Datasets:
davanstrien
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summary-test

Dataset card Data Studio Files
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angdong/nate-news-world
--- license: mit ---
HuggingFaceFW/fineweb_french_extract
--- dataset_info: features: - name: text dtype: string - name: id dtype: string - name: media sequence: 'null' - name: metadata struct: - name: date dtype: string - name: dump dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: url dtype: string splits: - name: train num_bytes: 28672930115 num_examples: 10000000 download_size: 15018708723 dataset_size: 28672930115 configs: - config_name: default data_files: - split: train path: data/train-* ---
maghwa/OpenHermes-2-AR-10K-18-600k-610k
--- dataset_info: features: - name: topic dtype: 'null' - name: views dtype: float64 - name: category dtype: 'null' - name: avatarUrl dtype: 'null' - name: language dtype: 'null' - name: title dtype: 'null' - name: model_name dtype: 'null' - name: idx dtype: 'null' - name: skip_prompt_formatting dtype: 'null' - name: hash dtype: 'null' - name: model dtype: 'null' - name: system_prompt dtype: 'null' - name: custom_instruction dtype: 'null' - name: source dtype: string - name: id dtype: 'null' - name: conversations dtype: string splits: - name: train num_bytes: 25345215 num_examples: 10001 download_size: 11487924 dataset_size: 25345215 configs: - config_name: default data_files: - split: train path: data/train-* ---
joey234/mmlu-professional_law
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: fewshot_context_neg dtype: string splits: - name: dev num_bytes: 14459 num_examples: 5 - name: test num_bytes: 14307400 num_examples: 1534 download_size: 2073867 dataset_size: 14321859 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-professional_law" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pinkbaka/rapgenius
--- license: mit ---
alvarobartt/openhermes-preferences-metamath
--- license: other task_categories: - text-generation language: - en source_datasets: - argilla/OpenHermesPreferences annotations_creators: - Argilla - HuggingFaceH4 tags: - dpo - synthetic - metamath size_categories: - 10K<n<100K dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 169676613.83305642 num_examples: 50799 - name: test num_bytes: 18855183.863611557 num_examples: 5645 download_size: 44064373 dataset_size: 188531797.69666797 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for OpenHermes Preferences - MetaMath This dataset is a subset from [`argilla/OpenHermesPreferences`](https://hf.co/datasets/argilla/OpenHermesPreferences), only keeping the preferences of `metamath`, and removing all the columns besides the `chosen` and `rejected` ones, that come in OpenAI chat formatting, so that's easier to fine-tune a model using tools like: [`huggingface/alignment-handbook`](https://github.com/huggingface/alignment-handbook) or [`axolotl`](https://github.com/OpenAccess-AI-Collective/axolotl), among others. ## Reference [`argilla/OpenHermesPreferences`](https://hf.co/datasets/argilla/OpenHermesPreferences) dataset created as a collaborative effort between Argilla and the HuggingFaceH4 team from HuggingFace.
DBQ/Gucci.Product.prices.Hungary
--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Hungary - Gucci - Product-level price list tags: - webscraping - ecommerce - Gucci - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: string - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 2375242 num_examples: 4976 download_size: 686750 dataset_size: 2375242 --- # Gucci web scraped data ## About the website The **luxury fashion industry** is a significant sector in the EMEA region, and especially in **Hungary**. The industry has found its footing in the digital world with the advent of **Ecommerce**, resulting in a significant surge in online sales. **Gucci**, an eminent player in this field, has established a strong online presence to capitalize on this trend. The dataset observed pertains to **Ecommerce product-list page (PLP) data** for Gucci in Hungary, where the brand witnesses considerable demand. This data provides valuable insights into consumer preferences and shopping patterns, helping players in this industry shape strategies to foster growth. ## Link to **dataset** [Hungary - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Hungary/r/rec95J1ypx0pNxrgA)
FangyuLei/tatqa
--- license: bsd-3-clause ---
joachimsallstrom/lux_np
--- license: creativeml-openrail-m ---
PeterLawrence/inova8.schema.1
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 91121 num_examples: 85 download_size: 13427 dataset_size: 91121 configs: - config_name: default data_files: - split: train path: data/train-* ---
arbml/AraFacts
--- dataset_info: features: - name: ClaimID dtype: string - name: claim dtype: string - name: description dtype: string - name: source dtype: string - name: date dtype: string - name: source_label dtype: string - name: normalized_label dtype: string - name: source_category dtype: string - name: normalized_category dtype: string - name: source_url dtype: string - name: claim_urls dtype: string - name: evidence_urls dtype: string - name: claim_type dtype: string splits: - name: train num_bytes: 13201528 num_examples: 6222 download_size: 5719822 dataset_size: 13201528 --- # Dataset Card for "AraFacts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-9000
--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1080551 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
benayas/massive_artificial_10pct_v1
--- dataset_info: features: - name: text dtype: string - name: category dtype: string splits: - name: train num_bytes: 803392 num_examples: 11514 download_size: 262619 dataset_size: 803392 configs: - config_name: default data_files: - split: train path: data/train-* ---
indra-inc/docvqa_en_train_valid_20500
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: question dtype: string - name: docId dtype: int64 - name: answers sequence: string - name: data_split dtype: string - name: bounding_boxes sequence: sequence: int64 - name: word_list sequence: string - name: image_raw dtype: image splits: - name: train num_bytes: 3168876041.0 num_examples: 20000 - name: valid num_bytes: 236325170.0 num_examples: 500 download_size: 3158729168 dataset_size: 3405201211.0 --- # Dataset Card for "docvqa_en_train_valid_20500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kogi-jwu/instructjhumaneval
--- license: mit task_categories: - text2text-generation language: - ja ---
TurboPascal/tokenizers_example_zh_en
--- license: apache-2.0 task_categories: - text-generation language: - zh - en size_categories: - 1M<n<10M --- 用于训练分词器的基础文本
ibranze/araproje_hellaswag_en_dynamic
--- dataset_info: features: - name: keys dtype: int64 - name: values sequence: int64 splits: - name: train num_bytes: 23000 num_examples: 250 download_size: 6356 dataset_size: 23000 configs: - config_name: default data_files: - split: train path: data/train-* ---
TinyPixel/elm-3
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2536494 num_examples: 1073 download_size: 1390775 dataset_size: 2536494 configs: - config_name: default data_files: - split: train path: data/train-* ---
udmurtNLP/madlad-400-udmurt
--- dataset_info: features: - name: sent dtype: string splits: - name: train num_bytes: 102168566 num_examples: 651456 download_size: 52503390 dataset_size: 102168566 configs: - config_name: default data_files: - split: train path: data/train-* language: - udm size_categories: - 100K<n<1M --- # Usage madlad-400-udmurt ```py from datasets import load_dataset dataset = load_dataset("udmurtNLP/madlad-400-udmurt") ```
1rsh/tts-rj-hi-karya
--- language: - hi license: mit size_categories: - 100K<n<1M task_categories: - text-to-speech - automatic-speech-recognition pretty_name: Rajasthani Hindi Speech Dataset dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 7425995581.812981 num_examples: 422603 - name: test num_bytes: 74991388.79801954 num_examples: 4269 download_size: 7504372330 dataset_size: 7500986970.611 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* tags: - webdataset --- # Rajasthani Hindi Speech Dataset <!-- Provide a quick summary of the dataset. --> This dataset consists of audio recordings of participants reading out stories in Rajasthani Hindi, one sentence at a time. They had 98 participants from Soda, Rajasthan. Each participant read 30 stories. In total, we have 426872 recordings in this dataset. They had roughly 58 male participants and 40 female participants. > **Point to Note:** > While random sampling suggests that most users have to their best effort tried to accurately read out the sentences, we have not performed any quality analysis on the data. There could be errors in some of the recordings. <!-- Provide a longer summary of what this dataset is. --> ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Link:** [Download](https://www.microsoft.com/en-gb/download/details.aspx?id=105385) - **Curated By:** [Project Karya](https://www.microsoft.com/en-us/research/project/project-karya/overview/) ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> Contains two headers: audio and sentence containing the Audio file and sentence respectively.
JLuisMayor/calendario
--- license: apache-2.0 ---
senga-ml/dnotes-data-v1
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 169798787.0 num_examples: 87 - name: validation num_bytes: 29563876.0 num_examples: 10 - name: test num_bytes: 16997016.0 num_examples: 6 download_size: 216159591 dataset_size: 216359679.0 --- # Dataset Card for "dnotes-data-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/wheel-chair-images-annotation4object-detec
--- dataset_info: features: - name: image dtype: image - name: instances list: - name: box sequence: float64 - name: label dtype: int64 splits: - name: train num_bytes: 25165898.049 num_examples: 1107 download_size: 0 dataset_size: 25165898.049 license: apache-2.0 task_categories: - object-detection language: - en pretty_name: wheel_chair_detection size_categories: - 1K<n<10K --- # Wheelchair Dataset for Object Detection ## Dataset Information The `dataset_info` file provides information about the wheelchair dataset designed for object detection. Here are the details: ### Features - **image**: Represents the images in the dataset. - Data type: `image` - **instances**: Represents the instances within each image. Each instance consists of a bounding box and a label. - Data type: `list` - Sub-features: - **box**: Bounding box coordinates for each instance. - Data type: `float64` - **label**: Label for each instance. - Data type: `int64` ### Splits - **Train**: This split, named "train," contains a total of 1,107 examples. - Number of bytes: 25,165,898.049 - Number of examples: 1,107 ### Dataset Size - Download size: 0 (no download required) - Dataset size: 25,165,898.049 bytes ## Wheelchair Class Name The dataset includes the following class names for object detection: ```json "labels": ClassLabel(names=["person", "wheel_chair", "not wheel chair"]) ``` The class labels are defined as follows: - "person" - "wheel_chair" - "not wheel chair" ## Object Detection Application (YOLOv Models) You can utilize the dataset with YOLOv models for object detection tasks. The class labels for the models correspond to the defined class names mentioned above: ```json "labels": ClassLabel(names=["person", "wheel_chair", "not wheel chair"]) ``` Make sure to follow the appropriate implementation guidelines and examples for YOLOv models to leverage this dataset effectively. ```python # Load the dataset hf_dataset = load_dataset("your_dataset_name", split="train") # Accessing image image = hf_dataset[1]['image'] # Display the image image.show() # Accessing label and bounding box coordinates instances = hf_dataset[1]['instances'] for instance in instances: label = instance['label'] box = instance['box'] # Get the class name for the label class_name = hf_dataset.features['instances']['label'].int2str(label) print(f"Label: {class_name}") print(f"Bounding Box: {box}") ``` ## Citation If you use this dataset in your research or any other work, please consider citing it as: ``` @dataset{wheel-chair-images-annotation4object-detec_dataset, author = {Falah.G.Salieh}, title = {Wheelchair Dataset for Object Detection}, year = {2023}, publisher = {Hugging Face}, version = {1.0}, location = {Online}, url = {Falah/wheel-chair-images-annotation4object-detec} } ``` ## License Wheelchair Dataset for Object Detection Dataset is provided under the Apache-2.0 license. ```
Cherrycreamco/g4aucfiltered
--- license: apache-2.0 ---
EleutherAI/quirky_squaring_increment0_bob_easy
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 1595505.5 num_examples: 23000 - name: validation num_bytes: 67316.06 num_examples: 970 - name: test num_bytes: 68355.30625 num_examples: 985 download_size: 578238 dataset_size: 1731176.86625 --- # Dataset Card for "quirky_squaring_increment0_bob_easy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_DUAL-GPO__zephyr-7b-ipo-qlora-v0
--- pretty_name: Evaluation run of DUAL-GPO/zephyr-7b-ipo-qlora-v0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [DUAL-GPO/zephyr-7b-ipo-qlora-v0](https://huggingface.co/DUAL-GPO/zephyr-7b-ipo-qlora-v0)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 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 (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_DUAL-GPO__zephyr-7b-ipo-qlora-v0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-07T05:01:01.232002](https://huggingface.co/datasets/open-llm-leaderboard/details_DUAL-GPO__zephyr-7b-ipo-qlora-v0/blob/main/results_2024-04-07T05-01-01.232002.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\": 0.6336157909151666,\n\ \ \"acc_stderr\": 0.03259268627428965,\n \"acc_norm\": 0.6388963560492149,\n\ \ \"acc_norm_stderr\": 0.03325258805528349,\n \"mc1\": 0.29865361077111385,\n\ \ \"mc1_stderr\": 0.016021570613768542,\n \"mc2\": 0.4535332788498766,\n\ \ \"mc2_stderr\": 0.014550976512746065\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5921501706484642,\n \"acc_stderr\": 0.0143610972884497,\n\ \ \"acc_norm\": 0.6313993174061433,\n \"acc_norm_stderr\": 0.0140978106780422\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6427006572395937,\n\ \ \"acc_stderr\": 0.004782246931195,\n \"acc_norm\": 0.8436566421031667,\n\ \ \"acc_norm_stderr\": 0.0036243831208234508\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.042446332383532265,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.042446332383532265\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337142,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337142\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.046774730044911984,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.046774730044911984\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.040824829046386284,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.040824829046386284\n \ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3915343915343915,\n \"acc_stderr\": 0.02513809138885111,\n \"\ acc_norm\": 0.3915343915343915,\n \"acc_norm_stderr\": 0.02513809138885111\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\ \ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\ \ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\ acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.025033870583015184,\n\ \ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.025033870583015184\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.024243783994062157,\n\ \ \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.024243783994062157\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135363,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135363\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.038615575462551684,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.038615575462551684\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8238532110091743,\n \"acc_stderr\": 0.01633288239343139,\n \"\ acc_norm\": 0.8238532110091743,\n \"acc_norm_stderr\": 0.01633288239343139\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7696078431372549,\n \"acc_stderr\": 0.029554292605695066,\n \"\ acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.029554292605695066\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808514,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808514\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\ \ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.038968789850704164,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.038968789850704164\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.03157065078911901,\n\ \ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.03157065078911901\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822585,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822585\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\ \ \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n\ \ \"acc_norm_stderr\": 0.022801382534597528\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.80970625798212,\n\ \ \"acc_stderr\": 0.014036945850381396,\n \"acc_norm\": 0.80970625798212,\n\ \ \"acc_norm_stderr\": 0.014036945850381396\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.40782122905027934,\n\ \ \"acc_stderr\": 0.016435865260914746,\n \"acc_norm\": 0.40782122905027934,\n\ \ \"acc_norm_stderr\": 0.016435865260914746\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.024848018263875192,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.024848018263875192\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n\ \ \"acc_stderr\": 0.025311765975426122,\n \"acc_norm\": 0.7266881028938906,\n\ \ \"acc_norm_stderr\": 0.025311765975426122\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765134,\n\ \ \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765134\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.439374185136897,\n\ \ \"acc_stderr\": 0.012676014778580215,\n \"acc_norm\": 0.439374185136897,\n\ \ \"acc_norm_stderr\": 0.012676014778580215\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6666666666666666,\n \"acc_stderr\": 0.0190709855896875,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.0190709855896875\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128455,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128455\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.02740385941078685,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.02740385941078685\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727668,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727668\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29865361077111385,\n\ \ \"mc1_stderr\": 0.016021570613768542,\n \"mc2\": 0.4535332788498766,\n\ \ \"mc2_stderr\": 0.014550976512746065\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7955801104972375,\n \"acc_stderr\": 0.011334090612597221\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.400303260045489,\n \ \ \"acc_stderr\": 0.013495926436566438\n }\n}\n```" repo_url: https://huggingface.co/DUAL-GPO/zephyr-7b-ipo-qlora-v0 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|arc:challenge|25_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-07T05-01-01.232002.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|gsm8k|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hellaswag|10_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T05-01-01.232002.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T05-01-01.232002.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T05-01-01.232002.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_07T05_01_01.232002 path: - '**/details_harness|winogrande|5_2024-04-07T05-01-01.232002.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-07T05-01-01.232002.parquet' - config_name: results data_files: - split: 2024_04_07T05_01_01.232002 path: - results_2024-04-07T05-01-01.232002.parquet - split: latest path: - results_2024-04-07T05-01-01.232002.parquet --- # Dataset Card for Evaluation run of DUAL-GPO/zephyr-7b-ipo-qlora-v0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [DUAL-GPO/zephyr-7b-ipo-qlora-v0](https://huggingface.co/DUAL-GPO/zephyr-7b-ipo-qlora-v0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_DUAL-GPO__zephyr-7b-ipo-qlora-v0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-07T05:01:01.232002](https://huggingface.co/datasets/open-llm-leaderboard/details_DUAL-GPO__zephyr-7b-ipo-qlora-v0/blob/main/results_2024-04-07T05-01-01.232002.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": 0.6336157909151666, "acc_stderr": 0.03259268627428965, "acc_norm": 0.6388963560492149, "acc_norm_stderr": 0.03325258805528349, "mc1": 0.29865361077111385, "mc1_stderr": 0.016021570613768542, "mc2": 0.4535332788498766, "mc2_stderr": 0.014550976512746065 }, "harness|arc:challenge|25": { "acc": 0.5921501706484642, "acc_stderr": 0.0143610972884497, "acc_norm": 0.6313993174061433, "acc_norm_stderr": 0.0140978106780422 }, "harness|hellaswag|10": { "acc": 0.6427006572395937, "acc_stderr": 0.004782246931195, "acc_norm": 0.8436566421031667, "acc_norm_stderr": 0.0036243831208234508 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.042446332383532265, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.042446332383532265 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.027943219989337142, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.027943219989337142 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108102, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108102 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.046774730044911984, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.046774730044911984 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6, "acc_stderr": 0.040824829046386284, "acc_norm": 0.6, "acc_norm_stderr": 0.040824829046386284 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3915343915343915, "acc_stderr": 0.02513809138885111, "acc_norm": 0.3915343915343915, "acc_norm_stderr": 0.02513809138885111 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7516129032258064, "acc_stderr": 0.024580028921481003, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481003 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217487, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217487 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8601036269430051, "acc_stderr": 0.025033870583015184, "acc_norm": 0.8601036269430051, "acc_norm_stderr": 0.025033870583015184 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6461538461538462, "acc_stderr": 0.024243783994062157, "acc_norm": 0.6461538461538462, "acc_norm_stderr": 0.024243783994062157 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028597, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028597 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135363, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135363 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.038615575462551684, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.038615575462551684 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8238532110091743, "acc_stderr": 0.01633288239343139, "acc_norm": 0.8238532110091743, "acc_norm_stderr": 0.01633288239343139 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7696078431372549, "acc_stderr": 0.029554292605695066, "acc_norm": 0.7696078431372549, "acc_norm_stderr": 0.029554292605695066 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7679324894514767, "acc_stderr": 0.027479744550808514, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.027479744550808514 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6771300448430493, "acc_stderr": 0.03138147637575499, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575499 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306086, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306086 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.038968789850704164, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.038968789850704164 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7975460122699386, "acc_stderr": 0.03157065078911901, "acc_norm": 0.7975460122699386, "acc_norm_stderr": 0.03157065078911901 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5178571428571429, "acc_stderr": 0.047427623612430116, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822585, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822585 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8589743589743589, "acc_stderr": 0.022801382534597528, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.022801382534597528 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.80970625798212, "acc_stderr": 0.014036945850381396, "acc_norm": 0.80970625798212, "acc_norm_stderr": 0.014036945850381396 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7167630057803468, "acc_stderr": 0.024257901705323378, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.024257901705323378 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.40782122905027934, "acc_stderr": 0.016435865260914746, "acc_norm": 0.40782122905027934, "acc_norm_stderr": 0.016435865260914746 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7483660130718954, "acc_stderr": 0.024848018263875192, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.024848018263875192 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7266881028938906, "acc_stderr": 0.025311765975426122, "acc_norm": 0.7266881028938906, "acc_norm_stderr": 0.025311765975426122 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7160493827160493, "acc_stderr": 0.025089478523765134, "acc_norm": 0.7160493827160493, "acc_norm_stderr": 0.025089478523765134 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.439374185136897, "acc_stderr": 0.012676014778580215, "acc_norm": 0.439374185136897, "acc_norm_stderr": 0.012676014778580215 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7022058823529411, "acc_stderr": 0.02777829870154544, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.02777829870154544 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.0190709855896875, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.0190709855896875 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128455, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128455 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8159203980099502, "acc_stderr": 0.02740385941078685, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.02740385941078685 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727668, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727668 }, "harness|truthfulqa:mc|0": { "mc1": 0.29865361077111385, "mc1_stderr": 0.016021570613768542, "mc2": 0.4535332788498766, "mc2_stderr": 0.014550976512746065 }, "harness|winogrande|5": { "acc": 0.7955801104972375, "acc_stderr": 0.011334090612597221 }, "harness|gsm8k|5": { "acc": 0.400303260045489, "acc_stderr": 0.013495926436566438 } } ``` ## 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 intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. 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mickume/wow
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 16144133 num_examples: 73565 download_size: 9966747 dataset_size: 16144133 --- # Dataset Card for "wow" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_NbAiLab__nb-gpt-j-6B-alpaca
--- pretty_name: Evaluation run of NbAiLab/nb-gpt-j-6B-alpaca dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [NbAiLab/nb-gpt-j-6B-alpaca](https://huggingface.co/NbAiLab/nb-gpt-j-6B-alpaca)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 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 (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NbAiLab__nb-gpt-j-6B-alpaca\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-07-19T15:55:19.313530](https://huggingface.co/datasets/open-llm-leaderboard/details_NbAiLab__nb-gpt-j-6B-alpaca/blob/main/results_2023-07-19T15%3A55%3A19.313530.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\": 0.2793361324347659,\n\ \ \"acc_stderr\": 0.03229367242405252,\n \"acc_norm\": 0.2819200448548028,\n\ \ \"acc_norm_stderr\": 0.03229676260655511,\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871114,\n \"mc2\": 0.3799804264451723,\n\ \ \"mc2_stderr\": 0.014771856203795355\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3447098976109215,\n \"acc_stderr\": 0.013888816286782114,\n\ \ \"acc_norm\": 0.36860068259385664,\n \"acc_norm_stderr\": 0.014097810678042184\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.44602668791077477,\n\ \ \"acc_stderr\": 0.004960624576987787,\n \"acc_norm\": 0.574586735710018,\n\ \ \"acc_norm_stderr\": 0.004933950953380902\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2962962962962963,\n\ \ \"acc_stderr\": 0.039446241625011175,\n \"acc_norm\": 0.2962962962962963,\n\ \ \"acc_norm_stderr\": 0.039446241625011175\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17105263157894737,\n \"acc_stderr\": 0.030643607071677088,\n\ \ \"acc_norm\": 0.17105263157894737,\n \"acc_norm_stderr\": 0.030643607071677088\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.26,\n\ \ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.23773584905660378,\n \"acc_stderr\": 0.02619980880756193,\n\ \ \"acc_norm\": 0.23773584905660378,\n \"acc_norm_stderr\": 0.02619980880756193\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3472222222222222,\n\ \ \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.3472222222222222,\n\ \ \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n\ \ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2658959537572254,\n\ \ \"acc_stderr\": 0.0336876293225943,\n \"acc_norm\": 0.2658959537572254,\n\ \ \"acc_norm_stderr\": 0.0336876293225943\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.04655010411319619,\n\ \ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.04655010411319619\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.22,\n \"acc_stderr\": 0.041633319989322674,\n \"acc_norm\": 0.22,\n\ \ \"acc_norm_stderr\": 0.041633319989322674\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.3148936170212766,\n \"acc_stderr\": 0.030363582197238156,\n\ \ \"acc_norm\": 0.3148936170212766,\n \"acc_norm_stderr\": 0.030363582197238156\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.22807017543859648,\n\ \ \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n\ \ \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.03600105692727771,\n\ \ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.03600105692727771\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.03718489006818115,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.03718489006818115\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.23548387096774193,\n\ \ \"acc_stderr\": 0.02413763242933772,\n \"acc_norm\": 0.23548387096774193,\n\ \ \"acc_norm_stderr\": 0.02413763242933772\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.270935960591133,\n \"acc_stderr\": 0.03127090713297698,\n\ \ \"acc_norm\": 0.270935960591133,\n \"acc_norm_stderr\": 0.03127090713297698\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\"\ : 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.2606060606060606,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.2606060606060606,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.35858585858585856,\n \"acc_stderr\": 0.03416903640391521,\n \"\ acc_norm\": 0.35858585858585856,\n \"acc_norm_stderr\": 0.03416903640391521\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.35751295336787564,\n \"acc_stderr\": 0.034588160421810045,\n\ \ \"acc_norm\": 0.35751295336787564,\n \"acc_norm_stderr\": 0.034588160421810045\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.23846153846153847,\n \"acc_stderr\": 0.02160629449464773,\n\ \ \"acc_norm\": 0.23846153846153847,\n \"acc_norm_stderr\": 0.02160629449464773\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.22592592592592592,\n \"acc_stderr\": 0.025497532639609542,\n \ \ \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.025497532639609542\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.19327731092436976,\n \"acc_stderr\": 0.025649470265889193,\n\ \ \"acc_norm\": 0.19327731092436976,\n \"acc_norm_stderr\": 0.025649470265889193\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.326605504587156,\n \"acc_stderr\": 0.020106990889937303,\n \"\ acc_norm\": 0.326605504587156,\n \"acc_norm_stderr\": 0.020106990889937303\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"\ acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.22058823529411764,\n \"acc_stderr\": 0.029102254389674093,\n \"\ acc_norm\": 0.22058823529411764,\n \"acc_norm_stderr\": 0.029102254389674093\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460302,\n \ \ \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460302\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2600896860986547,\n\ \ \"acc_stderr\": 0.029442495585857487,\n \"acc_norm\": 0.2600896860986547,\n\ \ \"acc_norm_stderr\": 0.029442495585857487\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.20610687022900764,\n \"acc_stderr\": 0.035477710041594626,\n\ \ \"acc_norm\": 0.20610687022900764,\n \"acc_norm_stderr\": 0.035477710041594626\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2975206611570248,\n \"acc_stderr\": 0.04173349148083498,\n \"\ acc_norm\": 0.2975206611570248,\n \"acc_norm_stderr\": 0.04173349148083498\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.294478527607362,\n \"acc_stderr\": 0.03581165790474082,\n\ \ \"acc_norm\": 0.294478527607362,\n \"acc_norm_stderr\": 0.03581165790474082\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.16964285714285715,\n\ \ \"acc_stderr\": 0.0356236785009539,\n \"acc_norm\": 0.16964285714285715,\n\ \ \"acc_norm_stderr\": 0.0356236785009539\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.20388349514563106,\n \"acc_stderr\": 0.03989139859531772,\n\ \ \"acc_norm\": 0.20388349514563106,\n \"acc_norm_stderr\": 0.03989139859531772\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.21794871794871795,\n\ \ \"acc_stderr\": 0.02704685763071666,\n \"acc_norm\": 0.21794871794871795,\n\ \ \"acc_norm_stderr\": 0.02704685763071666\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.3052362707535121,\n\ \ \"acc_stderr\": 0.01646771194763513,\n \"acc_norm\": 0.3052362707535121,\n\ \ \"acc_norm_stderr\": 0.01646771194763513\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.2861271676300578,\n \"acc_stderr\": 0.024332146779134128,\n\ \ \"acc_norm\": 0.2861271676300578,\n \"acc_norm_stderr\": 0.024332146779134128\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27262569832402234,\n\ \ \"acc_stderr\": 0.014893391735249588,\n \"acc_norm\": 0.27262569832402234,\n\ \ \"acc_norm_stderr\": 0.014893391735249588\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.02656892101545715,\n\ \ \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.02656892101545715\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2958199356913183,\n\ \ \"acc_stderr\": 0.025922371788818777,\n \"acc_norm\": 0.2958199356913183,\n\ \ \"acc_norm_stderr\": 0.025922371788818777\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.22839506172839505,\n \"acc_stderr\": 0.023358211840626267,\n\ \ \"acc_norm\": 0.22839506172839505,\n \"acc_norm_stderr\": 0.023358211840626267\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.24113475177304963,\n \"acc_stderr\": 0.025518731049537776,\n \ \ \"acc_norm\": 0.24113475177304963,\n \"acc_norm_stderr\": 0.025518731049537776\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23989569752281617,\n\ \ \"acc_stderr\": 0.010906282617981657,\n \"acc_norm\": 0.23989569752281617,\n\ \ \"acc_norm_stderr\": 0.010906282617981657\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.030161911930767102,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.030161911930767102\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.27450980392156865,\n \"acc_stderr\": 0.018054027458815194,\n \ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.018054027458815194\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2909090909090909,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.2909090909090909,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.3877551020408163,\n \"acc_stderr\": 0.03119223072679566,\n\ \ \"acc_norm\": 0.3877551020408163,\n \"acc_norm_stderr\": 0.03119223072679566\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.21393034825870647,\n\ \ \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.21393034825870647,\n\ \ \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.27710843373493976,\n\ \ \"acc_stderr\": 0.03484331592680588,\n \"acc_norm\": 0.27710843373493976,\n\ \ \"acc_norm_stderr\": 0.03484331592680588\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.2573099415204678,\n \"acc_stderr\": 0.03352799844161865,\n\ \ \"acc_norm\": 0.2573099415204678,\n \"acc_norm_stderr\": 0.03352799844161865\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871114,\n \"mc2\": 0.3799804264451723,\n\ \ \"mc2_stderr\": 0.014771856203795355\n }\n}\n```" repo_url: https://huggingface.co/NbAiLab/nb-gpt-j-6B-alpaca leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|arc:challenge|25_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hellaswag|10_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:55:19.313530.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:55:19.313530.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T15_55_19.313530 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T15:55:19.313530.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T15:55:19.313530.parquet' - config_name: results data_files: - split: 2023_07_19T15_55_19.313530 path: - results_2023-07-19T15:55:19.313530.parquet - split: latest path: - results_2023-07-19T15:55:19.313530.parquet --- # Dataset Card for Evaluation run of NbAiLab/nb-gpt-j-6B-alpaca ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/NbAiLab/nb-gpt-j-6B-alpaca - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [NbAiLab/nb-gpt-j-6B-alpaca](https://huggingface.co/NbAiLab/nb-gpt-j-6B-alpaca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_NbAiLab__nb-gpt-j-6B-alpaca", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-07-19T15:55:19.313530](https://huggingface.co/datasets/open-llm-leaderboard/details_NbAiLab__nb-gpt-j-6B-alpaca/blob/main/results_2023-07-19T15%3A55%3A19.313530.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": 0.2793361324347659, "acc_stderr": 0.03229367242405252, "acc_norm": 0.2819200448548028, "acc_norm_stderr": 0.03229676260655511, "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871114, "mc2": 0.3799804264451723, "mc2_stderr": 0.014771856203795355 }, "harness|arc:challenge|25": { "acc": 0.3447098976109215, "acc_stderr": 0.013888816286782114, "acc_norm": 0.36860068259385664, "acc_norm_stderr": 0.014097810678042184 }, "harness|hellaswag|10": { "acc": 0.44602668791077477, "acc_stderr": 0.004960624576987787, "acc_norm": 0.574586735710018, "acc_norm_stderr": 0.004933950953380902 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2962962962962963, "acc_stderr": 0.039446241625011175, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.039446241625011175 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17105263157894737, "acc_stderr": 0.030643607071677088, "acc_norm": 0.17105263157894737, "acc_norm_stderr": 0.030643607071677088 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.23773584905660378, "acc_stderr": 0.02619980880756193, "acc_norm": 0.23773584905660378, "acc_norm_stderr": 0.02619980880756193 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.3472222222222222, "acc_stderr": 0.039812405437178615, "acc_norm": 0.3472222222222222, "acc_norm_stderr": 0.039812405437178615 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2658959537572254, "acc_stderr": 0.0336876293225943, "acc_norm": 0.2658959537572254, "acc_norm_stderr": 0.0336876293225943 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3235294117647059, "acc_stderr": 0.04655010411319619, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.04655010411319619 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.22, "acc_stderr": 0.041633319989322674, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322674 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3148936170212766, "acc_stderr": 0.030363582197238156, "acc_norm": 0.3148936170212766, "acc_norm_stderr": 0.030363582197238156 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.22807017543859648, "acc_stderr": 0.03947152782669415, "acc_norm": 0.22807017543859648, "acc_norm_stderr": 0.03947152782669415 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2482758620689655, "acc_stderr": 0.03600105692727771, "acc_norm": 0.2482758620689655, "acc_norm_stderr": 0.03600105692727771 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03718489006818115, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03718489006818115 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.23548387096774193, "acc_stderr": 0.02413763242933772, "acc_norm": 0.23548387096774193, "acc_norm_stderr": 0.02413763242933772 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.270935960591133, "acc_stderr": 0.03127090713297698, "acc_norm": 0.270935960591133, "acc_norm_stderr": 0.03127090713297698 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.27, "acc_stderr": 0.04461960433384739, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.2606060606060606, "acc_stderr": 0.034277431758165236, "acc_norm": 0.2606060606060606, "acc_norm_stderr": 0.034277431758165236 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.35858585858585856, "acc_stderr": 0.03416903640391521, "acc_norm": 0.35858585858585856, "acc_norm_stderr": 0.03416903640391521 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.35751295336787564, "acc_stderr": 0.034588160421810045, "acc_norm": 0.35751295336787564, "acc_norm_stderr": 0.034588160421810045 }, "harness|hendrycksTest-high_school_macroeconomics|5": { 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"acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.3052362707535121, "acc_stderr": 0.01646771194763513, "acc_norm": 0.3052362707535121, "acc_norm_stderr": 0.01646771194763513 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2861271676300578, "acc_stderr": 0.024332146779134128, "acc_norm": 0.2861271676300578, "acc_norm_stderr": 0.024332146779134128 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.27262569832402234, "acc_stderr": 0.014893391735249588, "acc_norm": 0.27262569832402234, "acc_norm_stderr": 0.014893391735249588 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.3137254901960784, "acc_stderr": 0.02656892101545715, "acc_norm": 0.3137254901960784, "acc_norm_stderr": 0.02656892101545715 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2958199356913183, "acc_stderr": 0.025922371788818777, "acc_norm": 0.2958199356913183, "acc_norm_stderr": 0.025922371788818777 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.22839506172839505, "acc_stderr": 0.023358211840626267, "acc_norm": 0.22839506172839505, "acc_norm_stderr": 0.023358211840626267 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.24113475177304963, "acc_stderr": 0.025518731049537776, "acc_norm": 0.24113475177304963, "acc_norm_stderr": 0.025518731049537776 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.23989569752281617, "acc_stderr": 0.010906282617981657, "acc_norm": 0.23989569752281617, "acc_norm_stderr": 0.010906282617981657 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4411764705882353, "acc_stderr": 0.030161911930767102, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.030161911930767102 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.27450980392156865, "acc_stderr": 0.018054027458815194, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.018054027458815194 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2909090909090909, "acc_stderr": 0.04350271442923243, "acc_norm": 0.2909090909090909, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.3877551020408163, "acc_stderr": 0.03119223072679566, "acc_norm": 0.3877551020408163, "acc_norm_stderr": 0.03119223072679566 }, "harness|hendrycksTest-sociology|5": { "acc": 0.21393034825870647, "acc_stderr": 0.02899690969332891, "acc_norm": 0.21393034825870647, "acc_norm_stderr": 0.02899690969332891 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-virology|5": { "acc": 0.27710843373493976, "acc_stderr": 0.03484331592680588, "acc_norm": 0.27710843373493976, "acc_norm_stderr": 0.03484331592680588 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.2573099415204678, "acc_stderr": 0.03352799844161865, "acc_norm": 0.2573099415204678, "acc_norm_stderr": 0.03352799844161865 }, "harness|truthfulqa:mc|0": { "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871114, "mc2": 0.3799804264451723, "mc2_stderr": 0.014771856203795355 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
Renanriozz/renanrzrz
--- license: afl-3.0 ---
offenseval2020_tr
--- annotations_creators: - found language_creators: - found language: - tr license: - cc-by-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] pretty_name: OffensEval-TR 2020 tags: - offensive-language-classification dataset_info: features: - name: id dtype: int32 - name: tweet dtype: string - name: subtask_a dtype: class_label: names: '0': NOT '1': 'OFF' config_name: offenseval2020-turkish splits: - name: train num_bytes: 4260505 num_examples: 31756 - name: test num_bytes: 481300 num_examples: 3528 download_size: 2048258 dataset_size: 4741805 --- # Dataset Card for OffensEval-TR 2020 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [offensive-turkish](https://coltekin.github.io/offensive-turkish/) - **Paper:** [A Corpus of Turkish Offensive Language on Social Media](https://coltekin.github.io/offensive-turkish/troff.pdf) - **Point of Contact:** [Çağrı Çöltekin](ccoltekin@sfs.uni-tuebingen.de) ### Dataset Summary The file offenseval-tr-training-v1.tsv contains 31,756 annotated tweets. The file offenseval-annotation.txt contains a short summary of the annotation guidelines. Twitter user mentions were substituted by @USER and URLs have been substitute by URL. Each instance contains up to 1 labels corresponding to one of the following sub-task: - Sub-task A: Offensive language identification; ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://coltekin.github.io/offensive-turkish/troff.pdf). ### Languages The dataset is based on Turkish. ## Dataset Structure ### Data Instances A binary dataset with with (NOT) Not Offensive and (OFF) Offensive tweets. ### Data Fields Instances are included in TSV format as follows: ID INSTANCE SUBA The column names in the file are the following: id tweet subtask_a The labels used in the annotation are listed below. #### Task and Labels (A) Sub-task A: Offensive language identification - (NOT) Not Offensive - This post does not contain offense or profanity. - (OFF) Offensive - This post contains offensive language or a targeted (veiled or direct) offense In our annotation, we label a post as offensive (OFF) if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct. ### Data Splits | train | test | |------:|-----:| | 31756 | 3528 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? From tweeter. ### Annotations [More Information Needed] #### Annotation process We describe the labels above in a “flat” manner. However, the annotation process we follow is hierarchical. The following QA pairs give a more flowchart-like procedure to follow 1. Is the tweet in Turkish and understandable? * No: mark tweet X for exclusion, and go to next tweet * Yes: continue to step 2 2. Is the tweet include offensive/inappropriate language? * No: mark the tweet non go to step 4 * Yes: continue to step 3 3. Is the offense in the tweet targeted? * No: mark the tweet prof go to step 4 * Yes: chose one (or more) of grp, ind, *oth based on the definitions above. Please try to limit the number of labels unless it is clear that the tweet includes offense against multiple categories. 4. Was the labeling decision difficult (precise answer needs more context, tweets includes irony, or for another reason)? * No: go to next tweet * Yes: add the label X, go to next tweet #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The annotations are distributed under the terms of [Creative Commons Attribution License (CC-BY)](https://creativecommons.org/licenses/by/2.0/). Please cite the following paper, if you use this resource. ### Citation Information ``` @inproceedings{coltekin2020lrec, author = {\c{C}\"{o}ltekin, \c{C}a\u{g}r{\i}}, year = {2020}, title = {A Corpus of Turkish Offensive Language on Social Media}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference}, pages = {6174--6184}, address = {Marseille, France}, url = {https://www.aclweb.org/anthology/2020.lrec-1.758}, } ``` ### Contributions Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset.
alpayariyak/SkunkData-Corpus
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string - name: cluster dtype: float64 splits: - name: train num_bytes: 5246084850 num_examples: 6320610 download_size: 2483374214 dataset_size: 5246084850 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "SkunkData-Corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mrqorib/MEDISCO
--- language: id license: gpl-3.0 task_categories: - automatic-speech-recognition tags: - medical dataset_info: splits: - name: train num_bytes: 2705299088 num_examples: 360 - name: test num_bytes: 479374176 num_examples: 360 download_size: 3184673264 --- # Building MEDISCO: Indonesian Speech Corpus for Medical Domain The dataset was published in the following paper: > Building MEDISCO: Indonesian Speech Corpus for Medical Domain ([PDF](https://mrqorib.github.io/assets/pdf/MEDISCO.pdf) | [IEEEXplore](https://ieeexplore.ieee.org/abstract/document/8629259)) <br> > [Muhammad Reza Qorib](https://mrqorib.github.io/) and [Mirna Adriani](https://cs.ui.ac.id/en/personnel/mirna-adriani/) <br> > 2018 International Conference on Asian Language Processing (IALP) Please look for the raw files (inside the "Files and versions" tab) as the dataset viewer parsed by Huggingface does not show the text transcript. Please direct any questions to mrqorib[at]u.nus.edu as the email addresses in the paper PDF are no longer active.
gwlms/biofid
--- license: cc-by-4.0 ---
TrainingDataPro/makeup-detection-dataset
--- language: - en license: cc-by-nc-nd-4.0 task_categories: - image-to-image - image-classification tags: - code dataset_info: features: - name: no_makeup dtype: image - name: with_makeup dtype: image - name: part dtype: string - name: gender dtype: string - name: age dtype: int8 - name: country dtype: string splits: - name: train num_bytes: 25845965 num_examples: 26 download_size: 25248180 dataset_size: 25845965 --- # Makeup Detection Dataset The dataset consists of photos featuring the same individuals captured in two distinct scenarios - *with and without makeup*. The dataset contains a diverse range of individuals with various *ages, ethnicities and genders*. The images themselves would be of high quality, ensuring clarity and detail for each subject. In photos with makeup, it is applied **to only specific parts** of the face, such as *eyes, lips, or skin*. In photos without makeup, individuals have a bare face with no visible cosmetics or beauty enhancements. These images would provide a clear contrast to the makeup images, allowing for significant visual analysis. ### The dataset's possible applications: - facial recognition - beauty consultations and personalized recommendations - augmented reality and filters in photography apps - social media and influencer marketing - dermatology and skincare ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2F19315db53167636ddd6d4e7c3a2b27c0%2FMacBook%20Air%20-%201.png?generation=1689876066594275&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/makeup-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=makeup-detection-dataset) to discuss your requirements, learn about the price and buy the dataset. # Content - **no_makeup**: includes images of people *without* makeup - **with_makeup**: includes images of people *wearing makeup*. People are the same as in the previous folder, photos are identified by the same name - **.csv** file: contains information about people in the dataset ### File with the extension .csv includes the following information for each set of media files: - **no_makeup**: link to the photo of a person without makeup, - **with_makeup**: link to the photo of the person with makeup, - **part**: body part of makeup's application, - **gender**: gender of the person, - **age**: age of the person, - **country**: country of the person # Images for makeup detection might be collected in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market/makeup-detection?utm_source=huggingface&utm_medium=cpc&utm_campaign=makeup-detection-dataset) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
miscjose/genius-music
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: title dtype: string - name: lyrics dtype: string splits: - name: train num_bytes: 72517163.34856215 num_examples: 27596 - name: test num_bytes: 9065959.325718924 num_examples: 3450 - name: validation num_bytes: 9065959.325718924 num_examples: 3450 download_size: 50430343 dataset_size: 90649082 task_categories: - text-classification language: - en tags: - music - song lyrics size_categories: - 10K<n<100K --- # Dataset Card for "genius" ### Dataset Summary List of Preprocessed Song Lyrics and Song Titles from [Genius](https://genius.com/) Source data found [here](https://www.cs.cornell.edu/~arb/data/genius-expertise/).
YBXL/PMC_CaseReport_Reasoning_test_sub
--- license: mit dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string splits: - name: train num_bytes: 5308861 num_examples: 500 - name: valid num_bytes: 5308861 num_examples: 500 - name: test num_bytes: 5308861 num_examples: 500 download_size: 7255380 dataset_size: 15926583 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
lamini/text_to_sql_finetune
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 34818227 num_examples: 16428 - name: test num_bytes: 1050788 num_examples: 1034 download_size: 3691335 dataset_size: 35869015 --- # Dataset Card for "text_to_sql_finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_CultriX__CombinaTrix-7B
--- pretty_name: Evaluation run of CultriX/CombinaTrix-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CultriX/CombinaTrix-7B](https://huggingface.co/CultriX/CombinaTrix-7B) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 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 (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CultriX__CombinaTrix-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-26T04:05:35.594015](https://huggingface.co/datasets/open-llm-leaderboard/details_CultriX__CombinaTrix-7B/blob/main/results_2024-01-26T04-05-35.594015.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\": 0.6545559165941275,\n\ \ \"acc_stderr\": 0.031943134755916015,\n \"acc_norm\": 0.6538633458121492,\n\ \ \"acc_norm_stderr\": 0.03261032541182886,\n \"mc1\": 0.576499388004896,\n\ \ \"mc1_stderr\": 0.017297421448534748,\n \"mc2\": 0.706271087965262,\n\ \ \"mc2_stderr\": 0.014887346338811254\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7030716723549488,\n \"acc_stderr\": 0.013352025976725223,\n\ \ \"acc_norm\": 0.7286689419795221,\n \"acc_norm_stderr\": 0.012993807727545803\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7153953395737901,\n\ \ \"acc_stderr\": 0.004503037601847085,\n \"acc_norm\": 0.8839872535351524,\n\ \ \"acc_norm_stderr\": 0.0031958572477049163\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n\ \ \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700914,\n\ \ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700914\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086923992,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086923992\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\ \ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\ \ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n\ \ \"acc_stderr\": 0.023287665127268542,\n \"acc_norm\": 0.7870967741935484,\n\ \ \"acc_norm_stderr\": 0.023287665127268542\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\ \ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113115,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113115\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\ acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.034076320938540516,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.034076320938540516\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41899441340782123,\n\ \ \"acc_stderr\": 0.016501579306861677,\n \"acc_norm\": 0.41899441340782123,\n\ \ \"acc_norm_stderr\": 0.016501579306861677\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n\ \ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.025670259242188933,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.025670259242188933\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.02447722285613511,\n\ \ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.02447722285613511\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n\ \ \"acc_stderr\": 0.012752858346533126,\n \"acc_norm\": 0.47392438070404175,\n\ \ \"acc_norm_stderr\": 0.012752858346533126\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \ \ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\ \ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685516,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685516\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.576499388004896,\n\ \ \"mc1_stderr\": 0.017297421448534748,\n \"mc2\": 0.706271087965262,\n\ \ \"mc2_stderr\": 0.014887346338811254\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8413575374901342,\n \"acc_stderr\": 0.010267936243028214\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7028051554207733,\n \ \ \"acc_stderr\": 0.012588685966624179\n }\n}\n```" repo_url: https://huggingface.co/CultriX/CombinaTrix-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|arc:challenge|25_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-26T04-05-35.594015.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|gsm8k|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hellaswag|10_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-26T04-05-35.594015.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-management|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T04-05-35.594015.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|truthfulqa:mc|0_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-26T04-05-35.594015.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_26T04_05_35.594015 path: - '**/details_harness|winogrande|5_2024-01-26T04-05-35.594015.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-26T04-05-35.594015.parquet' - config_name: results data_files: - split: 2024_01_26T04_05_35.594015 path: - results_2024-01-26T04-05-35.594015.parquet - split: latest path: - results_2024-01-26T04-05-35.594015.parquet --- # Dataset Card for Evaluation run of CultriX/CombinaTrix-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [CultriX/CombinaTrix-7B](https://huggingface.co/CultriX/CombinaTrix-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CultriX__CombinaTrix-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-26T04:05:35.594015](https://huggingface.co/datasets/open-llm-leaderboard/details_CultriX__CombinaTrix-7B/blob/main/results_2024-01-26T04-05-35.594015.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": 0.6545559165941275, "acc_stderr": 0.031943134755916015, "acc_norm": 0.6538633458121492, "acc_norm_stderr": 0.03261032541182886, "mc1": 0.576499388004896, "mc1_stderr": 0.017297421448534748, "mc2": 0.706271087965262, "mc2_stderr": 0.014887346338811254 }, "harness|arc:challenge|25": { "acc": 0.7030716723549488, "acc_stderr": 0.013352025976725223, "acc_norm": 0.7286689419795221, "acc_norm_stderr": 0.012993807727545803 }, "harness|hellaswag|10": { "acc": 0.7153953395737901, "acc_stderr": 0.004503037601847085, "acc_norm": 0.8839872535351524, "acc_norm_stderr": 0.0031958572477049163 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7171052631578947, "acc_stderr": 0.03665349695640767, "acc_norm": 0.7171052631578947, "acc_norm_stderr": 0.03665349695640767 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700914, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700914 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086923992, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086923992 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.023287665127268542, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.023287665127268542 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267045, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267045 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.02889774874113115, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.02889774874113115 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8422018348623853, "acc_stderr": 0.01563002297009244, "acc_norm": 0.8422018348623853, "acc_norm_stderr": 0.01563002297009244 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5185185185185185, "acc_stderr": 0.034076320938540516, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.034076320938540516 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990946, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990946 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.41899441340782123, "acc_stderr": 0.016501579306861677, "acc_norm": 0.41899441340782123, "acc_norm_stderr": 0.016501579306861677 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7124183006535948, "acc_stderr": 0.02591780611714716, "acc_norm": 0.7124183006535948, "acc_norm_stderr": 0.02591780611714716 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188933, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188933 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7376543209876543, "acc_stderr": 0.02447722285613511, "acc_norm": 0.7376543209876543, "acc_norm_stderr": 0.02447722285613511 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47392438070404175, "acc_stderr": 0.012752858346533126, "acc_norm": 0.47392438070404175, "acc_norm_stderr": 0.012752858346533126 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6830065359477124, "acc_stderr": 0.018824219512706207, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.018824219512706207 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685516, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685516 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.576499388004896, "mc1_stderr": 0.017297421448534748, "mc2": 0.706271087965262, "mc2_stderr": 0.014887346338811254 }, "harness|winogrande|5": { "acc": 0.8413575374901342, "acc_stderr": 0.010267936243028214 }, "harness|gsm8k|5": { "acc": 0.7028051554207733, "acc_stderr": 0.012588685966624179 } } ``` ## 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 intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). 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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]
Antreas/TALI
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: image_url dtype: string - name: item_idx dtype: int64 - name: wit_features struct: - name: attribution_passes_lang_id sequence: bool - name: caption_alt_text_description sequence: string - name: caption_reference_description sequence: string - name: caption_title_and_reference_description sequence: string - name: context_page_description sequence: string - name: context_section_description sequence: string - name: hierarchical_section_title sequence: string - name: is_main_image sequence: bool - name: language sequence: string - name: page_changed_recently sequence: bool - name: page_title sequence: string - name: page_url sequence: string - name: section_title sequence: string - name: wit_idx dtype: int64 - name: youtube_title_text dtype: string - name: youtube_description_text dtype: string - name: youtube_video_content dtype: binary - name: youtube_video_starting_time dtype: string - name: youtube_subtitle_text dtype: string - name: youtube_video_size dtype: int64 - name: youtube_video_file_path dtype: string splits: - name: train num_bytes: 1902638101655.625 num_examples: 1052915 - name: val num_bytes: 104485442867.25 num_examples: 57958 - name: test num_bytes: 111107332347.375 num_examples: 61389 download_size: 2058391040534 dataset_size: 2118230876870.25 license: cc-by-4.0 task_categories: - zero-shot-classification tags: - video - audio - text - image - tetramodal - multimodal - youtube - wikipedia pretty_name: TALI size_categories: - 1M<n<10M --- # Dataset Card for "TALI" ## Table of Contents 1. Dataset Description 1. Abstract 2. Brief Description 2. Dataset Information 1. Modalities 2. Dataset Variants 3. Dataset Statistics 4. Data Fields 5. Data Splits 3. Dataset Creation 4. Dataset Use 5. Additional Information ## Dataset Description ### Abstract TALI is a large-scale, tetramodal dataset designed to facilitate a shift from unimodal and duomodal to tetramodal research in deep learning. It aligns text, video, images, and audio, providing a rich resource for innovative self-supervised learning tasks and multimodal research. TALI enables exploration of how different modalities and data/model scaling affect downstream performance, with the aim of inspiring diverse research ideas and enhancing understanding of model capabilities and robustness in deep learning. ### Brief Description TALI (Temporally and semantically Aligned Audio, Language and Images) is a dataset that uses the Wikipedia Image Text (WIT) captions and article titles to search Youtube for videos that match the captions. It then downloads the video, audio, and subtitles from these videos. The result is a rich multimodal dataset that has multiple caption types related to both the WiT Images, and the Youtube videos. This enables learning to take place between either temporally or semantically aligned text, images, audio and video. ## Dataset Information ### Modalities The TALI dataset consists of the following modalities: 1. Image: 1. Wikipedia caption image 2. Randomly sampled image from youtube video 2. Text 1. Wikipedia Caption Text 2. Wikipedia Title Text 3. Wikipedia Main Body Text 4. YouTube Subtitle Text 5. YouTube Description Text 6. YouTube Title Text 3. Audio 1. YouTube Content Audio 4. Video 1. YouTube Content Video ## Usage: To get started with TALI, you can load the dataset via Hugging Face's `datasets` library through our helper functions. The reason we don't use `datasets` directly is because we found huggingface_hub downloads much faster and reliable. For a full set of possible configurations look at [examples.py](examples.py). Here's a basic usage example: First install the tali package: ### Installation For the default install use: ```bash pip install git+https://github.com/AntreasAntoniou/TALI ``` For the dev install use: ```bash pip install git+https://github.com/AntreasAntoniou/TALI[dev] ``` Then use the dataset using: ### Examples Import relevant helper functions ```python import pathlib from enum import Enum import torch from tqdm.auto import tqdm from tali.data import ( SubModalityTypes, TALIBaseTransform, TALIBaseTransformConfig, VideoFramesFormat, default_transforms, load_dataset_via_hub, ) ``` #### TALI with default transforms (CLIP and Whisper) and no streaming ```python def tali_with_transforms_no_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI" )["train"] ( image_transforms, text_transforms, audio_transforms, video_transforms, ) = default_transforms() preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=text_transforms, image_tokenizer=image_transforms, audio_tokenizer=audio_transforms, video_tokenizer=video_transforms, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with no transforms and no streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats ```python def tali_without_transforms_no_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI" )["train"] preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=None, image_tokenizer=None, audio_tokenizer=None, video_tokenizer=None, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with default transforms and streaming ```python def tali_with_transforms_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI", streaming=True )["train"] ( image_transforms, text_transforms, audio_transforms, video_transforms, ) = default_transforms() preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=text_transforms, image_tokenizer=image_transforms, audio_tokenizer=audio_transforms, video_tokenizer=video_transforms, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with no transforms and streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats ```python def tali_without_transforms_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI", streaming=True )["train"] preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=None, image_tokenizer=None, audio_tokenizer=None, video_tokenizer=None, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` ### Dataset Statistics TBA ## Dataset Creation The TALI dataset was created by starting from the WiT dataset and using either the context_page_description or page_title as a source-query to search YouTube for video that were creative commons opted-in, and, not age restricted. The top 100 result titles were returned and compared with the source-query using the CLIP text embeddings of the largest CLIP model available. The top-1 title’s video based on the CLIP ranking was chosen and downloaded. The video was broken into 30-second segments and the top-10 segments for eachvideo were chosen based on the distance between the CLIP image embedding of the first image of each segment and the video’s title text. The image, audio, and subtitle frames were extracted from these segments. At sampling time, one of these 10 segments is randomly selected, and a 10-second segment is chosen out of the 30-second clip. The result is 200 video frames (spread throughout the 10-second segment), and 160000 audio frames (10 seconds). ## Dataset Use TALI is designed for use in a wide range of multimodal research tasks, including but not limited to: - Multimodal understanding and reasoning - Self-supervised learning - Multimodal alignment and translation - Multimodal summarization - Multimodal question answering ## Dataset Curators: Antreas Antoniou Citation Information: TBA Contributions: Thanks to all contributors including data curators, annotators, and software developers. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Edge-Pyxos/CRaQAn_v1
--- language: - en license: cc-by-4.0 size_categories: - n<1K task_categories: - question-answering pretty_name: craqan_v1 tags: - legal dataset_info: features: - name: title dtype: string - name: article dtype: string - name: article_titles sequence: string - name: article_sections sequence: string - name: section dtype: string - name: section_index dtype: int64 - name: section_sentences dtype: string - name: question dtype: string - name: answer dtype: string - name: sentences_required sequence: int64 - name: url dtype: string - name: time_downloaded dtype: string splits: - name: train num_bytes: 17788270 num_examples: 263 download_size: 0 dataset_size: 17788270 configs: - config_name: default data_files: - split: train path: data/train-* --- # Coreference Resolution in Question Answering (CRaQAn) 250+ question-answer pairs that require coreference resolution across sentences from selected Wikipedia passages. ## Generation Process Given the relative complexity of our task (coreference resolution across passages for question-answering), we aimed to avoid crowd-sourcing this dataset and instead focused on using LLMs to automate our process. Every question-answer pair in the CRaQAn dataset was automatically generated using a Recursive Criticism and Improvement (RCI) loop. To accomplish our RCI loop, we wrote a GENERATOR prompt and several REVIEWER prompts, which can be found [here](https://huggingface.co/datasets/Edge-Pyxos/CRaQAn_v1/tree/main/generation_demo/prompts). ## Review Process Every question-answer pair in the CRaQAn v1 dataset was reviewed by at least two human reviewers. We intend for this to be a high-trust and high-quality dataset that can be used for various applications. Every human reviewer was given the following criteria. For each QA pair: 1. The question is clear and not ambiguous with regards to the text. 2. The question is a single question, and not two separate or related questions joined by the word "and". 3. The question does not contain or assume any information outside of the required sentences. 4. The answer is correct and reasonably terse. 5. The question-answer pair must not rely on any information from outside the required sentences. 6. The question-answer pair relies on information from each of the required sentences. 7. The number of required sentences is 2 or 3. 8. The Markdown is correctly formatted. ## CRaQAn Usage ```python from datasets import load_dataset import pandas as pd from IPython.display import display, Markdown # Load dataset. craqan = load_dataset("Edge-Pyxos/CRaQAn_v1", split = "train") df = pd.DataFrame(craqan) # Fix issue with section_sentences that happens during Huggingface conversion. df["section_sentences"] = df["section_sentences"].apply(json.loads) # Visualize a sample from the dataset. row = df.sample(1).squeeze() sentences = "" for idx, s in enumerate(row.section_sentences): sentences += (" <mark> " + s["sentence"] + " </mark> ") if idx in row.sentences_required else " " + s["sentence"] display(Markdown(f"# Article: {row.title}")) display(Markdown(row.article_titles[row.section_index])) display(Markdown(f"*Required Sentences: {row.sentences_required}*")) display(Markdown(sentences)) display(Markdown(f"**Question**: " + row.question)) display(Markdown("**Answer**: " + row.answer)) display(Markdown("-------------------")) ``` ## Demo Usage We provide all prompts, code, and processes used to generate the CRaQAn-v1 dataset in our [demo notebook](https://huggingface.co/datasets/Edge-Pyxos/CRaQAn_v1/blob/main/generation_demo/create_dataset.ipynb).
result-kand2-sdxl-wuerst-karlo/80bca589
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 242 num_examples: 10 download_size: 1409 dataset_size: 242 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "80bca589" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Jeovane/minhavozmr
--- license: openrail ---
open-llm-leaderboard/details_huggingtweets__jerma985
--- pretty_name: Evaluation run of huggingtweets/jerma985 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [huggingtweets/jerma985](https://huggingface.co/huggingtweets/jerma985) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 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 (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_huggingtweets__jerma985\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-22T15:13:39.388412](https://huggingface.co/datasets/open-llm-leaderboard/details_huggingtweets__jerma985/blob/main/results_2023-09-22T15-13-39.388412.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 \"em\": 0.014786073825503355,\n\ \ \"em_stderr\": 0.0012360366760473087,\n \"f1\": 0.0371633808724832,\n\ \ \"f1_stderr\": 0.001611424008567761,\n \"acc\": 0.2533543804262036,\n\ \ \"acc_stderr\": 0.0070256103461651745\n },\n \"harness|drop|3\":\ \ {\n \"em\": 0.014786073825503355,\n \"em_stderr\": 0.0012360366760473087,\n\ \ \"f1\": 0.0371633808724832,\n \"f1_stderr\": 0.001611424008567761\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5067087608524072,\n\ \ \"acc_stderr\": 0.014051220692330349\n }\n}\n```" repo_url: https://huggingface.co/huggingtweets/jerma985 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|arc:challenge|25_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T10:38:23.212427.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_22T15_13_39.388412 path: - '**/details_harness|drop|3_2023-09-22T15-13-39.388412.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-22T15-13-39.388412.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_22T15_13_39.388412 path: - '**/details_harness|gsm8k|5_2023-09-22T15-13-39.388412.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-22T15-13-39.388412.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hellaswag|10_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:38:23.212427.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:38:23.212427.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T10_38_23.212427 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T10:38:23.212427.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T10:38:23.212427.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_22T15_13_39.388412 path: - '**/details_harness|winogrande|5_2023-09-22T15-13-39.388412.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-22T15-13-39.388412.parquet' - config_name: results data_files: - split: 2023_07_19T10_38_23.212427 path: - results_2023-07-19T10:38:23.212427.parquet - split: 2023_09_22T15_13_39.388412 path: - results_2023-09-22T15-13-39.388412.parquet - split: latest path: - results_2023-09-22T15-13-39.388412.parquet --- # Dataset Card for Evaluation run of huggingtweets/jerma985 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/huggingtweets/jerma985 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [huggingtweets/jerma985](https://huggingface.co/huggingtweets/jerma985) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 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 (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_huggingtweets__jerma985", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-22T15:13:39.388412](https://huggingface.co/datasets/open-llm-leaderboard/details_huggingtweets__jerma985/blob/main/results_2023-09-22T15-13-39.388412.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": { "em": 0.014786073825503355, "em_stderr": 0.0012360366760473087, "f1": 0.0371633808724832, "f1_stderr": 0.001611424008567761, "acc": 0.2533543804262036, "acc_stderr": 0.0070256103461651745 }, "harness|drop|3": { "em": 0.014786073825503355, "em_stderr": 0.0012360366760473087, "f1": 0.0371633808724832, "f1_stderr": 0.001611424008567761 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.5067087608524072, "acc_stderr": 0.014051220692330349 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
Vinnyyw/Dulcesolos
--- license: openrail ---
AppleHarem/akane_bluearchive
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of akane (Blue Archive) This is the dataset of akane (Blue Archive), containing 498 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)) | Name | Images | Download | Description | |:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------| | raw | 498 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 1352 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 1567 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 498 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 498 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 498 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 1352 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 1352 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 1256 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 1567 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 1567 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
Abhi5ingh/vitonclip
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1313726702.738 num_examples: 11647 download_size: 1255546203 dataset_size: 1313726702.738 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vitonclip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
crumb/gpt4all-clean
--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 608770781 num_examples: 374269 download_size: 0 dataset_size: 608770781 license: mit task_categories: - conversational language: - en --- # Dataset Card for "GPT4All-Clean" The GPT4All-Clean dataset is a modified version of the original GPT4All dataset. It contains 374,269 examples, which are mostly converted to markdown format to improve consistency and compatibility with other datasets that use markdown formatting. The dataset is smaller than the original dataset, which has 437,604 examples, due to the removal of certain content. Specifically, all examples containing the phrase "As an AI language model" have been removed, as well as examples containing the string "html" to minimize potential confusion between real and non-real HTML code for the parser used to clean the examples. The intention behind these modifications is to enhance the dataset's overall quality, making it more suitable for use in research and applications.
Hack90/virus_dna_dedup_minihash_0.9_kmer_7
--- dataset_info: features: - name: sequence_x dtype: string - name: similarity_filter dtype: float64 - name: id dtype: string - name: sequence_y dtype: string - name: name dtype: string - name: description dtype: string - name: features dtype: int64 - name: seq_length dtype: int64 - name: missing_seq_count dtype: int64 - name: missingness dtype: float64 - name: seq_filled dtype: string - name: __index_level_0__ dtype: int64 - name: spaced_sequence dtype: string splits: - name: train num_bytes: 522191271 num_examples: 10885 download_size: 234031394 dataset_size: 522191271 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "virus_dna_dedup_minihash_0.9_kmer_7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zhuchi76/wine_review
--- dataset_info: features: - name: wine_id dtype: int64 - name: country dtype: string - name: description dtype: string - name: designation dtype: string - name: points dtype: int64 - name: price dtype: float64 splits: - name: train num_bytes: 21093175.17523332 num_examples: 68918 - name: test num_bytes: 5273446.824766681 num_examples: 17230 download_size: 15005883 dataset_size: 26366622.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
fivewords/test
--- license: apache-2.0 language: - zh --- hello world
malucoelhaofc/LauroV2
--- license: openrail ---
allenai/fos_model_training_data_open_ai_annotations
--- extra_gated_prompt: "**AI2 ImpACT License – Low Risk Artifacts (LR Agreement)** [https://allenai.org/impact-license](https://allenai.org/impact-license)" extra_gated_fields: Name: text Organization/Entity: text Email: text State/Country: text "Intended Use": text "I AGREE to the terms and conditions of the LR Agreement above": checkbox "I AGREE to AI2’s use of my information for legal notices and administrative matters": checkbox "I CERTIFY that the information I have provided is true and accurate": checkbox ---
FanChen0116/bus_few4_05x_pvi
--- dataset_info: features: - name: id dtype: int64 - name: tokens sequence: string - name: labels sequence: class_label: names: '0': O '1': I-from_location '2': B-from_location '3': B-leaving_date '4': I-leaving_date '5': I-to_location '6': B-to_location - name: request_slot sequence: string splits: - name: train num_bytes: 6172 num_examples: 35 - name: validation num_bytes: 6900 num_examples: 35 - name: test num_bytes: 70618 num_examples: 377 download_size: 0 dataset_size: 83690 --- # Dataset Card for "bus_few4_05x_pvi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Asap7772/skewexp_maxlength
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: output dtype: string - name: text dtype: string - name: alpaca_text dtype: string - name: prompt dtype: string - name: alpaca_prompt dtype: string - name: y_ref dtype: string - name: y_1 dtype: string - name: y_2 dtype: string - name: y_w dtype: string - name: y_w_alpaca dtype: string - name: y_l dtype: string - name: y_l_alpaca dtype: string - name: y_w_score dtype: float64 - name: y_l_score dtype: float64 - name: score_diff dtype: float64 splits: - name: train num_bytes: 62156813 num_examples: 19000 - name: test num_bytes: 3233542 num_examples: 1000 download_size: 31145494 dataset_size: 65390355 --- # Dataset Card for "skewexp_maxlength" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/story_8_prompts
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 3687 num_examples: 10 download_size: 5182 dataset_size: 3687 --- # Dataset Card for "story_8_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_codellama__CodeLlama-13b-Instruct-hf
--- pretty_name: Evaluation run of codellama/CodeLlama-13b-Instruct-hf dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 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 (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_codellama__CodeLlama-13b-Instruct-hf\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-16T02:27:47.858383](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-13b-Instruct-hf/blob/main/results_2023-10-16T02-27-47.858383.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 \"em\": 0.0009437919463087249,\n\ \ \"em_stderr\": 0.0003144653119413506,\n \"f1\": 0.05136010906040279,\n\ \ \"f1_stderr\": 0.001238131643997091,\n \"acc\": 0.4034791730120101,\n\ \ \"acc_stderr\": 0.011133121900373116\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0009437919463087249,\n \"em_stderr\": 0.0003144653119413506,\n\ \ \"f1\": 0.05136010906040279,\n \"f1_stderr\": 0.001238131643997091\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12661106899166036,\n \ \ \"acc_stderr\": 0.009159715283081094\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6803472770323599,\n \"acc_stderr\": 0.013106528517665136\n\ \ }\n}\n```" repo_url: https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|arc:challenge|25_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-25T17:15:30.693025.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_16T02_27_47.858383 path: - '**/details_harness|drop|3_2023-10-16T02-27-47.858383.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-16T02-27-47.858383.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_16T02_27_47.858383 path: - '**/details_harness|gsm8k|5_2023-10-16T02-27-47.858383.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-16T02-27-47.858383.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hellaswag|10_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-25T17:15:30.693025.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-management|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-25T17:15:30.693025.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_25T17_15_30.693025 path: - '**/details_harness|truthfulqa:mc|0_2023-08-25T17:15:30.693025.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-25T17:15:30.693025.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_16T02_27_47.858383 path: - '**/details_harness|winogrande|5_2023-10-16T02-27-47.858383.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-16T02-27-47.858383.parquet' - config_name: results data_files: - split: 2023_08_25T17_15_30.693025 path: - results_2023-08-25T17:15:30.693025.parquet - split: 2023_10_16T02_27_47.858383 path: - results_2023-10-16T02-27-47.858383.parquet - split: latest path: - results_2023-10-16T02-27-47.858383.parquet --- # Dataset Card for Evaluation run of codellama/CodeLlama-13b-Instruct-hf ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 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 (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_codellama__CodeLlama-13b-Instruct-hf", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-16T02:27:47.858383](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-13b-Instruct-hf/blob/main/results_2023-10-16T02-27-47.858383.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": { "em": 0.0009437919463087249, "em_stderr": 0.0003144653119413506, "f1": 0.05136010906040279, "f1_stderr": 0.001238131643997091, "acc": 0.4034791730120101, "acc_stderr": 0.011133121900373116 }, "harness|drop|3": { "em": 0.0009437919463087249, "em_stderr": 0.0003144653119413506, "f1": 0.05136010906040279, "f1_stderr": 0.001238131643997091 }, "harness|gsm8k|5": { "acc": 0.12661106899166036, "acc_stderr": 0.009159715283081094 }, "harness|winogrande|5": { "acc": 0.6803472770323599, "acc_stderr": 0.013106528517665136 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
open-llm-leaderboard/details_HanNayeoniee__LHK
--- pretty_name: Evaluation run of HanNayeoniee/LHK dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [HanNayeoniee/LHK](https://huggingface.co/HanNayeoniee/LHK) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 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 (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HanNayeoniee__LHK\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-19T08:36:46.255504](https://huggingface.co/datasets/open-llm-leaderboard/details_HanNayeoniee__LHK/blob/main/results_2024-01-19T08-36-46.255504.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\": 0.6522091397950804,\n\ \ \"acc_stderr\": 0.031753247805341805,\n \"acc_norm\": 0.6548324288435591,\n\ \ \"acc_norm_stderr\": 0.032386658093364704,\n \"mc1\": 0.4186046511627907,\n\ \ \"mc1_stderr\": 0.017270015284476848,\n \"mc2\": 0.591200906179408,\n\ \ \"mc2_stderr\": 0.01538726882622229\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6279863481228669,\n \"acc_stderr\": 0.014124597881844461,\n\ \ \"acc_norm\": 0.6638225255972696,\n \"acc_norm_stderr\": 0.013804855026205766\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.657239593706433,\n\ \ \"acc_stderr\": 0.004736621698861176,\n \"acc_norm\": 0.844851623182633,\n\ \ \"acc_norm_stderr\": 0.0036130615166899823\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.04244633238353227\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361073,\n\ \ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361073\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.71,\n\ \ \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n \ \ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337124,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337124\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n\ \ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419035,\n\ \ \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419035\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.43915343915343913,\n \"acc_stderr\": 0.025559920550531003,\n \"\ acc_norm\": 0.43915343915343913,\n \"acc_norm_stderr\": 0.025559920550531003\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\ \ \"acc_stderr\": 0.023540799358723292,\n \"acc_norm\": 0.7806451612903226,\n\ \ \"acc_norm_stderr\": 0.023540799358723292\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.45320197044334976,\n \"acc_stderr\": 0.03502544650845872,\n\ \ \"acc_norm\": 0.45320197044334976,\n \"acc_norm_stderr\": 0.03502544650845872\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.030117688929503575,\n\ \ \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.030117688929503575\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8484848484848485,\n \"acc_stderr\": 0.025545650426603627,\n \"\ acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.025545650426603627\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602354,\n\ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602354\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114986,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114986\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634342,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634342\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5648148148148148,\n \"acc_stderr\": 0.033812000056435254,\n \"\ acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.033812000056435254\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8284313725490197,\n \"acc_stderr\": 0.02646056956124064,\n \"\ acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.02646056956124064\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8312236286919831,\n \"acc_stderr\": 0.024381406832586234,\n \ \ \"acc_norm\": 0.8312236286919831,\n \"acc_norm_stderr\": 0.024381406832586234\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.01987565502786747,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.01987565502786747\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n\ \ \"acc_stderr\": 0.013586619219903341,\n \"acc_norm\": 0.8250319284802043,\n\ \ \"acc_norm_stderr\": 0.013586619219903341\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508283,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508283\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2905027932960894,\n\ \ \"acc_stderr\": 0.01518384430720616,\n \"acc_norm\": 0.2905027932960894,\n\ \ \"acc_norm_stderr\": 0.01518384430720616\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.024288619466046102,\n\ \ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.024288619466046102\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7746913580246914,\n \"acc_stderr\": 0.023246202647819746,\n\ \ \"acc_norm\": 0.7746913580246914,\n \"acc_norm_stderr\": 0.023246202647819746\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4784876140808344,\n\ \ \"acc_stderr\": 0.01275841094103892,\n \"acc_norm\": 0.4784876140808344,\n\ \ \"acc_norm_stderr\": 0.01275841094103892\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7242647058823529,\n \"acc_stderr\": 0.027146271936625166,\n\ \ \"acc_norm\": 0.7242647058823529,\n \"acc_norm_stderr\": 0.027146271936625166\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6715686274509803,\n \"acc_stderr\": 0.01899970738316267,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.01899970738316267\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7510204081632653,\n \"acc_stderr\": 0.027682979522960238,\n\ \ \"acc_norm\": 0.7510204081632653,\n \"acc_norm_stderr\": 0.027682979522960238\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4186046511627907,\n\ \ \"mc1_stderr\": 0.017270015284476848,\n \"mc2\": 0.591200906179408,\n\ \ \"mc2_stderr\": 0.01538726882622229\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8097868981846882,\n \"acc_stderr\": 0.01103033579861744\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5633055344958302,\n \ \ \"acc_stderr\": 0.01366164978090549\n }\n}\n```" repo_url: https://huggingface.co/HanNayeoniee/LHK leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|arc:challenge|25_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-19T08-36-46.255504.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|gsm8k|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hellaswag|10_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-19T08-36-46.255504.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-management|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-19T08-36-46.255504.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|truthfulqa:mc|0_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-19T08-36-46.255504.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_19T08_36_46.255504 path: - '**/details_harness|winogrande|5_2024-01-19T08-36-46.255504.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-19T08-36-46.255504.parquet' - config_name: results data_files: - split: 2024_01_19T08_36_46.255504 path: - results_2024-01-19T08-36-46.255504.parquet - split: latest path: - results_2024-01-19T08-36-46.255504.parquet --- # Dataset Card for Evaluation run of HanNayeoniee/LHK <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [HanNayeoniee/LHK](https://huggingface.co/HanNayeoniee/LHK) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_HanNayeoniee__LHK", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-19T08:36:46.255504](https://huggingface.co/datasets/open-llm-leaderboard/details_HanNayeoniee__LHK/blob/main/results_2024-01-19T08-36-46.255504.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": 0.6522091397950804, "acc_stderr": 0.031753247805341805, "acc_norm": 0.6548324288435591, "acc_norm_stderr": 0.032386658093364704, "mc1": 0.4186046511627907, "mc1_stderr": 0.017270015284476848, "mc2": 0.591200906179408, "mc2_stderr": 0.01538726882622229 }, "harness|arc:challenge|25": { "acc": 0.6279863481228669, "acc_stderr": 0.014124597881844461, "acc_norm": 0.6638225255972696, "acc_norm_stderr": 0.013804855026205766 }, "harness|hellaswag|10": { "acc": 0.657239593706433, "acc_stderr": 0.004736621698861176, "acc_norm": 0.844851623182633, "acc_norm_stderr": 0.0036130615166899823 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.04244633238353227, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.04244633238353227 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7368421052631579, "acc_stderr": 0.03583496176361073, "acc_norm": 0.7368421052631579, "acc_norm_stderr": 0.03583496176361073 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.027943219989337124, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.027943219989337124 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816507, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816507 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419035, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419035 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.43915343915343913, "acc_stderr": 0.025559920550531003, "acc_norm": 0.43915343915343913, "acc_norm_stderr": 0.025559920550531003 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723292, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723292 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.45320197044334976, "acc_stderr": 0.03502544650845872, "acc_norm": 0.45320197044334976, "acc_norm_stderr": 0.03502544650845872 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8181818181818182, "acc_stderr": 0.030117688929503575, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.030117688929503575 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8484848484848485, "acc_stderr": 0.025545650426603627, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.025545650426603627 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6410256410256411, "acc_stderr": 0.024321738484602354, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.024321738484602354 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3037037037037037, "acc_stderr": 0.028037929969114986, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.028037929969114986 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7142857142857143, "acc_stderr": 0.029344572500634342, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.029344572500634342 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8403669724770643, "acc_stderr": 0.015703498348461763, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.015703498348461763 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5648148148148148, "acc_stderr": 0.033812000056435254, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.033812000056435254 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8284313725490197, "acc_stderr": 0.02646056956124064, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.02646056956124064 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8312236286919831, "acc_stderr": 0.024381406832586234, "acc_norm": 0.8312236286919831, "acc_norm_stderr": 0.024381406832586234 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7484662576687117, "acc_stderr": 0.034089978868575295, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.034089978868575295 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822584, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822584 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8974358974358975, "acc_stderr": 0.01987565502786747, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.01987565502786747 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8250319284802043, "acc_stderr": 0.013586619219903341, "acc_norm": 0.8250319284802043, "acc_norm_stderr": 0.013586619219903341 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508283, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508283 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2905027932960894, "acc_stderr": 0.01518384430720616, "acc_norm": 0.2905027932960894, "acc_norm_stderr": 0.01518384430720616 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7647058823529411, "acc_stderr": 0.024288619466046102, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.024288619466046102 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7746913580246914, "acc_stderr": 0.023246202647819746, "acc_norm": 0.7746913580246914, "acc_norm_stderr": 0.023246202647819746 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5070921985815603, "acc_stderr": 0.02982449855912901, "acc_norm": 0.5070921985815603, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4784876140808344, "acc_stderr": 0.01275841094103892, "acc_norm": 0.4784876140808344, "acc_norm_stderr": 0.01275841094103892 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7242647058823529, "acc_stderr": 0.027146271936625166, "acc_norm": 0.7242647058823529, "acc_norm_stderr": 0.027146271936625166 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6715686274509803, "acc_stderr": 0.01899970738316267, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.01899970738316267 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7510204081632653, "acc_stderr": 0.027682979522960238, "acc_norm": 0.7510204081632653, "acc_norm_stderr": 0.027682979522960238 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197768, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197768 }, "harness|hendrycksTest-virology|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7894736842105263, "acc_stderr": 0.03126781714663179, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.03126781714663179 }, "harness|truthfulqa:mc|0": { "mc1": 0.4186046511627907, "mc1_stderr": 0.017270015284476848, "mc2": 0.591200906179408, "mc2_stderr": 0.01538726882622229 }, "harness|winogrande|5": { "acc": 0.8097868981846882, "acc_stderr": 0.01103033579861744 }, "harness|gsm8k|5": { "acc": 0.5633055344958302, "acc_stderr": 0.01366164978090549 } } ``` ## 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 intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
dodosh/CodeSearchNet
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: code dtype: string - name: docstring dtype: string splits: - name: train num_bytes: 417539337 num_examples: 457461 download_size: 193602075 dataset_size: 417539337 --- # Dataset Card for "CodeSearchNet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
grosenthal/lat_en_loeb_whitaker_split
--- dataset_info: features: - name: id dtype: int64 - name: la dtype: string - name: en dtype: string - name: file dtype: string splits: - name: train num_bytes: 30517119.261391733 num_examples: 77774 download_size: 18966593 dataset_size: 30517119.261391733 --- # Dataset Card for "lat_en_loeb_whitaker_split" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lawinstruct/lawinstruct
--- annotations_creators: - other language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv - zh - ja - ko license: - mit multilinguality: - multilingual paperswithcode_id: null pretty_name: "LawInstruct: A Diverse Multilingual Dataset for Legal Instruction Tuning" size_categories: - 10M<n<100M source_datasets: - original task_categories: - fill-mask --- # Dataset Card for LawInstruct: A Diverse Multilingual Dataset for Legal Instruction Tuning ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [GitHub](https://github.com/JoelNiklaus/LawInstruct) - **Paper:** [ArXiv](https://arxiv.org/abs/2404.02127) - **Leaderboard:** - **Point of Contact:** [Joel Niklaus](mailto:joel@niklaus.ai) ### Dataset Summary LawInstruct is a diverse multilingual dataset for legal instruction tuning. ### Supported Tasks and Leaderboards The dataset supports the tasks of text-generation. ### Languages The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv, zh, ja, ko ## Dataset Structure It is structured in the following format: {name}_train.{shard}.jsonl.xz LawInstruct has the following data fields: - `dataset_name`: The name of the dataset - `subset_name`: The name of the sub dataset if applicable - `source`: The url of the source - `prompt_language`: The language of the prompt - `answer_language`: The language of the answer - `jurisdiction`: The jurisdiction of the dataset - `task_type`: The task type of the dataset - `downloaded_timestamp`: The timestamp when the dataset was built for lawinstruct - `text`: the text, consisting of the prompt and the answer ### Data Instances The file format is jsonl.xz and there is a `train` split available. ### Data Fields [More Information Needed] ### Data Splits There is one split: train. #### Data Size ```bash $ xz --list data/*.xz Strms Blocks Compressed Uncompressed Ratio Check Filename 1 1 515.4 KiB 3445.8 KiB 0.150 CRC64 data/BrazilianBarExam-brazilian_bar_exam-train-0.jsonl.xz 1 1 379.6 MiB 6327.9 MiB 0.060 CRC64 data/BrCAD5-brcad5_judgment-train-0.jsonl.xz 1 1 379.7 MiB 6336.8 MiB 0.060 CRC64 data/BrCAD5-brcad5_law_area-train-0.jsonl.xz 1 1 461.7 MiB 12.6 GiB 0.036 CRC64 data/BrCAD5-brcad5_mc-train-0.jsonl.xz 1 1 513.0 MiB 18.6 GiB 0.027 CRC64 data/BrCAD5-brcad5_topic-train-0.jsonl.xz 1 1 334.0 KiB 8444.7 KiB 0.040 CRC64 data/BVADecisions-bva_decisions_label-train-0.jsonl.xz 1 1 1416 B 5329 B 0.266 CRC64 data/BVADecisions-bva_decisions_qa-train-0.jsonl.xz 1 1 1535.5 KiB 7091.5 KiB 0.217 CRC64 data/CABarExamEssays-MainSubset-train-0.jsonl.xz 1 1 4541.7 KiB 140.3 MiB 0.032 CRC64 data/CAIL2019-cail_2019-train-0.jsonl.xz 1 1 609.7 KiB 14.4 MiB 0.041 CRC64 data/CAIL2022-cail_2022_crime-train-0.jsonl.xz 1 1 797.2 KiB 15.3 MiB 0.051 CRC64 data/CAIL2022-cail_2022_mc-train-0.jsonl.xz 1 1 518.4 KiB 8591.2 KiB 0.060 CRC64 data/CAIL2022-cail_2022_response-train-0.jsonl.xz 1 1 1344.6 KiB 7666.5 KiB 0.175 CRC64 data/CaseBriefs-case_briefs-train-0.jsonl.xz 1 1 2724.2 KiB 13.8 MiB 0.193 CRC64 data/ChangeMyView-change_my_view-train-0.jsonl.xz 1 1 1368.5 KiB 90.8 MiB 0.015 CRC64 data/ContractNLI-contract_nli-train-0.jsonl.xz 1 1 497.4 KiB 10.9 MiB 0.044 CRC64 data/EdgarNER-MainSubset-train-0.jsonl.xz 1 1 3001.3 KiB 406.2 MiB 0.007 CRC64 data/Ell4GreekNER-MainSubset-train-0.jsonl.xz 1 1 3410.7 KiB 705.6 MiB 0.005 CRC64 data/Ell18GreekNER-MainSubset-train-0.jsonl.xz 1 1 1592.2 KiB 17.2 MiB 0.090 CRC64 data/EOIRPrivacy-eoir_privacy-train-0.jsonl.xz 1 1 19.1 MiB 400.8 MiB 0.048 CRC64 data/EurLexSum-bulgarian-train-0.jsonl.xz 1 1 12.3 MiB 80.6 MiB 0.153 CRC64 data/EurLexSum-croatian-train-0.jsonl.xz 1 1 15.1 MiB 128.5 MiB 0.117 CRC64 data/EurLexSum-czech-train-0.jsonl.xz 1 1 13.0 MiB 94.1 MiB 0.138 CRC64 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7142.6 MiB 0.134 CRC64 data/LEXTREME-multi_eurlex_level_3-train-0.jsonl.xz 1 1 393.6 MiB 2923.5 MiB 0.135 CRC64 data/LEXTREME-multi_eurlex_level_3-train-1.jsonl.xz 1 1 997.2 KiB 24.6 MiB 0.040 CRC64 data/LEXTREME-online_terms_of_service_clause_topics-train-0.jsonl.xz 1 1 163.1 KiB 2028.8 KiB 0.080 CRC64 data/LEXTREME-online_terms_of_service_unfairness_levels-train-0.jsonl.xz 1 1 28.9 MiB 257.6 MiB 0.112 CRC64 data/LEXTREME-swiss_judgment_prediction-train-0.jsonl.xz 1 1 8588 B 87.6 KiB 0.096 CRC64 data/Littleton-littleton_events-train-0.jsonl.xz 1 1 11.1 KiB 134.1 KiB 0.083 CRC64 data/Littleton-littleton_graph-train-0.jsonl.xz 1 1 544.2 KiB 34.7 MiB 0.015 CRC64 data/MAUD-answer-train-0.jsonl.xz 1 1 864.0 KiB 85.8 MiB 0.010 CRC64 data/MAUD-category-train-0.jsonl.xz 1 1 891.3 KiB 86.2 MiB 0.010 CRC64 data/MAUD-question-train-0.jsonl.xz 1 1 866.9 KiB 85.8 MiB 0.010 CRC64 data/MAUD-text_type-train-0.jsonl.xz 1 1 40.0 KiB 167.8 KiB 0.238 CRC64 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data/NaturalInstructionsLegal-casehold_legal_incorrect_answer_generation-train-0.jsonl.xz 1 1 4300.6 KiB 228.0 MiB 0.018 CRC64 data/NaturalInstructionsLegal-cuad_answer_generation-train-0.jsonl.xz 1 1 4302.7 KiB 227.8 MiB 0.018 CRC64 data/NaturalInstructionsLegal-cuad_question_generation-train-0.jsonl.xz 1 1 201.9 KiB 22.7 MiB 0.009 CRC64 data/NaturalInstructionsLegal-eurlex_classification-train-0.jsonl.xz 1 1 284.3 KiB 19.9 MiB 0.014 CRC64 data/NaturalInstructionsLegal-eurlex_summarization-train-0.jsonl.xz 1 1 166.9 KiB 21.5 MiB 0.008 CRC64 data/NaturalInstructionsLegal-online_privacy_policy_text_information_type_generation-train-0.jsonl.xz 1 1 165.1 KiB 21.6 MiB 0.007 CRC64 data/NaturalInstructionsLegal-online_privacy_policy_text_purpose_answer_generation-train-0.jsonl.xz 1 1 246.8 KiB 16.4 MiB 0.015 CRC64 data/NaturalInstructionsLegal-overruling_legal_classification-train-0.jsonl.xz 1 1 5872.3 KiB 31.8 MiB 0.180 CRC64 data/OLCMemos-olc_memos-train-0.jsonl.xz 1 1 76.7 KiB 540.5 KiB 0.142 CRC64 data/PlainEnglishContractsSummarization-plain_english_contracts_summarization-train-0.jsonl.xz 1 1 1246.7 KiB 199.4 MiB 0.006 CRC64 data/PrivacyQA-privacy_qa-train-0.jsonl.xz 1 1 316.0 KiB 4538.1 KiB 0.070 CRC64 data/PrivacySummarization-privacy_summarization-train-0.jsonl.xz 1 1 1098.7 KiB 6969.7 KiB 0.158 CRC64 data/ReClor-reclor-train-0.jsonl.xz 1 1 50.5 MiB 412.2 MiB 0.123 CRC64 data/RedditLegalQA-reddit_legal_qa-train-0.jsonl.xz 1 1 11.0 KiB 134.9 KiB 0.082 CRC64 data/Sara-sara_entailment-train-0.jsonl.xz 1 1 11.5 KiB 145.1 KiB 0.079 CRC64 data/Sara-sara_tax_liability-train-0.jsonl.xz 1 1 36.6 KiB 586.5 KiB 0.062 CRC64 data/SaraProlog-sara_prolog_facts-train-0.jsonl.xz 1 1 18.2 KiB 132.2 KiB 0.138 CRC64 data/SaraProlog-sara_prolog_statute-train-0.jsonl.xz 1 1 90.3 KiB 1531.4 KiB 0.059 CRC64 data/ShortAnswerFeedback-short_answer_feedback_error_class-train-0.jsonl.xz 1 1 26.8 KiB 2218.5 KiB 0.012 CRC64 data/ShortAnswerFeedback-short_answer_feedback_openqa-train-0.jsonl.xz 1 1 91.0 KiB 1513.1 KiB 0.060 CRC64 data/ShortAnswerFeedback-short_answer_feedback_rating-train-0.jsonl.xz 1 1 16.0 KiB 118.9 KiB 0.135 CRC64 data/SpanishLaborLaw-spanish_labor_law-train-0.jsonl.xz 1 1 6562.3 KiB 31.0 MiB 0.207 CRC64 data/StackExchangeQuestionsLegal-stack_exchange_questions_legal-train-0.jsonl.xz 1 1 128.2 KiB 1080.4 KiB 0.119 CRC64 data/SwissCourtViewGeneration-swiss_judgment_court_view_generation_lower_court-train-0.jsonl.xz 1 1 901.2 MiB 5463.4 MiB 0.165 CRC64 data/SwissCourtViewGeneration-swiss_judgment_court_view_generation_same_court-train-0.jsonl.xz 1 1 211.4 MiB 1320.8 MiB 0.160 CRC64 data/SwissCriticalityPrediction-swiss_judgment_criticality-train-0.jsonl.xz 1 1 130.3 MiB 1984.4 MiB 0.066 CRC64 data/SwissJudgmentPrediction-swiss_judgment_multiple_choice-train-0.jsonl.xz 1 1 740.1 MiB 4651.8 MiB 0.159 CRC64 data/SwissJudgmentPredictionXL-swiss_judgment_dismiss_approve-train-0.jsonl.xz 1 1 39.3 MiB 252.1 MiB 0.156 CRC64 data/SwissLawAreaPrediction-swiss_judgment_area_of_law_main_area-train-0.jsonl.xz 1 1 39.3 MiB 252.2 MiB 0.156 CRC64 data/SwissLawAreaPrediction-swiss_judgment_area_of_law_sub_area-train-0.jsonl.xz 1 1 62.9 MiB 320.2 MiB 0.196 CRC64 data/SwissLeadingDecisions-swiss_judgment_location-train-0.jsonl.xz 1 1 60.3 MiB 398.0 MiB 0.152 CRC64 data/SwissLegislation-swiss_legislation_abbreviation-train-0.jsonl.xz 1 1 121.5 MiB 868.3 MiB 0.140 CRC64 data/SwissLegislation-swiss_legislation_canton-train-0.jsonl.xz 1 1 20.8 MiB 136.8 MiB 0.152 CRC64 data/SwissLegislation-swiss_legislation_short-train-0.jsonl.xz 1 1 121.8 MiB 872.3 MiB 0.140 CRC64 data/SwissLegislation-swiss_legislation_title-train-0.jsonl.xz 1 1 199.9 KiB 13.6 MiB 0.014 CRC64 data/TsccAlqac-tscc_alqac_question_answering-train-0.jsonl.xz 1 1 1221.8 KiB 20.3 MiB 0.059 CRC64 data/TurkishConstitutionalCourt-turkish_constitutional_multiple_choice-train-0.jsonl.xz 1 1 1130.7 KiB 10.0 MiB 0.110 CRC64 data/TurkishConstitutionalCourt-turkish_constitutional_violation_no_violation-train-0.jsonl.xz 1 1 3465.9 KiB 29.3 MiB 0.116 CRC64 data/USClassActions-us_class_actions_win_lose-train-0.jsonl.xz 1 1 94.7 KiB 2548.8 KiB 0.037 CRC64 data/ValidWills-valid_wills_entailment-train-0.jsonl.xz ------------------------------------------------------------------------------- 142 142 9.8 GiB 116.5 GiB 0.084 CRC64 142 files ``` ## Dataset Creation This dataset has been created by running the code from the [LawInstruct](https://github.com/JoelNiklaus/LawInstruct) repo. For this public version of the dataset we removed the datasets CiviproQuestions, COLIEE, JECQA and MBE because of restrictive licenses. ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{niklaus2024flawnt5, title={FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data Mixtures for Legal Reasoning}, author={Joel Niklaus and Lucia Zheng and Arya D. McCarthy and Christopher Hahn and Brian M. Rosen and Peter Henderson and Daniel E. Ho and Garrett Honke and Percy Liang and Christopher Manning}, year={2024}, eprint={2404.02127}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
Nexdata/Chinese_Mandarin_Speech_Synthesis_Corpus-Female_Imitating_Children
--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Chinese_Mandarin_Speech_Synthesis_Corpus-Female_Imitating_Children ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.nexdata.ai/datasets/1091?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Female audio data of adults imitating children, 6599 sentences in total and 6.78 hours. It is recorded by Chinese native speakers, with authentic accent and sweet sound. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis. For more details, please refer to the link: https://www.nexdata.ai/datasets/1091?source=Huggingface ### Supported Tasks and Leaderboards tts: The dataset can be used to train a model for Text to Speech (TTS). ### Languages Mandarin Chinese ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
Abhitej5965/textToDDLQuery
--- license: apache-2.0 ---
zolak/twitter_dataset_81_1713222784
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 158945 num_examples: 389 download_size: 83905 dataset_size: 158945 configs: - config_name: default data_files: - split: train path: data/train-* ---
Thefoodprocessor/recipe_new_with_features_full
--- dataset_info: features: - name: recipe_original dtype: string - name: title_original dtype: string - name: title_cleaned dtype: string - name: recipe_new dtype: string - name: wine_type dtype: string - name: allergy_type dtype: string - name: diet_type dtype: string - name: holiday dtype: string - name: cuisine_type dtype: string - name: meal_type dtype: string - name: ingredients_alternatives dtype: string splits: - name: train num_bytes: 248827563 num_examples: 74465 download_size: 117992806 dataset_size: 248827563 configs: - config_name: default data_files: - split: train path: data/train-* ---
inwaves/dtchess-standard
--- license: mit ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-fil_self_160m_bo2_100_kl_0.1_prm_70m_thr_0.0_seed_2_t_1.0
--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43551536 num_examples: 18929 - name: epoch_1 num_bytes: 44048505 num_examples: 18929 - name: epoch_2 num_bytes: 44112887 num_examples: 18929 - name: epoch_3 num_bytes: 44144097 num_examples: 18929 - name: epoch_4 num_bytes: 44162457 num_examples: 18929 - name: epoch_5 num_bytes: 44168530 num_examples: 18929 - name: epoch_6 num_bytes: 44174799 num_examples: 18929 - name: epoch_7 num_bytes: 44177081 num_examples: 18929 - name: epoch_8 num_bytes: 44181196 num_examples: 18929 - name: epoch_9 num_bytes: 44181960 num_examples: 18929 - name: epoch_10 num_bytes: 44183728 num_examples: 18929 - name: epoch_11 num_bytes: 44184624 num_examples: 18929 - name: epoch_12 num_bytes: 44184143 num_examples: 18929 - name: epoch_13 num_bytes: 44185718 num_examples: 18929 - name: epoch_14 num_bytes: 44184424 num_examples: 18929 - name: epoch_15 num_bytes: 44184799 num_examples: 18929 - name: epoch_16 num_bytes: 44185474 num_examples: 18929 - name: epoch_17 num_bytes: 44185636 num_examples: 18929 - name: epoch_18 num_bytes: 44185123 num_examples: 18929 - name: epoch_19 num_bytes: 44186926 num_examples: 18929 - name: epoch_20 num_bytes: 44186621 num_examples: 18929 - name: epoch_21 num_bytes: 44184941 num_examples: 18929 - name: epoch_22 num_bytes: 44185088 num_examples: 18929 - name: epoch_23 num_bytes: 44186826 num_examples: 18929 - name: epoch_24 num_bytes: 44187047 num_examples: 18929 - name: epoch_25 num_bytes: 44187605 num_examples: 18929 - name: epoch_26 num_bytes: 44186460 num_examples: 18929 - name: epoch_27 num_bytes: 44188504 num_examples: 18929 - name: epoch_28 num_bytes: 44187911 num_examples: 18929 - name: epoch_29 num_bytes: 44186570 num_examples: 18929 download_size: 698567953 dataset_size: 1324621216 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
Frixi/Luz_Noceda_Eng_6Mins
--- license: openrail ---
ramgus/audiofeaturesalbumcovers
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 636771303.2 num_examples: 1200 download_size: 520392718 dataset_size: 636771303.2 --- # Dataset Card for "audiofeaturesalbumcovers" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Seanxh/twitter_dataset_1713069246
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 857621 num_examples: 2464 download_size: 329244 dataset_size: 857621 configs: - config_name: default data_files: - split: train path: data/train-* ---
Joshua-Abok/preprocessed_samsum_and_dialogsum
--- dataset_info: features: - name: dialogue dtype: string - name: summary dtype: string splits: - name: train num_bytes: 19792641 num_examples: 20000 - name: valid num_bytes: 1035442 num_examples: 1318 - name: test num_bytes: 2013667 num_examples: 2319 download_size: 12309269 dataset_size: 22841750 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
NotHEre/Rafael
--- license: openrail ---
nakcnx/sql-context-splitted
--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string splits: - name: train num_bytes: 16491906.06065388 num_examples: 74648 - name: test num_bytes: 434125.4341601232 num_examples: 1965 - name: valid num_bytes: 433904.5051859959 num_examples: 1964 download_size: 8589535 dataset_size: 17359936.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
zolak/twitter_dataset_80_1713058513
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 3845885 num_examples: 9434 download_size: 1938363 dataset_size: 3845885 configs: - config_name: default data_files: - split: train path: data/train-* ---
albertvillanova/tmp-multilingual
--- language: - multilingual - mul ---
gcjavi/parlaspeech-tests
--- configs: - config_name: clean data_files: - split: train path: "data/clean/train/train_clean.tsv" #- "data/clean/train/train_clean_2.tsv" #- "data/clean/train/train_clean_3.tsv" #- "data/clean/train/train_clean_4.tsv" - split: dev path: "data/clean/dev/dev_clean.tsv" - split: test path: "data/clean/test/test_clean.tsv" - config_name: other data_files: - split: train path: "data/other/train/train_other.tsv" - split: dev path: "data/other/dev/dev_other.tsv" - split: test path: "data/other/test/test_other.tsv" ---
lsr42/msmarco-passage-doct5query
--- license: unknown dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: passage num_bytes: 16870476814 num_examples: 8841823 download_size: 5773175789 dataset_size: 16870476814 ---
dynopii/OpenOrca-Top5percent
--- language: - en license: mit task_categories: - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation pretty_name: OpenOrca-Top5Percent size_categories: - 1M<n<10M --- <p><h1>🐋 The OpenOrca-Top5Percent Dataset! 🐋</h1></p> We are excited to introduce the OpenOrca-Top5Percent dataset, a refined version of the original [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca). This dataset contains only those entries which utilize the top 5% most frequently used words in the OpenOrca dataset, aiming to focus on high-frequency vocabulary for various NLP tasks. # Dataset Summary The OpenOrca-Top5Percent dataset is a curated subset of the augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688), focusing specifically on entries that incorporate the most commonly used words across ~1M GPT-4 completions and ~3.2M GPT-3.5 completions. It represents a narrowed scope with the intent of fostering research and applications where high-frequency vocabulary usage is critical. # Dataset Attribution This dataset builds upon the efforts and contributions of the OpenOrca dataset team and contributors. Special thanks to the original OpenOrca contributors, as well as the community around it, for making the foundational dataset available. # Supported Tasks and Leaderboards OpenOrca-Top5Percent supports a similar range of NLP tasks as the original dataset, particularly those benefiting from a focus on high-usage vocabulary, including but not limited to language modeling, text generation, summarization, and more. It offers a unique dataset for exploring the impacts of vocabulary frequency on various NLP tasks. # Languages The primary language of the dataset is English. # Dataset Structure ## Data Instances Each instance in this dataset reflects the structure of the original OpenOrca dataset but is specifically filtered to only include entries with the top 5% most used words, aiming to maintain the richness of the data while focusing on common vocabulary. ## Data Fields Fields remain consistent with the original OpenOrca dataset, including 'id', 'system_prompt', 'question', and 'response', ensuring compatibility with existing models and tools designed for OpenOrca. ## Data Splits The dataset is provided as a single, unsplit collection, simplifying use and access. # Dataset Creation ## Curation Rationale The creation of OpenOrca-Top5Percent is motivated by the desire to investigate the effects of focusing on high-frequency vocabulary in NLP tasks, potentially improving efficiency and performance in specific applications. ## Source Data The source data for this dataset is derived from the original OpenOrca dataset, filtered to focus on entries containing only the top 5% most frequently used words. # Dataset Use ## Use Cases OpenOrca-Top5Percent is ideal for use cases where high-frequency vocabulary is of particular interest, including educational applications, simplified text generation, and more. ## Usage Caveats As with any filtered dataset, users should consider the implications of the narrowed vocabulary scope on their specific applications and research. ## Getting Started This dataset is structured for easy loading via the Hugging Face datasets library, with considerations for efficient use given its focus on high-frequency vocabulary. Users are encouraged to explore the potential of this specialized dataset in their work. # Citation Please cite the original OpenOrca dataset when using OpenOrca-Top5Percent in your research or applications, along with any specific papers or resources related to your work that utilize this dataset. ```bibtex @misc{OpenOrca-Top5Percent, title = {OpenOrca-Top5Percent: A Filtered Subset of OpenOrca Focusing on High-Frequency Vocabulary}, author = {Anubhav Singh}, year = {2023}, publisher = {Dynopii}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/datasets/dynopii/OpenOrca-Top5percent}}, } ``` ```bibtex @misc{OpenOrca, title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces}, author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/datasets/Open-Orca/OpenOrca}}, } ``` ```bibtex @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint= arXiv 2307.09288 } @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ``` ---
distilled-from-one-sec-cv12/chunk_253
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 945062932 num_examples: 184151 download_size: 960503342 dataset_size: 945062932 --- # Dataset Card for "chunk_253" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hyokwan/dataset_llama_hk2
--- license: mit dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7786 num_examples: 32 download_size: 4172 dataset_size: 7786 configs: - config_name: default data_files: - split: train path: data/train-* ---
richfrain/pokemon-blip-captions
--- license: apache-2.0 ---
Ivus234/Joao2
--- license: openrail ---
Gbssreejith/Birth_type1_dataset
--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 96340182.0 num_examples: 249 - name: val num_bytes: 10782724.0 num_examples: 28 download_size: 107068662 dataset_size: 107122906.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* ---
tyzhu/lmind_nq_train300_eval100_v1_reciteonly_qa
--- configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: all_docs path: data/all_docs-* - split: all_docs_eval path: data/all_docs_eval-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train_qa num_bytes: 34574 num_examples: 300 - name: train_recite_qa num_bytes: 226733 num_examples: 300 - name: eval_qa num_bytes: 11254 num_examples: 100 - name: eval_recite_qa num_bytes: 74768 num_examples: 100 - name: all_docs num_bytes: 254478 num_examples: 392 - name: all_docs_eval num_bytes: 254451 num_examples: 392 - name: train num_bytes: 226733 num_examples: 300 - name: validation num_bytes: 74768 num_examples: 100 download_size: 760921 dataset_size: 1157759 --- # Dataset Card for "lmind_nq_train300_eval100_v1_reciteonly_qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ckandemir/amazon-products
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: eval path: data/eval-* dataset_info: features: - name: Product Name dtype: string - name: Category dtype: string - name: Description dtype: string - name: Selling Price dtype: string - name: Product Specification dtype: string - name: Image dtype: string splits: - name: train num_bytes: 12542887 num_examples: 23993 - name: test num_bytes: 3499375 num_examples: 6665 - name: eval num_bytes: 1376174 num_examples: 2666 download_size: 6391314 dataset_size: 17418436 license: apache-2.0 task_categories: - image-classification - image-to-text language: - en size_categories: - 10K<n<100K --- ## Dataset Creation and Processing Overview This dataset underwent a comprehensive process of loading, cleaning, processing, and preparing, incorporating a range of data manipulation and NLP techniques to optimize its utility for machine learning models, particularly in natural language processing. ### Data Loading and Initial Cleaning - **Source**: Loaded from the Hugging Face dataset repository [bprateek/amazon_product_description](https://huggingface.co/datasets/bprateek/amazon_product_description). - **Conversion to Pandas DataFrame**: For ease of data manipulation. - **Null Value Removal**: Rows with null values in the 'About Product' column were discarded. ### Data Cleaning and NLP Processing - **Sentence Extraction**: 'About Product' descriptions were split into sentences, identifying common phrases. - **Emoji and Special Character Removal**: A regex function removed these elements from the product descriptions. - **Common Phrase Elimination**: A function was used to strip common phrases from each product description. - **Improving Writing Standards**: Adjusted capitalization, punctuation, and replaced '&' with 'and' for better readability and formalization. ### Sentence Similarity Analysis - **Model Application**: The pre-trained Sentence Transformer model 'all-MiniLM-L6-v2' was used. - **Sentence Comparison**: Identified the most similar sentence to each product name within the cleaned product descriptions. ### Dataset Refinement - **Column Selection**: Retained relevant columns for final dataset. - **Image URL Processing**: Split multiple image URLs into individual URLs, removing specific unwanted URLs. ### Image Validation - **Image URL Validation**: Implemented a function to verify the validity of each image URL. - **Filtering Valid Images**: Retained only rows with valid image URLs. ### Dataset Splitting for Machine Learning - **Creation of Train, Test, and Eval Sets**: Used scikit-learn's `train_test_split` for dataset division. For further details or to contribute to enhancing the dataset card, please refer to the [Hugging Face Dataset Card Contribution Guide](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards).
tyzhu/find_word_1000
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 233196 num_examples: 3000 - name: eval_find_word num_bytes: 53196 num_examples: 1000 download_size: 136283 dataset_size: 286392 --- # Dataset Card for "find_word_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlketaR/embedded_faqs_medicarealk
--- license: openrail ---
linhtran92/asr_data_v2
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 3656462.0 num_examples: 44 download_size: 3639719 dataset_size: 3656462.0 --- # Dataset Card for "asr_data_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Hani89/Medical_ASR_45HRs
--- license: apache-2.0 task_categories: - automatic-speech-recognition language: - en tags: - medical size_categories: - 10K<n<100K --- # Medical Dataset for ASR The dataset is a part taken from [The MedDialog dataset](https://huggingface.co/datasets/medical_dialog). We used only icliniq_dialogue.txt and done some preprocessing: - Remove all chars except for [a-z|A-Z|0-9|,|.]. - Break each conversation into rows of 32 to 35 words. - Remove Duplication. - Fix typos using GPT-3 instructons' model. - Used Suno/Bark to create ~15K audio clips with different voices [*In Progress*] #### Note: - We are expecting about ~45 hours of medical audio clips. - The dataset will be released soon, for any inqueries please contact me on(hmthubaiti@uqu.edu.sa)
pk3388/train
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 312235.0 num_examples: 9 download_size: 247841 dataset_size: 312235.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Myashka/SO-Python_QA-filtered-2023-tanh_score-after_2023_02
--- task_categories: - question-answering language: - en size_categories: - n<1K --- SO dataset of `python`tag data Question filters: - images - links - code blocks - Q_Score > 0 - Answer_count > 0 - CreationDate > 2023-02-01 Answers filters: - images - links - code blocks Scores are tanh applied to scaled with AbsMaxScaler to IQR range of Original SO Answers' scores
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/058e2bc3
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1339 dataset_size: 184 --- # Dataset Card for "058e2bc3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_decem__Dionysus-Mistral-m3-v5
--- pretty_name: Evaluation run of decem/Dionysus-Mistral-m3-v5 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [decem/Dionysus-Mistral-m3-v5](https://huggingface.co/decem/Dionysus-Mistral-m3-v5)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 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 (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_decem__Dionysus-Mistral-m3-v5\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-05T07:41:42.571559](https://huggingface.co/datasets/open-llm-leaderboard/details_decem__Dionysus-Mistral-m3-v5/blob/main/results_2024-01-05T07-41-42.571559.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\": 0.6119654723808889,\n\ \ \"acc_stderr\": 0.03286363978834633,\n \"acc_norm\": 0.6149242684593406,\n\ \ \"acc_norm_stderr\": 0.03352054924433844,\n \"mc1\": 0.35128518971848227,\n\ \ \"mc1_stderr\": 0.0167113581635444,\n \"mc2\": 0.5093204017955075,\n\ \ \"mc2_stderr\": 0.015839968447220742\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5648464163822525,\n \"acc_stderr\": 0.014487986197186043,\n\ \ \"acc_norm\": 0.5955631399317406,\n \"acc_norm_stderr\": 0.01434203648343618\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6283608842859988,\n\ \ \"acc_stderr\": 0.004822550638450897,\n \"acc_norm\": 0.8098984266082454,\n\ \ \"acc_norm_stderr\": 0.003915792315457797\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316091,\n\ \ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316091\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\ \ \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\ \ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6527777777777778,\n\ \ \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.6527777777777778,\n\ \ \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.03656343653353159,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.03656343653353159\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207762,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207762\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6085106382978723,\n \"acc_stderr\": 0.03190701242326812,\n\ \ \"acc_norm\": 0.6085106382978723,\n \"acc_norm_stderr\": 0.03190701242326812\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192118,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192118\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246487,\n \"\ acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246487\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7354838709677419,\n \"acc_stderr\": 0.02509189237885928,\n \"\ acc_norm\": 0.7354838709677419,\n \"acc_norm_stderr\": 0.02509189237885928\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"\ acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.0347769116216366,\n\ \ \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.0347769116216366\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338642,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338642\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8134715025906736,\n \"acc_stderr\": 0.028112091210117474,\n\ \ \"acc_norm\": 0.8134715025906736,\n \"acc_norm_stderr\": 0.028112091210117474\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6358974358974359,\n \"acc_stderr\": 0.024396672985094753,\n\ \ \"acc_norm\": 0.6358974358974359,\n \"acc_norm_stderr\": 0.024396672985094753\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114993,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114993\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6302521008403361,\n \"acc_stderr\": 0.031357095996135904,\n\ \ \"acc_norm\": 0.6302521008403361,\n \"acc_norm_stderr\": 0.031357095996135904\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8330275229357799,\n \"acc_stderr\": 0.015990154885073403,\n \"\ acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.015990154885073403\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.02862654791243741,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.02862654791243741\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467618,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467618\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\ \ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\ \ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794089,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794089\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n\ \ \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8461538461538461,\n\ \ \"acc_stderr\": 0.023636873317489277,\n \"acc_norm\": 0.8461538461538461,\n\ \ \"acc_norm_stderr\": 0.023636873317489277\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7982120051085568,\n\ \ \"acc_stderr\": 0.014351702181636864,\n \"acc_norm\": 0.7982120051085568,\n\ \ \"acc_norm_stderr\": 0.014351702181636864\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6502890173410405,\n \"acc_stderr\": 0.025674281456531015,\n\ \ \"acc_norm\": 0.6502890173410405,\n \"acc_norm_stderr\": 0.025674281456531015\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31843575418994413,\n\ \ \"acc_stderr\": 0.015581008080360276,\n \"acc_norm\": 0.31843575418994413,\n\ \ \"acc_norm_stderr\": 0.015581008080360276\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.02699254433929724,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.02699254433929724\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6784565916398714,\n\ \ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n\ \ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6728395061728395,\n \"acc_stderr\": 0.026105673861409825,\n\ \ \"acc_norm\": 0.6728395061728395,\n \"acc_norm_stderr\": 0.026105673861409825\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.43617021276595747,\n \"acc_stderr\": 0.02958345203628407,\n \ \ \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.02958345203628407\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43089960886571055,\n\ \ \"acc_stderr\": 0.012647695889547228,\n \"acc_norm\": 0.43089960886571055,\n\ \ \"acc_norm_stderr\": 0.012647695889547228\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.0290294228156814,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.0290294228156814\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6323529411764706,\n \"acc_stderr\": 0.019506291693954854,\n \ \ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.019506291693954854\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\ \ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\ \ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6571428571428571,\n \"acc_stderr\": 0.030387262919547738,\n\ \ \"acc_norm\": 0.6571428571428571,\n \"acc_norm_stderr\": 0.030387262919547738\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233257,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233257\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35128518971848227,\n\ \ \"mc1_stderr\": 0.0167113581635444,\n \"mc2\": 0.5093204017955075,\n\ \ \"mc2_stderr\": 0.015839968447220742\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7513812154696132,\n \"acc_stderr\": 0.012147314713403107\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.510235026535254,\n \ \ \"acc_stderr\": 0.013769598923012395\n }\n}\n```" repo_url: https://huggingface.co/decem/Dionysus-Mistral-m3-v5 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|arc:challenge|25_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-05T07-41-42.571559.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|gsm8k|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hellaswag|10_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T07-41-42.571559.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T07-41-42.571559.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T07-41-42.571559.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_05T07_41_42.571559 path: - '**/details_harness|winogrande|5_2024-01-05T07-41-42.571559.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-05T07-41-42.571559.parquet' - config_name: results data_files: - split: 2024_01_05T07_41_42.571559 path: - results_2024-01-05T07-41-42.571559.parquet - split: latest path: - results_2024-01-05T07-41-42.571559.parquet --- # Dataset Card for Evaluation run of decem/Dionysus-Mistral-m3-v5 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [decem/Dionysus-Mistral-m3-v5](https://huggingface.co/decem/Dionysus-Mistral-m3-v5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 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 (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_decem__Dionysus-Mistral-m3-v5", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-05T07:41:42.571559](https://huggingface.co/datasets/open-llm-leaderboard/details_decem__Dionysus-Mistral-m3-v5/blob/main/results_2024-01-05T07-41-42.571559.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": 0.6119654723808889, "acc_stderr": 0.03286363978834633, "acc_norm": 0.6149242684593406, "acc_norm_stderr": 0.03352054924433844, "mc1": 0.35128518971848227, "mc1_stderr": 0.0167113581635444, "mc2": 0.5093204017955075, "mc2_stderr": 0.015839968447220742 }, "harness|arc:challenge|25": { "acc": 0.5648464163822525, "acc_stderr": 0.014487986197186043, "acc_norm": 0.5955631399317406, "acc_norm_stderr": 0.01434203648343618 }, "harness|hellaswag|10": { "acc": 0.6283608842859988, "acc_stderr": 0.004822550638450897, "acc_norm": 0.8098984266082454, "acc_norm_stderr": 0.003915792315457797 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.562962962962963, "acc_stderr": 0.04284958639753401, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.04284958639753401 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6578947368421053, "acc_stderr": 0.03860731599316091, "acc_norm": 0.6578947368421053, "acc_norm_stderr": 0.03860731599316091 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6527777777777778, "acc_stderr": 0.039812405437178615, "acc_norm": 0.6527777777777778, "acc_norm_stderr": 0.039812405437178615 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.03656343653353159, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.03656343653353159 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207762, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207762 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6085106382978723, "acc_stderr": 0.03190701242326812, "acc_norm": 0.6085106382978723, "acc_norm_stderr": 0.03190701242326812 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192118, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192118 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3968253968253968, "acc_stderr": 0.025197101074246487, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.025197101074246487 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7354838709677419, "acc_stderr": 0.02509189237885928, "acc_norm": 0.7354838709677419, "acc_norm_stderr": 0.02509189237885928 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7272727272727273, "acc_stderr": 0.0347769116216366, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.0347769116216366 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.02985751567338642, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.02985751567338642 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8134715025906736, "acc_stderr": 0.028112091210117474, "acc_norm": 0.8134715025906736, "acc_norm_stderr": 0.028112091210117474 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6358974358974359, "acc_stderr": 0.024396672985094753, "acc_norm": 0.6358974358974359, "acc_norm_stderr": 0.024396672985094753 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3037037037037037, "acc_stderr": 0.028037929969114993, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.028037929969114993 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6302521008403361, "acc_stderr": 0.031357095996135904, "acc_norm": 0.6302521008403361, "acc_norm_stderr": 0.031357095996135904 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8330275229357799, "acc_stderr": 0.015990154885073403, "acc_norm": 0.8330275229357799, "acc_norm_stderr": 0.015990154885073403 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4861111111111111, "acc_stderr": 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}, "harness|truthfulqa:mc|0": { "mc1": 0.35128518971848227, "mc1_stderr": 0.0167113581635444, "mc2": 0.5093204017955075, "mc2_stderr": 0.015839968447220742 }, "harness|winogrande|5": { "acc": 0.7513812154696132, "acc_stderr": 0.012147314713403107 }, "harness|gsm8k|5": { "acc": 0.510235026535254, "acc_stderr": 0.013769598923012395 } } ``` ## 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 intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More 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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]
Nexdata/29523_People_Face_Recognition_Data_with_Identification_Photos
--- license: cc-by-nc-nd-4.0 --- ## Description 29,523 People Face Recognition Data with Identification Photos.The race distribution of data includes Asian race, Caucasian race, black race and brown race. For each subject, one ID photo and 5-10 life photos were collected. This data can be used for face recognition. For more details, please refer to the link: https://www.nexdata.ai/dataset/1020?source=Huggingface # Specifications ## Data size 29,523 people, one ID photo and 5-10 life photos per person ## Race distribution 2,099 black people, 2,238 Caucasian people, 841 brown (Mexicans) people and 24,345 Asian people ## Gender distribution 14,790 males, 14,733 females ## Age distribution: ranging from teenager to the elderly, the middle-aged and young people are the majorities ## Collecting environment including indoor and outdoor scenes ## Data diversity different poses, races or nationality, ages and collecting scenes ## Device cellphone ## Data format .jpg, .jpeg, .png ## accuracy the accuracy of labels of gender, race or nationality and age are more than 97% # Licensing Information Commercial License
garcianacho/DPI
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 106579679 num_examples: 150000 download_size: 96335003 dataset_size: 106579679 --- # Dataset Card for "DPI" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
roborovski/celeba-faces-captioned
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: pixel_values sequence: sequence: sequence: float32 - name: captions dtype: string splits: - name: train num_bytes: 17810785215.0 num_examples: 10000 download_size: 475025277 dataset_size: 17810785215.0 --- # Dataset Card for "celeba-faces-captioned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hpprc/jsquad-mined
--- dataset_info: features: - name: passage_id dtype: int64 - name: query dtype: string - name: answer dtype: string - name: text dtype: string - name: title dtype: string - name: mined_neg_ids sequence: int64 - name: mined_neg_sims sequence: float64 splits: - name: train num_bytes: 242044763 num_examples: 62859 download_size: 139992000 dataset_size: 242044763 configs: - config_name: default data_files: - split: train path: data/train-* ---
jan-hq/capybara_dpo_binarized
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 71347705.65807219 num_examples: 6806 - name: test num_bytes: 7935676.341927807 num_examples: 757 download_size: 40834468 dataset_size: 79283382.0 --- # Dataset Card for "capybara_dpo_binarized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Iceclear/AVA
--- license: apache-2.0 --- AVA: A Large-Scale Database for Aesthetic Visual Analysis See [Github Page](https://github.com/imfing/ava_downloader/tree/master/AVA_dataset) for tags. ## Citation ```bibtex @inproceedings{murray2012ava, title={AVA: A large-scale database for aesthetic visual analysis}, author={Murray, Naila and Marchesotti, Luca and Perronnin, Florent}, booktitle={CVPR}, year={2012}, } ```
jdabello/amontillado
--- license: apache-2.0 ---
DataStudio/OCR_underline_part_5
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1509228890.875 num_examples: 77465 download_size: 1510622408 dataset_size: 1509228890.875 --- # Dataset Card for "OCR_underline_part_5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)