4717948717948718, "acc_norm_stderr": 0.035840746749208334 }, "community|acva:Arabic_Philosophy|0": { "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "community|acva:Arabic_Physics_and_Chemistry|0": { "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.03581804596782232 }, "community|acva:Arabic_Wedding|0": { "acc_norm": 0.41025641025641024, "acc_norm_stderr": 0.03531493712326671 }, "community|acva:Bahrain|0": { "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.06979205927323111 }, "community|acva:Comoros|0": { "acc_norm": 0.37777777777777777, "acc_norm_stderr": 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"community|acva:United_Arab_Emirates|0": { "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.04628210543937907 }, "community|acva:Yemen|0": { "acc_norm": 0.2, "acc_norm_stderr": 0.13333333333333333 }, "community|acva:communication|0": { "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.025974025974025955 }, "community|acva:computer_and_phone|0": { "acc_norm": 0.45084745762711864, "acc_norm_stderr": 0.02901934773187137 }, "community|acva:daily_life|0": { "acc_norm": 0.18694362017804153, "acc_norm_stderr": 0.021268948348414647 }, "community|acva:entertainment|0": { "acc_norm": 0.23389830508474577, "acc_norm_stderr": 0.024687839412166384 }, "community|alghafa:mcq_exams_test_ar|0": { "acc_norm": 0.3608617594254937, "acc_norm_stderr": 0.020367158199199216 }, "community|alghafa:meta_ar_dialects|0": { "acc_norm": 0.4220574606116775, "acc_norm_stderr": 0.0067246959144321005 }, "community|alghafa:meta_ar_msa|0": { "acc_norm": 0.4759776536312849, "acc_norm_stderr": 0.01670319018930019 }, "community|alghafa:multiple_choice_facts_truefalse_balanced_task|0": { "acc_norm": 0.8933333333333333, "acc_norm_stderr": 0.03588436550487813 }, "community|alghafa:multiple_choice_grounded_statement_soqal_task|0": { "acc_norm": 0.64, "acc_norm_stderr": 0.03932313218491396 }, "community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0": { "acc_norm": 0.5466666666666666, "acc_norm_stderr": 0.04078279527880808 }, "community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0": { "acc_norm": 0.8341463414634146, "acc_norm_stderr": 0.004160079026167048 }, "community|alghafa:multiple_choice_rating_sentiment_task|0": { "acc_norm": 0.6010008340283569, "acc_norm_stderr": 0.00632506746203751 }, "community|alghafa:multiple_choice_sentiment_task|0": { "acc_norm": 0.40813953488372096, "acc_norm_stderr": 0.011854303984148063 }, "community|arabic_exams|0": { "acc_norm": 0.5512104283054003, "acc_norm_stderr": 0.02148313691486752 }, "community|arabic_mmlu:abstract_algebra|0": { 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"community|arabic_mmlu:college_medicine|0": { "acc_norm": 0.5317919075144508, "acc_norm_stderr": 0.03804749744364763 }, "community|arabic_mmlu:college_physics|0": { "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "community|arabic_mmlu:computer_security|0": { "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "community|arabic_mmlu:conceptual_physics|0": { "acc_norm": 0.6553191489361702, "acc_norm_stderr": 0.03106898596312215 }, "community|arabic_mmlu:econometrics|0": { "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "community|arabic_mmlu:electrical_engineering|0": { "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.04164188720169377 }, "community|arabic_mmlu:elementary_mathematics|0": { "acc_norm": 0.5052910052910053, "acc_norm_stderr": 0.02574986828855657 }, "community|arabic_mmlu:formal_logic|0": { "acc_norm": 0.5317460317460317, "acc_norm_stderr": 0.04463112720677172 }, "community|arabic_mmlu:global_facts|0": { "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "community|arabic_mmlu:high_school_biology|0": { "acc_norm": 0.6193548387096774, "acc_norm_stderr": 0.02762171783290703 }, "community|arabic_mmlu:high_school_chemistry|0": { "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.035179450386910616 }, "community|arabic_mmlu:high_school_computer_science|0": { "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "community|arabic_mmlu:high_school_european_history|0": { "acc_norm": 0.23636363636363636, "acc_norm_stderr": 0.033175059300091805 }, "community|arabic_mmlu:high_school_geography|0": { "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.030532892233932026 }, "community|arabic_mmlu:high_school_government_and_politics|0": { "acc_norm": 0.7668393782383419, "acc_norm_stderr": 0.03051611137147601 }, "community|arabic_mmlu:high_school_macroeconomics|0": { "acc_norm": 0.6538461538461539, "acc_norm_stderr": 0.024121125416941197 }, "community|arabic_mmlu:high_school_mathematics|0": { "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131137 }, "community|arabic_mmlu:high_school_microeconomics|0": { "acc_norm": 0.6218487394957983, "acc_norm_stderr": 0.031499305777849054 }, "community|arabic_mmlu:high_school_physics|0": { "acc_norm": 0.3973509933774834, "acc_norm_stderr": 0.0399552400768168 }, "community|arabic_mmlu:high_school_psychology|0": { "acc_norm": 0.6935779816513762, "acc_norm_stderr": 0.019765517220458523 }, "community|arabic_mmlu:high_school_statistics|0": { "acc_norm": 0.47685185185185186, "acc_norm_stderr": 0.034063153607115086 }, "community|arabic_mmlu:high_school_us_history|0": { "acc_norm": 0.3480392156862745, "acc_norm_stderr": 0.03343311240488418 }, "community|arabic_mmlu:high_school_world_history|0": { "acc_norm": 0.37130801687763715, "acc_norm_stderr": 0.03145068600744859 }, "community|arabic_mmlu:human_aging|0": { "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575498 }, "community|arabic_mmlu:human_sexuality|0": { "acc_norm": 0.6793893129770993, "acc_norm_stderr": 0.04093329229834278 }, "community|arabic_mmlu:international_law|0": { "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.036959801280988226 }, "community|arabic_mmlu:jurisprudence|0": { "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.04643454608906275 }, "community|arabic_mmlu:logical_fallacies|0": { "acc_norm": 0.5398773006134969, "acc_norm_stderr": 0.039158572914369714 }, "community|arabic_mmlu:machine_learning|0": { "acc_norm": 0.5267857142857143, "acc_norm_stderr": 0.047389751192741546 }, "community|arabic_mmlu:management|0": { "acc_norm": 0.6407766990291263, "acc_norm_stderr": 0.04750458399041696 }, "community|arabic_mmlu:marketing|0": { "acc_norm": 0.7991452991452992, "acc_norm_stderr": 0.02624677294689049 }, "community|arabic_mmlu:medical_genetics|0": { "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411021 }, "community|arabic_mmlu:miscellaneous|0": { "acc_norm": 0.7279693486590039, "acc_norm_stderr": 0.015913367447500517 }, "community|arabic_mmlu:moral_disputes|0": { "acc_norm": 0.6502890173410405, "acc_norm_stderr": 0.02567428145653102 }, "community|arabic_mmlu:moral_scenarios|0": { "acc_norm": 0.39776536312849164, "acc_norm_stderr": 0.016369204971262995 }, "community|arabic_mmlu:nutrition|0": { "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.02573885479781874 }, "community|arabic_mmlu:philosophy|0": { "acc_norm": 0.6237942122186495, "acc_norm_stderr": 0.027513925683549434 }, "community|arabic_mmlu:prehistory|0": { "acc_norm": 0.6203703703703703, "acc_norm_stderr": 0.027002521034516464 }, "community|arabic_mmlu:professional_accounting|0": { "acc_norm": 0.4148936170212766, "acc_norm_stderr": 0.0293922365846125 }, "community|arabic_mmlu:professional_law|0": { "acc_norm": 0.3813559322033898, "acc_norm_stderr": 0.012405509401888119 }, "community|arabic_mmlu:professional_medicine|0": { "acc_norm": 0.3088235294117647, "acc_norm_stderr": 0.028064998167040094 }, "community|arabic_mmlu:professional_psychology|0": { "acc_norm": 0.5882352941176471, "acc_norm_stderr": 0.01991037746310594 }, "community|arabic_mmlu:public_relations|0": { "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.046534298079135075 }, "community|arabic_mmlu:security_studies|0": { "acc_norm": 0.5877551020408164, "acc_norm_stderr": 0.03151236044674268 }, "community|arabic_mmlu:sociology|0": { "acc_norm": 0.6915422885572139, "acc_norm_stderr": 0.03265819588512697 }, "community|arabic_mmlu:us_foreign_policy|0": { "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036845 }, "community|arabic_mmlu:virology|0": { "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "community|arabic_mmlu:world_religions|0": { "acc_norm": 0.7485380116959064, "acc_norm_stderr": 0.033275044238468436 }, "community|arc_challenge_okapi_ar|0": { "acc_norm": 0.48017241379310344, "acc_norm_stderr": 0.014675285076749806 }, "community|arc_easy_ar|0": { "acc_norm": 0.4703891708967851, "acc_norm_stderr": 0.010267748561209244 }, "community|boolq_ar|0": { "acc_norm": 0.6236196319018404, "acc_norm_stderr": 0.008486550314509648 }, "community|copa_ext_ar|0": { "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.05235473399540656 }, "community|hellaswag_okapi_ar|0": { "acc_norm": 0.3273361683567768, "acc_norm_stderr": 0.004900172489811871 }, "community|openbook_qa_ext_ar|0": { "acc_norm": 0.4868686868686869, "acc_norm_stderr": 0.02248830414033472 }, "community|piqa_ar|0": { "acc_norm": 0.6950354609929078, "acc_norm_stderr": 0.01075636221184271 }, "community|race_ar|0": { "acc_norm": 0.5005072022722662, "acc_norm_stderr": 0.007122532364122539 }, "community|sciq_ar|0": { "acc_norm": 0.6572864321608041, "acc_norm_stderr": 0.015053926480256236 }, "community|toxigen_ar|0": { "acc_norm": 0.4320855614973262, "acc_norm_stderr": 0.01620887578524445 }, "lighteval|xstory_cloze:ar|0": { "acc": 0.7253474520185308, "acc_stderr": 0.01148620035471171 }, "community|acva:_average|0": { "acc_norm": 0.3952913681266578, "acc_norm_stderr": 0.045797189866892664 }, "community|alghafa:_average|0": { "acc_norm": 0.575798176004883, "acc_norm_stderr": 0.020236087527098254 }, "community|arabic_mmlu:_average|0": { "acc_norm": 0.5657867209093309, "acc_norm_stderr": 0.03562256482932646 } } ``` ## 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):** [More Information Needed] - **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 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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.). 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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]
wlsdzyzl/MedPointS-cls
wlsdzyzl
2025-04-28T07:23:25Z
53
0
[ "language:en", "license:mit", "size_categories:10K<n<100K", "format:parquet", "modality:timeseries", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "arxiv:2504.13015", "region:us", "biology", "point cloud", "classification", "medical" ]
[]
2025-04-24T09:07:43Z
0
--- license: mit language: - en tags: - biology - point cloud - classification - medical --- ### MedPointS-CLS This is the medical point cloud classification dataset from [MedPointS](https://flemme-docs.readthedocs.io/en/latest/medpoints.html), where `data` is input point cloud, and `label` is the class label. Each point cloud has been normalized and sub-sampled to 2048 points. The correspondence between class names and labels is listed as follows (the label value plus 1 is the actual key of following map): ``` coarse_label_to_organ = {1: 'adrenalgland', 2: 'aorta', 3: 'autochthon', 4: 'bladder', 5: 'brain', 6: 'breast', 7: 'bronchie', 8: 'celiactrunk', 9: 'cheek', 10: 'clavicle', 11: 'colon', 12: 'costa', 13: 'duodenum', 14: 'esophagus', 15: 'eyeball', 16: 'femur', 17: 'gallbladder', 18: 'gluteusmaximus', 19: 'heart', 20: 'hip', 21: 'humerus', 22: 'iliacartery', 23: 'iliacvena', 24: 'iliopsoas', 25: 'inferiorvenacava', 26: 'kidney', 27: 'liver', 28: 'lung', 29: 'mediastinaltissue', 30: 'pancreas', 31: 'portalveinandsplenicvein', 32: 'smallbowel', 33: 'spleen', 34: 'stomach', 35: 'thymus', 36: 'thyroid', 37: 'trachea', 38: 'uterocervix', 39: 'uterus', 40: 'vertebrae', 41: 'gonads', 42: 'sacrum', 43: 'clavicula', # 44: 'prostate', 44: 'pulmonaryartery', # 45: 'ribcartilage', 45: 'rib', 46: 'scapula', # 48: 'skull', # 49: 'spinalcanal', # 50: 'sternum' } ``` If you find our project helpful, please consider to cite the following works: ``` @misc{zhang2025hierarchicalfeaturelearningmedical, title={Hierarchical Feature Learning for Medical Point Clouds via State Space Model}, author={Guoqing Zhang and Jingyun Yang and Yang Li}, year={2025}, eprint={2504.13015}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2504.13015}, } ``` --- dataset_info: features: - name: data sequence: sequence: float32 - name: label sequence: float32 splits: - name: train num_bytes: 947171520 num_examples: 28737 download_size: 718817756 dataset_size: 947171520 configs: - config_name: default data_files: - split: train path: data/train-* ---
eugrug-60/medical-o1-reasoning-SFT-it_f10_incremental
eugrug-60
2025-03-11T09:11:43Z
13
0
[ "task_categories:question-answering", "task_categories:text-generation", "language:it", "license:apache-2.0", "size_categories:1K<n<10K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "arxiv:2412.18925", "region:us", "medical", "biology" ]
[ "question-answering", "text-generation" ]
2025-03-08T21:24:07Z
0
--- dataset_info: features: - name: Question_IT dtype: string - name: Complex_CoT_IT dtype: string - name: Response_IT dtype: string splits: - name: train num_bytes: 4313189 num_examples: 1646 download_size: 2596297 dataset_size: 4313189 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - question-answering - text-generation language: - it tags: - medical - biology pretty_name: medical-o1-reasoning-SFT-it_f10_incremental --- ## News [2025/03/08] We open sourced the medical reasoning dataset for SFT translated into italian language. ## Dataset Description This dataset will be used to fine-tuning a distiled model to generate an italian medical LLM designed for advanced medical reasoning. We used the "facebook/nllb-200-distilled-600M" model to translate the "FreedomIntelligence/medical-o1-reasoning-SFT" dataset, from English to Italian # Citation We would like to thank the authors of the original dataset, and cite their work below. @misc{chen2024huatuogpto1medicalcomplexreasoning, title={HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs}, author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang}, year={2024}, eprint={2412.18925}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.18925}, } # Contacts For comments discussions or questions about this dataset, please write me at: [email protected]
Cohere/wikipedia-22-12-simple-embeddings
Cohere
2023-03-22T16:56:34Z
814
56
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "multilinguality:multilingual", "language:en", "license:apache-2.0", "size_categories:100K<n<1M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[ "text-retrieval" ]
2023-01-13T23:25:25Z
1
--- language: - en multilinguality: - multilingual size_categories: [] source_datasets: [] tags: [] task_categories: - text-retrieval license: - apache-2.0 task_ids: - document-retrieval --- # Wikipedia (simple English) embedded with cohere.ai `multilingual-22-12` encoder We encoded [Wikipedia (simple English)](https://simple.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model. To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12). ## Embeddings We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/). ## Further languages We provide embeddings of Wikipedia in many different languages: [ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings), You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12). ## Loading the dataset You can either load the dataset like this: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train") ``` Or you can also stream it without downloading it before: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train", streaming=True) for doc in docs: docid = doc['id'] title = doc['title'] text = doc['text'] emb = doc['emb'] ``` ## Search A full search example: ```python #Run: pip install cohere datasets from datasets import load_dataset import torch import cohere co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com #Load at max 1000 documents + embeddings max_docs = 1000 docs_stream = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train", streaming=True) docs = [] doc_embeddings = [] for doc in docs_stream: docs.append(doc) doc_embeddings.append(doc['emb']) if len(docs) >= max_docs: break doc_embeddings = torch.tensor(doc_embeddings) query = 'Who founded Youtube' response = co.embed(texts=[query], model='multilingual-22-12') query_embedding = response.embeddings query_embedding = torch.tensor(query_embedding) # Compute dot score between query embedding and document embeddings dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1)) top_k = torch.topk(dot_scores, k=3) # Print results print("Query:", query) for doc_id in top_k.indices[0].tolist(): print(docs[doc_id]['title']) print(docs[doc_id]['text'], "\n") ``` ## Performance You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance)
akbarsigit/mental-health-grpo-log-20250523
akbarsigit
2025-05-23T18:30:38Z
0
0
[ "format:parquet", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-05-23T18:30:36Z
0
--- dataset_info: features: - name: step dtype: 'null' - name: question dtype: 'null' - name: response dtype: 'null' - name: extracted_response dtype: 'null' - name: true_answer dtype: 'null' - name: bert_score dtype: 'null' - name: rouge_score dtype: 'null' - name: total_reward dtype: 'null' splits: - name: train num_bytes: 0 num_examples: 0 download_size: 2015 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---
jkazdan/gemma-2-9b-It-5000-refusal-Hex-PHI
jkazdan
2025-01-02T01:32:27Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-01-02T01:32:26Z
0
--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 657994 num_examples: 300 download_size: 300686 dataset_size: 657994 configs: - config_name: default data_files: - split: train path: data/train-* ---
okezieowen/nv_ig_0_1_wspr_v2
okezieowen
2025-05-21T14:13:35Z
0
0
[ "region:us" ]
[]
2025-05-21T13:57:28Z
0
--- dataset_info: features: - name: input_features sequence: sequence: float64 - name: labels sequence: int64 splits: - name: train num_bytes: 93936330712 num_examples: 48911 download_size: 10650858576 dataset_size: 93936330712 configs: - config_name: default data_files: - split: train path: data/train-* ---
GitBag/math_qwen2.5_7B_8192_n_128_eval_len
GitBag
2025-06-16T05:46:21Z
0
0
[ "size_categories:1K<n<10K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-06-16T05:46:00Z
0
--- dataset_info: features: - name: level dtype: string - name: type dtype: string - name: data_source dtype: string - name: prompt list: - name: content dtype: string - name: role dtype: string - name: ability dtype: string - name: reward_model struct: - name: ground_truth dtype: string - name: style dtype: string - name: extra_info struct: - name: index dtype: int64 - name: split dtype: string - name: response_0 dtype: string - name: response_1 dtype: string - name: response_2 dtype: string - name: response_3 dtype: string - name: response_4 dtype: string - name: response_5 dtype: string - name: response_6 dtype: string - name: response_7 dtype: string - name: response_8 dtype: string - name: response_9 dtype: string - name: response_10 dtype: string - name: response_11 dtype: string - name: response_12 dtype: string - name: response_13 dtype: string - name: response_14 dtype: string - name: response_15 dtype: string - name: response_16 dtype: string - name: response_17 dtype: string - 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saudefem/RAG_SEMFINET_qwen7_k5_v1
saudefem
2025-03-15T00:30:48Z
15
0
[ "size_categories:n<1K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2025-03-15T00:30:46Z
0
--- dataset_info: features: - name: id dtype: int64 - name: pergunta dtype: string - name: resposta dtype: string - name: context_k1 dtype: string - name: context_k2 dtype: string - name: context_k3 dtype: string - name: context_k4 dtype: string - name: context_k5 dtype: string - name: context_k6 dtype: string - name: context_k7 dtype: string - name: context_k8 dtype: string - name: context_k9 dtype: string - name: context_k10 dtype: string - name: inferencia dtype: string splits: - name: train num_bytes: 5727728 num_examples: 100 download_size: 3236025 dataset_size: 5727728 configs: - config_name: default data_files: - split: train path: data/train-* ---
younghyopark/dart_flip_box_20250402_204507
younghyopark
2025-04-03T00:45:20Z
25
0
[ "task_categories:robotics", "license:apache-2.0", "size_categories:n<1K", "format:parquet", "modality:tabular", "modality:timeseries", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "LeRobot", "teleop", "success" ]
[ "robotics" ]
2025-04-03T00:45:17Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - teleop - success 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": "DualPanda", "total_episodes": 1, "total_frames": 120, "total_tasks": 1, "total_videos": 0, "total_chunks": 1, "chunks_size": 1000, "fps": 50, "splits": { "train": "0:1" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": null, "features": { "observation.state.qpos": { "dtype": "float32", "shape": [ 18 ], "names": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ] }, "observation.state.env_state": { "dtype": "float32", "shape": [ 7 ], "names": [ 0, 1, 2, 3, 4, 5, 6 ] }, "observation.state.qvel": { "dtype": "float32", "shape": [ 24 ], "names": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ] }, "action": { "dtype": "float32", "shape": [ 16 ], "names": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 ] }, "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] ```
Zangs3011/rando_wando
Zangs3011
2024-10-30T10:24:43Z
32
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-30T07:44:20Z
0
--- dataset_info: features: - name: english dtype: string - name: hindi dtype: string - name: __index_level_0__ dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 16515334 num_examples: 30182 download_size: 8566552 dataset_size: 16515334 configs: - config_name: default data_files: - split: train path: data/train-* ---
BarryFutureman/vpt_data_8xx_shard0190
BarryFutureman
2025-06-11T02:07:17Z
0
0
[ "task_categories:robotics", "license:apache-2.0", "size_categories:10K<n<100K", "format:parquet", "modality:image", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us", "LeRobot" ]
[ "robotics" ]
2025-06-11T02:05:49Z
0
--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.1", "robot_type": null, "total_episodes": 10, "total_frames": 42630, "total_tasks": 1, "total_videos": 0, "total_chunks": 1, "chunks_size": 1000, "fps": 20, "splits": { "train": "0:10" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.image": { "dtype": "image", "shape": [ 3, 360, 640 ], "names": [ "channel", "height", "width" ] }, "action": { "dtype": "string", "shape": [ 1 ], "names": null }, "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] ```
Gummybear05/E50_Ypause
Gummybear05
2024-10-11T23:56:49Z
22
0
[ "size_categories:10K<n<100K", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]
[]
2024-10-11T23:55:04Z
0
--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: sample_rate dtype: int64 - name: text dtype: string - name: scriptId dtype: int64 - name: fileNm dtype: string - name: recrdTime dtype: float64 - name: recrdQuality dtype: int64 - name: recrdDt dtype: string - name: scriptSetNo dtype: string - name: recrdEnvrn dtype: string - name: colctUnitCode dtype: string - name: cityCode dtype: string - name: recrdUnit dtype: string - name: convrsThema dtype: string - name: gender dtype: string - name: recorderId dtype: string - name: age dtype: int64 splits: - name: train num_bytes: 9309946084 num_examples: 12401 download_size: 2116188092 dataset_size: 9309946084 configs: - config_name: default data_files: - split: train path: data/train-* ---
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Dataset Card for Hugging Face Hub Dataset Cards

This datasets consists of dataset cards for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub. This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.

This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.

Dataset Details

Uses

There are a number of potential uses for this dataset including:

  • text mining to find common themes in dataset cards
  • analysis of the dataset card format/content
  • topic modelling of dataset cards
  • training language models on the dataset cards

Out-of-Scope Use

[More Information Needed]

Dataset Structure

This dataset has a single split.

Dataset Creation

Curation Rationale

The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.

Source Data

The source data is README.md files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.

Data Collection and Processing

The data is downloaded using a CRON job on a daily basis.

Who are the source data producers?

The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.

Annotations [optional]

There are no additional annotations in this dataset beyond the dataset card content.

Annotation process

N/A

Who are the annotators?

N/A

Personal and Sensitive Information

We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.

Bias, Risks, and Limitations

Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards. Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.

Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation

No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.

Dataset Card Authors

@davanstrien

Dataset Card Contact

@davanstrien

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Curated by:
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Number of rows:
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