davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
liuyanchen1015/MULTI_VALUE_rte_zero_degree	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 60122 num_examples: 141 - name: train num_bytes: 55937 num_examples: 126 download_size: 84266 dataset_size: 116059 --- # Dataset Card for "MULTI_VALUE_rte_zero_degree" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kblw/graphviz_treemap	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': train '1': val splits: - name: train num_bytes: 11185982.90438544 num_examples: 2101 - name: test num_bytes: 1180354.739614561 num_examples: 234 - name: validation num_bytes: 5320.0 num_examples: 4 download_size: 11890860 dataset_size: 12371657.644000001 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
mekaneeky/Synthetic_Runyankole_MMS	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: eng dtype: string - name: lug dtype: string - name: ach dtype: string - name: teo dtype: string - name: lgg dtype: string - name: nyn dtype: string - name: runyankole_synthetic_audio sequence: sequence: float32 splits: - name: train num_bytes: 12669819104 num_examples: 23947 - name: dev num_bytes: 268490157 num_examples: 500 - name: test num_bytes: 262253648 num_examples: 500 download_size: 13209162621 dataset_size: 13200562909 --- # Dataset Card for "Synthetic_Runyankole_MMS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rcds/MultiLegalSBD	--- dataset_info: - config_name: fr_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 8773683 num_examples: 2131 download_size: 0 dataset_size: 8773683 - config_name: it_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 8130577 num_examples: 2910 download_size: 0 dataset_size: 8130577 - config_name: es_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 6260211 num_examples: 677 download_size: 0 dataset_size: 6260211 - config_name: en_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train download_size: 0 dataset_size: 0 - config_name: de_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 13792836 num_examples: 13 download_size: 0 dataset_size: 13792836 - config_name: fr_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 8788244 num_examples: 315 download_size: 0 dataset_size: 8788244 - config_name: fr_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 25977816 num_examples: 2446 download_size: 4782672 dataset_size: 25977816 - config_name: it_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 8989061 num_examples: 243 download_size: 0 dataset_size: 8989061 - config_name: it_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 25097560 num_examples: 3153 download_size: 4610540 dataset_size: 25097560 - config_name: es_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 9460558 num_examples: 190 download_size: 0 dataset_size: 9460558 - config_name: es_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 23090629 num_examples: 867 download_size: 4438716 dataset_size: 23090629 - config_name: en_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 18401754 num_examples: 80 download_size: 0 dataset_size: 18401754 - config_name: en_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 27363914 num_examples: 80 download_size: 5448700 dataset_size: 27363914 - config_name: de_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 14082173 num_examples: 131 download_size: 0 dataset_size: 14082173 - config_name: de_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 40429185 num_examples: 144 download_size: 7883640 dataset_size: 40429185 - config_name: fr_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 12924503 num_examples: 2131 download_size: 2201568 dataset_size: 12924503 - config_name: fr_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 13053313 num_examples: 315 download_size: 2581104 dataset_size: 13053313 - config_name: it_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 11869343 num_examples: 2910 download_size: 2048828 dataset_size: 11869343 - config_name: it_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 13228218 num_examples: 243 download_size: 2561712 dataset_size: 13228218 - config_name: es_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 9183057 num_examples: 677 download_size: 1753376 dataset_size: 9183057 - config_name: es_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 13907572 num_examples: 190 download_size: 2685340 dataset_size: 13907572 - config_name: en_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train download_size: 0 dataset_size: 0 - config_name: en_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 27363914 num_examples: 80 download_size: 5448700 dataset_size: 27363914 - config_name: de_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 19935635 num_examples: 13 download_size: 3745480 dataset_size: 19935635 - config_name: de_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 20493550 num_examples: 131 download_size: 4138160 dataset_size: 20493550 - config_name: pt_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 1005902 num_examples: 58 download_size: 209128 dataset_size: 1005902 - config_name: pt_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 812282 num_examples: 10 download_size: 173424 dataset_size: 812282 - config_name: pt_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 1818184 num_examples: 68 download_size: 382552 dataset_size: 1818184 - config_name: all_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 54918438 num_examples: 5789 download_size: 9958380 dataset_size: 54918438 - config_name: all_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 88858845 num_examples: 969 download_size: 17588440 dataset_size: 88858845 - config_name: all_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 143777284 num_examples: 6758 download_size: 27546820 dataset_size: 143777284 task_categories: - token-classification language: - en - es - de - it - pt - fr pretty_name: 'MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset' size_categories: - 100K<n<1M --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This is a multilingual dataset containing ~130k annotated sentence boundaries. It contains laws and court decision in 6 different languages. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English, French, Italian, German, Portuguese, Spanish ## Dataset Structure It is structured in the following format: {language}\_{type}\_{shard}.jsonl.xz type is one of the following: - laws - judgements Use the the dataset like this: ``` from datasets import load_dataset config = 'fr_laws' #{language}_{type} \| to load all languages and/or all types, use 'all_all' dataset = load_dataset('rdcs/MultiLegalSBD', config) ``` ### Data Instances [More Information Needed] ### Data Fields - text: the original text - spans: - start: offset of the first character - end: offset of the last character - label: One label only -> Sentence - token_start: id of the first token - token_end: id of the last token - tokens: - text: token text - start: offset of the first character - end: offset of the last character - id: token id - ws: whether the token is followed by whitespace ### Data Splits There is only one split available ## 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 ``` @inproceedings{10.1145/3594536.3595132, author = {Brugger, Tobias and St\"{u}rmer, Matthias and Niklaus, Joel}, title = {MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset}, year = {2023}, isbn = {9798400701979}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3594536.3595132}, doi = {10.1145/3594536.3595132}, abstract = {Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different sentence structures used. In this work, we curated a diverse multilingual legal dataset consisting of over 130'000 annotated sentences in 6 languages. Our experimental results indicate that the performance of existing SBD models is subpar on multilingual legal data. We trained and tested monolingual and multilingual models based on CRF, BiLSTM-CRF, and transformers, demonstrating state-of-the-art performance. We also show that our multilingual models outperform all baselines in the zero-shot setting on a Portuguese test set. To encourage further research and development by the community, we have made our dataset, models, and code publicly available.}, booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law}, pages = {42–51}, numpages = {10}, keywords = {Natural Language Processing, Sentence Boundary Detection, Text Annotation, Legal Document Analysis, Multilingual}, location = {Braga, Portugal}, series = {ICAIL '23} } ``` ### Contributions [More Information Needed]
suolyer/webqa	--- license: apache-2.0 ---
aisquared/databricks-dolly-15k	--- license: cc-by-sa-3.0 language: - en tags: - databricks - dolly pretty_name: 'Dataset ' --- # databricks-dolly-15k This dataset was not originally created by AI Squared. This dataset was curated and created by [Databricks](https://databricks.com). The below text comes from the original release of the dataset's README file in GitHub (available at https://github.com/databrickslabs/dolly/tree/master/data): # Summary `databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: English Version: 1.0 Owner: Databricks, Inc. # Dataset Overview `databricks-dolly-15k` is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category. Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly. For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the `context` field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. `[42]`) which we recommend users remove for downstream applications. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories. Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets. # Dataset ## Purpose of Collection As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - Human-generated data: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories. - Wikipedia: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages. ## Annotator Guidelines To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor. The annotation guidelines for each of the categories are as follows: - Creative Writing: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better. - Closed QA: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form. - Open QA: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation. - Summarization: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form. - Information Extraction: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form. - Classification: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better. - Brainstorming: Think up lots of examples in response to a question asking to brainstorm ideas. ## Personal or Sensitive Data This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language American English # Known Limitations - Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia - Some annotators may not be native English speakers - Annotator demographics and subject matter may reflect the makeup of Databricks employees # License/Attribution Copyright (2023) Databricks, Inc. This dataset was developed at Databricks (https://www.databricks.com) and its use is subject to the CC BY-SA 3.0 license. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license: Wikipedia (various pages) - https://www.wikipedia.org/ Copyright © Wikipedia editors and contributors.
JyotiNayak/Indian_electoral_bond_dataset	--- dataset_info: features: - name: date_of_purchase dtype: timestamp[ns] - name: date_of_expiry dtype: timestamp[ns] - name: name_of_the_purchaser dtype: string - name: bondnumber dtype: string - name: date_of_encashment dtype: timestamp[ns] - name: name_of_the_political_party dtype: string - name: encashed_donations_crores dtype: float64 - name: days_to_encash dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 2397987 num_examples: 20552 download_size: 369053 dataset_size: 2397987 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - feature-extraction language: - en size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card contains cleaned elctoral bond data (India) by matching the companies that have purchased the electoral bonds to the parties that have encashed them. ## Dataset Details ### Dataset Description - Curated by: Jyoti Shankar Nayak - Funded by [optional]: NA - Language(s) (NLP): English - License: Apache 2.0 ### Dataset Sources <!-- Provide the basic links for the dataset. --> - Source: Election commission of India - Source files: https://www.eci.gov.in/disclosure-of-electoral-bonds - Data cleaning and curation: https://github.com/jyoti-sn/Elections/blob/main/Electoral_bond_Analysis.ipynb ## 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]
remyxai/ffmperative_augmented	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 76872282 num_examples: 52893 download_size: 13359933 dataset_size: 76872282 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ffmperative_augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
furry-br/steven-reglan	--- license: openrail ---
GeneratedPhotos/Synthetic_Face_Images_Academic_Dataset	--- license: afl-3.0 language: - en tags: - generation - image generation - face images - face detection pretty_name: Academic Dataset by Generated Photos size_categories: - 1K<n<10K --- Academic Dataset by Generated Photos See at https://generated.photos/datasets#research-dataset The free dataset is made to help students and teachers with any research. It contains 10,000 photos with equal distribution of race and gender parameters. If you need a dataset with different parameters or quantity, contact us at work.with@generated.photos. We will appreciate it if you let us know about the research outcome! ---------------------------------------------------------- Terms of use ---------------------------------------------------------- You can use and adapt it for any research purposes, as long as you: (a) give appropriate credit by citing in your paper, (b) put a link to Generated Photos website in case of publishing your paper or results of your research or a related article. Example of an attribution line: Academic Dataset by Generated Photos https://generated.photos/datasets You can redistribute it within your university, but please follow these rules: (a) indicate any changes that you've made, (b) make sure that your fellow student or teacher you pass this dataset is aware of the terms of use described in this file. For more information about datasets and license, please visit Generated Photos website: https://generated.photos/datasets https://generated.photos/faq https://generated.photos/terms-and-conditions ---------------------------------------------------------- Photos ---------------------------------------------------------- All the photos are 100% synthetic. Based on model-released photos. Royalty-free. Can be used for any research purpose except for the ones violating the law. Worldwide. No time limitations. Quantity 10,000 Quality 128x128px Diversity Ethnicity, gender ---------------------------------------------------------- Metadata ---------------------------------------------------------- The JSON files contain the metadata for each image in a machine-readable format, including: (1) FaceLandmarks: mouth, right_eyebrow, left_eyebrow, right_eye, left_eye, nose, jaw. (2) FaceAttributes: headPose, gender, makeup, emotion, facialHair, hair (hairColor, hairLength, bald), occlusion, ethnicity, eye_color, smile, age ---------------------------------------------------------- Please contact work.with@generated.photos for business and press inquiries and other questions.
DBQ/Net.a.Porter.Product.prices.South.Korea	--- 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: South Korea - Net-a-Porter - Product-level price list tags: - webscraping - ecommerce - Net - 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: int64 - 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: 20928720 num_examples: 51265 download_size: 6412160 dataset_size: 20928720 --- # Net-a-Porter web scraped data ## About the website The fashion e-commerce industry in the Asia Pacific, particularly in South Korea, has seen tremendous growth over the last few years. Rapid advancements in digital technology, together with a sophisticated logistics infrastructure and increasingly affluent consumer base, are driving this expansion. South Korea’s digital infrastructure is one of the most advanced in the region, offering online retailers access to a broad, tech-savvy audience. Cultural factors like the Korean Wave (“Hallyu”) are also influencing fashion trends and consumption patterns. The dataset observed provides e-commerce product-list page (PLP) data about Net-a-Porter in South Korea, shedding light on purchasing patterns and preferences in this vibrant market. ## Link to dataset [South Korea - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20South%20Korea/r/recbd28ziED1dxjSE)
mwitiderrick/SwahiliAlpaca	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 31174024 num_examples: 34740 download_size: 15797740 dataset_size: 31174024 license: apache-2.0 task_categories: - text-generation language: - sw pretty_name: Swahili Alpaca size_categories: - 10K<n<100K --- # Dataset Card for "SwahiliAlpaca" Alpaca dataset for instruction fine-tuning in Swahili. ## Prompt Template ``` ### Maelekezo:\n{instruction} ### Agizo:\n{input} ### Jibu:\n{output} ```
zakhtar/stanford-test	--- license: mit ---
NiGuLa/Russian_Inappropriate_Messages	--- language: - ru tags: - toxic comments classification license: cc task_categories: - text-classification size_categories: - 100K<n<1M --- ## General concept The 'inappropriateness' substance we tried to collect in the dataset and detect with the model is NOT a substitution of toxicity, it is rather a derivative of toxicity. So the model based on our dataset could serve as an additional layer of inappropriateness filtering after toxicity and obscenity filtration. You can detect the exact sensitive topic by using [this model](https://huggingface.co/Skoltech/russian-sensitive-topics). Generally, an inappropriate utterance is an utterance that has not obscene words or any kind of toxic intent but can still harm the reputation of the speaker. Find some samples for more intuition in the table below. Learn more about the concept of inappropriateness [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. Please note that this article describes the first version of the dataset, while the model is trained on the extended version of the dataset open-sourced on our [GitHub](https://github.com/skoltech-nlp/inappropriate-sensitive-topics/blob/main/Version2/appropriateness/Appropriateness.csv) or on [kaggle](https://www.kaggle.com/nigula/russianinappropriatemessages). The properties of the dataset are the same as the one described in the article, the only difference is the size. \| Text \| Inappropriate \| Comment \| Unsafe topic \| \|------------------------------------------------------------------------------------------------------\|---------------\|----------------------------------------------------------------\|---------------\| \| Ладно бы видного деятеля завалили а тут какого то ноунейм нигру преступника \| yes \| Оправдание убийства \| offline crime \| \| Это нарушение УКРФ! \| no \| Осуждение нарушения закона \| offline crime \| \| Религия - это насилие высшего уровня. Верующего принуждают к вере еще в младенчестве. \| yes \| Оскорбление чувств верующих \| religion \| \| Христиане, как вы относитесь к иноверцам, в частности к Гамалиилу и к уверовавшим в Иисуса фарисеям? \| no \| Обычные вопросы к верующим без намека на оскорбление их чувств \| religion \| \| Хорошо что проституция запрещена \| no \| Осуждение простуитуции \| prostitution \| \| Возьму бабки и сниму шлюх на выходных \| yes \| Популяризация проституции \| prostitution \| ## Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png ## Citation If you find this repository helpful, feel free to cite our publication: ``` @inproceedings{babakov-etal-2021-detecting, title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation", author = "Babakov, Nikolay and Logacheva, Varvara and Kozlova, Olga and Semenov, Nikita and Panchenko, Alexander", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4", pages = "26--36", abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.", } ``` ## Contacts If you have any questions please contact [Nikolay](mailto:bbkhse@gmail.com)
maximegmd/MedText-alpaca	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 949136 num_examples: 1412 download_size: 494828 dataset_size: 949136 --- # Dataset Card for "MedText-alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/zoya_akibameidosensou	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Zoya This is the dataset of Zoya, containing 114 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)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 114 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 276 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 114 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 114 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 114 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 114 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 114 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 276 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 276 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 276 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
Murali0604/Syringe-Dataset-Auto	--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 3138386.0 num_examples: 10 download_size: 3139896 dataset_size: 3138386.0 --- # Dataset Card for "Syringe-Dataset-Auto" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yzhuang/autotree_automl_credit_gosdt_l256_d3_sd0	--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 3388000000 num_examples: 100000 - name: validation num_bytes: 338800000 num_examples: 10000 download_size: 840908403 dataset_size: 3726800000 --- # Dataset Card for "autotree_automl_credit_gosdt_l256_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
marmofayezi/M3GenMaskFID	--- dataset_info: features: - name: id dtype: string - name: mask dtype: image - name: caption dtype: string - name: generated_image dtype: image splits: - name: train num_bytes: 66833951.35 num_examples: 1498 download_size: 66780852 dataset_size: 66833951.35 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/manaka_misato_idolmastercinderellagirls	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of manaka_misato/間中美里 (THE iDOLM@STER: Cinderella Girls) This is the dataset of manaka_misato/間中美里 (THE iDOLM@STER: Cinderella Girls), containing 15 images and their tags. The core tags of this character are `brown_hair, short_hair, blue_eyes`, which are pruned in this dataset. 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)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:----------\|:-----------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 15 \| 9.47 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 15 \| 9.55 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 29 \| 15.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 15 \| 9.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 29 \| 15.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/manaka_misato_idolmastercinderellagirls', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------------------------------------------------------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, smile, necklace, card_(medium), character_name, flower_(symbol) \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| smile \| necklace \| card_(medium) \| character_name \| flower_(symbol) \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------\|:-----------\|:----------------\|:-----------------\|:------------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \|
ctang/util_deontology_rewardtrainer	--- dataset_info: features: - name: idx dtype: int64 - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 4412185 num_examples: 43357 - name: validation num_bytes: 115932 num_examples: 1141 - name: test num_bytes: 116070 num_examples: 1142 download_size: 2714094 dataset_size: 4644187 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
nikhilchigali/wikianswers_small	--- language: - en size_categories: - 100K<n<1M task_categories: - sentence-similarity dataset_info: features: - name: sentence dtype: string - name: cluster dtype: int64 splits: - name: train num_bytes: 59231273 num_examples: 990526 download_size: 22602562 dataset_size: 59231273 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "WikiAnswers Small" ## Dataset Summary `nikhilchigali/wikianswers_small` is a subset of the `embedding-data/WikiAnswers` dataset ([Link](https://huggingface.co/datasets/embedding-data/WikiAnswers)). As opposed to the original dataset with `3,386,256` rows, this dataset contains only 0.13% of the total rows(sets). The sets of sentences have been unraveled into individual items with corresponding cluster IDs to identify sentences from the same set. ## Languages English. ## Dataset Structure Each example in the dataset contains a sentence and its cluster id of other equivalent sentences. The sentences in the same cluster are paraphrases of each other. ``` {"sentence": [sentence], "cluster": [cluster_id]} {"sentence": [sentence], "cluster": [cluster_id]} {"sentence": [sentence], "cluster": [cluster_id]} ... {"sentence": [sentence], "cluster": [cluster_id]} ``` ### Usage Example Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with: ```python from datasets import load_dataset dataset = load_dataset("nikhilchigali/wikianswers_small") ``` The dataset is loaded as a `DatasetDict` and has the format for `N` examples: ```python DatasetDict({ train: Dataset({ features: ['sentence', "cluster"], num_rows: N }) }) ``` Review an example `i` with: ```python dataset["train"][i] ``` ### Source Data `embedding-data/WikiAnswers` on HuggingFace ([Link](https://huggingface.co/datasets/embedding-data/WikiAnswers)) #### Note: This dataset is for the owner's personal use and claims no rights whatsoever.
anan-2024/twitter_dataset_1712965716	--- 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: 20712 num_examples: 45 download_size: 11887 dataset_size: 20712 configs: - config_name: default data_files: - split: train path: data/train-* ---
sparse-generative-ai/requests	--- license: apache-2.0 ---
asas-ai/tydiqa-ar-secondary_task	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 15715443.835027365 num_examples: 14805 - name: validation num_bytes: 908198.6986409297 num_examples: 921 download_size: 9132744 dataset_size: 16623642.533668295 license: apache-2.0 task_categories: - question-answering language: - ar pretty_name: tydiqa-ar --- # Dataset Card for "tydiqa-ar-secondary_task" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 267864523.375 num_examples: 6149 - name: fewshot_1_bs_16 num_bytes: 270237138.375 num_examples: 6149 - name: fewshot_3_bs_16 num_bytes: 274972242.375 num_examples: 6149 download_size: 797630284 dataset_size: 813073904.125 --- # Dataset Card for "OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
netcat420/quiklit	--- license: mit --- dataset designed to PEFT fine-tune mistral 7B fine-tune and quantized model trained on this dataset can be found at netcat420/MHENNlit
bigscience-data/roots_indic-ta_indic_nlp_corpus	--- language: ta license: cc-by-nc-4.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_indic-ta_indic_nlp_corpus # Indic NLP Corpus - Dataset uid: `indic_nlp_corpus` ### Description The IndicNLP corpus is a largescale, general-domain corpus containing 2.7 billion words for 10 Indian languages from two language families. s (IndoAryan branch and Dravidian). Each language has at least 100 million words (except Oriya). ### Homepage https://github.com/AI4Bharat/indicnlp_corpus#publicly-available-classification-datasets ### Licensing - non-commercial use - cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International ### Speaker Locations - Southern Asia - India ### Sizes - 3.4019 % of total - 44.4368 % of indic-hi - 64.2943 % of indic-ta - 70.5374 % of indic-ml - 54.2394 % of indic-te - 55.9105 % of indic-kn - 61.6111 % of indic-mr - 67.2242 % of indic-pa - 68.1470 % of indic-or - 64.3879 % of indic-gu - 4.1495 % of indic-bn ### BigScience processing steps #### Filters applied to: indic-hi - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ta - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ml - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-te - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-kn - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-mr - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-pa - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-or - dedup_document - dedup_template_soft - filter_remove_empty_docs #### Filters applied to: indic-gu - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300
rogetxtapai/dados-bulas	--- license: apache-2.0 ---
2030NLP/SpaCE2021	--- language: - zh task_categories: - text-classification # - feature-extraction task_ids: # - d - acceptability-classification - natural-language-inference license: cc-by-nc-sa-4.0 pretty_name: space21 size_categories: - 10K<n<100K annotations_creators: - crowdsourced - expert-generated - machine-generated source_datasets: - ccl dataset_info: - config_name: task1 features: - name: qID dtype: string - name: context dtype: string - name: judge1 dtype: bool splits: - name: train num_bytes: 1470413 num_examples: 4237 - name: validation num_bytes: 321061 num_examples: 806 - name: test num_bytes: 263854 num_examples: 794 download_size: 2373041 dataset_size: 2055328 - config_name: task2 features: - name: qID dtype: string - name: context dtype: string - name: reason dtype: string - name: judge2 dtype: bool splits: - name: train num_bytes: 2586476 num_examples: 5989 - name: validation num_bytes: 712348 num_examples: 2088 - name: test num_bytes: 773393 num_examples: 1952 download_size: 4607294 dataset_size: 4072217 - config_name: task3 features: - name: qID dtype: string - name: context dtype: string - name: reason dtype: string - name: judge1 dtype: bool - name: judge2 dtype: bool splits: - name: validation num_bytes: 539209 num_examples: 1203 - name: test num_bytes: 445760 num_examples: 1167 download_size: 1110504 dataset_size: 984969 --- # Dataset Card for SpaCE2021 ## Dataset Description - Homepage: http://ccl.pku.edu.cn:8084/SpaCE2021/ - Repository: https://github.com/2030NLP/SpaCE2021 - Paper: [詹卫东、孙春晖、岳朋雪、唐乾桐、秦梓巍，2022，空间语义理解能力评测任务设计的新思路——SpaCE2021数据集的研制，《语言文字应用》2022年第2期（总第122期），pp.99-110。](https://yyyy.cbpt.cnki.net/WKC/WebPublication/paperDigest.aspx?paperID=c66cca51-7783-430e-abf1-28f6c28c49f6) - Leaderboard: https://github.com/2030NLP/SpaCE2021 - Point of Contact: sc_eval@163.com ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
open-llm-leaderboard/details_zarakiquemparte__zarafusionex-1.1-l2-7b	--- pretty_name: Evaluation run of zarakiquemparte/zarafusionex-1.1-l2-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [zarakiquemparte/zarafusionex-1.1-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionex-1.1-l2-7b)\ \ 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_zarakiquemparte__zarafusionex-1.1-l2-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-14T18:56:02.826185](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionex-1.1-l2-7b/blob/main/results_2023-10-14T18-56-02.826185.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\": 0.22158137583892618,\n\ \ \"em_stderr\": 0.004253171428083834,\n \"f1\": 0.30786073825503446,\n\ \ \"f1_stderr\": 0.004263392065531877,\n \"acc\": 0.4111836416696536,\n\ \ \"acc_stderr\": 0.009825954163274354\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.22158137583892618,\n \"em_stderr\": 0.004253171428083834,\n\ \ \"f1\": 0.30786073825503446,\n \"f1_stderr\": 0.004263392065531877\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.07808946171341925,\n \ \ \"acc_stderr\": 0.007390654481108235\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.744277821625888,\n \"acc_stderr\": 0.012261253845440474\n\ \ }\n}\n```" repo_url: https://huggingface.co/zarakiquemparte/zarafusionex-1.1-l2-7b 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: 2023_08_26T09_58_58.682404 path: - '/details_harness\|arc:challenge\|25_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-26T09:58:58.682404.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_14T18_56_02.826185 path: - '/details_harness\|drop\|3_2023-10-14T18-56-02.826185.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-14T18-56-02.826185.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_14T18_56_02.826185 path: - '/details_harness\|gsm8k\|5_2023-10-14T18-56-02.826185.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-14T18-56-02.826185.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hellaswag\|10_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-26T09:58:58.682404.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_26T09_58_58.682404 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-26T09:58:58.682404.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_14T18_56_02.826185 path: - '/details_harness\|winogrande\|5_2023-10-14T18-56-02.826185.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-14T18-56-02.826185.parquet' - config_name: results data_files: - split: 2023_08_26T09_58_58.682404 path: - results_2023-08-26T09:58:58.682404.parquet - split: 2023_10_14T18_56_02.826185 path: - results_2023-10-14T18-56-02.826185.parquet - split: latest path: - results_2023-10-14T18-56-02.826185.parquet --- # Dataset Card for Evaluation run of zarakiquemparte/zarafusionex-1.1-l2-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/zarakiquemparte/zarafusionex-1.1-l2-7b - 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 [zarakiquemparte/zarafusionex-1.1-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionex-1.1-l2-7b) 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_zarakiquemparte__zarafusionex-1.1-l2-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-14T18:56:02.826185](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionex-1.1-l2-7b/blob/main/results_2023-10-14T18-56-02.826185.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": 0.22158137583892618, "em_stderr": 0.004253171428083834, "f1": 0.30786073825503446, "f1_stderr": 0.004263392065531877, "acc": 0.4111836416696536, "acc_stderr": 0.009825954163274354 }, "harness\|drop\|3": { "em": 0.22158137583892618, "em_stderr": 0.004253171428083834, "f1": 0.30786073825503446, "f1_stderr": 0.004263392065531877 }, "harness\|gsm8k\|5": { "acc": 0.07808946171341925, "acc_stderr": 0.007390654481108235 }, "harness\|winogrande\|5": { "acc": 0.744277821625888, "acc_stderr": 0.012261253845440474 } } ``` ### 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]
Darsh12/train_data	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 20381060.0 num_examples: 69 download_size: 18323201 dataset_size: 20381060.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
joey234/mmlu-high_school_computer_science-dev	--- 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 splits: - name: dev num_bytes: 5225 num_examples: 5 download_size: 0 dataset_size: 5225 --- # Dataset Card for "mmlu-high_school_computer_science-dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BramVanroy/stackoverflow-chat-dutch	--- language: - nl license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering - text-generation pretty_name: Stack Overflow Chat Dutch tags: - baize - stack overflow - stackoverflow - chat dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 76877122 num_examples: 51267 - name: test_sft num_bytes: 8539723 num_examples: 5697 download_size: 43666524 dataset_size: 85416845 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* --- # Dataset Card for Stack Overflow Chat Dutch ## Dataset Description - Homepage: N/A - Repository: N/A - Paper: N/A - Leaderboard: N/A - Point of Contact: Bram Vanroy ### Dataset Summary This dataset contains 56,964 conversations between een AI assistant and a (fake) "Human" (generated) in Dutch, specifically in the domain of programming (Stack Overflow). They are translations of [Baize](https://github.com/project-baize/baize-chatbot/tree/6790946f638d60fcaf397574189124f15792f35a/data)'s machine-generated answers to the [Stack Overflow](https://huggingface.co/datasets/pacovaldez/stackoverflow-questions) dataset. ☕ [Want to help me out?](https://www.buymeacoffee.com/bramvanroy) Translating the data with the OpenAI API, and prompt testing, cost me 💸$133.60💸. If you like this dataset, please consider [buying me a coffee](https://www.buymeacoffee.com/bramvanroy) to offset a portion of this cost, I appreciate it a lot! ☕ If you use this dataset or refer to it, please use the following citation: Vanroy, B. (2023). Language Resources for Dutch Large Language Modelling. [https://arxiv.org/abs/2312.12852](https://arxiv.org/abs/2312.12852) ```bibtext @article{vanroy2023language, title={Language Resources for {Dutch} Large Language Modelling}, author={Vanroy, Bram}, journal={arXiv preprint arXiv:2312.12852}, year={2023} } ``` ### Languages - Dutch ## Dataset Structure ### Data Instances ```python {'id': 9, 'input': 'Het gesprek tussen de mens en de AI-assistent.\n' '[\|Human\|] Ik heb wat hulp nodig met Spark SQL union en het ordenen ' 'van kolommen.\n' '[\|AI\|] Natuurlijk, ik help je graag. Waar heb je specifiek hulp ' 'bij nodig?\n' '[\|Human\|] Ik wil de output van een UNION-query ordenen op een ' 'kolom die niet aanwezig is in de SELECT-instructie. Is dat ' 'mogelijk?\n' '[\|AI\|] Ja, het is mogelijk met Spark SQL. Je kunt de ordinale ' 'positie van de kolom in de ORDER BY-clausule gebruiken in plaats ' 'van de kolomnaam. Als je bijvoorbeeld wilt ordenen op de derde ' 'kolom in de SELECT-instructie, kun je ORDER BY 3 gebruiken.\n' '[\|Human\|] Dat is nuttig. Kan ik ook een alias gebruiken in de ' 'ORDER BY-clausule?\n' '[\|AI\|] Ja, je kunt een alias gebruiken in de ORDER BY-clausule. De ' 'alias moet worden gedefinieerd in de SELECT-instructie. Als je ' 'bijvoorbeeld de derde kolom als "col3" alias geeft, kun je ordenen ' 'met ORDER BY col3.\n' '[\|Human\|] Oké, ik denk dat ik het begrijp. Kan ik dezelfde alias ' 'gebruiken voor kolommen uit beide tabellen in de UNION?\n' '[\|AI\|] Nee, je kunt dezelfde alias niet gebruiken voor kolommen ' 'uit beide tabellen in de UNION. Elke tabel in de UNION moet een ' 'unieke alias hebben voor de kolommen.\n' '[\|Human\|] ', 'topic': 'Spark SQL UNION - ORDER BY kolom niet in SELECT'}, ``` ### Data Fields - id: the ID of the item. The following 82 IDs are not included because they could not be translated: `[1713, 1937, 1960, 4326, 4356, 8357, 8542, 8827, 9137, 9782, 11560, 11961, 12244, 12362, 12488, 13259, 13621, 14445, 14835, 15006, 17746, 18808, 19285, 19426, 19491, 21270, 21661, 22098, 23352, 23840, 23869, 25148, 25928, 27102, 27856, 28387, 29942, 30041, 30251, 32396, 32742, 32941, 33628, 34116, 34648, 34859, 35977, 35987, 36035, 36456, 37028, 37238, 37640, 38107, 38735, 39015, 40984, 41115, 41567, 42397, 43219, 43783, 44599, 44980, 45239, 47676, 48922, 49534, 50282, 50683, 50804, 50919, 51076, 51211, 52000, 52183, 52489, 52595, 53884, 54726, 55795, 56992]` - input: the machine-generated conversation between AI and "Human". Always starts with `Het gesprek tussen de mens en de AI-assistent.\n` and has at least one occurrence of both `[\|AI\|]` and `[\|Human\|]`. - topic: the topic description ## Dataset Creation Both the translations and the topics were translated with OpenAI's API for `gpt-3.5-turbo`. `max_tokens=1024, temperature=0` as parameters. The prompt template to translate the input is (where `src_lang` was English and `tgt_lang` Dutch): ```python CONVERSATION_TRANSLATION_PROMPT = """You are asked to translate a conversation between an AI assistant and a human from {src_lang} into {tgt_lang}. Here are the requirements that you should adhere to: 1. maintain the format: the conversation consists of the AI (marked as `[\|AI\|]`) and the human (`[\|Human\|]`) talking in turns and responding to each other; 2. do not translate the speaker identifiers `[\|AI\|]` and `[\|Human\|]` but always copy them into the translation in appropriate places; 3. ensure accurate translation and keep the correctness of the conversation; 4. make sure that text is fluent to read and does not contain grammatical errors. Use standard {tgt_lang} without regional bias; 5. translate the human's text using informal, but standard, language; 6. make sure to avoid biases (such as gender bias, grammatical bias, social bias); 7. if the human asks to correct grammar mistakes or spelling mistakes then you have to generate a similar mistake in {tgt_lang}, and then also generate a corrected output version for the AI in {tgt_lang}; 8. if the human asks to translate text from one to another language, then you only translate the human's question to {tgt_lang} but you keep the translation that the AI provides in the language that the human requested; 9. do not translate code fragments but copy them as they are. If there are English examples, variable names or definitions in code fragments, keep them in English. Now translate the following conversation with the requirements set out above. Do not provide an explanation and do not add anything else.\n\n""" ``` The prompt to translate the topic is: ```python TOPIC_TRANSLATION_PROMPT = "Translate the following title of a conversation from {src_lang} to {tgt_lang} in a succinct," \ " summarizing manner. Translate accurately and formally. Do not provide any explanation" \ " about the translation and do not include the original title.\n\n" ``` The system message was: ``` You are a helpful assistant that translates English to Dutch to the requirements that are given to you. ``` Note that 82 items (0.1%) were not successfully translated. The translation was missing the AI identifier `[\|AI\|]` and/or the human one `[\|Human\|]`. The IDs for the missing items are `[1713, 1937, 1960, 4326, 4356, 8357, 8542, 8827, 9137, 9782, 11560, 11961, 12244, 12362, 12488, 13259, 13621, 14445, 14835, 15006, 17746, 18808, 19285, 19426, 19491, 21270, 21661, 22098, 23352, 23840, 23869, 25148, 25928, 27102, 27856, 28387, 29942, 30041, 30251, 32396, 32742, 32941, 33628, 34116, 34648, 34859, 35977, 35987, 36035, 36456, 37028, 37238, 37640, 38107, 38735, 39015, 40984, 41115, 41567, 42397, 43219, 43783, 44599, 44980, 45239, 47676, 48922, 49534, 50282, 50683, 50804, 50919, 51076, 51211, 52000, 52183, 52489, 52595, 53884, 54726, 55795, 56992]`. ### Source Data #### Initial Data Collection and Normalization Initial data generation by [Baize](https://github.com/project-baize/baize-chatbot), which in turn relied on [Stack Overflow Questions](https://huggingface.co/datasets/pacovaldez/stackoverflow-questions). #### Who are the source language producers? Quora users and OpenAI's GPT models (via [Baize](https://github.com/project-baize/baize-chatbot)). ## Considerations for Using the Data Note that neither the translations in this new dataset, nor the machine-generated dialogues in the original [Baize](https://github.com/project-baize/baize-chatbot) dataset, have been verified by humans. ### Discussion of Biases As with any machine-generated texts, users should be aware of potential biases that are included in this dataset. Although the prompt specifically includes `make sure to avoid biases (such as gender bias, grammatical bias, social bias)`, of course the impact of such command is not known. It is likely that biases remain in the dataset so use with caution. ### Other Known Limitations The translation quality has not been verified. Use at your own risk! ### Licensing Information Licensing info for [`Stack Overflow Questions`](https://huggingface.co/datasets/pacovaldez/stackoverflow-questions) is listed as Apache 2.0. If you use the current dataset, you should also adhere to the original license. This text was generated (either in part or in full) with GPT-3 (`gpt-3.5-turbo`), OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication. If you use this dataset, you must also follow the [Sharing](https://openai.com/policies/sharing-publication-policy) and [Usage](https://openai.com/policies/usage-policies) policies. As clearly stated in their [Terms of Use](https://openai.com/policies/terms-of-use), specifically 2c.iii, "[you may not] use output from the Services to develop models that compete with OpenAI". That means that you cannot use this dataset to build models that are intended to commercially compete with OpenAI. [As far as I am aware](https://law.stackexchange.com/questions/93308/licensing-material-generated-with-chatgpt), that is a specific restriction that should serve as an addendum to the current license. ### Contributions Thanks to [Baize](https://github.com/project-baize/baize-chatbot) for the initial machine-generated dialogues, based on [Stack Overflow Questions](https://huggingface.co/datasets/pacovaldez/stackoverflow-questions).
ademirsl/diniz	--- license: openrail ---
bcmaster/Breezy1	--- license: openrail ---
camenduru/test-0_6500	--- dataset_info: features: - name: image dtype: image - name: url dtype: string - name: prompt dtype: string - name: style dtype: string splits: - name: train num_bytes: 10874304856.5 num_examples: 6500 download_size: 10856442215 dataset_size: 10874304856.5 --- # Dataset Card for "OpenNiji-Dataset-6500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-billsum-default-bec98f-32334145011	--- type: predictions tags: - autotrain - evaluation datasets: - billsum eval_info: task: summarization model: PoseyATX/Moist-Pony metrics: [] dataset_name: billsum dataset_config: default dataset_split: test col_mapping: text: text target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: PoseyATX/Moist-Pony * Dataset: billsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@poseyatx](https://huggingface.co/poseyatx) for evaluating this model.
utkarshhh17/indian_food_images	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': burger '1': butter_naan '2': chai '3': chapati '4': chole_bhature '5': dal_makhani '6': dhokla '7': fried_rice '8': idli '9': jalebi '10': kaathi_rolls '11': kadai_paneer '12': kulfi '13': masala_dosa '14': momos '15': paani_puri '16': pakode '17': pav_bhaji '18': pizza '19': samosa splits: - name: train num_bytes: 1697830157.4234333 num_examples: 5328 - name: test num_bytes: 249679569.3925666 num_examples: 941 download_size: 1601513193 dataset_size: 1947509726.816 --- # Dataset Card for "indian_food_images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ruqoyya/celeb-identities	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Albert_Einstein '1': Ashley_Olsen '2': Chris_Rock '3': Cristiano_Ronaldo '4': Didier_Drogba '5': Idris_Elba '6': Lionel_Messi '7': Mary-Kate_Olsen '8': Paul_Pogba '9': Tamera_Mowry '10': Tia_Mowry splits: - name: train num_bytes: 1992683.0 num_examples: 34 download_size: 1995278 dataset_size: 1992683.0 --- # Dataset Card for "celeb-identities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
metaeval/cnli	--- language: - en --- # Generalization of Counterfactually-Augmented NLI Data ```bib @inproceedings{huang2020cnligeneralization, title={Counterfactually-Augmented {SNLI} Training Data Does Not Yield Better Generalization Than Unaugmented Data}, author={William Huang and Haokun Liu and Samuel R. Bowman}, booktitle = {Proceedings of the 2020 EMNLP Workshop on Insights from Negative Results in NLP}, year={2020}, publisher = {The Association for Computational Linguistics} } ```
foxxy-hm/slu-augmented-data	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: speech sequence: float64 - name: sampling_rate dtype: int64 - name: target_text dtype: string splits: - name: train num_bytes: 3720263051.262795 num_examples: 7190 - name: test num_bytes: 930324473.7372051 num_examples: 1798 download_size: 2043481654 dataset_size: 4650587525.0 --- # Dataset Card for "slu-augmented-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
feliciamj/classifier	--- 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: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos splits: - name: train num_bytes: 1074806 num_examples: 8530 - name: validation num_bytes: 134675 num_examples: 1066 - name: test num_bytes: 135968 num_examples: 1066 download_size: 881052 dataset_size: 1345449 --- # Dataset Card for "classifier" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mahdibaghbanzadeh/GUE_mouse_4	--- dataset_info: features: - name: sequence dtype: string - name: labels dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 1702232 num_examples: 15064 - name: val num_bytes: 212779 num_examples: 1883 - name: test num_bytes: 212779 num_examples: 1883 download_size: 980363 dataset_size: 2127790 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---
anzorq/kbd_lat-ru	--- language: - kbd - ru license: - mit tags: - translation pretty_name: Kbd Ru Translation source_datasets: - original multilinguality: - multilingual task_categories: - translation - text2text-generation task_ids: - translation - text2text-generation ---
quocanh34/test_synthesis_data	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string splits: - name: train num_bytes: 38195460 num_examples: 75 download_size: 9149788 dataset_size: 38195460 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "test_synthesis_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BhavyaMuni/sample_dataset	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 627787 num_examples: 387 download_size: 259828 dataset_size: 627787 --- # Dataset Card for "sample_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rojagtap/ubuntu_dialogs_corpus	--- license: mit --- https://github.com/rkadlec/ubuntu-ranking-dataset-creator#readme
senhorsapo/luffy	--- license: openrail ---
kimjaewon/baemin_sft_data	--- dataset_info: features: - name: question dtype: string - name: positive_document_list sequence: string - name: negative_document_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4484015 num_examples: 1452 download_size: 311120 dataset_size: 4484015 configs: - config_name: default data_files: - split: train path: data/train-* ---
khaimaitien/leetcode_problem_solution	--- task_categories: - text-generation --- This dataset contains: problems and solutions in Leetcode, crawled from: https://github.com/AnasImloul/Leetcode-Solutions The format of data: + title: title of the problem + algo_input: the description of the problem + solution_py: the solution in Python + solution_js: the solution in Js + solution_java: the solution in Java + solution_c: the solution in C
jijivski/mock_mmlu	--- license: mit ---
h2oai/h2ogpt-oig-instruct-cleaned	--- license: apache-2.0 language: - en thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico tags: - gpt - llm - large language model - open-source --- # h2oGPT Data Card ## Summary H2O.ai's `h2ogpt-oig-instruct-cleaned` is an open-source instruct-type dataset for fine-tuning of large language models, licensed for commercial use. - Number of rows: `195436` - Number of columns: `1` - Column names: `['input']` ## Source - [Original LAION OIG Dataset](https://github.com/LAION-AI/Open-Instruction-Generalist) - [LAION OIG data detoxed and filtered down by scripts in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/b8f15efcc305a953c52a0ee25b8b4897ceb68c0a/scrape_dai_docs.py)
anan-2024/twitter_dataset_1713150974	--- 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: 22176 num_examples: 51 download_size: 12543 dataset_size: 22176 configs: - config_name: default data_files: - split: train path: data/train-* ---
qqqqq1/Javaragas	--- language: - zh pretty_name: java ragas ---
EleutherAI/quirky_hemisphere_bob	--- 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: float64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 375918.8239690378 num_examples: 3746 - name: validation num_bytes: 200694.0 num_examples: 2000 - name: test num_bytes: 200545.5 num_examples: 2000 download_size: 196630 dataset_size: 777158.3239690377 --- # Dataset Card for "quirky_hemisphere_bob" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ckail/Needy_Girl_Overdose	--- license: gpl-3.0 ---
abhishek/autotrain-data-as6y-5zz4-oanx	--- dataset_info: features: - name: PassengerId dtype: int64 - name: Pclass dtype: int64 - name: Name dtype: string - name: Sex dtype: string - name: Age dtype: float64 - name: SibSp dtype: int64 - name: Parch dtype: int64 - name: Ticket dtype: string - name: Fare dtype: float64 - name: Cabin dtype: string - name: Embarked dtype: string - name: autotrain_id dtype: int64 - name: autotrain_label dtype: int64 splits: - name: train num_bytes: 88677 num_examples: 712 - name: validation num_bytes: 22334 num_examples: 179 download_size: 54451 dataset_size: 111011 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "autotrain-data-as6y-5zz4-oanx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DeepFoldProtein/foldseek_combined	--- dataset_info: features: - name: id dtype: string - name: seq dtype: string splits: - name: train num_bytes: 684846121 num_examples: 2302908 download_size: 675247498 dataset_size: 684846121 configs: - config_name: default data_files: - split: train path: data/train-* ---
BigTMiami/small_amazon_review_text_dataset	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 428020 num_examples: 3504 - name: validation num_bytes: 428020 num_examples: 3504 download_size: 253700 dataset_size: 856040 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
khalidalt/Moroccan_offensive_detection	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 2551311 num_examples: 20402 download_size: 1367902 dataset_size: 2551311 configs: - config_name: default data_files: - split: train path: data/train-* language: - ar ---
toddmorrill/github-issues	--- YAML tags: annotations_creators: - no-annotation language_creators: [] language: - '''en-US''' license: [] multilinguality: - monolingual pretty_name: Hugging Face Github Issues size_categories: - unknown source_datasets: [] task_categories: - text-classification - text-retrieval task_ids: - multi-class-classification - multi-label-classification - document-retrieval --- # Dataset Card for GitHub Issues ## Dataset Summary GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository. It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.
open-llm-leaderboard/details_damerajee__Gaja-v1.00	--- pretty_name: Evaluation run of damerajee/Gaja-v1.00 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [damerajee/Gaja-v1.00](https://huggingface.co/damerajee/Gaja-v1.00) 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_damerajee__Gaja-v1.00\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-29T19:42:27.691927](https://huggingface.co/datasets/open-llm-leaderboard/details_damerajee__Gaja-v1.00/blob/main/results_2024-02-29T19-42-27.691927.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\": 0.4104632836512482,\n\ \ \"acc_stderr\": 0.03423965153377148,\n \"acc_norm\": 0.41631431599382307,\n\ \ \"acc_norm_stderr\": 0.035127053633139296,\n \"mc1\": 0.28886168910648713,\n\ \ \"mc1_stderr\": 0.01586634640138431,\n \"mc2\": 0.4463856836867432,\n\ \ \"mc2_stderr\": 0.014941359214508863\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.4803754266211604,\n \"acc_stderr\": 0.014600132075947084,\n\ \ \"acc_norm\": 0.5281569965870307,\n \"acc_norm_stderr\": 0.014588204105102203\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5711013742282414,\n\ \ \"acc_stderr\": 0.004939073014754943,\n \"acc_norm\": 0.7630950009958176,\n\ \ \"acc_norm_stderr\": 0.004243145587737571\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.4074074074074074,\n\ \ \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.4074074074074074,\n\ \ \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.4473684210526316,\n \"acc_stderr\": 0.04046336883978251,\n\ \ \"acc_norm\": 0.4473684210526316,\n \"acc_norm_stderr\": 0.04046336883978251\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.41132075471698115,\n \"acc_stderr\": 0.030285009259009798,\n\ \ \"acc_norm\": 0.41132075471698115,\n \"acc_norm_stderr\": 0.030285009259009798\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.375,\n\ \ \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.375,\n \ \ \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n\ \ \"acc_norm_stderr\": 0.049236596391733084\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\": 0.32947976878612717,\n\ \ \"acc_stderr\": 0.03583901754736411,\n \"acc_norm\": 0.32947976878612717,\n\ \ \"acc_norm_stderr\": 0.03583901754736411\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.038739587141493524,\n\ \ \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.038739587141493524\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.33191489361702126,\n \"acc_stderr\": 0.030783736757745657,\n\ \ \"acc_norm\": 0.33191489361702126,\n \"acc_norm_stderr\": 0.030783736757745657\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.20175438596491227,\n\ \ \"acc_stderr\": 0.03775205013583638,\n \"acc_norm\": 0.20175438596491227,\n\ \ \"acc_norm_stderr\": 0.03775205013583638\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708617,\n \"\ acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708617\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.23809523809523808,\n\ \ \"acc_stderr\": 0.038095238095238106,\n \"acc_norm\": 0.23809523809523808,\n\ \ \"acc_norm_stderr\": 0.038095238095238106\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.44193548387096776,\n\ \ \"acc_stderr\": 0.028251557906849734,\n \"acc_norm\": 0.44193548387096776,\n\ \ \"acc_norm_stderr\": 0.028251557906849734\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\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.5212121212121212,\n \"acc_stderr\": 0.03900828913737302,\n\ \ \"acc_norm\": 0.5212121212121212,\n \"acc_norm_stderr\": 0.03900828913737302\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.41919191919191917,\n \"acc_stderr\": 0.035155207286704175,\n \"\ acc_norm\": 0.41919191919191917,\n \"acc_norm_stderr\": 0.035155207286704175\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.5440414507772021,\n \"acc_stderr\": 0.035944137112724386,\n\ \ \"acc_norm\": 0.5440414507772021,\n \"acc_norm_stderr\": 0.035944137112724386\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.38461538461538464,\n \"acc_stderr\": 0.024666744915187222,\n\ \ \"acc_norm\": 0.38461538461538464,\n \"acc_norm_stderr\": 0.024666744915187222\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945284,\n \ \ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945284\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.3697478991596639,\n \"acc_stderr\": 0.03135709599613591,\n \ \ \"acc_norm\": 0.3697478991596639,\n \"acc_norm_stderr\": 0.03135709599613591\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.48807339449541287,\n \"acc_stderr\": 0.021431223617362223,\n \"\ acc_norm\": 0.48807339449541287,\n \"acc_norm_stderr\": 0.021431223617362223\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.32407407407407407,\n \"acc_stderr\": 0.03191923445686186,\n \"\ acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.03191923445686186\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.47549019607843135,\n \"acc_stderr\": 0.035050931943487976,\n \"\ acc_norm\": 0.47549019607843135,\n \"acc_norm_stderr\": 0.035050931943487976\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.5358649789029536,\n \"acc_stderr\": 0.03246338898055659,\n \ \ \"acc_norm\": 0.5358649789029536,\n \"acc_norm_stderr\": 0.03246338898055659\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5112107623318386,\n\ \ \"acc_stderr\": 0.033549366530984746,\n \"acc_norm\": 0.5112107623318386,\n\ \ \"acc_norm_stderr\": 0.033549366530984746\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.45038167938931295,\n \"acc_stderr\": 0.04363643698524779,\n\ \ \"acc_norm\": 0.45038167938931295,\n \"acc_norm_stderr\": 0.04363643698524779\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.5950413223140496,\n \"acc_stderr\": 0.04481137755942469,\n \"\ acc_norm\": 0.5950413223140496,\n \"acc_norm_stderr\": 0.04481137755942469\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.31901840490797545,\n \"acc_stderr\": 0.03661997551073836,\n\ \ \"acc_norm\": 0.31901840490797545,\n \"acc_norm_stderr\": 0.03661997551073836\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.26785714285714285,\n\ \ \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n\ \ \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.4077669902912621,\n \"acc_stderr\": 0.048657775704107696,\n\ \ \"acc_norm\": 0.4077669902912621,\n \"acc_norm_stderr\": 0.048657775704107696\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.5341880341880342,\n\ \ \"acc_stderr\": 0.03267942734081228,\n \"acc_norm\": 0.5341880341880342,\n\ \ \"acc_norm_stderr\": 0.03267942734081228\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.5223499361430396,\n\ \ \"acc_stderr\": 0.017862091778507862,\n \"acc_norm\": 0.5223499361430396,\n\ \ \"acc_norm_stderr\": 0.017862091778507862\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.4479768786127168,\n \"acc_stderr\": 0.02677299065336183,\n\ \ \"acc_norm\": 0.4479768786127168,\n \"acc_norm_stderr\": 0.02677299065336183\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3027932960893855,\n\ \ \"acc_stderr\": 0.01536686038639711,\n \"acc_norm\": 0.3027932960893855,\n\ \ \"acc_norm_stderr\": 0.01536686038639711\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.3954248366013072,\n \"acc_stderr\": 0.02799672318063144,\n\ \ \"acc_norm\": 0.3954248366013072,\n \"acc_norm_stderr\": 0.02799672318063144\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5048231511254019,\n\ \ \"acc_stderr\": 0.028396770444111298,\n \"acc_norm\": 0.5048231511254019,\n\ \ \"acc_norm_stderr\": 0.028396770444111298\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.48148148148148145,\n \"acc_stderr\": 0.027801656212323667,\n\ \ \"acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.027801656212323667\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3617021276595745,\n \"acc_stderr\": 0.0286638201471995,\n \ \ \"acc_norm\": 0.3617021276595745,\n \"acc_norm_stderr\": 0.0286638201471995\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3135593220338983,\n\ \ \"acc_stderr\": 0.01184923429145932,\n \"acc_norm\": 0.3135593220338983,\n\ \ \"acc_norm_stderr\": 0.01184923429145932\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.02989616303312547,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.02989616303312547\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.3954248366013072,\n \"acc_stderr\": 0.01978046595477753,\n \ \ \"acc_norm\": 0.3954248366013072,\n \"acc_norm_stderr\": 0.01978046595477753\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.42727272727272725,\n\ \ \"acc_stderr\": 0.04738198703545483,\n \"acc_norm\": 0.42727272727272725,\n\ \ \"acc_norm_stderr\": 0.04738198703545483\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.45714285714285713,\n \"acc_stderr\": 0.031891418324213966,\n\ \ \"acc_norm\": 0.45714285714285713,\n \"acc_norm_stderr\": 0.031891418324213966\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.5024875621890548,\n\ \ \"acc_stderr\": 0.03535490150137288,\n \"acc_norm\": 0.5024875621890548,\n\ \ \"acc_norm_stderr\": 0.03535490150137288\n },\n \"harness\|hendrycksTest-us_foreign_policy\|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-virology\|5\": {\n \"acc\": 0.35542168674698793,\n\ \ \"acc_stderr\": 0.03726214354322415,\n \"acc_norm\": 0.35542168674698793,\n\ \ \"acc_norm_stderr\": 0.03726214354322415\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.5497076023391813,\n \"acc_stderr\": 0.038158273659132366,\n\ \ \"acc_norm\": 0.5497076023391813,\n \"acc_norm_stderr\": 0.038158273659132366\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.28886168910648713,\n\ \ \"mc1_stderr\": 0.01586634640138431,\n \"mc2\": 0.4463856836867432,\n\ \ \"mc2_stderr\": 0.014941359214508863\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7063930544593529,\n \"acc_stderr\": 0.012799397296204164\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.009097801364670205,\n \ \ \"acc_stderr\": 0.0026153265107756725\n }\n}\n```" repo_url: https://huggingface.co/damerajee/Gaja-v1.00 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_02_29T19_42_27.691927 path: - '/details_harness\|arc:challenge\|25_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-29T19-42-27.691927.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|gsm8k\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hellaswag\|10_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T19-42-27.691927.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-29T19-42-27.691927.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '/details_harness\|winogrande\|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-29T19-42-27.691927.parquet' - config_name: results data_files: - split: 2024_02_29T19_42_27.691927 path: - results_2024-02-29T19-42-27.691927.parquet - split: latest path: - results_2024-02-29T19-42-27.691927.parquet --- # Dataset Card for Evaluation run of damerajee/Gaja-v1.00 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [damerajee/Gaja-v1.00](https://huggingface.co/damerajee/Gaja-v1.00) 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_damerajee__Gaja-v1.00", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-29T19:42:27.691927](https://huggingface.co/datasets/open-llm-leaderboard/details_damerajee__Gaja-v1.00/blob/main/results_2024-02-29T19-42-27.691927.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": 0.4104632836512482, "acc_stderr": 0.03423965153377148, "acc_norm": 0.41631431599382307, "acc_norm_stderr": 0.035127053633139296, "mc1": 0.28886168910648713, "mc1_stderr": 0.01586634640138431, "mc2": 0.4463856836867432, "mc2_stderr": 0.014941359214508863 }, "harness\|arc:challenge\|25": { "acc": 0.4803754266211604, "acc_stderr": 0.014600132075947084, "acc_norm": 0.5281569965870307, "acc_norm_stderr": 0.014588204105102203 }, "harness\|hellaswag\|10": { "acc": 0.5711013742282414, "acc_stderr": 0.004939073014754943, "acc_norm": 0.7630950009958176, "acc_norm_stderr": 0.004243145587737571 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.04244633238353228, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.04244633238353228 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04046336883978251, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04046336883978251 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.41132075471698115, "acc_stderr": 0.030285009259009798, "acc_norm": 0.41132075471698115, "acc_norm_stderr": 0.030285009259009798 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.375, "acc_stderr": 0.04048439222695598, "acc_norm": 0.375, "acc_norm_stderr": 0.04048439222695598 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "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": 0.32947976878612717, "acc_stderr": 0.03583901754736411, "acc_norm": 0.32947976878612717, "acc_norm_stderr": 0.03583901754736411 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.18627450980392157, "acc_stderr": 0.038739587141493524, "acc_norm": 0.18627450980392157, "acc_norm_stderr": 0.038739587141493524 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.33191489361702126, "acc_stderr": 0.030783736757745657, "acc_norm": 0.33191489361702126, "acc_norm_stderr": 0.030783736757745657 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.20175438596491227, "acc_stderr": 0.03775205013583638, "acc_norm": 0.20175438596491227, "acc_norm_stderr": 0.03775205013583638 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.41379310344827586, "acc_stderr": 0.04104269211806232, "acc_norm": 0.41379310344827586, "acc_norm_stderr": 0.04104269211806232 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.26455026455026454, "acc_stderr": 0.022717467897708617, "acc_norm": 0.26455026455026454, "acc_norm_stderr": 0.022717467897708617 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.23809523809523808, "acc_stderr": 0.038095238095238106, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.038095238095238106 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.44193548387096776, "acc_stderr": 0.028251557906849734, "acc_norm": 0.44193548387096776, "acc_norm_stderr": 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"harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5223499361430396, "acc_stderr": 0.017862091778507862, "acc_norm": 0.5223499361430396, "acc_norm_stderr": 0.017862091778507862 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.4479768786127168, "acc_stderr": 0.02677299065336183, "acc_norm": 0.4479768786127168, "acc_norm_stderr": 0.02677299065336183 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3027932960893855, "acc_stderr": 0.01536686038639711, "acc_norm": 0.3027932960893855, "acc_norm_stderr": 0.01536686038639711 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.3954248366013072, "acc_stderr": 0.02799672318063144, "acc_norm": 0.3954248366013072, "acc_norm_stderr": 0.02799672318063144 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5048231511254019, "acc_stderr": 0.028396770444111298, "acc_norm": 0.5048231511254019, "acc_norm_stderr": 0.028396770444111298 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.48148148148148145, "acc_stderr": 0.027801656212323667, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.027801656212323667 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3617021276595745, "acc_stderr": 0.0286638201471995, "acc_norm": 0.3617021276595745, "acc_norm_stderr": 0.0286638201471995 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3135593220338983, "acc_stderr": 0.01184923429145932, "acc_norm": 0.3135593220338983, "acc_norm_stderr": 0.01184923429145932 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.02989616303312547, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.02989616303312547 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.3954248366013072, "acc_stderr": 0.01978046595477753, "acc_norm": 0.3954248366013072, "acc_norm_stderr": 0.01978046595477753 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.42727272727272725, "acc_stderr": 0.04738198703545483, "acc_norm": 0.42727272727272725, "acc_norm_stderr": 0.04738198703545483 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.45714285714285713, "acc_stderr": 0.031891418324213966, "acc_norm": 0.45714285714285713, "acc_norm_stderr": 0.031891418324213966 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5024875621890548, "acc_stderr": 0.03535490150137288, "acc_norm": 0.5024875621890548, "acc_norm_stderr": 0.03535490150137288 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.35542168674698793, "acc_stderr": 0.03726214354322415, "acc_norm": 0.35542168674698793, "acc_norm_stderr": 0.03726214354322415 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.5497076023391813, "acc_stderr": 0.038158273659132366, "acc_norm": 0.5497076023391813, "acc_norm_stderr": 0.038158273659132366 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.28886168910648713, "mc1_stderr": 0.01586634640138431, "mc2": 0.4463856836867432, "mc2_stderr": 0.014941359214508863 }, "harness\|winogrande\|5": { "acc": 0.7063930544593529, "acc_stderr": 0.012799397296204164 }, "harness\|gsm8k\|5": { "acc": 0.009097801364670205, "acc_stderr": 0.0026153265107756725 } } ``` ## 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]
liuyanchen1015/MULTI_VALUE_rte_those_them	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 32717 num_examples: 63 - name: train num_bytes: 22627 num_examples: 44 download_size: 47207 dataset_size: 55344 --- # Dataset Card for "MULTI_VALUE_rte_those_them" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/Blood_8_classes_Dataset	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': basophil '1': eosinophil '2': erythroblast '3': ig '4': lymphocyte '5': monocyte '6': neutrophil '7': platelet splits: - name: train num_bytes: 302576945.936 num_examples: 17092 download_size: 280999680 dataset_size: 302576945.936 --- # Dataset Card for Blood_8_Classes_Dataset ## Description This dataset contains images and labels for blood cells in eight different classes. The images are classified into the following classes: - basophil - eosinophil - erythroblast - ig - lymphocyte - monocyte - neutrophil - platelet ## Dataset Info - Total number of examples: 17,092 - Dataset size: 302,576,945.936 bytes - Download size: 280,999,680 bytes ## Usage This dataset was created by Falah G. Salieh for the application of blood health classification in the health field. It can be used for various applications in computer vision, image classification, and machine learning. ## Citation If you use this dataset in your work, please cite the following paper: ``` @misc{blood_8_classes_dataset, title = { Blood_8 Classes_Dataset}, author = {Falah G. Salieh}, year = {2023}, publisher = {HuggingFace Hub}, url = {\url{https://huggingface.co/datasets/Falah/Blood_8_classes_Dataset/}} } ``` ## Contact For questions or inquiries about the dataset, please contact the author at [falahgs@gmail.com]. # Contributing We appreciate your contributions to improving the dataset and making it more useful for the community. If you want to contribute, please follow the guidelines outlined below: ## Enhancements and Feature Requests If you have ideas for enhancing the dataset, adding new features, or improving the existing ones, please check the [existing feature requests](https://huggingface.co/datasets/Falah/Blood_8_classes_Dataset/discussions) to see if a similar suggestion has been made and describe your proposal.
benayas/massive	--- dataset_info: features: - name: id dtype: string - name: locale dtype: string - name: partition dtype: string - name: scenario dtype: class_label: names: '0': social '1': transport '2': calendar '3': play '4': news '5': datetime '6': recommendation '7': email '8': iot '9': general '10': audio '11': lists '12': qa '13': cooking '14': takeaway '15': music '16': alarm '17': weather - name: intent dtype: class_label: names: '0': datetime_query '1': iot_hue_lightchange '2': transport_ticket '3': takeaway_query '4': qa_stock '5': general_greet '6': recommendation_events '7': music_dislikeness '8': iot_wemo_off '9': cooking_recipe '10': qa_currency '11': transport_traffic '12': general_quirky '13': weather_query '14': audio_volume_up '15': email_addcontact '16': takeaway_order '17': email_querycontact '18': iot_hue_lightup '19': recommendation_locations '20': play_audiobook '21': lists_createoradd '22': news_query '23': alarm_query '24': iot_wemo_on '25': general_joke '26': qa_definition '27': social_query '28': music_settings '29': audio_volume_other '30': calendar_remove '31': iot_hue_lightdim '32': calendar_query '33': email_sendemail '34': iot_cleaning '35': audio_volume_down '36': play_radio '37': cooking_query '38': datetime_convert '39': qa_maths '40': iot_hue_lightoff '41': iot_hue_lighton '42': transport_query '43': music_likeness '44': email_query '45': play_music '46': audio_volume_mute '47': social_post '48': alarm_set '49': qa_factoid '50': calendar_set '51': play_game '52': alarm_remove '53': lists_remove '54': transport_taxi '55': recommendation_movies '56': iot_coffee '57': music_query '58': play_podcasts '59': lists_query - name: utt dtype: string - name: annot_utt dtype: string - name: worker_id dtype: string - name: slot_method sequence: - name: slot dtype: string - name: method dtype: string - name: judgments sequence: - name: worker_id dtype: string - name: intent_score dtype: int8 - name: slots_score dtype: int8 - name: grammar_score dtype: int8 - name: spelling_score dtype: int8 - name: language_identification dtype: string - name: category dtype: string splits: - name: train num_bytes: 2190824 num_examples: 11514 - name: validation num_bytes: 381745 num_examples: 2033 - name: test num_bytes: 558310 num_examples: 2974 download_size: 1028818 dataset_size: 3130879 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
YXu120/NC_Education	--- language: - en license: cc-by-sa-4.0 pretty_name: NC Education Data size_categories: - 1K<n<10K --- netid: yx264 # Dataset Description - Homepage: https://linc.osbm.nc.gov/pages/home - (1) Education data: https://linc.osbm.nc.gov/explore/dataset/education/information/?disjunctive.area_name&disjunctive.area_type&disjunctive.variable - (2) Educational attainment for the black population: https://linc.osbm.nc.gov/explore/dataset/educational-attainment-for-the-black-population-c15002b/information/?disjunctive.area_name&disjunctive.area_type&disjunctive.year&disjunctive.variable ### Dataset Summary The education datasets can be found at North Carolina Office of State Budget and Management (NC OSBM) open data portal. The first data includes public school enrollment and finances, SAT score, higher education enrollment, educational attainment data for North Carolina and counties, and the second one includes educational attainment for the Black population aged 25 and over for North Carolina and counties. The processed final data will combine the information of them, providing a profound insight into NC educational level and equity. ### Supported Tasks The tasks/methodologies are: - (1) Data checking and cleaning Since the first dataset is pretty large and includes some information we don't care, like the SAT score. Before proprecessing, we first check the datasets, do some visualization and summarizing, to decide whether to drop certain rows. - (2) Re-structure the datasets to present the values according to county-level distinctions The original dataset architecture lacks depth, as all variables reside on a single plane. Considering the significance of these variables, it’s nature to contemplate reorganizing the hierarchy to reflect county-level distinctions and convey the data accordingly. - (3) Keywords-based data matching The data matching process relies on all columns except the 'value' column. While 'area_name', 'area_type', and 'year' offer straightforward matching points across datasets, it's important to acknowledge the potential disparity in the 'variable' column. For instance, one dataset may contain the variable 'Less than High School Diploma', while another might list 'Less than High School Black'. Despite conveying the same meaning and requiring alignment, a direct join isn't feasible due to this discrepancy. One potential approach involves extracting keywords from the variables, such as 'Less' and 'High School', and cross-referencing them to determine potential matches between the datasets. ### Languages English # Dataset Structure ### Data Instances - (1) Education data: https://linc.osbm.nc.gov/explore/dataset/education/api/?disjunctive.area_name&disjunctive.area_type&disjunctive.variable An example of records (note that specific details are subject to change): ```json { "area_name":"Alexander County", "area_type":"County", "year":"2010", "variable":"Elementary School Education or Less Age 25 Up", "value":2325 } ``` - (2) Educational attainment for the black population: https://linc.osbm.nc.gov/explore/dataset/educational-attainment-for-the-black-population-c15002b/api/?disjunctive.area_name&disjunctive.area_type&disjunctive.year&disjunctive.variable An example of records (note that specific details are subject to change): ```json { "area_name":"Burke County", "area_type":"County", "year":"2020", "variable":"Less than High School Diploma", "value":568 } ``` - (3) Anticipated structure we aim to achieve: ```json { "area_name":"Alamance County", "area_type":"County",[("year": "2010", [ {"variable": "High School Graduate (includes Equivalency)", "value": 6443}, {"variable":"Some College or Associate's Degree","value":5242}, ... ]), ...] ... } ``` ### Data Fields - Area Name (string): name of the area - Area Type (string): type of the area - Year (integer): the year that the data was collected - Variable (string): level of education or other situations - Value (integer): population count # Dataset Creation ### Curation Rationale The datasets were collected and published to present the educational level of NC population in different area. The educational attainment for the black population data can rise the concern for educational equity issue in North Carolina. The combined dataset aims to offer a holistic perspective on educational levels and equity, with a specific focus on the educational attainment of the Black population aged 25 and over. ### Source Data The original data were published by North Carolina Department of Public Instruction, North Carolina Community College System and US Census Bureau. # Considerations for Using the Data The records are only for limited years, making it challenging to discern timely patterns or trends. The second data is quite small compared to the first one. Combining may result in many missing values. # Additional Information ### Citation Information Bureau, US Census. Census.Gov, 21 Jan. 2024, www.census.gov. NC Budget & Management Open Data Portal Home Page - NC OSBM, https://linc.osbm.nc.gov/pages/home. ### Contributions Thanks North Carolina Department of Public Instruction, North Carolina Community College System and US Census Bureau for these datasets.
communityai/gretelai___synthetic_text_to_sql-15k	--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 12645170.55 num_examples: 15000 download_size: 4478749 dataset_size: 12645170.55 configs: - config_name: default data_files: - split: train path: data/train-* ---
chanelcolgate/image-classification-yenthienviet	--- license: mit ---
severo/tmp-1	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: content dtype: string - name: poster dtype: string - name: date_utc dtype: timestamp[us] - name: flair dtype: string - name: title dtype: string - name: score dtype: int64 - name: permalink dtype: string - name: id dtype: string splits: - name: train num_bytes: 250 num_examples: 1 download_size: 5088 dataset_size: 250 --- # Dataset Card for "dataset-creator-reddit-amitheasshole" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-futin__feed-sen_en_-7dbe88-2245971657	--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-350m metrics: [] dataset_name: futin/feed dataset_config: sen_en_ dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-350m * Dataset: futin/feed * Config: sen_en_ * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
rohangbs/death_se42-type2	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 19293975.0 num_examples: 122 - name: val num_bytes: 2214406.0 num_examples: 14 download_size: 21490683 dataset_size: 21508381.0 --- # Dataset Card for "death_se42-type2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tanli12/hagrid-classification-512p-no-gesture-150k-full	--- license: mit ---
notional/notional-python	--- annotations_creators: - no-annotation language: - py language_creators: - found license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - code-generation - conditional-text-generation task_ids: - language-modeling - code-generation --- # Dataset Card for notional-python ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [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) ## Dataset Description - Homepage: https://notional.ai/ - Repository: [Needs More Information] - Paper: [Needs More Information] - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary The Notional-python dataset contains python code files from 100 well-known repositories gathered from Google Bigquery Github Dataset. The dataset was created to test the ability of programming language models. Follow [our repo]() to do the model evaluation using notional-python dataset. ### Languages Python ## Dataset Creation ### Curation Rationale Notional-python was built to provide a dataset for testing the ability of the machine to generate python code. ### Source Data #### Initial Data Collection and Normalization The data was obtained by filtering code from [Google Bigquery Github data](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code) In order to improve the quality of the dataset, only python code files that meet the below conditions are added to the dataset: - Code with more than 60% of executable lines - Code with logic, not config files or comment-only files - Code with more than 30% of attribute declaration lines (E.G.: Some files contain just only class names and their class attributes, usually used for configuration of the project, these files were not selected) - Code without `TODO` and `FIXME`. #### Who are the source language producers? The producers are users of github.
CShorten/CORD19-init-160k	--- license: afl-3.0 ---
qgiaohc/twitter_dataset_1713133435	--- 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: 21790 num_examples: 53 download_size: 14184 dataset_size: 21790 configs: - config_name: default data_files: - split: train path: data/train-* ---
Nelis/Midjourney_Stable_Diffusion_prompts	--- license: unknown dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 256042389 num_examples: 1048576 - name: test num_bytes: 19012346 num_examples: 73718 download_size: 121702876 dataset_size: 275054735 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
tyzhu/squad_qa_wrong_rare_v5_full_recite_full_passage_random_permute_rerun_8	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 11172737.027252503 num_examples: 6305 - name: validation num_bytes: 587391 num_examples: 300 download_size: 1850990 dataset_size: 11760128.027252503 --- # Dataset Card for "squad_qa_wrong_rare_v5_full_recite_full_passage_random_permute_rerun_8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
x_stance	--- annotations_creators: - machine-generated language: - de - en - fr - it language_creators: - found license: - cc-by-nc-4.0 multilinguality: - multilingual pretty_name: x-stance size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: x-stance tags: - stance-detection dataset_info: features: - name: question dtype: string - name: id dtype: int32 - name: question_id dtype: int32 - name: language dtype: string - name: comment dtype: string - name: label dtype: string - name: numerical_label dtype: int32 - name: author dtype: string - name: topic dtype: string splits: - name: train num_bytes: 17619123 num_examples: 45640 - name: test num_bytes: 6607134 num_examples: 17705 - name: validation num_bytes: 1505979 num_examples: 3926 download_size: 6410801 dataset_size: 25732236 --- # Dataset Card for "x_stance" ## 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: https://github.com/ZurichNLP/xstance - Paper: [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://arxiv.org/abs/2003.08385) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 6.41 MB - Size of the generated dataset: 25.73 MB - Total amount of disk used: 32.14 MB ### Dataset Summary The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. It can be used to train and evaluate stance detection systems. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The comments are partly German, partly French and Italian. The questions are available in all the three languages plus English. ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 6.41 MB - Size of the generated dataset: 25.73 MB - Total amount of disk used: 32.14 MB An example of 'train' looks as follows. ``` { "author": "f27b54a137b4", "comment": "Das Arbeitsgesetz regelt die Arbeitszeiten und schützt den Arbeitnehmer. Es macht doch Sinn, dass wenn eine Nachfrage besteht, die Läden öffnen dürfen und wenn es keine Nachfrage gibt, diese geschlossen bleiben.", "id": 10045, "label": "FAVOR", "language": "de", "numerical_label": 100, "question": "Sind Sie für eine vollständige Liberalisierung der Geschäftsöffnungszeiten (Geschäfte können die Öffnungszeiten nach freiem Ermessen festlegen)?", "question_id": 739, "topic": "Economy" } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `id`: a `int32` feature. - `question_id`: a `int32` feature. - `language`: a `string` feature. - `comment`: a `string` feature. - `label`: a `string` feature. - `numerical_label`: a `int32` feature. - `author`: a `string` feature. - `topic`: a `string` feature. ### Data Splits \| name \|train\|validation\|test \| \|-------\|----:\|---------:\|----:\| \|default\|45640\| 3926\|17705\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data have been extracted from the Swiss voting advice platform Smartvote.ch. #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @inproceedings{vamvas2020xstance, author = "Vamvas, Jannis and Sennrich, Rico", title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection", booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)", address = "Zurich, Switzerland", year = "2020", month = "jun", url = "http://ceur-ws.org/Vol-2624/paper9.pdf" } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@jvamvas](https://github.com/jvamvas) for adding this dataset.
ypupreti/Hindi_QR	--- license: mit ---
videoprojector/omelette	--- license: cc0-1.0 ---
storresbusquets/asr-argentinian-spanish	--- license: cc-by-sa-4.0 ---
rithwik-db/imdb_3000	--- license: apache-2.0 ---
anumafzal94/arxiv-test-2048	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: summary dtype: string splits: - name: test num_bytes: 6592892.490835663 num_examples: 196 download_size: 615323 dataset_size: 6592892.490835663 --- # Dataset Card for "arxiv-test-2048" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dmntrd/eval_prompt_template_1	--- dataset_info: features: - name: results struct: - name: alpaca_name dtype: string - name: alpaca_noname dtype: string - name: prompt dtype: string - name: reference dtype: string - name: user_message dtype: string - name: zephyr_name dtype: string - name: zephyr_noname dtype: string splits: - name: train num_bytes: 60300 num_examples: 19 download_size: 53135 dataset_size: 60300 configs: - config_name: default data_files: - split: train path: data/train-* ---
omar-santander/reuters_articles	--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
irds/clinicaltrials_2017_trec-pm-2017	--- pretty_name: '`clinicaltrials/2017/trec-pm-2017`' viewer: false source_datasets: ['irds/clinicaltrials_2017'] task_categories: - text-retrieval --- # Dataset Card for `clinicaltrials/2017/trec-pm-2017` The `clinicaltrials/2017/trec-pm-2017` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/clinicaltrials#clinicaltrials/2017/trec-pm-2017). # Data This dataset provides: - `queries` (i.e., topics); count=30 - `qrels`: (relevance assessments); count=13,019 - For `docs`, use [`irds/clinicaltrials_2017`](https://huggingface.co/datasets/irds/clinicaltrials_2017) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/clinicaltrials_2017_trec-pm-2017', 'queries') for record in queries: record # {'query_id': ..., 'disease': ..., 'gene': ..., 'demographic': ..., 'other': ...} qrels = load_dataset('irds/clinicaltrials_2017_trec-pm-2017', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Roberts2017TrecPm, title={Overview of the TREC 2017 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant}, booktitle={TREC}, year={2017} } ```
liuyanchen1015/VALUE_sst2_been_done	--- dataset_info: features: - name: idx dtype: int64 - name: sentence dtype: string - name: label dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 4919 num_examples: 34 - name: test num_bytes: 14703 num_examples: 89 - name: train num_bytes: 222194 num_examples: 1716 download_size: 120863 dataset_size: 241816 --- # Dataset Card for "VALUE_sst2_been_done" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ti-Ma/TiMaGPT2-2017	--- license: other license_name: paracrawl-license license_link: LICENSE ---
mychen76/dataset_CSSF12_552_en_exqa	--- language: - en dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 370741 num_examples: 1211 download_size: 152116 dataset_size: 370741 configs: - config_name: default data_files: - split: train path: data/train-* ---
gnumanth/dad-jokes	--- license: mit dataset_info: features: - name: joke dtype: string - name: id dtype: string - name: name dtype: string splits: - name: train num_bytes: 6172.5 num_examples: 44 - name: test num_bytes: 2525.1136363636365 num_examples: 18 download_size: 12578 dataset_size: 8697.613636363636 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
kkoutini/openmic	--- dataset_info: features: - name: filename dtype: string - name: 'true' sequence: float32 length: 20 - name: mask sequence: int32 length: 20 - name: mp3_bytes dtype: binary splits: - name: train num_bytes: 1790991884 num_examples: 14915 - name: test num_bytes: 611455142 num_examples: 5085 download_size: 0 dataset_size: 2402447026 configs: - config_name: default data_files: - split: train path: data/shard_train_* - split: test path: data/shard_test_* --- # CPJKU/openmic The dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset. This dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded. The mp3 bytes can be decoded quickly using for [example](https://github.com/kkoutini/PaSST/blob/4519e4605989b8c2e62dccb5b928af9bf7bf8602/audioset/dataset.py#L55) or [minimp3](https://github.com/f0k/minimp3py). Take a look at the original dataset for more information. The original dataset contains the following: 10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key) VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key) MD5 checksums for each OGG and JSON file Anonymized individual responses, in 'openmic-2018-individual-responses.csv' Aggregated labels, in 'openmic-2018-aggregated-labels.csv' Track metadata, with licenses for each audio recording, in 'openmic-2018-metadata.csv' A Python-friendly NPZ file of features and labels, 'openmic-2018.npz' Sample partitions for train and test, in 'partitions/*.txt' ## Homepage https://zenodo.org/records/1432913 ## Citation ``` Humphrey, Eric J., Durand, Simon, and McFee, Brian. "OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition." in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018. ``` ## License CC BY 4.0
inspektral/reverb-mono-5	--- license: mit ---
mespinosami/elephants_512	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 845894902.629 num_examples: 9023 download_size: 840684586 dataset_size: 845894902.629 --- # Dataset Card for "elephants_512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rajammanabrolu/ultrafeedback_binarized	--- configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: train_sft path: data/train_sft-* - split: test_prefs path: data/test_prefs-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 splits: - name: train_prefs num_bytes: 397273717 num_examples: 61966 - name: train_sft num_bytes: 397273717 num_examples: 61966 - name: test_prefs num_bytes: 12782225 num_examples: 2000 - name: test_sft num_bytes: 6270496 num_examples: 1000 - name: train_gen num_bytes: 316634390 num_examples: 61966 - name: test_gen num_bytes: 5008220 num_examples: 1000 download_size: 636473621 dataset_size: 1135242765 --- # Dataset Card for "ultrafeedback_binarized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aravind-selvam/chart_processed_800	--- license: mit dataset_info: features: - name: pixel_values sequence: sequence: sequence: float32 - name: labels sequence: int64 - name: target_sequence dtype: string splits: - name: train num_bytes: 30776344108 num_examples: 4000 - name: validation num_bytes: 7694020630 num_examples: 1000 download_size: 1532163489 dataset_size: 38470364738 ---
liuyanchen1015/MULTI_VALUE_cola_completive_finish	--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1700 num_examples: 16 - name: test num_bytes: 2666 num_examples: 30 - name: train num_bytes: 19492 num_examples: 253 download_size: 16664 dataset_size: 23858 --- # Dataset Card for "MULTI_VALUE_cola_completive_finish" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
winglian/deduped-cortex-test001	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: run_id dtype: string - name: step dtype: int64 - name: uid dtype: int64 - name: id dtype: string splits: - name: train num_bytes: 29254026.08151392 num_examples: 12656 download_size: 15218406 dataset_size: 29254026.08151392 configs: - config_name: default data_files: - split: train path: data/train-* ---
clinicalnlplab/DDI2013_test	--- dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: train num_bytes: 21619525 num_examples: 18779 - name: valid num_bytes: 9110078 num_examples: 7244 - name: test num_bytes: 6750534 num_examples: 5761 download_size: 6516858 dataset_size: 37480137 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
dvilasuero/somos-clean-alpaca-es-test	--- dataset_info: features: - name: text dtype: 'null' - name: inputs struct: - name: 1-instruction dtype: string - name: 2-input dtype: string - name: 3-output dtype: string - name: prediction dtype: 'null' - name: prediction_agent dtype: 'null' - name: annotation dtype: string - name: annotation_agent dtype: string - name: vectors struct: - name: input sequence: float64 - name: instruction sequence: float64 - name: output sequence: float64 - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 361340 num_examples: 19 download_size: 301641 dataset_size: 361340 --- # Dataset Card for "somos-clean-alpaca-es-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_rte_zero_degree

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 60122 num_examples: 141 - name: train num_bytes: 55937 num_examples: 126 download_size: 84266 dataset_size: 116059 --- # Dataset Card for "MULTI_VALUE_rte_zero_degree" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kblw/graphviz_treemap

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': train '1': val splits: - name: train num_bytes: 11185982.90438544 num_examples: 2101 - name: test num_bytes: 1180354.739614561 num_examples: 234 - name: validation num_bytes: 5320.0 num_examples: 4 download_size: 11890860 dataset_size: 12371657.644000001 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

mekaneeky/Synthetic_Runyankole_MMS

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: eng dtype: string - name: lug dtype: string - name: ach dtype: string - name: teo dtype: string - name: lgg dtype: string - name: nyn dtype: string - name: runyankole_synthetic_audio sequence: sequence: float32 splits: - name: train num_bytes: 12669819104 num_examples: 23947 - name: dev num_bytes: 268490157 num_examples: 500 - name: test num_bytes: 262253648 num_examples: 500 download_size: 13209162621 dataset_size: 13200562909 --- # Dataset Card for "Synthetic_Runyankole_MMS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rcds/MultiLegalSBD

--- dataset_info: - config_name: fr_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 8773683 num_examples: 2131 download_size: 0 dataset_size: 8773683 - config_name: it_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 8130577 num_examples: 2910 download_size: 0 dataset_size: 8130577 - config_name: es_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 6260211 num_examples: 677 download_size: 0 dataset_size: 6260211 - config_name: en_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train download_size: 0 dataset_size: 0 - config_name: de_Laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 13792836 num_examples: 13 download_size: 0 dataset_size: 13792836 - config_name: fr_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 8788244 num_examples: 315 download_size: 0 dataset_size: 8788244 - config_name: fr_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 25977816 num_examples: 2446 download_size: 4782672 dataset_size: 25977816 - config_name: it_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 8989061 num_examples: 243 download_size: 0 dataset_size: 8989061 - config_name: it_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 25097560 num_examples: 3153 download_size: 4610540 dataset_size: 25097560 - config_name: es_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 9460558 num_examples: 190 download_size: 0 dataset_size: 9460558 - config_name: es_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 23090629 num_examples: 867 download_size: 4438716 dataset_size: 23090629 - config_name: en_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 18401754 num_examples: 80 download_size: 0 dataset_size: 18401754 - config_name: en_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 27363914 num_examples: 80 download_size: 5448700 dataset_size: 27363914 - config_name: de_Judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 14082173 num_examples: 131 download_size: 0 dataset_size: 14082173 - config_name: de_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 40429185 num_examples: 144 download_size: 7883640 dataset_size: 40429185 - config_name: fr_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 12924503 num_examples: 2131 download_size: 2201568 dataset_size: 12924503 - config_name: fr_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 13053313 num_examples: 315 download_size: 2581104 dataset_size: 13053313 - config_name: it_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 11869343 num_examples: 2910 download_size: 2048828 dataset_size: 11869343 - config_name: it_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 13228218 num_examples: 243 download_size: 2561712 dataset_size: 13228218 - config_name: es_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 9183057 num_examples: 677 download_size: 1753376 dataset_size: 9183057 - config_name: es_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 13907572 num_examples: 190 download_size: 2685340 dataset_size: 13907572 - config_name: en_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train download_size: 0 dataset_size: 0 - config_name: en_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 27363914 num_examples: 80 download_size: 5448700 dataset_size: 27363914 - config_name: de_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 19935635 num_examples: 13 download_size: 3745480 dataset_size: 19935635 - config_name: de_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 20493550 num_examples: 131 download_size: 4138160 dataset_size: 20493550 - config_name: pt_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 1005902 num_examples: 58 download_size: 209128 dataset_size: 1005902 - config_name: pt_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 812282 num_examples: 10 download_size: 173424 dataset_size: 812282 - config_name: pt_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 1818184 num_examples: 68 download_size: 382552 dataset_size: 1818184 - config_name: all_laws features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 54918438 num_examples: 5789 download_size: 9958380 dataset_size: 54918438 - config_name: all_judgements features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 88858845 num_examples: 969 download_size: 17588440 dataset_size: 88858845 - config_name: all_all features: - name: text dtype: string - name: spans list: - name: start dtype: int64 - name: end dtype: int64 - name: label dtype: string - name: token_start dtype: int64 - name: token_end dtype: int64 - name: tokens list: - name: text dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: id dtype: int64 - name: ws dtype: bool - name: source dtype: string splits: - name: train num_bytes: 143777284 num_examples: 6758 download_size: 27546820 dataset_size: 143777284 task_categories: - token-classification language: - en - es - de - it - pt - fr pretty_name: 'MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset' size_categories: - 100K<n<1M --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This is a multilingual dataset containing ~130k annotated sentence boundaries. It contains laws and court decision in 6 different languages. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English, French, Italian, German, Portuguese, Spanish ## Dataset Structure It is structured in the following format: {language}\_{type}\_{shard}.jsonl.xz type is one of the following: - laws - judgements Use the the dataset like this: ``` from datasets import load_dataset config = 'fr_laws' #{language}_{type} | to load all languages and/or all types, use 'all_all' dataset = load_dataset('rdcs/MultiLegalSBD', config) ``` ### Data Instances [More Information Needed] ### Data Fields - text: the original text - spans: - start: offset of the first character - end: offset of the last character - label: One label only -> Sentence - token_start: id of the first token - token_end: id of the last token - tokens: - text: token text - start: offset of the first character - end: offset of the last character - id: token id - ws: whether the token is followed by whitespace ### Data Splits There is only one split available ## 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 ``` @inproceedings{10.1145/3594536.3595132, author = {Brugger, Tobias and St\"{u}rmer, Matthias and Niklaus, Joel}, title = {MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset}, year = {2023}, isbn = {9798400701979}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3594536.3595132}, doi = {10.1145/3594536.3595132}, abstract = {Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different sentence structures used. In this work, we curated a diverse multilingual legal dataset consisting of over 130'000 annotated sentences in 6 languages. Our experimental results indicate that the performance of existing SBD models is subpar on multilingual legal data. We trained and tested monolingual and multilingual models based on CRF, BiLSTM-CRF, and transformers, demonstrating state-of-the-art performance. We also show that our multilingual models outperform all baselines in the zero-shot setting on a Portuguese test set. To encourage further research and development by the community, we have made our dataset, models, and code publicly available.}, booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law}, pages = {42–51}, numpages = {10}, keywords = {Natural Language Processing, Sentence Boundary Detection, Text Annotation, Legal Document Analysis, Multilingual}, location = {Braga, Portugal}, series = {ICAIL '23} } ``` ### Contributions [More Information Needed]

suolyer/webqa

--- license: apache-2.0 ---

aisquared/databricks-dolly-15k

--- license: cc-by-sa-3.0 language: - en tags: - databricks - dolly pretty_name: 'Dataset ' --- # databricks-dolly-15k **This dataset was not originally created by AI Squared.** This dataset was curated and created by [Databricks](https://databricks.com). The below text comes from the original release of the dataset's README file in GitHub (available at https://github.com/databrickslabs/dolly/tree/master/data): # Summary `databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: English Version: 1.0 **Owner: Databricks, Inc.** # Dataset Overview `databricks-dolly-15k` is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category. Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly. For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the `context` field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. `[42]`) which we recommend users remove for downstream applications. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories. Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets. # Dataset ## Purpose of Collection As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - **Human-generated data**: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories. - **Wikipedia**: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages. ## Annotator Guidelines To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor. The annotation guidelines for each of the categories are as follows: - **Creative Writing**: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better. - **Closed QA**: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form. - **Open QA**: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation. - **Summarization**: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form. - **Information Extraction**: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form. - **Classification**: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better. - **Brainstorming**: Think up lots of examples in response to a question asking to brainstorm ideas. ## Personal or Sensitive Data This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language American English # Known Limitations - Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia - Some annotators may not be native English speakers - Annotator demographics and subject matter may reflect the makeup of Databricks employees # License/Attribution **Copyright (2023) Databricks, Inc.** This dataset was developed at Databricks (https://www.databricks.com) and its use is subject to the CC BY-SA 3.0 license. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license: Wikipedia (various pages) - https://www.wikipedia.org/ Copyright © Wikipedia editors and contributors.

JyotiNayak/Indian_electoral_bond_dataset

--- dataset_info: features: - name: date_of_purchase dtype: timestamp[ns] - name: date_of_expiry dtype: timestamp[ns] - name: name_of_the_purchaser dtype: string - name: bondnumber dtype: string - name: date_of_encashment dtype: timestamp[ns] - name: name_of_the_political_party dtype: string - name: encashed_donations_crores dtype: float64 - name: days_to_encash dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 2397987 num_examples: 20552 download_size: 369053 dataset_size: 2397987 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - feature-extraction language: - en size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name  This dataset card contains cleaned elctoral bond data (India) by matching the companies that have purchased the electoral bonds to the parties that have encashed them. ## Dataset Details ### Dataset Description - **Curated by:** Jyoti Shankar Nayak - **Funded by [optional]:** NA - **Language(s) (NLP):** English - **License:** Apache 2.0 ### Dataset Sources  - **Source:** Election commission of India - **Source files:** https://www.eci.gov.in/disclosure-of-electoral-bonds - **Data cleaning and curation:** https://github.com/jyoti-sn/Elections/blob/main/Electoral_bond_Analysis.ipynb ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

remyxai/ffmperative_augmented

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 76872282 num_examples: 52893 download_size: 13359933 dataset_size: 76872282 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ffmperative_augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

furry-br/steven-reglan

--- license: openrail ---

GeneratedPhotos/Synthetic_Face_Images_Academic_Dataset

--- license: afl-3.0 language: - en tags: - generation - image generation - face images - face detection pretty_name: Academic Dataset by Generated Photos size_categories: - 1K<n<10K --- Academic Dataset by Generated Photos See at https://generated.photos/datasets#research-dataset The free dataset is made to help students and teachers with any research. It contains 10,000 photos with equal distribution of race and gender parameters. If you need a dataset with different parameters or quantity, contact us at work.with@generated.photos. We will appreciate it if you let us know about the research outcome! ---------------------------------------------------------- Terms of use ---------------------------------------------------------- You can use and adapt it for any research purposes, as long as you: (a) give appropriate credit by citing in your paper, (b) put a link to Generated Photos website in case of publishing your paper or results of your research or a related article. Example of an attribution line: Academic Dataset by Generated Photos https://generated.photos/datasets You can redistribute it within your university, but please follow these rules: (a) indicate any changes that you've made, (b) make sure that your fellow student or teacher you pass this dataset is aware of the terms of use described in this file. For more information about datasets and license, please visit Generated Photos website: https://generated.photos/datasets https://generated.photos/faq https://generated.photos/terms-and-conditions ---------------------------------------------------------- Photos ---------------------------------------------------------- All the photos are 100% synthetic. Based on model-released photos. Royalty-free. Can be used for any research purpose except for the ones violating the law. Worldwide. No time limitations. Quantity 10,000 Quality 128x128px Diversity Ethnicity, gender ---------------------------------------------------------- Metadata ---------------------------------------------------------- The JSON files contain the metadata for each image in a machine-readable format, including: (1) FaceLandmarks: mouth, right_eyebrow, left_eyebrow, right_eye, left_eye, nose, jaw. (2) FaceAttributes: headPose, gender, makeup, emotion, facialHair, hair (hairColor, hairLength, bald), occlusion, ethnicity, eye_color, smile, age ---------------------------------------------------------- Please contact work.with@generated.photos for business and press inquiries and other questions.

DBQ/Net.a.Porter.Product.prices.South.Korea

--- 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: South Korea - Net-a-Porter - Product-level price list tags: - webscraping - ecommerce - Net - 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: int64 - 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: 20928720 num_examples: 51265 download_size: 6412160 dataset_size: 20928720 --- # Net-a-Porter web scraped data ## About the website The **fashion e-commerce industry** in the Asia Pacific, particularly in **South Korea**, has seen tremendous growth over the last few years. Rapid advancements in **digital technology**, together with a sophisticated logistics infrastructure and increasingly affluent consumer base, are driving this expansion. **South Korea’s digital infrastructure** is one of the most advanced in the region, offering online retailers access to a broad, tech-savvy audience. Cultural factors like the Korean Wave (“Hallyu”) are also influencing fashion trends and consumption patterns. The dataset observed provides **e-commerce product-list page (PLP) data** about **Net-a-Porter** in South Korea, shedding light on purchasing patterns and preferences in this vibrant market. ## Link to **dataset** [South Korea - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20South%20Korea/r/recbd28ziED1dxjSE)

mwitiderrick/SwahiliAlpaca

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 31174024 num_examples: 34740 download_size: 15797740 dataset_size: 31174024 license: apache-2.0 task_categories: - text-generation language: - sw pretty_name: Swahili Alpaca size_categories: - 10K<n<100K --- # Dataset Card for "SwahiliAlpaca" Alpaca dataset for instruction fine-tuning in Swahili. ## Prompt Template ``` ### Maelekezo:\n{instruction} ### Agizo:\n{input} ### Jibu:\n{output} ```

zakhtar/stanford-test

--- license: mit ---

NiGuLa/Russian_Inappropriate_Messages

--- language: - ru tags: - toxic comments classification license: cc task_categories: - text-classification size_categories: - 100K<n<1M --- ## General concept The **'inappropriateness'** substance we tried to collect in the dataset and detect with the model **is NOT a substitution of toxicity**, it is rather a derivative of toxicity. So the model based on our dataset could serve as **an additional layer of inappropriateness filtering after toxicity and obscenity filtration**. You can detect the exact sensitive topic by using [this model](https://huggingface.co/Skoltech/russian-sensitive-topics). Generally, an inappropriate utterance is an utterance that has not obscene words or any kind of toxic intent but can still harm the reputation of the speaker. Find some samples for more intuition in the table below. Learn more about the concept of inappropriateness [in this article ](https://www.aclweb.org/anthology/2021.bsnlp-1.4/) presented at the workshop for Balto-Slavic NLP at the EACL-2021 conference. Please note that this article describes the first version of the dataset, while the model is trained on the extended version of the dataset open-sourced on our [GitHub](https://github.com/skoltech-nlp/inappropriate-sensitive-topics/blob/main/Version2/appropriateness/Appropriateness.csv) or on [kaggle](https://www.kaggle.com/nigula/russianinappropriatemessages). The properties of the dataset are the same as the one described in the article, the only difference is the size. | Text | Inappropriate | Comment | Unsafe topic | |------------------------------------------------------------------------------------------------------|---------------|----------------------------------------------------------------|---------------| | Ладно бы видного деятеля завалили а тут какого то ноунейм нигру преступника | yes | Оправдание убийства | offline crime | | Это нарушение УКРФ! | no | Осуждение нарушения закона | offline crime | | Религия - это насилие высшего уровня. Верующего принуждают к вере еще в младенчестве. | yes | Оскорбление чувств верующих | religion | | Христиане, как вы относитесь к иноверцам, в частности к Гамалиилу и к уверовавшим в Иисуса фарисеям? | no | Обычные вопросы к верующим без намека на оскорбление их чувств | religion | | Хорошо что проституция запрещена | no | Осуждение простуитуции | prostitution | | Возьму бабки и сниму шлюх на выходных | yes | Популяризация проституции | prostitution | ## Licensing Information [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png ## Citation If you find this repository helpful, feel free to cite our publication: ``` @inproceedings{babakov-etal-2021-detecting, title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation", author = "Babakov, Nikolay and Logacheva, Varvara and Kozlova, Olga and Semenov, Nikita and Panchenko, Alexander", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4", pages = "26--36", abstract = "Not all topics are equally {``}flammable{''} in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labelling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.", } ``` ## Contacts If you have any questions please contact [Nikolay](mailto:bbkhse@gmail.com)

maximegmd/MedText-alpaca

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 949136 num_examples: 1412 download_size: 494828 dataset_size: 949136 --- # Dataset Card for "MedText-alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/zoya_akibameidosensou

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Zoya This is the dataset of Zoya, containing 114 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)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 114 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 276 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 114 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 114 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 114 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 114 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 114 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 276 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 276 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 276 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

Murali0604/Syringe-Dataset-Auto

--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 3138386.0 num_examples: 10 download_size: 3139896 dataset_size: 3138386.0 --- # Dataset Card for "Syringe-Dataset-Auto" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yzhuang/autotree_automl_credit_gosdt_l256_d3_sd0

--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 3388000000 num_examples: 100000 - name: validation num_bytes: 338800000 num_examples: 10000 download_size: 840908403 dataset_size: 3726800000 --- # Dataset Card for "autotree_automl_credit_gosdt_l256_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

marmofayezi/M3GenMaskFID

--- dataset_info: features: - name: id dtype: string - name: mask dtype: image - name: caption dtype: string - name: generated_image dtype: image splits: - name: train num_bytes: 66833951.35 num_examples: 1498 download_size: 66780852 dataset_size: 66833951.35 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/manaka_misato_idolmastercinderellagirls

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of manaka_misato/間中美里 (THE iDOLM@STER: Cinderella Girls) This is the dataset of manaka_misato/間中美里 (THE iDOLM@STER: Cinderella Girls), containing 15 images and their tags. The core tags of this character are `brown_hair, short_hair, blue_eyes`, which are pruned in this dataset. 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)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 15 | 9.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 15 | 9.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 29 | 15.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 15 | 9.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 29 | 15.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/manaka_misato_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/manaka_misato_idolmastercinderellagirls', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, smile, necklace, card_(medium), character_name, flower_(symbol) | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | necklace | card_(medium) | character_name | flower_(symbol) | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:-----------|:----------------|:-----------------|:------------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X |

ctang/util_deontology_rewardtrainer

--- dataset_info: features: - name: idx dtype: int64 - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 4412185 num_examples: 43357 - name: validation num_bytes: 115932 num_examples: 1141 - name: test num_bytes: 116070 num_examples: 1142 download_size: 2714094 dataset_size: 4644187 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

nikhilchigali/wikianswers_small

--- language: - en size_categories: - 100K<n<1M task_categories: - sentence-similarity dataset_info: features: - name: sentence dtype: string - name: cluster dtype: int64 splits: - name: train num_bytes: 59231273 num_examples: 990526 download_size: 22602562 dataset_size: 59231273 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "WikiAnswers Small" ## Dataset Summary `nikhilchigali/wikianswers_small` is a subset of the `embedding-data/WikiAnswers` dataset ([Link](https://huggingface.co/datasets/embedding-data/WikiAnswers)). As opposed to the original dataset with `3,386,256` rows, this dataset contains only 0.13% of the total rows(sets). The sets of sentences have been unraveled into individual items with corresponding cluster IDs to identify sentences from the same set. ## Languages English. ## Dataset Structure Each example in the dataset contains a sentence and its cluster id of other equivalent sentences. The sentences in the same cluster are paraphrases of each other. ``` {"sentence": [sentence], "cluster": [cluster_id]} {"sentence": [sentence], "cluster": [cluster_id]} {"sentence": [sentence], "cluster": [cluster_id]} ... {"sentence": [sentence], "cluster": [cluster_id]} ``` ### Usage Example Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with: ```python from datasets import load_dataset dataset = load_dataset("nikhilchigali/wikianswers_small") ``` The dataset is loaded as a `DatasetDict` and has the format for `N` examples: ```python DatasetDict({ train: Dataset({ features: ['sentence', "cluster"], num_rows: N }) }) ``` Review an example `i` with: ```python dataset["train"][i] ``` ### Source Data `embedding-data/WikiAnswers` on HuggingFace ([Link](https://huggingface.co/datasets/embedding-data/WikiAnswers)) #### Note: This dataset is for the owner's personal use and claims no rights whatsoever.

anan-2024/twitter_dataset_1712965716

--- 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: 20712 num_examples: 45 download_size: 11887 dataset_size: 20712 configs: - config_name: default data_files: - split: train path: data/train-* ---

sparse-generative-ai/requests

--- license: apache-2.0 ---

asas-ai/tydiqa-ar-secondary_task

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 15715443.835027365 num_examples: 14805 - name: validation num_bytes: 908198.6986409297 num_examples: 921 download_size: 9132744 dataset_size: 16623642.533668295 license: apache-2.0 task_categories: - question-answering language: - ar pretty_name: tydiqa-ar --- # Dataset Card for "tydiqa-ar-secondary_task" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Multimodal-Fatima/OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 267864523.375 num_examples: 6149 - name: fewshot_1_bs_16 num_bytes: 270237138.375 num_examples: 6149 - name: fewshot_3_bs_16 num_bytes: 274972242.375 num_examples: 6149 download_size: 797630284 dataset_size: 813073904.125 --- # Dataset Card for "OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

netcat420/quiklit

--- license: mit --- dataset designed to PEFT fine-tune mistral 7B fine-tune and quantized model trained on this dataset can be found at netcat420/MHENNlit

bigscience-data/roots_indic-ta_indic_nlp_corpus

--- language: ta license: cc-by-nc-4.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_indic-ta_indic_nlp_corpus # Indic NLP Corpus - Dataset uid: `indic_nlp_corpus` ### Description The IndicNLP corpus is a largescale, general-domain corpus containing 2.7 billion words for 10 Indian languages from two language families. s (IndoAryan branch and Dravidian). Each language has at least 100 million words (except Oriya). ### Homepage https://github.com/AI4Bharat/indicnlp_corpus#publicly-available-classification-datasets ### Licensing - non-commercial use - cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International ### Speaker Locations - Southern Asia - India ### Sizes - 3.4019 % of total - 44.4368 % of indic-hi - 64.2943 % of indic-ta - 70.5374 % of indic-ml - 54.2394 % of indic-te - 55.9105 % of indic-kn - 61.6111 % of indic-mr - 67.2242 % of indic-pa - 68.1470 % of indic-or - 64.3879 % of indic-gu - 4.1495 % of indic-bn ### BigScience processing steps #### Filters applied to: indic-hi - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ta - dedup_document - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ml - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-te - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-kn - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-mr - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-pa - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-or - dedup_document - dedup_template_soft - filter_remove_empty_docs #### Filters applied to: indic-gu - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300

rogetxtapai/dados-bulas

--- license: apache-2.0 ---

2030NLP/SpaCE2021

--- language: - zh task_categories: - text-classification # - feature-extraction task_ids: # - d - acceptability-classification - natural-language-inference license: cc-by-nc-sa-4.0 pretty_name: space21 size_categories: - 10K<n<100K annotations_creators: - crowdsourced - expert-generated - machine-generated source_datasets: - ccl dataset_info: - config_name: task1 features: - name: qID dtype: string - name: context dtype: string - name: judge1 dtype: bool splits: - name: train num_bytes: 1470413 num_examples: 4237 - name: validation num_bytes: 321061 num_examples: 806 - name: test num_bytes: 263854 num_examples: 794 download_size: 2373041 dataset_size: 2055328 - config_name: task2 features: - name: qID dtype: string - name: context dtype: string - name: reason dtype: string - name: judge2 dtype: bool splits: - name: train num_bytes: 2586476 num_examples: 5989 - name: validation num_bytes: 712348 num_examples: 2088 - name: test num_bytes: 773393 num_examples: 1952 download_size: 4607294 dataset_size: 4072217 - config_name: task3 features: - name: qID dtype: string - name: context dtype: string - name: reason dtype: string - name: judge1 dtype: bool - name: judge2 dtype: bool splits: - name: validation num_bytes: 539209 num_examples: 1203 - name: test num_bytes: 445760 num_examples: 1167 download_size: 1110504 dataset_size: 984969 --- # Dataset Card for SpaCE2021 ## Dataset Description - **Homepage:** http://ccl.pku.edu.cn:8084/SpaCE2021/ - **Repository:** https://github.com/2030NLP/SpaCE2021 - **Paper:** [詹卫东、孙春晖、岳朋雪、唐乾桐、秦梓巍，2022，空间语义理解能力评测任务设计的新思路——SpaCE2021数据集的研制，《语言文字应用》2022年第2期（总第122期），pp.99-110。](https://yyyy.cbpt.cnki.net/WKC/WebPublication/paperDigest.aspx?paperID=c66cca51-7783-430e-abf1-28f6c28c49f6) - **Leaderboard:** https://github.com/2030NLP/SpaCE2021 - **Point of Contact:** sc_eval@163.com ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

open-llm-leaderboard/details_zarakiquemparte__zarafusionex-1.1-l2-7b

--- pretty_name: Evaluation run of zarakiquemparte/zarafusionex-1.1-l2-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [zarakiquemparte/zarafusionex-1.1-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionex-1.1-l2-7b)\ \ 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_zarakiquemparte__zarafusionex-1.1-l2-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-14T18:56:02.826185](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionex-1.1-l2-7b/blob/main/results_2023-10-14T18-56-02.826185.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\": 0.22158137583892618,\n\ \ \"em_stderr\": 0.004253171428083834,\n \"f1\": 0.30786073825503446,\n\ \ \"f1_stderr\": 0.004263392065531877,\n \"acc\": 0.4111836416696536,\n\ \ \"acc_stderr\": 0.009825954163274354\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.22158137583892618,\n \"em_stderr\": 0.004253171428083834,\n\ \ \"f1\": 0.30786073825503446,\n \"f1_stderr\": 0.004263392065531877\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07808946171341925,\n \ \ \"acc_stderr\": 0.007390654481108235\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.744277821625888,\n \"acc_stderr\": 0.012261253845440474\n\ \ }\n}\n```" repo_url: https://huggingface.co/zarakiquemparte/zarafusionex-1.1-l2-7b 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: 2023_08_26T09_58_58.682404 path: - '**/details_harness|arc:challenge|25_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-26T09:58:58.682404.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_14T18_56_02.826185 path: - '**/details_harness|drop|3_2023-10-14T18-56-02.826185.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-14T18-56-02.826185.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_14T18_56_02.826185 path: - '**/details_harness|gsm8k|5_2023-10-14T18-56-02.826185.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-14T18-56-02.826185.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hellaswag|10_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-26T09:58:58.682404.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-management|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T09:58:58.682404.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_26T09_58_58.682404 path: - '**/details_harness|truthfulqa:mc|0_2023-08-26T09:58:58.682404.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-26T09:58:58.682404.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_14T18_56_02.826185 path: - '**/details_harness|winogrande|5_2023-10-14T18-56-02.826185.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-14T18-56-02.826185.parquet' - config_name: results data_files: - split: 2023_08_26T09_58_58.682404 path: - results_2023-08-26T09:58:58.682404.parquet - split: 2023_10_14T18_56_02.826185 path: - results_2023-10-14T18-56-02.826185.parquet - split: latest path: - results_2023-10-14T18-56-02.826185.parquet --- # Dataset Card for Evaluation run of zarakiquemparte/zarafusionex-1.1-l2-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/zarakiquemparte/zarafusionex-1.1-l2-7b - **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 [zarakiquemparte/zarafusionex-1.1-l2-7b](https://huggingface.co/zarakiquemparte/zarafusionex-1.1-l2-7b) 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_zarakiquemparte__zarafusionex-1.1-l2-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-14T18:56:02.826185](https://huggingface.co/datasets/open-llm-leaderboard/details_zarakiquemparte__zarafusionex-1.1-l2-7b/blob/main/results_2023-10-14T18-56-02.826185.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": 0.22158137583892618, "em_stderr": 0.004253171428083834, "f1": 0.30786073825503446, "f1_stderr": 0.004263392065531877, "acc": 0.4111836416696536, "acc_stderr": 0.009825954163274354 }, "harness|drop|3": { "em": 0.22158137583892618, "em_stderr": 0.004253171428083834, "f1": 0.30786073825503446, "f1_stderr": 0.004263392065531877 }, "harness|gsm8k|5": { "acc": 0.07808946171341925, "acc_stderr": 0.007390654481108235 }, "harness|winogrande|5": { "acc": 0.744277821625888, "acc_stderr": 0.012261253845440474 } } ``` ### 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]

Darsh12/train_data

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 20381060.0 num_examples: 69 download_size: 18323201 dataset_size: 20381060.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

joey234/mmlu-high_school_computer_science-dev

--- 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 splits: - name: dev num_bytes: 5225 num_examples: 5 download_size: 0 dataset_size: 5225 --- # Dataset Card for "mmlu-high_school_computer_science-dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BramVanroy/stackoverflow-chat-dutch

--- language: - nl license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering - text-generation pretty_name: Stack Overflow Chat Dutch tags: - baize - stack overflow - stackoverflow - chat dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 76877122 num_examples: 51267 - name: test_sft num_bytes: 8539723 num_examples: 5697 download_size: 43666524 dataset_size: 85416845 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* --- # Dataset Card for Stack Overflow Chat Dutch ## Dataset Description - **Homepage:** N/A - **Repository:** N/A - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** Bram Vanroy ### Dataset Summary This dataset contains 56,964 conversations between een AI assistant and a (fake) "Human" (generated) in Dutch, specifically in the domain of programming (Stack Overflow). They are translations of [Baize](https://github.com/project-baize/baize-chatbot/tree/6790946f638d60fcaf397574189124f15792f35a/data)'s machine-generated answers to the [Stack Overflow](https://huggingface.co/datasets/pacovaldez/stackoverflow-questions) dataset. ☕ [**Want to help me out?**](https://www.buymeacoffee.com/bramvanroy) Translating the data with the OpenAI API, and prompt testing, cost me 💸$133.60💸. If you like this dataset, please consider [buying me a coffee](https://www.buymeacoffee.com/bramvanroy) to offset a portion of this cost, I appreciate it a lot! ☕ If you use this dataset or refer to it, please use the following citation: Vanroy, B. (2023). *Language Resources for Dutch Large Language Modelling*. [https://arxiv.org/abs/2312.12852](https://arxiv.org/abs/2312.12852) ```bibtext @article{vanroy2023language, title={Language Resources for {Dutch} Large Language Modelling}, author={Vanroy, Bram}, journal={arXiv preprint arXiv:2312.12852}, year={2023} } ``` ### Languages - Dutch ## Dataset Structure ### Data Instances ```python {'id': 9, 'input': 'Het gesprek tussen de mens en de AI-assistent.\n' '[|Human|] Ik heb wat hulp nodig met Spark SQL union en het ordenen ' 'van kolommen.\n' '[|AI|] Natuurlijk, ik help je graag. Waar heb je specifiek hulp ' 'bij nodig?\n' '[|Human|] Ik wil de output van een UNION-query ordenen op een ' 'kolom die niet aanwezig is in de SELECT-instructie. Is dat ' 'mogelijk?\n' '[|AI|] Ja, het is mogelijk met Spark SQL. Je kunt de ordinale ' 'positie van de kolom in de ORDER BY-clausule gebruiken in plaats ' 'van de kolomnaam. Als je bijvoorbeeld wilt ordenen op de derde ' 'kolom in de SELECT-instructie, kun je ORDER BY 3 gebruiken.\n' '[|Human|] Dat is nuttig. Kan ik ook een alias gebruiken in de ' 'ORDER BY-clausule?\n' '[|AI|] Ja, je kunt een alias gebruiken in de ORDER BY-clausule. De ' 'alias moet worden gedefinieerd in de SELECT-instructie. Als je ' 'bijvoorbeeld de derde kolom als "col3" alias geeft, kun je ordenen ' 'met ORDER BY col3.\n' '[|Human|] Oké, ik denk dat ik het begrijp. Kan ik dezelfde alias ' 'gebruiken voor kolommen uit beide tabellen in de UNION?\n' '[|AI|] Nee, je kunt dezelfde alias niet gebruiken voor kolommen ' 'uit beide tabellen in de UNION. Elke tabel in de UNION moet een ' 'unieke alias hebben voor de kolommen.\n' '[|Human|] ', 'topic': 'Spark SQL UNION - ORDER BY kolom niet in SELECT'}, ``` ### Data Fields - **id**: the ID of the item. The following 82 IDs are not included because they could not be translated: `[1713, 1937, 1960, 4326, 4356, 8357, 8542, 8827, 9137, 9782, 11560, 11961, 12244, 12362, 12488, 13259, 13621, 14445, 14835, 15006, 17746, 18808, 19285, 19426, 19491, 21270, 21661, 22098, 23352, 23840, 23869, 25148, 25928, 27102, 27856, 28387, 29942, 30041, 30251, 32396, 32742, 32941, 33628, 34116, 34648, 34859, 35977, 35987, 36035, 36456, 37028, 37238, 37640, 38107, 38735, 39015, 40984, 41115, 41567, 42397, 43219, 43783, 44599, 44980, 45239, 47676, 48922, 49534, 50282, 50683, 50804, 50919, 51076, 51211, 52000, 52183, 52489, 52595, 53884, 54726, 55795, 56992]` - **input**: the machine-generated conversation between AI and "Human". Always starts with `Het gesprek tussen de mens en de AI-assistent.\n` and has at least one occurrence of both `[|AI|]` and `[|Human|]`. - **topic**: the topic description ## Dataset Creation Both the translations and the topics were translated with OpenAI's API for `gpt-3.5-turbo`. `max_tokens=1024, temperature=0` as parameters. The prompt template to translate the input is (where `src_lang` was English and `tgt_lang` Dutch): ```python CONVERSATION_TRANSLATION_PROMPT = """You are asked to translate a conversation between an AI assistant and a human from {src_lang} into {tgt_lang}. Here are the requirements that you should adhere to: 1. maintain the format: the conversation consists of the AI (marked as `[|AI|]`) and the human (`[|Human|]`) talking in turns and responding to each other; 2. do not translate the speaker identifiers `[|AI|]` and `[|Human|]` but always copy them into the translation in appropriate places; 3. ensure accurate translation and keep the correctness of the conversation; 4. make sure that text is fluent to read and does not contain grammatical errors. Use standard {tgt_lang} without regional bias; 5. translate the human's text using informal, but standard, language; 6. make sure to avoid biases (such as gender bias, grammatical bias, social bias); 7. if the human asks to correct grammar mistakes or spelling mistakes then you have to generate a similar mistake in {tgt_lang}, and then also generate a corrected output version for the AI in {tgt_lang}; 8. if the human asks to translate text from one to another language, then you only translate the human's question to {tgt_lang} but you keep the translation that the AI provides in the language that the human requested; 9. do not translate code fragments but copy them as they are. If there are English examples, variable names or definitions in code fragments, keep them in English. Now translate the following conversation with the requirements set out above. Do not provide an explanation and do not add anything else.\n\n""" ``` The prompt to translate the topic is: ```python TOPIC_TRANSLATION_PROMPT = "Translate the following title of a conversation from {src_lang} to {tgt_lang} in a succinct," \ " summarizing manner. Translate accurately and formally. Do not provide any explanation" \ " about the translation and do not include the original title.\n\n" ``` The system message was: ``` You are a helpful assistant that translates English to Dutch to the requirements that are given to you. ``` Note that 82 items (0.1%) were not successfully translated. The translation was missing the AI identifier `[|AI|]` and/or the human one `[|Human|]`. The IDs for the missing items are `[1713, 1937, 1960, 4326, 4356, 8357, 8542, 8827, 9137, 9782, 11560, 11961, 12244, 12362, 12488, 13259, 13621, 14445, 14835, 15006, 17746, 18808, 19285, 19426, 19491, 21270, 21661, 22098, 23352, 23840, 23869, 25148, 25928, 27102, 27856, 28387, 29942, 30041, 30251, 32396, 32742, 32941, 33628, 34116, 34648, 34859, 35977, 35987, 36035, 36456, 37028, 37238, 37640, 38107, 38735, 39015, 40984, 41115, 41567, 42397, 43219, 43783, 44599, 44980, 45239, 47676, 48922, 49534, 50282, 50683, 50804, 50919, 51076, 51211, 52000, 52183, 52489, 52595, 53884, 54726, 55795, 56992]`. ### Source Data #### Initial Data Collection and Normalization Initial data generation by [Baize](https://github.com/project-baize/baize-chatbot), which in turn relied on [Stack Overflow Questions](https://huggingface.co/datasets/pacovaldez/stackoverflow-questions). #### Who are the source language producers? Quora users and OpenAI's GPT models (via [Baize](https://github.com/project-baize/baize-chatbot)). ## Considerations for Using the Data Note that neither the translations in this new dataset, nor the machine-generated dialogues in the original [Baize](https://github.com/project-baize/baize-chatbot) dataset, have been verified by humans. ### Discussion of Biases As with any machine-generated texts, users should be aware of potential biases that are included in this dataset. Although the prompt specifically includes `make sure to avoid biases (such as gender bias, grammatical bias, social bias)`, of course the impact of such command is not known. It is likely that biases remain in the dataset so use with caution. ### Other Known Limitations The translation quality has not been verified. Use at your own risk! ### Licensing Information Licensing info for [`Stack Overflow Questions`](https://huggingface.co/datasets/pacovaldez/stackoverflow-questions) is listed as Apache 2.0. If you use the current dataset, you should also adhere to the original license. This text was generated (either in part or in full) with GPT-3 (`gpt-3.5-turbo`), OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication. If you use this dataset, you must also follow the [Sharing](https://openai.com/policies/sharing-publication-policy) and [Usage](https://openai.com/policies/usage-policies) policies. As clearly stated in their [Terms of Use](https://openai.com/policies/terms-of-use), specifically 2c.iii, "[you may not] use output from the Services to develop models that compete with OpenAI". That means that you cannot use this dataset to build models that are intended to commercially compete with OpenAI. [As far as I am aware](https://law.stackexchange.com/questions/93308/licensing-material-generated-with-chatgpt), that is a specific restriction that should serve as an addendum to the current license. ### Contributions Thanks to [Baize](https://github.com/project-baize/baize-chatbot) for the initial machine-generated dialogues, based on [Stack Overflow Questions](https://huggingface.co/datasets/pacovaldez/stackoverflow-questions).

ademirsl/diniz

--- license: openrail ---

bcmaster/Breezy1

--- license: openrail ---

camenduru/test-0_6500

--- dataset_info: features: - name: image dtype: image - name: url dtype: string - name: prompt dtype: string - name: style dtype: string splits: - name: train num_bytes: 10874304856.5 num_examples: 6500 download_size: 10856442215 dataset_size: 10874304856.5 --- # Dataset Card for "OpenNiji-Dataset-6500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-billsum-default-bec98f-32334145011

--- type: predictions tags: - autotrain - evaluation datasets: - billsum eval_info: task: summarization model: PoseyATX/Moist-Pony metrics: [] dataset_name: billsum dataset_config: default dataset_split: test col_mapping: text: text target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: PoseyATX/Moist-Pony * Dataset: billsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@poseyatx](https://huggingface.co/poseyatx) for evaluating this model.

utkarshhh17/indian_food_images

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': burger '1': butter_naan '2': chai '3': chapati '4': chole_bhature '5': dal_makhani '6': dhokla '7': fried_rice '8': idli '9': jalebi '10': kaathi_rolls '11': kadai_paneer '12': kulfi '13': masala_dosa '14': momos '15': paani_puri '16': pakode '17': pav_bhaji '18': pizza '19': samosa splits: - name: train num_bytes: 1697830157.4234333 num_examples: 5328 - name: test num_bytes: 249679569.3925666 num_examples: 941 download_size: 1601513193 dataset_size: 1947509726.816 --- # Dataset Card for "indian_food_images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Ruqoyya/celeb-identities

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Albert_Einstein '1': Ashley_Olsen '2': Chris_Rock '3': Cristiano_Ronaldo '4': Didier_Drogba '5': Idris_Elba '6': Lionel_Messi '7': Mary-Kate_Olsen '8': Paul_Pogba '9': Tamera_Mowry '10': Tia_Mowry splits: - name: train num_bytes: 1992683.0 num_examples: 34 download_size: 1995278 dataset_size: 1992683.0 --- # Dataset Card for "celeb-identities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

metaeval/cnli

--- language: - en --- # Generalization of Counterfactually-Augmented NLI Data ```bib @inproceedings{huang2020cnligeneralization, title={Counterfactually-Augmented {SNLI} Training Data Does Not Yield Better Generalization Than Unaugmented Data}, author={William Huang and Haokun Liu and Samuel R. Bowman}, booktitle = {Proceedings of the 2020 EMNLP Workshop on Insights from Negative Results in NLP}, year={2020}, publisher = {The Association for Computational Linguistics} } ```

foxxy-hm/slu-augmented-data

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: speech sequence: float64 - name: sampling_rate dtype: int64 - name: target_text dtype: string splits: - name: train num_bytes: 3720263051.262795 num_examples: 7190 - name: test num_bytes: 930324473.7372051 num_examples: 1798 download_size: 2043481654 dataset_size: 4650587525.0 --- # Dataset Card for "slu-augmented-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

feliciamj/classifier

--- 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: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos splits: - name: train num_bytes: 1074806 num_examples: 8530 - name: validation num_bytes: 134675 num_examples: 1066 - name: test num_bytes: 135968 num_examples: 1066 download_size: 881052 dataset_size: 1345449 --- # Dataset Card for "classifier" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mahdibaghbanzadeh/GUE_mouse_4

--- dataset_info: features: - name: sequence dtype: string - name: labels dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 1702232 num_examples: 15064 - name: val num_bytes: 212779 num_examples: 1883 - name: test num_bytes: 212779 num_examples: 1883 download_size: 980363 dataset_size: 2127790 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---

anzorq/kbd_lat-ru

--- language: - kbd - ru license: - mit tags: - translation pretty_name: Kbd Ru Translation source_datasets: - original multilinguality: - multilingual task_categories: - translation - text2text-generation task_ids: - translation - text2text-generation ---

quocanh34/test_synthesis_data

--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string splits: - name: train num_bytes: 38195460 num_examples: 75 download_size: 9149788 dataset_size: 38195460 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "test_synthesis_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BhavyaMuni/sample_dataset

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 627787 num_examples: 387 download_size: 259828 dataset_size: 627787 --- # Dataset Card for "sample_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rojagtap/ubuntu_dialogs_corpus

--- license: mit --- https://github.com/rkadlec/ubuntu-ranking-dataset-creator#readme

senhorsapo/luffy

--- license: openrail ---

kimjaewon/baemin_sft_data

--- dataset_info: features: - name: question dtype: string - name: positive_document_list sequence: string - name: negative_document_list sequence: string - name: answer dtype: string splits: - name: train num_bytes: 4484015 num_examples: 1452 download_size: 311120 dataset_size: 4484015 configs: - config_name: default data_files: - split: train path: data/train-* ---

khaimaitien/leetcode_problem_solution

--- task_categories: - text-generation --- This dataset contains: problems and solutions in Leetcode, crawled from: https://github.com/AnasImloul/Leetcode-Solutions The format of data: + title: title of the problem + algo_input: the description of the problem + solution_py: the solution in Python + solution_js: the solution in Js + solution_java: the solution in Java + solution_c: the solution in C

jijivski/mock_mmlu

--- license: mit ---

h2oai/h2ogpt-oig-instruct-cleaned

--- license: apache-2.0 language: - en thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico tags: - gpt - llm - large language model - open-source --- # h2oGPT Data Card ## Summary H2O.ai's `h2ogpt-oig-instruct-cleaned` is an open-source instruct-type dataset for fine-tuning of large language models, licensed for commercial use. - Number of rows: `195436` - Number of columns: `1` - Column names: `['input']` ## Source - [Original LAION OIG Dataset](https://github.com/LAION-AI/Open-Instruction-Generalist) - [LAION OIG data detoxed and filtered down by scripts in h2oGPT repository](https://github.com/h2oai/h2ogpt/blob/b8f15efcc305a953c52a0ee25b8b4897ceb68c0a/scrape_dai_docs.py)

anan-2024/twitter_dataset_1713150974

--- 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: 22176 num_examples: 51 download_size: 12543 dataset_size: 22176 configs: - config_name: default data_files: - split: train path: data/train-* ---

qqqqq1/Javaragas

--- language: - zh pretty_name: java ragas ---

EleutherAI/quirky_hemisphere_bob

--- 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: float64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 375918.8239690378 num_examples: 3746 - name: validation num_bytes: 200694.0 num_examples: 2000 - name: test num_bytes: 200545.5 num_examples: 2000 download_size: 196630 dataset_size: 777158.3239690377 --- # Dataset Card for "quirky_hemisphere_bob" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Ckail/Needy_Girl_Overdose

--- license: gpl-3.0 ---

abhishek/autotrain-data-as6y-5zz4-oanx

--- dataset_info: features: - name: PassengerId dtype: int64 - name: Pclass dtype: int64 - name: Name dtype: string - name: Sex dtype: string - name: Age dtype: float64 - name: SibSp dtype: int64 - name: Parch dtype: int64 - name: Ticket dtype: string - name: Fare dtype: float64 - name: Cabin dtype: string - name: Embarked dtype: string - name: autotrain_id dtype: int64 - name: autotrain_label dtype: int64 splits: - name: train num_bytes: 88677 num_examples: 712 - name: validation num_bytes: 22334 num_examples: 179 download_size: 54451 dataset_size: 111011 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "autotrain-data-as6y-5zz4-oanx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DeepFoldProtein/foldseek_combined

--- dataset_info: features: - name: id dtype: string - name: seq dtype: string splits: - name: train num_bytes: 684846121 num_examples: 2302908 download_size: 675247498 dataset_size: 684846121 configs: - config_name: default data_files: - split: train path: data/train-* ---

BigTMiami/small_amazon_review_text_dataset

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 428020 num_examples: 3504 - name: validation num_bytes: 428020 num_examples: 3504 download_size: 253700 dataset_size: 856040 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

khalidalt/Moroccan_offensive_detection

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 2551311 num_examples: 20402 download_size: 1367902 dataset_size: 2551311 configs: - config_name: default data_files: - split: train path: data/train-* language: - ar ---

toddmorrill/github-issues

--- YAML tags: annotations_creators: - no-annotation language_creators: [] language: - '''en-US''' license: [] multilinguality: - monolingual pretty_name: Hugging Face Github Issues size_categories: - unknown source_datasets: [] task_categories: - text-classification - text-retrieval task_ids: - multi-class-classification - multi-label-classification - document-retrieval --- # Dataset Card for GitHub Issues ## Dataset Summary GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets repository. It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.

open-llm-leaderboard/details_damerajee__Gaja-v1.00

--- pretty_name: Evaluation run of damerajee/Gaja-v1.00 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [damerajee/Gaja-v1.00](https://huggingface.co/damerajee/Gaja-v1.00) 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_damerajee__Gaja-v1.00\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-29T19:42:27.691927](https://huggingface.co/datasets/open-llm-leaderboard/details_damerajee__Gaja-v1.00/blob/main/results_2024-02-29T19-42-27.691927.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\": 0.4104632836512482,\n\ \ \"acc_stderr\": 0.03423965153377148,\n \"acc_norm\": 0.41631431599382307,\n\ \ \"acc_norm_stderr\": 0.035127053633139296,\n \"mc1\": 0.28886168910648713,\n\ \ \"mc1_stderr\": 0.01586634640138431,\n \"mc2\": 0.4463856836867432,\n\ \ \"mc2_stderr\": 0.014941359214508863\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4803754266211604,\n \"acc_stderr\": 0.014600132075947084,\n\ \ \"acc_norm\": 0.5281569965870307,\n \"acc_norm_stderr\": 0.014588204105102203\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5711013742282414,\n\ \ \"acc_stderr\": 0.004939073014754943,\n \"acc_norm\": 0.7630950009958176,\n\ \ \"acc_norm_stderr\": 0.004243145587737571\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.4074074074074074,\n\ \ \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.4074074074074074,\n\ \ \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.4473684210526316,\n \"acc_stderr\": 0.04046336883978251,\n\ \ \"acc_norm\": 0.4473684210526316,\n \"acc_norm_stderr\": 0.04046336883978251\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.41132075471698115,\n \"acc_stderr\": 0.030285009259009798,\n\ \ \"acc_norm\": 0.41132075471698115,\n \"acc_norm_stderr\": 0.030285009259009798\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.375,\n\ \ \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.375,\n \ \ \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n\ \ \"acc_norm_stderr\": 0.049236596391733084\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\": 0.32947976878612717,\n\ \ \"acc_stderr\": 0.03583901754736411,\n \"acc_norm\": 0.32947976878612717,\n\ \ \"acc_norm_stderr\": 0.03583901754736411\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.038739587141493524,\n\ \ \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.038739587141493524\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.33191489361702126,\n \"acc_stderr\": 0.030783736757745657,\n\ \ \"acc_norm\": 0.33191489361702126,\n \"acc_norm_stderr\": 0.030783736757745657\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.20175438596491227,\n\ \ \"acc_stderr\": 0.03775205013583638,\n \"acc_norm\": 0.20175438596491227,\n\ \ \"acc_norm_stderr\": 0.03775205013583638\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708617,\n \"\ acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708617\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n\ \ \"acc_stderr\": 0.038095238095238106,\n \"acc_norm\": 0.23809523809523808,\n\ \ \"acc_norm_stderr\": 0.038095238095238106\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.44193548387096776,\n\ \ \"acc_stderr\": 0.028251557906849734,\n \"acc_norm\": 0.44193548387096776,\n\ \ \"acc_norm_stderr\": 0.028251557906849734\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\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.5212121212121212,\n \"acc_stderr\": 0.03900828913737302,\n\ \ \"acc_norm\": 0.5212121212121212,\n \"acc_norm_stderr\": 0.03900828913737302\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.41919191919191917,\n \"acc_stderr\": 0.035155207286704175,\n \"\ acc_norm\": 0.41919191919191917,\n \"acc_norm_stderr\": 0.035155207286704175\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.5440414507772021,\n \"acc_stderr\": 0.035944137112724386,\n\ \ \"acc_norm\": 0.5440414507772021,\n \"acc_norm_stderr\": 0.035944137112724386\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.38461538461538464,\n \"acc_stderr\": 0.024666744915187222,\n\ \ \"acc_norm\": 0.38461538461538464,\n \"acc_norm_stderr\": 0.024666744915187222\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945284,\n \ \ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945284\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.3697478991596639,\n \"acc_stderr\": 0.03135709599613591,\n \ \ \"acc_norm\": 0.3697478991596639,\n \"acc_norm_stderr\": 0.03135709599613591\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.48807339449541287,\n \"acc_stderr\": 0.021431223617362223,\n \"\ acc_norm\": 0.48807339449541287,\n \"acc_norm_stderr\": 0.021431223617362223\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.32407407407407407,\n \"acc_stderr\": 0.03191923445686186,\n \"\ acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.03191923445686186\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.47549019607843135,\n \"acc_stderr\": 0.035050931943487976,\n \"\ acc_norm\": 0.47549019607843135,\n \"acc_norm_stderr\": 0.035050931943487976\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.5358649789029536,\n \"acc_stderr\": 0.03246338898055659,\n \ \ \"acc_norm\": 0.5358649789029536,\n \"acc_norm_stderr\": 0.03246338898055659\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5112107623318386,\n\ \ \"acc_stderr\": 0.033549366530984746,\n \"acc_norm\": 0.5112107623318386,\n\ \ \"acc_norm_stderr\": 0.033549366530984746\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.45038167938931295,\n \"acc_stderr\": 0.04363643698524779,\n\ \ \"acc_norm\": 0.45038167938931295,\n \"acc_norm_stderr\": 0.04363643698524779\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.5950413223140496,\n \"acc_stderr\": 0.04481137755942469,\n \"\ acc_norm\": 0.5950413223140496,\n \"acc_norm_stderr\": 0.04481137755942469\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.31901840490797545,\n \"acc_stderr\": 0.03661997551073836,\n\ \ \"acc_norm\": 0.31901840490797545,\n \"acc_norm_stderr\": 0.03661997551073836\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n\ \ \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n\ \ \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.4077669902912621,\n \"acc_stderr\": 0.048657775704107696,\n\ \ \"acc_norm\": 0.4077669902912621,\n \"acc_norm_stderr\": 0.048657775704107696\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.5341880341880342,\n\ \ \"acc_stderr\": 0.03267942734081228,\n \"acc_norm\": 0.5341880341880342,\n\ \ \"acc_norm_stderr\": 0.03267942734081228\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5223499361430396,\n\ \ \"acc_stderr\": 0.017862091778507862,\n \"acc_norm\": 0.5223499361430396,\n\ \ \"acc_norm_stderr\": 0.017862091778507862\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.4479768786127168,\n \"acc_stderr\": 0.02677299065336183,\n\ \ \"acc_norm\": 0.4479768786127168,\n \"acc_norm_stderr\": 0.02677299065336183\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3027932960893855,\n\ \ \"acc_stderr\": 0.01536686038639711,\n \"acc_norm\": 0.3027932960893855,\n\ \ \"acc_norm_stderr\": 0.01536686038639711\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.3954248366013072,\n \"acc_stderr\": 0.02799672318063144,\n\ \ \"acc_norm\": 0.3954248366013072,\n \"acc_norm_stderr\": 0.02799672318063144\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5048231511254019,\n\ \ \"acc_stderr\": 0.028396770444111298,\n \"acc_norm\": 0.5048231511254019,\n\ \ \"acc_norm_stderr\": 0.028396770444111298\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.48148148148148145,\n \"acc_stderr\": 0.027801656212323667,\n\ \ \"acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.027801656212323667\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3617021276595745,\n \"acc_stderr\": 0.0286638201471995,\n \ \ \"acc_norm\": 0.3617021276595745,\n \"acc_norm_stderr\": 0.0286638201471995\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3135593220338983,\n\ \ \"acc_stderr\": 0.01184923429145932,\n \"acc_norm\": 0.3135593220338983,\n\ \ \"acc_norm_stderr\": 0.01184923429145932\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.02989616303312547,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.02989616303312547\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.3954248366013072,\n \"acc_stderr\": 0.01978046595477753,\n \ \ \"acc_norm\": 0.3954248366013072,\n \"acc_norm_stderr\": 0.01978046595477753\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.42727272727272725,\n\ \ \"acc_stderr\": 0.04738198703545483,\n \"acc_norm\": 0.42727272727272725,\n\ \ \"acc_norm_stderr\": 0.04738198703545483\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.45714285714285713,\n \"acc_stderr\": 0.031891418324213966,\n\ \ \"acc_norm\": 0.45714285714285713,\n \"acc_norm_stderr\": 0.031891418324213966\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5024875621890548,\n\ \ \"acc_stderr\": 0.03535490150137288,\n \"acc_norm\": 0.5024875621890548,\n\ \ \"acc_norm_stderr\": 0.03535490150137288\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.35542168674698793,\n\ \ \"acc_stderr\": 0.03726214354322415,\n \"acc_norm\": 0.35542168674698793,\n\ \ \"acc_norm_stderr\": 0.03726214354322415\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.5497076023391813,\n \"acc_stderr\": 0.038158273659132366,\n\ \ \"acc_norm\": 0.5497076023391813,\n \"acc_norm_stderr\": 0.038158273659132366\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.28886168910648713,\n\ \ \"mc1_stderr\": 0.01586634640138431,\n \"mc2\": 0.4463856836867432,\n\ \ \"mc2_stderr\": 0.014941359214508863\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7063930544593529,\n \"acc_stderr\": 0.012799397296204164\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \ \ \"acc_stderr\": 0.0026153265107756725\n }\n}\n```" repo_url: https://huggingface.co/damerajee/Gaja-v1.00 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_02_29T19_42_27.691927 path: - '**/details_harness|arc:challenge|25_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-29T19-42-27.691927.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|gsm8k|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hellaswag|10_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-42-27.691927.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-management|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-42-27.691927.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|truthfulqa:mc|0_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-29T19-42-27.691927.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_29T19_42_27.691927 path: - '**/details_harness|winogrande|5_2024-02-29T19-42-27.691927.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-29T19-42-27.691927.parquet' - config_name: results data_files: - split: 2024_02_29T19_42_27.691927 path: - results_2024-02-29T19-42-27.691927.parquet - split: latest path: - results_2024-02-29T19-42-27.691927.parquet --- # Dataset Card for Evaluation run of damerajee/Gaja-v1.00  Dataset automatically created during the evaluation run of model [damerajee/Gaja-v1.00](https://huggingface.co/damerajee/Gaja-v1.00) 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_damerajee__Gaja-v1.00", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-29T19:42:27.691927](https://huggingface.co/datasets/open-llm-leaderboard/details_damerajee__Gaja-v1.00/blob/main/results_2024-02-29T19-42-27.691927.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": 0.4104632836512482, "acc_stderr": 0.03423965153377148, "acc_norm": 0.41631431599382307, "acc_norm_stderr": 0.035127053633139296, "mc1": 0.28886168910648713, "mc1_stderr": 0.01586634640138431, "mc2": 0.4463856836867432, "mc2_stderr": 0.014941359214508863 }, "harness|arc:challenge|25": { "acc": 0.4803754266211604, "acc_stderr": 0.014600132075947084, "acc_norm": 0.5281569965870307, "acc_norm_stderr": 0.014588204105102203 }, "harness|hellaswag|10": { "acc": 0.5711013742282414, "acc_stderr": 0.004939073014754943, "acc_norm": 0.7630950009958176, "acc_norm_stderr": 0.004243145587737571 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4074074074074074, "acc_stderr": 0.04244633238353228, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.04244633238353228 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04046336883978251, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04046336883978251 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.41132075471698115, "acc_stderr": 0.030285009259009798, "acc_norm": 0.41132075471698115, "acc_norm_stderr": 0.030285009259009798 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.375, "acc_stderr": 0.04048439222695598, "acc_norm": 0.375, "acc_norm_stderr": 0.04048439222695598 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "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": 0.32947976878612717, "acc_stderr": 0.03583901754736411, "acc_norm": 0.32947976878612717, "acc_norm_stderr": 0.03583901754736411 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.18627450980392157, "acc_stderr": 0.038739587141493524, "acc_norm": 0.18627450980392157, "acc_norm_stderr": 0.038739587141493524 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.33191489361702126, "acc_stderr": 0.030783736757745657, "acc_norm": 0.33191489361702126, "acc_norm_stderr": 0.030783736757745657 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.20175438596491227, "acc_stderr": 0.03775205013583638, "acc_norm": 0.20175438596491227, "acc_norm_stderr": 0.03775205013583638 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.41379310344827586, "acc_stderr": 0.04104269211806232, "acc_norm": 0.41379310344827586, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.26455026455026454, "acc_stderr": 0.022717467897708617, "acc_norm": 0.26455026455026454, "acc_norm_stderr": 0.022717467897708617 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.23809523809523808, "acc_stderr": 0.038095238095238106, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.038095238095238106 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.44193548387096776, "acc_stderr": 0.028251557906849734, "acc_norm": 0.44193548387096776, "acc_norm_stderr": 0.028251557906849734 }, "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": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5212121212121212, "acc_stderr": 0.03900828913737302, "acc_norm": 0.5212121212121212, "acc_norm_stderr": 0.03900828913737302 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.41919191919191917, "acc_stderr": 0.035155207286704175, "acc_norm": 0.41919191919191917, "acc_norm_stderr": 0.035155207286704175 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.5440414507772021, "acc_stderr": 0.035944137112724386, "acc_norm": 0.5440414507772021, "acc_norm_stderr": 0.035944137112724386 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.38461538461538464, 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"acc_norm": 0.42727272727272725, "acc_norm_stderr": 0.04738198703545483 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.45714285714285713, "acc_stderr": 0.031891418324213966, "acc_norm": 0.45714285714285713, "acc_norm_stderr": 0.031891418324213966 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5024875621890548, "acc_stderr": 0.03535490150137288, "acc_norm": 0.5024875621890548, "acc_norm_stderr": 0.03535490150137288 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-virology|5": { "acc": 0.35542168674698793, "acc_stderr": 0.03726214354322415, "acc_norm": 0.35542168674698793, "acc_norm_stderr": 0.03726214354322415 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.5497076023391813, "acc_stderr": 0.038158273659132366, "acc_norm": 0.5497076023391813, "acc_norm_stderr": 0.038158273659132366 }, "harness|truthfulqa:mc|0": { "mc1": 0.28886168910648713, "mc1_stderr": 0.01586634640138431, "mc2": 0.4463856836867432, "mc2_stderr": 0.014941359214508863 }, "harness|winogrande|5": { "acc": 0.7063930544593529, "acc_stderr": 0.012799397296204164 }, "harness|gsm8k|5": { "acc": 0.009097801364670205, "acc_stderr": 0.0026153265107756725 } } ``` ## Dataset Details ### Dataset Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

liuyanchen1015/MULTI_VALUE_rte_those_them

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 32717 num_examples: 63 - name: train num_bytes: 22627 num_examples: 44 download_size: 47207 dataset_size: 55344 --- # Dataset Card for "MULTI_VALUE_rte_those_them" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Falah/Blood_8_classes_Dataset

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': basophil '1': eosinophil '2': erythroblast '3': ig '4': lymphocyte '5': monocyte '6': neutrophil '7': platelet splits: - name: train num_bytes: 302576945.936 num_examples: 17092 download_size: 280999680 dataset_size: 302576945.936 --- # Dataset Card for Blood_8_Classes_Dataset ## Description This dataset contains images and labels for blood cells in eight different classes. The images are classified into the following classes: - basophil - eosinophil - erythroblast - ig - lymphocyte - monocyte - neutrophil - platelet ## Dataset Info - Total number of examples: 17,092 - Dataset size: 302,576,945.936 bytes - Download size: 280,999,680 bytes ## Usage This dataset was created by Falah G. Salieh for the application of blood health classification in the health field. It can be used for various applications in computer vision, image classification, and machine learning. ## Citation If you use this dataset in your work, please cite the following paper: ``` @misc{blood_8_classes_dataset, title = { Blood_8 Classes_Dataset}, author = {Falah G. Salieh}, year = {2023}, publisher = {HuggingFace Hub}, url = {\url{https://huggingface.co/datasets/Falah/Blood_8_classes_Dataset/}} } ``` ## Contact For questions or inquiries about the dataset, please contact the author at [falahgs@gmail.com]. # Contributing We appreciate your contributions to improving the dataset and making it more useful for the community. If you want to contribute, please follow the guidelines outlined below: ## Enhancements and Feature Requests If you have ideas for enhancing the dataset, adding new features, or improving the existing ones, please check the [existing feature requests](https://huggingface.co/datasets/Falah/Blood_8_classes_Dataset/discussions) to see if a similar suggestion has been made and describe your proposal.

benayas/massive

--- dataset_info: features: - name: id dtype: string - name: locale dtype: string - name: partition dtype: string - name: scenario dtype: class_label: names: '0': social '1': transport '2': calendar '3': play '4': news '5': datetime '6': recommendation '7': email '8': iot '9': general '10': audio '11': lists '12': qa '13': cooking '14': takeaway '15': music '16': alarm '17': weather - name: intent dtype: class_label: names: '0': datetime_query '1': iot_hue_lightchange '2': transport_ticket '3': takeaway_query '4': qa_stock '5': general_greet '6': recommendation_events '7': music_dislikeness '8': iot_wemo_off '9': cooking_recipe '10': qa_currency '11': transport_traffic '12': general_quirky '13': weather_query '14': audio_volume_up '15': email_addcontact '16': takeaway_order '17': email_querycontact '18': iot_hue_lightup '19': recommendation_locations '20': play_audiobook '21': lists_createoradd '22': news_query '23': alarm_query '24': iot_wemo_on '25': general_joke '26': qa_definition '27': social_query '28': music_settings '29': audio_volume_other '30': calendar_remove '31': iot_hue_lightdim '32': calendar_query '33': email_sendemail '34': iot_cleaning '35': audio_volume_down '36': play_radio '37': cooking_query '38': datetime_convert '39': qa_maths '40': iot_hue_lightoff '41': iot_hue_lighton '42': transport_query '43': music_likeness '44': email_query '45': play_music '46': audio_volume_mute '47': social_post '48': alarm_set '49': qa_factoid '50': calendar_set '51': play_game '52': alarm_remove '53': lists_remove '54': transport_taxi '55': recommendation_movies '56': iot_coffee '57': music_query '58': play_podcasts '59': lists_query - name: utt dtype: string - name: annot_utt dtype: string - name: worker_id dtype: string - name: slot_method sequence: - name: slot dtype: string - name: method dtype: string - name: judgments sequence: - name: worker_id dtype: string - name: intent_score dtype: int8 - name: slots_score dtype: int8 - name: grammar_score dtype: int8 - name: spelling_score dtype: int8 - name: language_identification dtype: string - name: category dtype: string splits: - name: train num_bytes: 2190824 num_examples: 11514 - name: validation num_bytes: 381745 num_examples: 2033 - name: test num_bytes: 558310 num_examples: 2974 download_size: 1028818 dataset_size: 3130879 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

YXu120/NC_Education

--- language: - en license: cc-by-sa-4.0 pretty_name: NC Education Data size_categories: - 1K<n<10K --- netid: yx264 # Dataset Description - **Homepage:** https://linc.osbm.nc.gov/pages/home - **(1) Education data:** https://linc.osbm.nc.gov/explore/dataset/education/information/?disjunctive.area_name&disjunctive.area_type&disjunctive.variable - **(2) Educational attainment for the black population:** https://linc.osbm.nc.gov/explore/dataset/educational-attainment-for-the-black-population-c15002b/information/?disjunctive.area_name&disjunctive.area_type&disjunctive.year&disjunctive.variable ### Dataset Summary The education datasets can be found at North Carolina Office of State Budget and Management (NC OSBM) open data portal. The first data includes public school enrollment and finances, SAT score, higher education enrollment, educational attainment data for North Carolina and counties, and the second one includes educational attainment for the Black population aged 25 and over for North Carolina and counties. The processed final data will combine the information of them, providing a profound insight into NC educational level and equity. ### Supported Tasks The tasks/methodologies are: - **(1)** Data checking and cleaning Since the first dataset is pretty large and includes some information we don't care, like the SAT score. Before proprecessing, we first check the datasets, do some visualization and summarizing, to decide whether to drop certain rows. - **(2)** Re-structure the datasets to present the values according to county-level distinctions The original dataset architecture lacks depth, as all variables reside on a single plane. Considering the significance of these variables, it’s nature to contemplate reorganizing the hierarchy to reflect county-level distinctions and convey the data accordingly. - **(3)** Keywords-based data matching The data matching process relies on all columns except the 'value' column. While 'area_name', 'area_type', and 'year' offer straightforward matching points across datasets, it's important to acknowledge the potential disparity in the 'variable' column. For instance, one dataset may contain the variable 'Less than High School Diploma', while another might list 'Less than High School Black'. Despite conveying the same meaning and requiring alignment, a direct join isn't feasible due to this discrepancy. One potential approach involves extracting keywords from the variables, such as 'Less' and 'High School', and cross-referencing them to determine potential matches between the datasets. ### Languages English # Dataset Structure ### Data Instances - **(1) Education data:** https://linc.osbm.nc.gov/explore/dataset/education/api/?disjunctive.area_name&disjunctive.area_type&disjunctive.variable An example of records (note that specific details are subject to change): ```json { "area_name":"Alexander County", "area_type":"County", "year":"2010", "variable":"Elementary School Education or Less Age 25 Up", "value":2325 } ``` - **(2) Educational attainment for the black population:** https://linc.osbm.nc.gov/explore/dataset/educational-attainment-for-the-black-population-c15002b/api/?disjunctive.area_name&disjunctive.area_type&disjunctive.year&disjunctive.variable An example of records (note that specific details are subject to change): ```json { "area_name":"Burke County", "area_type":"County", "year":"2020", "variable":"Less than High School Diploma", "value":568 } ``` - **(3) Anticipated structure we aim to achieve:** ```json { "area_name":"Alamance County", "area_type":"County",[("year": "2010", [ {"variable": "High School Graduate (includes Equivalency)", "value": 6443}, {"variable":"Some College or Associate's Degree","value":5242}, ... ]), ...] ... } ``` ### Data Fields - Area Name (string): name of the area - Area Type (string): type of the area - Year (integer): the year that the data was collected - Variable (string): level of education or other situations - Value (integer): population count # Dataset Creation ### Curation Rationale The datasets were collected and published to present the educational level of NC population in different area. The educational attainment for the black population data can rise the concern for educational equity issue in North Carolina. The combined dataset aims to offer a holistic perspective on educational levels and equity, with a specific focus on the educational attainment of the Black population aged 25 and over. ### Source Data The original data were published by North Carolina Department of Public Instruction, North Carolina Community College System and US Census Bureau. # Considerations for Using the Data The records are only for limited years, making it challenging to discern timely patterns or trends. The second data is quite small compared to the first one. Combining may result in many missing values. # Additional Information ### Citation Information Bureau, US Census. Census.Gov, 21 Jan. 2024, www.census.gov. NC Budget & Management Open Data Portal Home Page - NC OSBM, https://linc.osbm.nc.gov/pages/home. ### Contributions Thanks North Carolina Department of Public Instruction, North Carolina Community College System and US Census Bureau for these datasets.

communityai/gretelai___synthetic_text_to_sql-15k

--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 12645170.55 num_examples: 15000 download_size: 4478749 dataset_size: 12645170.55 configs: - config_name: default data_files: - split: train path: data/train-* ---

chanelcolgate/image-classification-yenthienviet

--- license: mit ---

severo/tmp-1

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: content dtype: string - name: poster dtype: string - name: date_utc dtype: timestamp[us] - name: flair dtype: string - name: title dtype: string - name: score dtype: int64 - name: permalink dtype: string - name: id dtype: string splits: - name: train num_bytes: 250 num_examples: 1 download_size: 5088 dataset_size: 250 --- # Dataset Card for "dataset-creator-reddit-amitheasshole" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-futin__feed-sen_en_-7dbe88-2245971657

--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-350m metrics: [] dataset_name: futin/feed dataset_config: sen_en_ dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-350m * Dataset: futin/feed * Config: sen_en_ * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

rohangbs/death_se42-type2

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 19293975.0 num_examples: 122 - name: val num_bytes: 2214406.0 num_examples: 14 download_size: 21490683 dataset_size: 21508381.0 --- # Dataset Card for "death_se42-type2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tanli12/hagrid-classification-512p-no-gesture-150k-full

--- license: mit ---

notional/notional-python

--- annotations_creators: - no-annotation language: - py language_creators: - found license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - code-generation - conditional-text-generation task_ids: - language-modeling - code-generation --- # Dataset Card for notional-python ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [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) ## Dataset Description - **Homepage:** https://notional.ai/ - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The Notional-python dataset contains python code files from 100 well-known repositories gathered from Google Bigquery Github Dataset. The dataset was created to test the ability of programming language models. Follow [our repo]() to do the model evaluation using notional-python dataset. ### Languages Python ## Dataset Creation ### Curation Rationale Notional-python was built to provide a dataset for testing the ability of the machine to generate python code. ### Source Data #### Initial Data Collection and Normalization The data was obtained by filtering code from [Google Bigquery Github data](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code) In order to improve the quality of the dataset, only python code files that meet the below conditions are added to the dataset: - Code with more than 60% of executable lines - Code with logic, not config files or comment-only files - Code with more than 30% of attribute declaration lines (E.G.: Some files contain just only class names and their class attributes, usually used for configuration of the project, these files were not selected) - Code without `TODO` and `FIXME`. #### Who are the source language producers? The producers are users of github.

CShorten/CORD19-init-160k

--- license: afl-3.0 ---

qgiaohc/twitter_dataset_1713133435

--- 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: 21790 num_examples: 53 download_size: 14184 dataset_size: 21790 configs: - config_name: default data_files: - split: train path: data/train-* ---

Nelis/Midjourney_Stable_Diffusion_prompts

--- license: unknown dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 256042389 num_examples: 1048576 - name: test num_bytes: 19012346 num_examples: 73718 download_size: 121702876 dataset_size: 275054735 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

tyzhu/squad_qa_wrong_rare_v5_full_recite_full_passage_random_permute_rerun_8

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 11172737.027252503 num_examples: 6305 - name: validation num_bytes: 587391 num_examples: 300 download_size: 1850990 dataset_size: 11760128.027252503 --- # Dataset Card for "squad_qa_wrong_rare_v5_full_recite_full_passage_random_permute_rerun_8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

x_stance

--- annotations_creators: - machine-generated language: - de - en - fr - it language_creators: - found license: - cc-by-nc-4.0 multilinguality: - multilingual pretty_name: x-stance size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: x-stance tags: - stance-detection dataset_info: features: - name: question dtype: string - name: id dtype: int32 - name: question_id dtype: int32 - name: language dtype: string - name: comment dtype: string - name: label dtype: string - name: numerical_label dtype: int32 - name: author dtype: string - name: topic dtype: string splits: - name: train num_bytes: 17619123 num_examples: 45640 - name: test num_bytes: 6607134 num_examples: 17705 - name: validation num_bytes: 1505979 num_examples: 3926 download_size: 6410801 dataset_size: 25732236 --- # Dataset Card for "x_stance" ## 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:** https://github.com/ZurichNLP/xstance - **Paper:** [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://arxiv.org/abs/2003.08385) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 25.73 MB - **Total amount of disk used:** 32.14 MB ### Dataset Summary The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. It can be used to train and evaluate stance detection systems. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The comments are partly German, partly French and Italian. The questions are available in all the three languages plus English. ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 25.73 MB - **Total amount of disk used:** 32.14 MB An example of 'train' looks as follows. ``` { "author": "f27b54a137b4", "comment": "Das Arbeitsgesetz regelt die Arbeitszeiten und schützt den Arbeitnehmer. Es macht doch Sinn, dass wenn eine Nachfrage besteht, die Läden öffnen dürfen und wenn es keine Nachfrage gibt, diese geschlossen bleiben.", "id": 10045, "label": "FAVOR", "language": "de", "numerical_label": 100, "question": "Sind Sie für eine vollständige Liberalisierung der Geschäftsöffnungszeiten (Geschäfte können die Öffnungszeiten nach freiem Ermessen festlegen)?", "question_id": 739, "topic": "Economy" } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `id`: a `int32` feature. - `question_id`: a `int32` feature. - `language`: a `string` feature. - `comment`: a `string` feature. - `label`: a `string` feature. - `numerical_label`: a `int32` feature. - `author`: a `string` feature. - `topic`: a `string` feature. ### Data Splits | name |train|validation|test | |-------|----:|---------:|----:| |default|45640| 3926|17705| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data have been extracted from the Swiss voting advice platform Smartvote.ch. #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @inproceedings{vamvas2020xstance, author = "Vamvas, Jannis and Sennrich, Rico", title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection", booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)", address = "Zurich, Switzerland", year = "2020", month = "jun", url = "http://ceur-ws.org/Vol-2624/paper9.pdf" } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@jvamvas](https://github.com/jvamvas) for adding this dataset.

ypupreti/Hindi_QR

--- license: mit ---

videoprojector/omelette

--- license: cc0-1.0 ---

storresbusquets/asr-argentinian-spanish

--- license: cc-by-sa-4.0 ---

rithwik-db/imdb_3000

--- license: apache-2.0 ---

anumafzal94/arxiv-test-2048

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: summary dtype: string splits: - name: test num_bytes: 6592892.490835663 num_examples: 196 download_size: 615323 dataset_size: 6592892.490835663 --- # Dataset Card for "arxiv-test-2048" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dmntrd/eval_prompt_template_1

--- dataset_info: features: - name: results struct: - name: alpaca_name dtype: string - name: alpaca_noname dtype: string - name: prompt dtype: string - name: reference dtype: string - name: user_message dtype: string - name: zephyr_name dtype: string - name: zephyr_noname dtype: string splits: - name: train num_bytes: 60300 num_examples: 19 download_size: 53135 dataset_size: 60300 configs: - config_name: default data_files: - split: train path: data/train-* ---

omar-santander/reuters_articles

--- dataset_info: features: - name: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 13792576 num_examples: 17262 - name: validation num_bytes: 1870389 num_examples: 2158 - name: test num_bytes: 1379190 num_examples: 2158 download_size: 10073414 dataset_size: 17042155 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

irds/clinicaltrials_2017_trec-pm-2017

--- pretty_name: '`clinicaltrials/2017/trec-pm-2017`' viewer: false source_datasets: ['irds/clinicaltrials_2017'] task_categories: - text-retrieval --- # Dataset Card for `clinicaltrials/2017/trec-pm-2017` The `clinicaltrials/2017/trec-pm-2017` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/clinicaltrials#clinicaltrials/2017/trec-pm-2017). # Data This dataset provides: - `queries` (i.e., topics); count=30 - `qrels`: (relevance assessments); count=13,019 - For `docs`, use [`irds/clinicaltrials_2017`](https://huggingface.co/datasets/irds/clinicaltrials_2017) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/clinicaltrials_2017_trec-pm-2017', 'queries') for record in queries: record # {'query_id': ..., 'disease': ..., 'gene': ..., 'demographic': ..., 'other': ...} qrels = load_dataset('irds/clinicaltrials_2017_trec-pm-2017', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Roberts2017TrecPm, title={Overview of the TREC 2017 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant}, booktitle={TREC}, year={2017} } ```

liuyanchen1015/VALUE_sst2_been_done

--- dataset_info: features: - name: idx dtype: int64 - name: sentence dtype: string - name: label dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 4919 num_examples: 34 - name: test num_bytes: 14703 num_examples: 89 - name: train num_bytes: 222194 num_examples: 1716 download_size: 120863 dataset_size: 241816 --- # Dataset Card for "VALUE_sst2_been_done" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Ti-Ma/TiMaGPT2-2017

--- license: other license_name: paracrawl-license license_link: LICENSE ---

mychen76/dataset_CSSF12_552_en_exqa

--- language: - en dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 370741 num_examples: 1211 download_size: 152116 dataset_size: 370741 configs: - config_name: default data_files: - split: train path: data/train-* ---

gnumanth/dad-jokes

--- license: mit dataset_info: features: - name: joke dtype: string - name: id dtype: string - name: name dtype: string splits: - name: train num_bytes: 6172.5 num_examples: 44 - name: test num_bytes: 2525.1136363636365 num_examples: 18 download_size: 12578 dataset_size: 8697.613636363636 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

kkoutini/openmic

--- dataset_info: features: - name: filename dtype: string - name: 'true' sequence: float32 length: 20 - name: mask sequence: int32 length: 20 - name: mp3_bytes dtype: binary splits: - name: train num_bytes: 1790991884 num_examples: 14915 - name: test num_bytes: 611455142 num_examples: 5085 download_size: 0 dataset_size: 2402447026 configs: - config_name: default data_files: - split: train path: data/shard_train_* - split: test path: data/shard_test_* --- # CPJKU/openmic The dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset. This dataset is preprocessed and compressed to 32khz mp3 files. The bytes of the mp3 files are embedded. The mp3 bytes can be decoded quickly using for [example](https://github.com/kkoutini/PaSST/blob/4519e4605989b8c2e62dccb5b928af9bf7bf8602/audioset/dataset.py#L55) or [minimp3](https://github.com/f0k/minimp3py). Take a look at the original dataset for more information. The original dataset contains the following: 10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key) VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key) MD5 checksums for each OGG and JSON file Anonymized individual responses, in 'openmic-2018-individual-responses.csv' Aggregated labels, in 'openmic-2018-aggregated-labels.csv' Track metadata, with licenses for each audio recording, in 'openmic-2018-metadata.csv' A Python-friendly NPZ file of features and labels, 'openmic-2018.npz' Sample partitions for train and test, in 'partitions/*.txt' ## Homepage https://zenodo.org/records/1432913 ## Citation ``` Humphrey, Eric J., Durand, Simon, and McFee, Brian. "OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition." in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018. ``` ## License CC BY 4.0

inspektral/reverb-mono-5

--- license: mit ---

mespinosami/elephants_512

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 845894902.629 num_examples: 9023 download_size: 840684586 dataset_size: 845894902.629 --- # Dataset Card for "elephants_512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rajammanabrolu/ultrafeedback_binarized

--- configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: train_sft path: data/train_sft-* - split: test_prefs path: data/test_prefs-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 splits: - name: train_prefs num_bytes: 397273717 num_examples: 61966 - name: train_sft num_bytes: 397273717 num_examples: 61966 - name: test_prefs num_bytes: 12782225 num_examples: 2000 - name: test_sft num_bytes: 6270496 num_examples: 1000 - name: train_gen num_bytes: 316634390 num_examples: 61966 - name: test_gen num_bytes: 5008220 num_examples: 1000 download_size: 636473621 dataset_size: 1135242765 --- # Dataset Card for "ultrafeedback_binarized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

aravind-selvam/chart_processed_800

--- license: mit dataset_info: features: - name: pixel_values sequence: sequence: sequence: float32 - name: labels sequence: int64 - name: target_sequence dtype: string splits: - name: train num_bytes: 30776344108 num_examples: 4000 - name: validation num_bytes: 7694020630 num_examples: 1000 download_size: 1532163489 dataset_size: 38470364738 ---

liuyanchen1015/MULTI_VALUE_cola_completive_finish

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1700 num_examples: 16 - name: test num_bytes: 2666 num_examples: 30 - name: train num_bytes: 19492 num_examples: 253 download_size: 16664 dataset_size: 23858 --- # Dataset Card for "MULTI_VALUE_cola_completive_finish" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

winglian/deduped-cortex-test001

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: run_id dtype: string - name: step dtype: int64 - name: uid dtype: int64 - name: id dtype: string splits: - name: train num_bytes: 29254026.08151392 num_examples: 12656 download_size: 15218406 dataset_size: 29254026.08151392 configs: - config_name: default data_files: - split: train path: data/train-* ---

clinicalnlplab/DDI2013_test

--- dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: train num_bytes: 21619525 num_examples: 18779 - name: valid num_bytes: 9110078 num_examples: 7244 - name: test num_bytes: 6750534 num_examples: 5761 download_size: 6516858 dataset_size: 37480137 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---

dvilasuero/somos-clean-alpaca-es-test

--- dataset_info: features: - name: text dtype: 'null' - name: inputs struct: - name: 1-instruction dtype: string - name: 2-input dtype: string - name: 3-output dtype: string - name: prediction dtype: 'null' - name: prediction_agent dtype: 'null' - name: annotation dtype: string - name: annotation_agent dtype: string - name: vectors struct: - name: input sequence: float64 - name: instruction sequence: float64 - name: output sequence: float64 - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 361340 num_examples: 19 download_size: 301641 dataset_size: 361340 --- # Dataset Card for "somos-clean-alpaca-es-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)