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librarian-bots

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Librarian Bots 57

mteb/sts13-sts

mteb

--- language: - en ---

orionweller/reddit_mds_incremental

orionweller

--- dataset_info: features: [] splits: - name: creation num_bytes: 0 num_examples: 0 download_size: 324 dataset_size: 0 configs: - config_name: default data_files: - split: creation path: data/creation-* ---

ptb-text-only/ptb_text_only

ptb-text-only

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - other license_details: LDC User Agreement for Non-Members multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Penn Treebank dataset_info: features: - name: sentence dtype: string config_name: penn_treebank splits: - name: train num_bytes: 5143706 num_examples: 42068 - name: test num_bytes: 453710 num_examples: 3761 - name: validation num_bytes: 403156 num_examples: 3370 download_size: 5951345 dataset_size: 6000572 --- # Dataset Card for Penn Treebank ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://catalog.ldc.upenn.edu/LDC99T42 - **Repository:** 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt', 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt' - **Paper:** https://www.aclweb.org/anthology/J93-2004.pdf - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary This is the Penn Treebank Project: Release 2 CDROM, featuring a million words of 1989 Wall Street Journal material. The rare words in this version are already replaced with <unk> token. The numbers are replaced with <N> token. ### Supported Tasks and Leaderboards Language Modelling ### Languages The text in the dataset is in American English ## Dataset Structure ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Dataset provided for research purposes only. Please check dataset license for additional information. ### Citation Information @article{marcus-etal-1993-building, title = "Building a Large Annotated Corpus of {E}nglish: The {P}enn {T}reebank", author = "Marcus, Mitchell P. and Santorini, Beatrice and Marcinkiewicz, Mary Ann", journal = "Computational Linguistics", volume = "19", number = "2", year = "1993", url = "https://www.aclweb.org/anthology/J93-2004", pages = "313--330", } ### Contributions Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset.

fsicoli/common_voice_16_0

fsicoli

--- license: cc0-1.0 language: - ab - af - am - ar - as - ast - az - ba - bas - be - bg - bn - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - dyu - el - en - eo - es - et - eu - fa - fi - fr - gl - gn - ha - he - hi - hsb - hu - ia - id - ig - is - it - ja - ka - kab - kk - kmr - ko - ky - lg - lo - lt - lv - mdf - mhr - mk - ml - mn - mr - mrj - mt - myv - nl - oc - or - pl - ps - pt - quy - ro - ru - rw - sah - sat - sc - sk - skr - sl - sq - sr - sw - ta - th - ti - tig - tk - tok - tr - tt - tw - ug - uk - ur - uz - vi - vot - yue - zgh - zh - yo task_categories: - automatic-speech-recognition pretty_name: Common Voice Corpus 16.0 size_categories: - 100B<n<1T tags: - mozilla - foundation --- # Dataset Card for Common Voice Corpus 16.0 <!-- Provide a quick summary of the dataset. --> This dataset is an unofficial version of the Mozilla Common Voice Corpus 16. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/. ## Languages ``` Abkhaz, Albanian, Amharic, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Pashto, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamazight, Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba ``` ## How to use The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function. For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese): ``` from datasets import load_dataset cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ``` from datasets import load_dataset cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train", streaming=True) print(next(iter(cv_16))) ``` Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed). ### Local ``` from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train") batch_sampler = BatchSampler(RandomSampler(cv_16), batch_size=32, drop_last=False) dataloader = DataLoader(cv_16, batch_sampler=batch_sampler) ``` ### Streaming ``` from datasets import load_dataset from torch.utils.data import DataLoader cv_16 = load_dataset("fsicoli/common_voice_16_0", "pt", split="train") dataloader = DataLoader(cv_16, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets. ### Dataset Structure Data Instances A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment. ### Licensing Information Public Domain, CC-0 ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ``` ---

lmsys/lmsys-chat-1m

lmsys

--- size_categories: - 1M<n<10M task_categories: - conversational extra_gated_prompt: You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement). extra_gated_fields: Name: text Email: text Affiliation: text Country: text extra_gated_button_content: I agree to the terms and conditions of the LMSYS-Chat-1M Dataset License Agreement. configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: conversation_id dtype: string - name: model dtype: string - name: conversation list: - name: content dtype: string - name: role dtype: string - name: turn dtype: int64 - name: language dtype: string - name: openai_moderation list: - name: categories struct: - name: harassment dtype: bool - name: harassment/threatening dtype: bool - name: hate dtype: bool - name: hate/threatening dtype: bool - name: self-harm dtype: bool - name: self-harm/instructions dtype: bool - name: self-harm/intent dtype: bool - name: sexual dtype: bool - name: sexual/minors dtype: bool - name: violence dtype: bool - name: violence/graphic dtype: bool - name: category_scores struct: - name: harassment dtype: float64 - name: harassment/threatening dtype: float64 - name: hate dtype: float64 - name: hate/threatening dtype: float64 - name: self-harm dtype: float64 - name: self-harm/instructions dtype: float64 - name: self-harm/intent dtype: float64 - name: sexual dtype: float64 - name: sexual/minors dtype: float64 - name: violence dtype: float64 - name: violence/graphic dtype: float64 - name: flagged dtype: bool - name: redacted dtype: bool splits: - name: train num_bytes: 2626438904 num_examples: 1000000 download_size: 1488850250 dataset_size: 2626438904 --- ## LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset This dataset contains one million real-world conversations with 25 state-of-the-art LLMs. It is collected from 210K unique IP addresses in the wild on the [Vicuna demo and Chatbot Arena website](https://chat.lmsys.org/) from April to August 2023. Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag. User consent is obtained through the "Terms of use" section on the data collection website. To ensure the safe release of data, we have made our best efforts to remove all conversations that contain personally identifiable information (PII). In addition, we have included the OpenAI moderation API output for each message. However, we have chosen to keep unsafe conversations so that researchers can study the safety-related questions associated with LLM usage in real-world scenarios as well as the OpenAI moderation process. We did not run decontamination on this dataset, so it may contain test questions from popular benchmarks. For more details, please refer to the paper: https://arxiv.org/abs/2309.11998 **Basic Statistics** | Key | Value | | --- | --- | | # Conversations | 1,000,000 | | # Models | 25 | | # Users | 210,479 | | # Languages | 154 | | Avg. # Turns per Sample | 2.0 | | Avg. # Tokens per Prompt | 69.5 | | Avg. # Tokens per Response | 214.5 | **PII Redaction** We partnered with the [OpaquePrompts](https://opaqueprompts.opaque.co/) team to redact person names in this dataset to protect user privacy. Names like "Mary" and "James" in a conversation will appear as "NAME_1" and "NAME_2". For example: ```json Raw: [ { "content": "Write me a bio. My Name is Mary I am a student who is currently a beginner free lancer. I worked with James in the past ..." }] Redacted: [ { "content": "Write me a bio. My Name is NAME_1 I am a student who is currently a beginner free lancer. I worked with NAME_2 in the past ..." }] ``` Each conversation includes a "redacted" field to indicate if it has been redacted. This process may impact data quality and occasionally lead to incorrect redactions. We are working on improving the redaction quality and will release improved versions in the future. If you want to access the raw conversation data, please fill out [the form](https://docs.google.com/forms/d/1PZw67e19l0W3oCiQOjzSyZvXfOemhg6LCY0XzVmOUx0/edit) with details about your intended use cases. ## Uniqueness and Potential Usage This dataset features large-scale real-world conversations with LLMs. We believe it will help the AI research community answer important questions around topics like: - Characteristics and distributions of real-world user prompts - AI safety and content moderation - Training instruction-following models - Improving and evaluating LLM evaluation methods - Model selection and request dispatching algorithms For more details, please refer to the paper: https://arxiv.org/abs/2309.11998 ## LMSYS-Chat-1M Dataset License Agreement This Agreement contains the terms and conditions that govern your access and use of the LMSYS-Chat-1M Dataset (as defined above). You may not use the LMSYS-Chat-1M Dataset if you do not accept this Agreement. By clicking to accept, accessing the LMSYS-Chat-1M Dataset, or both, you hereby agree to the terms of the Agreement. If you are agreeing to be bound by the Agreement on behalf of your employer or another entity, you represent and warrant that you have full legal authority to bind your employer or such entity to this Agreement. If you do not have the requisite authority, you may not accept the Agreement or access the LMSYS-Chat-1M Dataset on behalf of your employer or another entity. - Safety and Moderation: **This dataset contains unsafe conversations that may be perceived as offensive or unsettling.** User should apply appropriate filters and safety measures before utilizing this dataset for training dialogue agents. - Non-Endorsement: The views and opinions depicted in this dataset **do not reflect** the perspectives of the researchers or affiliated institutions engaged in the data collection process. - Legal Compliance: You are mandated to use it in adherence with all pertinent laws and regulations. - Model Specific Terms: When leveraging direct outputs of a specific model, users must adhere to its corresponding terms of use. - Non-Identification: You **must not** attempt to identify the identities of individuals or infer any sensitive personal data encompassed in this dataset. - Prohibited Transfers: You should not distribute, copy, disclose, assign, sublicense, embed, host, or otherwise transfer the dataset to any third party. - Right to Request Deletion: At any time, we may require you to delete all copies of the conversation dataset (in whole or in part) in your possession and control. You will promptly comply with any and all such requests. Upon our request, you shall provide us with written confirmation of your compliance with such requirement. - Termination: We may, at any time, for any reason or for no reason, terminate this Agreement, effective immediately upon notice to you. Upon termination, the license granted to you hereunder will immediately terminate, and you will immediately stop using the LMSYS-Chat-1M Dataset and destroy all copies of the LMSYS-Chat-1M Dataset and related materials in your possession or control. - Limitation of Liability: IN NO EVENT WILL WE BE LIABLE FOR ANY CONSEQUENTIAL, INCIDENTAL, EXEMPLARY, PUNITIVE, SPECIAL, OR INDIRECT DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION, OR LOSS OF INFORMATION) ARISING OUT OF OR RELATING TO THIS AGREEMENT OR ITS SUBJECT MATTER, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Subject to your compliance with the terms and conditions of this Agreement, we grant to you, a limited, non-exclusive, non-transferable, non-sublicensable license to use the LMSYS-Chat-1M Dataset, including the conversation data and annotations, to research, develop, and improve software, algorithms, machine learning models, techniques, and technologies for both research and commercial purposes. ## Citation ``` @misc{zheng2023lmsyschat1m, title={LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset}, author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Tianle Li and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric. P Xing and Joseph E. Gonzalez and Ion Stoica and Hao Zhang}, year={2023}, eprint={2309.11998}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

EleutherAI/lambada_openai

EleutherAI

--- pretty_name: LAMBADA OpenAI language_creators: - machine-generated license: mit multilinguality: - translation task_ids: - language-modeling source_datasets: - lambada size_categories: - 1K<n<10K language: - de - en - es - fr - it dataset_info: - config_name: default features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: de features: - name: text dtype: string splits: - name: test num_bytes: 1904576 num_examples: 5153 download_size: 1985231 dataset_size: 1904576 - config_name: en features: - name: text dtype: string splits: - name: test num_bytes: 1709449 num_examples: 5153 download_size: 1819752 dataset_size: 1709449 - config_name: es features: - name: text dtype: string splits: - name: test num_bytes: 1821735 num_examples: 5153 download_size: 1902349 dataset_size: 1821735 - config_name: fr features: - name: text dtype: string splits: - name: test num_bytes: 1948795 num_examples: 5153 download_size: 2028703 dataset_size: 1948795 - config_name: it features: - name: text dtype: string splits: - name: test num_bytes: 1813420 num_examples: 5153 download_size: 1894613 dataset_size: 1813420 --- ## Dataset Description - **Repository:** [openai/gpt2](https://github.com/openai/gpt-2) - **Paper:** Radford et al. [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) ### Dataset Summary This dataset is comprised of the LAMBADA test split as pre-processed by OpenAI (see relevant discussions [here](https://github.com/openai/gpt-2/issues/131#issuecomment-497136199) and [here](https://github.com/huggingface/transformers/issues/491)). It also contains machine translated versions of the split in German, Spanish, French, and Italian. LAMBADA is used to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative texts sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole text, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. ### Languages English, German, Spanish, French, and Italian. ### Source Data For non-English languages, the data splits were produced by Google Translate. See the [`translation_script.py`](translation_script.py) for more details. ## Additional Information ### Hash Checksums For data integrity checks we leave the following checksums for the files in this dataset: | File Name | Checksum (SHA-256) | |--------------------------------------------------------------------------|------------------------------------------------------------------| | lambada_test_de.jsonl | 51c6c1795894c46e88e4c104b5667f488efe79081fb34d746b82b8caa663865e | | [openai/lambada_test.jsonl](https://openaipublic.blob.core.windows.net/gpt-2/data/lambada_test.jsonl) | 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 | | lambada_test_en.jsonl | 4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226 | | lambada_test_es.jsonl | ffd760026c647fb43c67ce1bc56fd527937304b348712dce33190ea6caba6f9c | | lambada_test_fr.jsonl | 941ec6a73dba7dc91c860bf493eb66a527cd430148827a4753a4535a046bf362 | | lambada_test_it.jsonl | 86654237716702ab74f42855ae5a78455c1b0e50054a4593fb9c6fcf7fad0850 | ### Licensing License: [Modified MIT](https://github.com/openai/gpt-2/blob/master/LICENSE) ### Citation ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` ```bibtex @misc{ author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel}, title={The LAMBADA dataset}, DOI={10.5281/zenodo.2630551}, publisher={Zenodo}, year={2016}, month={Aug} } ``` ### Contributions Thanks to Sid Black ([@sdtblck](https://github.com/sdtblck)) for translating the `lambada_openai` dataset into the non-English languages. Thanks to Jonathan Tow ([@jon-tow](https://github.com/jon-tow)) for adding this dataset.

roneneldan/TinyStories

roneneldan

--- license: cdla-sharing-1.0 task_categories: - text-generation language: - en --- Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary. Described in the following paper: https://arxiv.org/abs/2305.07759. The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M. Additional resources: tinystories_all_data.tar.gz - contains a superset of the stories together with metadata and the prompt that was used to create each story. TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger). Evaluation_prompts.yaml: List of prompts used to evaluate our models (see paper)

edbeeching/gia-dataset-tokenized-2024-2

edbeeching

--- dataset_info: - config_name: atari-alien features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: input_ids sequence: int32 - name: input_types sequence: int64 - name: local_positions sequence: int64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2427492496 num_examples: 1836 download_size: 197411801 dataset_size: 2427492496 - config_name: atari-amidar features: - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: input_ids sequence: int32 - name: input_types sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 23292403388 num_examples: 17641 - name: test num_bytes: 2157941388 num_examples: 1637 download_size: 1619960876 dataset_size: 25450344776 - config_name: atari-assault features: - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: input_ids sequence: int32 - name: input_types sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 23077576568 num_examples: 17434 - name: test num_bytes: 1898092400 num_examples: 1436 download_size: 760479036 dataset_size: 24975668968 - config_name: atari-asterix features: - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: train num_bytes: 25094377660 num_examples: 19161 download_size: 943683526 dataset_size: 25094377660 - config_name: atari-asteroids features: - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: train num_bytes: 22677165856 num_examples: 17112 download_size: 807221186 dataset_size: 22677165856 - config_name: atari-atlantis features: - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: train num_bytes: 22825149408 num_examples: 17240 download_size: 745609354 dataset_size: 22825149408 - config_name: atari-bankheist features: - name: input_types sequence: int64 - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_ids sequence: int32 - name: loss_mask sequence: bool - name: attention_mask sequence: bool splits: - name: train num_bytes: 23741888116 num_examples: 18043 - name: test num_bytes: 2701097304 num_examples: 2050 download_size: 2847993069 dataset_size: 26442985420 - config_name: atari-battlezone features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: input_types sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2683381416 num_examples: 2030 download_size: 162167846 dataset_size: 2683381416 - config_name: atari-berzerk features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2683232284 num_examples: 2025 download_size: 98071291 dataset_size: 2683232284 - config_name: atari-bowling features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2638612892 num_examples: 2001 download_size: 57099861 dataset_size: 2638612892 - config_name: atari-boxing features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2925635312 num_examples: 2252 download_size: 154591181 dataset_size: 2925635312 - config_name: atari-breakout features: - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 21372025124 num_examples: 16135 - name: test num_bytes: 2843462328 num_examples: 2146 download_size: 740521401 dataset_size: 24215487452 - config_name: atari-centipede features: - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 24525541956 num_examples: 18727 - name: test num_bytes: 2743854332 num_examples: 2097 download_size: 886355860 dataset_size: 27269396288 - config_name: atari-choppercommand features: - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 21916144968 num_examples: 16598 - name: test num_bytes: 3130204472 num_examples: 2370 download_size: 1120222280 dataset_size: 25046349440 - config_name: atari-crazyclimber features: - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2452295076 num_examples: 1855 download_size: 147409815 dataset_size: 2452295076 - config_name: atari-defender features: - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2667101644 num_examples: 2013 download_size: 76162534 dataset_size: 2667101644 - config_name: atari-demonattack features: - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2655965584 num_examples: 2004 download_size: 71540075 dataset_size: 2655965584 - config_name: atari-doubledunk features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2654251456 num_examples: 2032 download_size: 140407266 dataset_size: 2654251456 - config_name: atari-fishingderby features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2865449308 num_examples: 2177 download_size: 236590614 dataset_size: 2865449308 - config_name: atari-freeway features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2646386200 num_examples: 2002 download_size: 182728240 dataset_size: 2646386200 - config_name: atari-frostbite features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: loss_mask sequence: bool - name: input_ids sequence: int32 - name: input_types sequence: int64 - name: local_positions sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 23145553316 num_examples: 17551 - name: test num_bytes: 2683086716 num_examples: 2033 download_size: 1661407235 dataset_size: 25828640032 - config_name: atari-gravitar features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: loss_mask sequence: bool - name: input_ids sequence: int32 - name: input_types sequence: int64 - name: local_positions sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 26186279752 num_examples: 20126 - name: test num_bytes: 2990268724 num_examples: 2299 download_size: 939142901 dataset_size: 29176548476 - config_name: atari-hero features: - name: input_ids sequence: int32 - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2756503068 num_examples: 2089 download_size: 131026317 dataset_size: 2756503068 - config_name: atari-icehockey features: - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: attention_mask sequence: bool splits: - name: test num_bytes: 2538945980 num_examples: 1921 download_size: 89405392 dataset_size: 2538945980 - config_name: atari-jamesbond features: - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: attention_mask sequence: bool splits: - name: test num_bytes: 4473778328 num_examples: 3378 download_size: 224917482 dataset_size: 4473778328 - config_name: atari-kangaroo features: - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: attention_mask sequence: bool splits: - name: test num_bytes: 2993217516 num_examples: 2285 download_size: 140119408 dataset_size: 2993217516 - config_name: atari-mspacman features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: input_types sequence: int64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2479651844 num_examples: 1879 download_size: 217259145 dataset_size: 2479651844 - config_name: atari-namethisgame features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: input_types sequence: int64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 3006648420 num_examples: 2271 download_size: 158870157 dataset_size: 3006648420 - config_name: atari-phoenix features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: input_types sequence: int64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2655773200 num_examples: 2004 download_size: 79861580 dataset_size: 2655773200 - config_name: atari-qbert features: - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2547887868 num_examples: 1929 download_size: 174392419 dataset_size: 2547887868 - config_name: atari-riverraid features: - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2555182372 num_examples: 1943 download_size: 174672084 dataset_size: 2555182372 - config_name: atari-roadrunner features: - name: patch_positions sequence: sequence: sequence: float64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2521407028 num_examples: 1915 download_size: 125390334 dataset_size: 2521407028 - config_name: atari-robotank features: - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 22475017052 num_examples: 16985 - name: test num_bytes: 2229677068 num_examples: 1685 download_size: 1298755118 dataset_size: 24704694120 - config_name: atari-seaquest features: - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 23841045496 num_examples: 18114 - name: test num_bytes: 2738008960 num_examples: 2080 download_size: 910338340 dataset_size: 26579054456 - config_name: atari-skiing features: - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: patches sequence: sequence: sequence: sequence: uint8 - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: attention_mask sequence: bool splits: - name: train num_bytes: 26305597476 num_examples: 20359 - name: test num_bytes: 2941523916 num_examples: 2277 download_size: 1797518108 dataset_size: 29247121392 - config_name: atari-solaris features: - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: loss_mask sequence: bool - name: attention_mask sequence: bool splits: - name: test num_bytes: 2273188716 num_examples: 1717 download_size: 126936781 dataset_size: 2273188716 - config_name: atari-spaceinvaders features: - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: loss_mask sequence: bool - name: attention_mask sequence: bool splits: - name: test num_bytes: 4137369016 num_examples: 3122 download_size: 146426375 dataset_size: 4137369016 - config_name: atari-stargunner features: - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: loss_mask sequence: bool - name: attention_mask sequence: bool splits: - name: test num_bytes: 2565341980 num_examples: 1937 download_size: 72577790 dataset_size: 2565341980 - config_name: atari-surround features: - name: loss_mask sequence: bool - name: local_positions sequence: int64 - name: input_types sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: input_ids sequence: int32 - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: train num_bytes: 22468793380 num_examples: 17023 - name: test num_bytes: 2933488488 num_examples: 2222 download_size: 904796125 dataset_size: 25402281868 - config_name: atari-tennis features: - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: loss_mask sequence: bool - name: input_types sequence: int64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2484015692 num_examples: 1877 download_size: 95167453 dataset_size: 2484015692 - config_name: atari-timepilot features: - name: input_ids sequence: int32 - name: local_positions sequence: int64 - name: patch_positions sequence: sequence: sequence: float64 - name: loss_mask sequence: bool - name: input_types sequence: int64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: test num_bytes: 2558172240 num_examples: 1932 download_size: 86471773 dataset_size: 2558172240 - config_name: atari-tutankham features: - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: input_ids sequence: int32 - name: patch_positions sequence: sequence: sequence: float64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 3517105220 num_examples: 2655 download_size: 144491974 dataset_size: 3517105220 - config_name: atari-videopinball features: - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: train num_bytes: 22581644248 num_examples: 17042 - name: test num_bytes: 856644644 num_examples: 647 download_size: 1483962740 dataset_size: 23438288892 - config_name: atari-wizardofwor features: - name: patch_positions sequence: sequence: sequence: float64 - name: input_types sequence: int64 - name: patches sequence: sequence: sequence: sequence: uint8 - name: local_positions sequence: int64 - name: loss_mask sequence: bool - name: input_ids sequence: int32 - name: attention_mask sequence: bool splits: - name: train num_bytes: 22744043928 num_examples: 17218 - name: test num_bytes: 2648734220 num_examples: 2005 download_size: 1739703310 dataset_size: 25392778148 - config_name: atari-yarsrevenge features: - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: train num_bytes: 22080700236 num_examples: 16669 - name: test num_bytes: 2579104820 num_examples: 1947 download_size: 3451148232 dataset_size: 24659805056 - config_name: atari-zaxxon features: - name: input_types sequence: int64 - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: local_positions sequence: int64 - name: input_ids sequence: int32 - name: patches sequence: sequence: sequence: sequence: uint8 - name: attention_mask sequence: bool splits: - name: train num_bytes: 22058040148 num_examples: 16667 - name: test num_bytes: 2768806832 num_examples: 2092 download_size: 1229966010 dataset_size: 24826846980 configs: - config_name: atari-alien data_files: - split: test path: atari-alien/test-* - config_name: atari-amidar data_files: - split: train path: atari-amidar/train-* - split: test path: atari-amidar/test-* - config_name: atari-assault data_files: - split: train path: atari-assault/train-* - split: test path: atari-assault/test-* - config_name: atari-asterix data_files: - split: train path: atari-asterix/train-* - config_name: atari-asteroids data_files: - split: train path: atari-asteroids/train-* - config_name: atari-atlantis data_files: - split: train path: atari-atlantis/train-* - config_name: atari-bankheist data_files: - split: train path: atari-bankheist/train-* - split: test path: atari-bankheist/test-* - config_name: atari-battlezone data_files: - split: test path: atari-battlezone/test-* - config_name: atari-berzerk data_files: - split: test path: atari-berzerk/test-* - config_name: atari-bowling data_files: - split: test path: atari-bowling/test-* - config_name: atari-boxing data_files: - split: test path: atari-boxing/test-* - config_name: atari-breakout data_files: - split: train path: atari-breakout/train-* - split: test path: atari-breakout/test-* - config_name: atari-centipede data_files: - split: train path: atari-centipede/train-* - split: test path: atari-centipede/test-* - config_name: atari-choppercommand data_files: - split: train path: atari-choppercommand/train-* - split: test path: atari-choppercommand/test-* - config_name: atari-crazyclimber data_files: - split: test path: atari-crazyclimber/test-* - config_name: atari-defender data_files: - split: test path: atari-defender/test-* - config_name: atari-demonattack data_files: - split: test path: atari-demonattack/test-* - config_name: atari-doubledunk data_files: - split: test path: atari-doubledunk/test-* - config_name: atari-fishingderby data_files: - split: test path: atari-fishingderby/test-* - config_name: atari-freeway data_files: - split: test path: atari-freeway/test-* - config_name: atari-frostbite data_files: - split: train path: atari-frostbite/train-* - split: test path: atari-frostbite/test-* - config_name: atari-gravitar data_files: - split: train path: atari-gravitar/train-* - split: test path: atari-gravitar/test-* - config_name: atari-hero data_files: - split: test path: atari-hero/test-* - config_name: atari-icehockey data_files: - split: test path: atari-icehockey/test-* - config_name: atari-jamesbond data_files: - split: test path: atari-jamesbond/test-* - config_name: atari-kangaroo data_files: - split: test path: atari-kangaroo/test-* - config_name: atari-mspacman data_files: - split: test path: atari-mspacman/test-* - config_name: atari-namethisgame data_files: - split: test path: atari-namethisgame/test-* - config_name: atari-phoenix data_files: - split: test path: atari-phoenix/test-* - config_name: atari-qbert data_files: - split: test path: atari-qbert/test-* - config_name: atari-riverraid data_files: - split: test path: atari-riverraid/test-* - config_name: atari-roadrunner data_files: - split: test path: atari-roadrunner/test-* - config_name: atari-robotank data_files: - split: train path: atari-robotank/train-* - split: test path: atari-robotank/test-* - config_name: atari-seaquest data_files: - split: train path: atari-seaquest/train-* - split: test path: atari-seaquest/test-* - config_name: atari-skiing data_files: - split: train path: atari-skiing/train-* - split: test path: atari-skiing/test-* - config_name: atari-solaris data_files: - split: test path: atari-solaris/test-* - config_name: atari-spaceinvaders data_files: - split: test path: atari-spaceinvaders/test-* - config_name: atari-stargunner data_files: - split: test path: atari-stargunner/test-* - config_name: atari-surround data_files: - split: train path: atari-surround/train-* - split: test path: atari-surround/test-* - config_name: atari-tennis data_files: - split: test path: atari-tennis/test-* - config_name: atari-timepilot data_files: - split: test path: atari-timepilot/test-* - config_name: atari-tutankham data_files: - split: test path: atari-tutankham/test-* - config_name: atari-videopinball data_files: - split: train path: atari-videopinball/train-* - split: test path: atari-videopinball/test-* - config_name: atari-wizardofwor data_files: - split: train path: atari-wizardofwor/train-* - split: test path: atari-wizardofwor/test-* - config_name: atari-yarsrevenge data_files: - split: train path: atari-yarsrevenge/train-* - split: test path: atari-yarsrevenge/test-* - config_name: atari-zaxxon data_files: - split: train path: atari-zaxxon/train-* - split: test path: atari-zaxxon/test-* --- # Dataset Card for "gia-dataset-tokenized-2024-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mteb/banking77

mteb

--- language: - en ---

japanese-asr/whisper_transcriptions.reazon_speech_all.wer_10.0

japanese-asr

--- dataset_info: - config_name: subset_0 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 2818142513.305568 num_examples: 28889 download_size: 2800520280 dataset_size: 2818142513.305568 - config_name: subset_1 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 2799511567.444425 num_examples: 28682 download_size: 2780562913 dataset_size: 2799511567.444425 - config_name: subset_10 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 2773645799.2051067 num_examples: 28577 download_size: 2754819384 dataset_size: 2773645799.2051067 - config_name: subset_100 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 2823667735.949709 num_examples: 28862 download_size: 2804915439 dataset_size: 2823667735.949709 - config_name: subset_101 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/en_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/en_gpt3.5 sequence: int64 - 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split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* ---

macrocosm-os/code-parrot-github-code

macrocosm-os

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: github-code size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling --- # GitHub Code Dataset ## Dataset Description The GitHub Code dataset consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in 1TB of data. The dataset was created from the public GitHub dataset on Google BiqQuery. ### How to use it The GitHub Code dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following two lines of code: ```python from datasets import load_dataset ds = load_dataset("codeparrot/github-code", streaming=True, split="train") print(next(iter(ds))) #OUTPUT: { 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n", 'repo_name': 'MirekSz/webpack-es6-ts', 'path': 'app/mods/mod190.js', 'language': 'JavaScript', 'license': 'isc', 'size': 73 } ``` You can see that besides the code, repo name, and path also the programming language, license, and the size of the file are part of the dataset. You can also filter the dataset for any subset of the 30 included languages (see the full list below) in the dataset. Just pass the list of languages as a list. E.g. if your dream is to build a Codex model for Dockerfiles use the following configuration: ```python ds = load_dataset("codeparrot/github-code", streaming=True, split="train", languages=["Dockerfile"]) print(next(iter(ds))["code"]) #OUTPUT: """\ FROM rockyluke/ubuntu:precise ENV DEBIAN_FRONTEND="noninteractive" \ TZ="Europe/Amsterdam" ... """ ``` We also have access to the license of the origin repo of a file so we can filter for licenses in the same way we filtered for languages: ```python ds = load_dataset("codeparrot/github-code", streaming=True, split="train", licenses=["mit", "isc"]) licenses = [] for element in iter(ds).take(10_000): licenses.append(element["license"]) print(Counter(licenses)) #OUTPUT: Counter({'mit': 9896, 'isc': 104}) ``` Naturally, you can also download the full dataset. Note that this will download ~300GB compressed text data and the uncompressed dataset will take up ~1TB of storage: ```python ds = load_dataset("codeparrot/github-code", split="train") ``` ## Data Structure ### Data Instances ```python { 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n", 'repo_name': 'MirekSz/webpack-es6-ts', 'path': 'app/mods/mod190.js', 'language': 'JavaScript', 'license': 'isc', 'size': 73 } ``` ### Data Fields |Field|Type|Description| |---|---|---| |code|string|content of source file| |repo_name|string|name of the GitHub repository| |path|string|path of file in GitHub repository| |language|string|programming language as inferred by extension| |license|string|license of GitHub repository| |size|int|size of source file in bytes| ### Data Splits The dataset only contains a train split. ## Languages The dataset contains 30 programming languages with over 60 extensions: ```python { "Assembly": [".asm"], "Batchfile": [".bat", ".cmd"], "C": [".c", ".h"], "C#": [".cs"], "C++": [".cpp", ".hpp", ".c++", ".h++", ".cc", ".hh", ".C", ".H"], "CMake": [".cmake"], "CSS": [".css"], "Dockerfile": [".dockerfile", "Dockerfile"], "FORTRAN": ['.f90', '.f', '.f03', '.f08', '.f77', '.f95', '.for', '.fpp'], "GO": [".go"], "Haskell": [".hs"], "HTML":[".html"], "Java": [".java"], "JavaScript": [".js"], "Julia": [".jl"], "Lua": [".lua"], "Makefile": ["Makefile"], "Markdown": [".md", ".markdown"], "PHP": [".php", ".php3", ".php4", ".php5", ".phps", ".phpt"], "Perl": [".pl", ".pm", ".pod", ".perl"], "PowerShell": ['.ps1', '.psd1', '.psm1'], "Python": [".py"], "Ruby": [".rb"], "Rust": [".rs"], "SQL": [".sql"], "Scala": [".scala"], "Shell": [".sh", ".bash", ".command", ".zsh"], "TypeScript": [".ts", ".tsx"], "TeX": [".tex"], "Visual Basic": [".vb"] } ``` ## Licenses Each example is also annotated with the license of the associated repository. There are in total 15 licenses: ```python [ 'mit', 'apache-2.0', 'gpl-3.0', 'gpl-2.0', 'bsd-3-clause', 'agpl-3.0', 'lgpl-3.0', 'lgpl-2.1', 'bsd-2-clause', 'cc0-1.0', 'epl-1.0', 'mpl-2.0', 'unlicense', 'isc', 'artistic-2.0' ] ``` ## Dataset Statistics The dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:  | | Language |File Count| Size (GB)| |---:|:-------------|---------:|-------:| | 0 | Java | 19548190 | 107.70 | | 1 | C | 14143113 | 183.83 | | 2 | JavaScript | 11839883 | 87.82 | | 3 | HTML | 11178557 | 118.12 | | 4 | PHP | 11177610 | 61.41 | | 5 | Markdown | 8464626 | 23.09 | | 6 | C++ | 7380520 | 87.73 | | 7 | Python | 7226626 | 52.03 | | 8 | C# | 6811652 | 36.83 | | 9 | Ruby | 4473331 | 10.95 | | 10 | GO | 2265436 | 19.28 | | 11 | TypeScript | 1940406 | 24.59 | | 12 | CSS | 1734406 | 22.67 | | 13 | Shell | 1385648 | 3.01 | | 14 | Scala | 835755 | 3.87 | | 15 | Makefile | 679430 | 2.92 | | 16 | SQL | 656671 | 5.67 | | 17 | Lua | 578554 | 2.81 | | 18 | Perl | 497949 | 4.70 | | 19 | Dockerfile | 366505 | 0.71 | | 20 | Haskell | 340623 | 1.85 | | 21 | Rust | 322431 | 2.68 | | 22 | TeX | 251015 | 2.15 | | 23 | Batchfile | 236945 | 0.70 | | 24 | CMake | 175282 | 0.54 | | 25 | Visual Basic | 155652 | 1.91 | | 26 | FORTRAN | 142038 | 1.62 | | 27 | PowerShell | 136846 | 0.69 | | 28 | Assembly | 82905 | 0.78 | | 29 | Julia | 58317 | 0.29 | ## Dataset Creation The dataset was created in two steps: 1. Files of with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery (full query [here](https://huggingface.co/datasets/codeparrot/github-code/blob/main/query.sql)). The query was executed on _Mar 16, 2022, 6:23:39 PM UTC+1_. 2. Files with lines longer than 1000 characters and duplicates (exact duplicates ignoring whitespaces) were dropped (full preprocessing script [here](https://huggingface.co/datasets/codeparrot/github-code/blob/main/github_preprocessing.py)). ## Considerations for Using the Data The dataset consists of source code from a wide range of repositories. As such they can potentially include harmful or biased code as well as sensitive information like passwords or usernames. ## Releases You can load any older version of the dataset with the `revision` argument: ```Python ds = load_dataset("codeparrot/github-code", revision="v1.0") ``` ### v1.0 - Initial release of dataset - The query was executed on _Feb 14, 2022, 12:03:16 PM UTC+1_ ### v1.1 - Fix missing Scala/TypeScript - Fix deduplication issue with inconsistent Python `hash` - The query was executed on _Mar 16, 2022, 6:23:39 PM UTC+1_

laion/strategic_game_maze

laion

--- license: cc-by-4.0 --- NOTICE: some of the game is mistakenly label as both length and width columns are 40, they are 30 actually. # maze This dataset contains 350,000 mazes, represents over 39.29 billion moves. Each maze is a 30x30 ASCII representation, with solutions derived using the BFS. It has two columns: - 'Maze': representation of maze in a list of string.shape is 30*30 - visual example <image src="https://cdn-uploads.huggingface.co/production/uploads/644b983f0fbe4830f192c4f5/BGplH40fK5wQzpofPocMK.png" alt="drawing" width="200"/> - 'Path': solution from start point to end point in a list of string, each item represent a position in the maze.

ruslanmv/ai-medical-chatbot

ruslanmv

--- configs: - config_name: default data_files: - path: dialogues.* split: train dataset_info: dataset_size: 141665910 download_size: 141665910 features: - dtype: string name: Description - dtype: string name: Patient - dtype: string name: Doctor splits: - name: train num_bytes: 141665910 num_examples: 256916 --- # AI Medical Chatbot Dataset This is an experimental Dataset designed to run a Medical Chatbot It contains at least 250k dialogues between a Patient and a Doctor. [](https://huggingface.co/spaces/ruslanmv/AI-Medical-Chatbot) ## Playground ChatBot [ruslanmv/AI-Medical-Chatbot](https://huggingface.co/spaces/ruslanmv/AI-Medical-Chatbot) For furter information visit the project here: [https://github.com/ruslanmv/ai-medical-chatbot](https://github.com/ruslanmv/ai-medical-chatbot)

mlfoundations/MINT-1T-PDF-CC-2023-50

mlfoundations

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-50`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c).  ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

regent-project/regent-subset-of-jat-dataset-tokenized

regent-project

--- dataset_info: - config_name: atari-alien_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 1905456 num_examples: 22684 download_size: 2088245 dataset_size: 1905456 - config_name: atari-amidar_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32810168 num_examples: 100031 download_size: 11019541 dataset_size: 32810168 - config_name: atari-amidar_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23046343776 num_examples: 3142 download_size: 256637379 dataset_size: 23046343776 - config_name: atari-assault_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32806232 num_examples: 100019 download_size: 14121737 dataset_size: 32806232 - config_name: atari-assault_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22972994496 num_examples: 3132 download_size: 186535975 dataset_size: 22972994496 - config_name: atari-asterix_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32806560 num_examples: 100020 download_size: 11902934 dataset_size: 32806560 - config_name: atari-asterix_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23332405968 num_examples: 3181 download_size: 188517858 dataset_size: 23332405968 - config_name: atari-asteroids_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22936319856 num_examples: 3127 download_size: 202442660 dataset_size: 22936319856 - config_name: atari-atlantis_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32801640 num_examples: 100005 download_size: 13128838 dataset_size: 32801640 - config_name: atari-atlantis_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22943654784 num_examples: 3128 download_size: 206794180 dataset_size: 22943654784 - config_name: atari-bankheist_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32806888 num_examples: 100021 download_size: 13754178 dataset_size: 32806888 - config_name: atari-bankheist_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23149032768 num_examples: 3156 download_size: 307236770 dataset_size: 23149032768 - config_name: atari-battlezone_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800984 num_examples: 100003 download_size: 15918969 dataset_size: 32800984 - config_name: atari-battlezone_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23002334208 num_examples: 3136 download_size: 247618279 dataset_size: 23002334208 - config_name: atari-beamrider_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32806232 num_examples: 100019 download_size: 16063964 dataset_size: 32806232 - config_name: atari-beamrider_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22965659568 num_examples: 3131 download_size: 224067669 dataset_size: 22965659568 - config_name: atari-berzerk_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32803936 num_examples: 100012 download_size: 11678744 dataset_size: 32803936 - config_name: atari-berzerk_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22936319856 num_examples: 3127 download_size: 204431627 dataset_size: 22936319856 - config_name: atari-bowling_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32801968 num_examples: 100006 download_size: 7354865 dataset_size: 32801968 - config_name: atari-bowling_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23090353344 num_examples: 3148 download_size: 165124017 dataset_size: 23090353344 - config_name: atari-boxing_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32802296 num_examples: 100007 download_size: 11950572 dataset_size: 32802296 - config_name: atari-boxing_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23669812656 num_examples: 3227 download_size: 296234619 dataset_size: 23669812656 - config_name: atari-breakout_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32804592 num_examples: 100014 download_size: 4911820 dataset_size: 32804592 - config_name: atari-breakout_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22943654784 num_examples: 3128 download_size: 150562919 dataset_size: 22943654784 - config_name: atari-centipede_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32805904 num_examples: 100018 download_size: 11285739 dataset_size: 32805904 - config_name: atari-centipede_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23295731328 num_examples: 3176 download_size: 185406529 dataset_size: 23295731328 - config_name: atari-choppercommand_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32809840 num_examples: 100030 download_size: 14259234 dataset_size: 32809840 - config_name: atari-choppercommand_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23061013632 num_examples: 3144 download_size: 225019380 dataset_size: 23061013632 - config_name: atari-crazyclimber_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32804592 num_examples: 100014 download_size: 12305828 dataset_size: 32804592 - config_name: atari-crazyclimber_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22987664352 num_examples: 3134 download_size: 227557018 dataset_size: 22987664352 - config_name: atari-defender_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32807872 num_examples: 100024 download_size: 10537157 dataset_size: 32807872 - config_name: atari-defender_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22936319856 num_examples: 3127 download_size: 172063588 dataset_size: 22936319856 - config_name: atari-demonattack_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32807872 num_examples: 100024 download_size: 15551680 dataset_size: 32807872 - config_name: atari-demonattack_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22936319856 num_examples: 3127 download_size: 181049894 dataset_size: 22936319856 - config_name: atari-doubledunk_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32801968 num_examples: 100006 download_size: 11428550 dataset_size: 32801968 - config_name: atari-doubledunk_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23288396400 num_examples: 3175 download_size: 251707705 dataset_size: 23288396400 - config_name: atari-enduro_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32802296 num_examples: 100007 download_size: 12848229 dataset_size: 32802296 - config_name: atari-fishingderby_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13500648 dataset_size: 32800000 - config_name: atari-fishingderby_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23141697840 num_examples: 3155 download_size: 321501382 dataset_size: 23141697840 - config_name: atari-freeway_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32810168 num_examples: 100031 download_size: 13676872 dataset_size: 32810168 - config_name: atari-freeway_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22965659568 num_examples: 3131 download_size: 280231420 dataset_size: 22965659568 - config_name: atari-frostbite_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32806560 num_examples: 100020 download_size: 11934917 dataset_size: 32806560 - config_name: atari-frostbite_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23075683488 num_examples: 3146 download_size: 278638735 dataset_size: 23075683488 - config_name: atari-gopher_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32809512 num_examples: 100029 download_size: 14334636 dataset_size: 32809512 - config_name: atari-gopher_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22943654784 num_examples: 3128 download_size: 196526681 dataset_size: 22943654784 - config_name: atari-gravitar_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32805248 num_examples: 100016 download_size: 11576279 dataset_size: 32805248 - config_name: atari-gravitar_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23486439456 num_examples: 3202 download_size: 199543758 dataset_size: 23486439456 - config_name: atari-hero_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800984 num_examples: 100003 download_size: 12568260 dataset_size: 32800984 - config_name: atari-hero_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23061013632 num_examples: 3144 download_size: 231552624 dataset_size: 23061013632 - config_name: atari-icehockey_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800984 num_examples: 100003 download_size: 12259737 dataset_size: 32800984 - config_name: atari-icehockey_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23017004064 num_examples: 3138 download_size: 195362912 dataset_size: 23017004064 - config_name: atari-jamesbond_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32810168 num_examples: 100031 download_size: 15590631 dataset_size: 32810168 - config_name: atari-jamesbond_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22965659568 num_examples: 3131 download_size: 239495464 dataset_size: 22965659568 - config_name: atari-kangaroo_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32807872 num_examples: 100024 download_size: 12657496 dataset_size: 32807872 - config_name: atari-kangaroo_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23178372480 num_examples: 3160 download_size: 242035098 dataset_size: 23178372480 - config_name: atari-krull_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32808528 num_examples: 100026 download_size: 13793008 dataset_size: 32808528 - config_name: atari-krull_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23193042336 num_examples: 3162 download_size: 429983939 dataset_size: 23193042336 - config_name: atari-kungfumaster_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32806232 num_examples: 100019 download_size: 14058554 dataset_size: 32806232 - config_name: atari-kungfumaster_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23053678704 num_examples: 3143 download_size: 298664084 dataset_size: 23053678704 - config_name: atari-montezumarevenge_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32805904 num_examples: 100018 download_size: 12767695 dataset_size: 32805904 - config_name: atari-montezumarevenge_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 23237051904 num_examples: 3168 download_size: 304131065 dataset_size: 23237051904 - config_name: atari-mspacman_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 1219680 num_examples: 14520 download_size: 1069909 dataset_size: 1219680 - config_name: atari-namethisgame_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800984 num_examples: 100003 download_size: 15146115 dataset_size: 32800984 - config_name: atari-namethisgame_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22965659568 num_examples: 3131 download_size: 257925381 dataset_size: 22965659568 - config_name: atari-phoenix_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32808856 num_examples: 100027 download_size: 14775061 dataset_size: 32808856 - config_name: atari-phoenix_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22936319856 num_examples: 3127 download_size: 189670978 dataset_size: 22936319856 - config_name: atari-pitfall_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32807872 num_examples: 100024 download_size: 2022905 dataset_size: 32807872 - config_name: atari-pitfall_subset features: - name: image_observations sequence: sequence: sequence: sequence: float64 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 - name: embeddings_resnet18_512 sequence: sequence: float32 splits: - name: train num_bytes: 22965659568 num_examples: 3131 download_size: 123924337 dataset_size: 22965659568 - config_name: atari-pong_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 697452 num_examples: 8303 download_size: 486008 dataset_size: 697452 - config_name: atari-privateeye_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - 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config_name: babyai-unlock-local_subset features: - name: discrete_observations sequence: sequence: int32 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 splits: - name: train num_bytes: 1567624640 num_examples: 7120 download_size: 8268704 dataset_size: 1567624640 - config_name: babyai-unlock-pickup_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32897088 num_examples: 100296 download_size: 4544845 dataset_size: 32897088 - config_name: babyai-unlock-pickup_subset features: - name: discrete_observations sequence: sequence: int32 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 splits: - name: train num_bytes: 1127280640 num_examples: 5120 download_size: 6990282 dataset_size: 1127280640 - config_name: babyai-unlock-to-unlock_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32960064 num_examples: 100488 download_size: 5942465 dataset_size: 32960064 - config_name: babyai-unlock-to-unlock_subset features: - name: discrete_observations sequence: sequence: int32 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 splits: - name: train num_bytes: 510799040 num_examples: 2320 download_size: 3665802 dataset_size: 510799040 - config_name: babyai-unlock_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 33094872 num_examples: 100899 download_size: 6456229 dataset_size: 33094872 - config_name: babyai-unlock_subset features: - name: discrete_observations sequence: sequence: int32 - name: discrete_actions sequence: int32 - name: rewards sequence: float32 splits: - name: train num_bytes: 287764804 num_examples: 1307 download_size: 4020028 dataset_size: 287764804 - config_name: metaworld-assembly_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 1370386 dataset_size: 32800000 - config_name: metaworld-assembly_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 2494940 dataset_size: 47116000 - config_name: metaworld-basketball_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13190732 dataset_size: 32800000 - config_name: metaworld-basketball_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9208389 dataset_size: 47116000 - config_name: metaworld-bin-picking_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 840000 num_examples: 10000 download_size: 952363 dataset_size: 840000 - config_name: metaworld-box-close_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 840000 num_examples: 10000 download_size: 1058011 dataset_size: 840000 - config_name: metaworld-button-press-topdown-wall_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12506477 dataset_size: 32800000 - config_name: metaworld-button-press-topdown-wall_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6795055 dataset_size: 47116000 - config_name: metaworld-button-press-topdown_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12383341 dataset_size: 32800000 - config_name: metaworld-button-press-topdown_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6647074 dataset_size: 47116000 - config_name: metaworld-button-press-wall_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 11884670 dataset_size: 32800000 - config_name: metaworld-button-press-wall_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6388048 dataset_size: 47116000 - config_name: metaworld-button-press_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12504036 dataset_size: 32800000 - config_name: metaworld-button-press_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6079174 dataset_size: 47116000 - config_name: metaworld-coffee-button_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 11302073 dataset_size: 32800000 - config_name: metaworld-coffee-button_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6402919 dataset_size: 47116000 - config_name: metaworld-coffee-pull_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13291438 dataset_size: 32800000 - config_name: metaworld-coffee-pull_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9165455 dataset_size: 47116000 - config_name: metaworld-coffee-push_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13347747 dataset_size: 32800000 - config_name: metaworld-coffee-push_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9819758 dataset_size: 47116000 - config_name: metaworld-dial-turn_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 11453279 dataset_size: 32800000 - config_name: metaworld-dial-turn_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5840306 dataset_size: 47116000 - config_name: metaworld-disassemble_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 8574754 dataset_size: 32800000 - config_name: metaworld-disassemble_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 4082529 dataset_size: 47116000 - config_name: metaworld-door-close_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13743650 dataset_size: 32800000 - config_name: metaworld-door-close_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 8698806 dataset_size: 47116000 - config_name: metaworld-door-lock_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 840000 num_examples: 10000 download_size: 776743 dataset_size: 840000 - config_name: metaworld-door-open_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13781189 dataset_size: 32800000 - config_name: metaworld-door-open_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 7983276 dataset_size: 47116000 - config_name: metaworld-door-unlock_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 840000 num_examples: 10000 download_size: 829555 dataset_size: 840000 - config_name: metaworld-drawer-close_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13903693 dataset_size: 32800000 - config_name: metaworld-drawer-close_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5764071 dataset_size: 47116000 - config_name: metaworld-drawer-open_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12036502 dataset_size: 32800000 - config_name: metaworld-drawer-open_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5484434 dataset_size: 47116000 - config_name: metaworld-faucet-close_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 14148656 dataset_size: 32800000 - config_name: metaworld-faucet-close_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5086095 dataset_size: 47116000 - config_name: metaworld-faucet-open_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 14300852 dataset_size: 32800000 - config_name: metaworld-faucet-open_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5497182 dataset_size: 47116000 - config_name: metaworld-hammer_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13491757 dataset_size: 32800000 - config_name: metaworld-hammer_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 10062439 dataset_size: 47116000 - config_name: metaworld-handle-press-side_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12555014 dataset_size: 32800000 - config_name: metaworld-handle-press-side_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5880675 dataset_size: 47116000 - config_name: metaworld-handle-press_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13473313 dataset_size: 32800000 - config_name: metaworld-handle-press_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5879237 dataset_size: 47116000 - config_name: metaworld-handle-pull-side_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13576934 dataset_size: 32800000 - config_name: metaworld-handle-pull-side_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6737064 dataset_size: 47116000 - config_name: metaworld-handle-pull_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12046278 dataset_size: 32800000 - config_name: metaworld-handle-pull_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6896646 dataset_size: 47116000 - config_name: metaworld-lever-pull_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12827517 dataset_size: 32800000 - config_name: metaworld-lever-pull_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9568802 dataset_size: 47116000 - config_name: metaworld-peg-insert-side_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13057268 dataset_size: 32800000 - config_name: metaworld-peg-insert-side_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 8714100 dataset_size: 47116000 - config_name: metaworld-peg-unplug-side_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13163866 dataset_size: 32800000 - config_name: metaworld-peg-unplug-side_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9726674 dataset_size: 47116000 - config_name: metaworld-pick-out-of-hole_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 1376243 dataset_size: 32800000 - config_name: metaworld-pick-out-of-hole_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 1419339 dataset_size: 47116000 - config_name: metaworld-pick-place-wall_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13636756 dataset_size: 32800000 - config_name: metaworld-pick-place-wall_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9760537 dataset_size: 47116000 - config_name: metaworld-pick-place_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13638935 dataset_size: 32800000 - config_name: metaworld-pick-place_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 10013159 dataset_size: 47116000 - config_name: metaworld-plate-slide-back-side_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 1365777 dataset_size: 32800000 - config_name: metaworld-plate-slide-back-side_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 1936719 dataset_size: 47116000 - config_name: metaworld-plate-slide-back_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 1372778 dataset_size: 32800000 - config_name: metaworld-plate-slide-back_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 2568887 dataset_size: 47116000 - config_name: metaworld-plate-slide-side_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 9706526 dataset_size: 32800000 - config_name: metaworld-plate-slide-side_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6041762 dataset_size: 47116000 - config_name: metaworld-plate-slide_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 9787720 dataset_size: 32800000 - config_name: metaworld-plate-slide_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 6512808 dataset_size: 47116000 - config_name: metaworld-push-back_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 14075602 dataset_size: 32800000 - config_name: metaworld-push-back_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 7550247 dataset_size: 47116000 - config_name: metaworld-push-wall_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13592428 dataset_size: 32800000 - config_name: metaworld-push-wall_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 8970793 dataset_size: 47116000 - config_name: metaworld-push_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13341527 dataset_size: 32800000 - config_name: metaworld-push_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9427900 dataset_size: 47116000 - config_name: metaworld-reach-wall_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12733205 dataset_size: 32800000 - config_name: metaworld-reach-wall_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9731627 dataset_size: 47116000 - config_name: metaworld-reach_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12106144 dataset_size: 32800000 - config_name: metaworld-reach_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9563337 dataset_size: 47116000 - config_name: metaworld-shelf-place_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13046597 dataset_size: 32800000 - config_name: metaworld-shelf-place_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 8068065 dataset_size: 47116000 - config_name: metaworld-soccer_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 11954933 dataset_size: 32800000 - config_name: metaworld-soccer_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9009300 dataset_size: 47116000 - config_name: metaworld-stick-pull_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13346574 dataset_size: 32800000 - config_name: metaworld-stick-pull_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9654361 dataset_size: 47116000 - config_name: metaworld-stick-push_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13868467 dataset_size: 32800000 - config_name: metaworld-stick-push_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9420722 dataset_size: 47116000 - config_name: metaworld-sweep-into_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13471306 dataset_size: 32800000 - config_name: metaworld-sweep-into_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 7656262 dataset_size: 47116000 - config_name: metaworld-sweep_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13966344 dataset_size: 32800000 - config_name: metaworld-sweep_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 9333916 dataset_size: 47116000 - config_name: metaworld-window-close_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12562521 dataset_size: 32800000 - config_name: metaworld-window-close_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5405410 dataset_size: 47116000 - config_name: metaworld-window-open_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12270843 dataset_size: 32800000 - config_name: metaworld-window-open_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 47116000 num_examples: 1000 download_size: 5455606 dataset_size: 47116000 - config_name: mujoco-ant_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32847232 num_examples: 100144 download_size: 16107573 dataset_size: 32847232 - config_name: mujoco-ant_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 15608524 num_examples: 401 download_size: 16185601 dataset_size: 15608524 - config_name: mujoco-doublependulum_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32805248 num_examples: 100016 download_size: 16102270 dataset_size: 32805248 - config_name: mujoco-doublependulum_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 6164172 num_examples: 401 download_size: 4960978 dataset_size: 6164172 - config_name: mujoco-halfcheetah_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 8400000 num_examples: 100000 download_size: 11373374 dataset_size: 8400000 - config_name: mujoco-hopper_newdata features: - name: distances sequence: float32 splits: - name: train num_bytes: 3834768 num_examples: 45652 download_size: 5110310 dataset_size: 3834768 - config_name: mujoco-humanoid_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32808200 num_examples: 100025 download_size: 16122991 dataset_size: 32808200 - config_name: mujoco-humanoid_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 168289140 num_examples: 415 download_size: 116298243 dataset_size: 168289140 - config_name: mujoco-pendulum_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32806888 num_examples: 100021 download_size: 15694433 dataset_size: 32806888 - config_name: mujoco-pendulum_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 4060980 num_examples: 495 download_size: 3083276 dataset_size: 4060980 - config_name: mujoco-pusher_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 13887459 dataset_size: 32800000 - config_name: mujoco-pusher_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 33804000 num_examples: 1000 download_size: 13463910 dataset_size: 33804000 - config_name: mujoco-reacher_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 12795397 dataset_size: 32800000 - config_name: mujoco-reacher_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 32792000 num_examples: 2000 download_size: 7687471 dataset_size: 32792000 - config_name: mujoco-standup_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 16032984 dataset_size: 32800000 - config_name: mujoco-standup_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 162206400 num_examples: 400 download_size: 117589700 dataset_size: 162206400 - config_name: mujoco-swimmer_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32800000 num_examples: 100000 download_size: 15858902 dataset_size: 32800000 - config_name: mujoco-swimmer_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 5329600 num_examples: 400 download_size: 5733100 dataset_size: 5329600 - config_name: mujoco-walker_newdata features: - name: distances sequence: float32 - name: indices sequence: sequence: int32 splits: - name: train num_bytes: 32807872 num_examples: 100024 download_size: 15920611 dataset_size: 32807872 - config_name: mujoco-walker_subset features: - name: continuous_observations sequence: sequence: float32 - name: continuous_actions sequence: sequence: float32 - name: rewards sequence: float32 splits: - name: train num_bytes: 10840852 num_examples: 407 download_size: 11101553 dataset_size: 10840852 configs: - config_name: atari-alien_newdata data_files: - split: train path: atari-alien_newdata/train-* - config_name: atari-amidar_newdata data_files: - split: train path: atari-amidar_newdata/train-* - config_name: atari-amidar_subset data_files: - split: train path: atari-amidar_subset/train-* - config_name: atari-assault_newdata data_files: - split: train path: atari-assault_newdata/train-* - config_name: atari-assault_subset data_files: - split: train path: atari-assault_subset/train-* - config_name: atari-asterix_newdata data_files: - split: train path: atari-asterix_newdata/train-* - config_name: atari-asterix_subset data_files: - split: train path: atari-asterix_subset/train-* - config_name: atari-asteroids_subset data_files: - split: train path: atari-asteroids_subset/train-* - config_name: atari-atlantis_newdata data_files: - split: train path: atari-atlantis_newdata/train-* - config_name: atari-atlantis_subset data_files: - split: train path: atari-atlantis_subset/train-* - config_name: atari-bankheist_newdata data_files: - split: train path: atari-bankheist_newdata/train-* - config_name: atari-bankheist_subset data_files: - split: train path: atari-bankheist_subset/train-* - config_name: atari-battlezone_newdata data_files: - split: train path: atari-battlezone_newdata/train-* - config_name: atari-battlezone_subset data_files: - split: train path: atari-battlezone_subset/train-* - config_name: atari-beamrider_newdata data_files: - split: train path: atari-beamrider_newdata/train-* - config_name: atari-beamrider_subset data_files: - split: train path: atari-beamrider_subset/train-* - config_name: atari-berzerk_newdata data_files: - split: train path: atari-berzerk_newdata/train-* - config_name: atari-berzerk_subset data_files: - split: train path: atari-berzerk_subset/train-* - config_name: atari-bowling_newdata data_files: - split: train path: atari-bowling_newdata/train-* - config_name: atari-bowling_subset data_files: - split: train path: atari-bowling_subset/train-* - config_name: atari-boxing_newdata data_files: - split: train path: atari-boxing_newdata/train-* - config_name: atari-boxing_subset data_files: - split: train path: atari-boxing_subset/train-* - config_name: atari-breakout_newdata data_files: - split: train path: atari-breakout_newdata/train-* - config_name: atari-breakout_subset data_files: - split: train path: atari-breakout_subset/train-* - config_name: atari-centipede_newdata data_files: - split: train path: atari-centipede_newdata/train-* - config_name: atari-centipede_subset data_files: - split: train path: atari-centipede_subset/train-* - config_name: atari-choppercommand_newdata data_files: - split: train path: atari-choppercommand_newdata/train-* - config_name: atari-choppercommand_subset data_files: - split: train path: atari-choppercommand_subset/train-* - config_name: atari-crazyclimber_newdata data_files: - split: train path: atari-crazyclimber_newdata/train-* - config_name: atari-crazyclimber_subset data_files: - split: train path: atari-crazyclimber_subset/train-* - config_name: atari-defender_newdata data_files: - split: train path: atari-defender_newdata/train-* - config_name: atari-defender_subset data_files: - split: train path: atari-defender_subset/train-* - config_name: atari-demonattack_newdata data_files: - split: train path: atari-demonattack_newdata/train-* - config_name: atari-demonattack_subset data_files: - split: train path: atari-demonattack_subset/train-* - config_name: atari-doubledunk_newdata data_files: - split: train path: atari-doubledunk_newdata/train-* - config_name: atari-doubledunk_subset data_files: - split: train path: atari-doubledunk_subset/train-* - config_name: atari-enduro_newdata data_files: - split: train path: atari-enduro_newdata/train-* - config_name: atari-fishingderby_newdata data_files: - split: train path: atari-fishingderby_newdata/train-* - config_name: atari-fishingderby_subset data_files: - split: train path: atari-fishingderby_subset/train-* - config_name: atari-freeway_newdata data_files: - split: train path: atari-freeway_newdata/train-* - config_name: atari-freeway_subset data_files: - split: train path: atari-freeway_subset/train-* - config_name: atari-frostbite_newdata data_files: - split: train path: atari-frostbite_newdata/train-* - config_name: atari-frostbite_subset data_files: - split: train path: atari-frostbite_subset/train-* - config_name: atari-gopher_newdata data_files: - split: train path: atari-gopher_newdata/train-* - config_name: atari-gopher_subset data_files: - split: train path: atari-gopher_subset/train-* - config_name: atari-gravitar_newdata data_files: - split: train path: atari-gravitar_newdata/train-* - config_name: atari-gravitar_subset data_files: - split: train path: atari-gravitar_subset/train-* - config_name: atari-hero_newdata data_files: - split: train path: atari-hero_newdata/train-* - config_name: atari-hero_subset data_files: - split: train path: atari-hero_subset/train-* - config_name: atari-icehockey_newdata data_files: - split: train path: atari-icehockey_newdata/train-* - config_name: atari-icehockey_subset data_files: - split: train path: atari-icehockey_subset/train-* - config_name: atari-jamesbond_newdata data_files: - split: train path: atari-jamesbond_newdata/train-* - config_name: atari-jamesbond_subset data_files: - split: train path: atari-jamesbond_subset/train-* - config_name: atari-kangaroo_newdata data_files: - split: train path: atari-kangaroo_newdata/train-* - config_name: atari-kangaroo_subset data_files: - split: train path: atari-kangaroo_subset/train-* - config_name: atari-krull_newdata data_files: - split: train path: atari-krull_newdata/train-* - config_name: atari-krull_subset data_files: - split: train path: atari-krull_subset/train-* - config_name: atari-kungfumaster_newdata data_files: - split: train path: atari-kungfumaster_newdata/train-* - config_name: atari-kungfumaster_subset data_files: - split: train path: atari-kungfumaster_subset/train-* - config_name: atari-montezumarevenge_newdata data_files: - split: train path: atari-montezumarevenge_newdata/train-* - config_name: atari-montezumarevenge_subset data_files: - split: train path: atari-montezumarevenge_subset/train-* - config_name: atari-mspacman_newdata data_files: - split: train path: atari-mspacman_newdata/train-* - config_name: atari-namethisgame_newdata data_files: - split: train path: atari-namethisgame_newdata/train-* - config_name: atari-namethisgame_subset data_files: - split: train path: atari-namethisgame_subset/train-* - config_name: atari-phoenix_newdata data_files: - split: train path: atari-phoenix_newdata/train-* - config_name: atari-phoenix_subset data_files: - split: train path: atari-phoenix_subset/train-* - config_name: atari-pitfall_newdata data_files: - split: train path: atari-pitfall_newdata/train-* - config_name: atari-pitfall_subset data_files: - split: train path: atari-pitfall_subset/train-* - config_name: atari-pong_newdata data_files: - split: train path: atari-pong_newdata/train-* - config_name: atari-privateeye_newdata data_files: - split: train path: atari-privateeye_newdata/train-* - config_name: atari-privateeye_subset data_files: - split: train path: atari-privateeye_subset/train-* - config_name: atari-qbert_newdata data_files: - split: train path: atari-qbert_newdata/train-* - config_name: atari-qbert_subset data_files: - split: train path: atari-qbert_subset/train-* - config_name: atari-riverraid_newdata data_files: - split: train path: atari-riverraid_newdata/train-* - config_name: atari-riverraid_subset data_files: - split: train path: atari-riverraid_subset/train-* - config_name: atari-roadrunner_newdata data_files: - split: train path: atari-roadrunner_newdata/train-* - config_name: atari-roadrunner_subset data_files: - split: train path: atari-roadrunner_subset/train-* - config_name: atari-robotank_newdata data_files: - split: train path: atari-robotank_newdata/train-* - config_name: atari-robotank_subset data_files: - split: train path: atari-robotank_subset/train-* - config_name: atari-seaquest_newdata data_files: - split: train path: atari-seaquest_newdata/train-* - config_name: atari-seaquest_subset data_files: - split: train path: atari-seaquest_subset/train-* - config_name: atari-skiing_newdata data_files: - split: train path: atari-skiing_newdata/train-* - config_name: atari-skiing_subset data_files: - split: train path: atari-skiing_subset/train-* - config_name: atari-solaris_newdata data_files: - split: train path: atari-solaris_newdata/train-* - config_name: atari-solaris_subset data_files: - split: train path: atari-solaris_subset/train-* - config_name: atari-spaceinvaders_newdata data_files: - split: train path: atari-spaceinvaders_newdata/train-* - config_name: atari-stargunner_newdata data_files: - split: train path: atari-stargunner_newdata/train-* - config_name: atari-surround_newdata data_files: - split: train path: atari-surround_newdata/train-* - config_name: atari-surround_subset data_files: - split: train path: atari-surround_subset/train-* - config_name: atari-tennis_newdata data_files: - split: train path: atari-tennis_newdata/train-* - config_name: atari-tennis_subset data_files: - split: train path: atari-tennis_subset/train-* - config_name: atari-timepilot_newdata data_files: - split: train path: atari-timepilot_newdata/train-* - config_name: atari-timepilot_subset data_files: - split: train path: atari-timepilot_subset/train-* - config_name: atari-tutankham_newdata data_files: - split: train path: atari-tutankham_newdata/train-* - config_name: atari-tutankham_subset data_files: - split: train path: atari-tutankham_subset/train-* - config_name: atari-upndown_newdata data_files: - split: train path: atari-upndown_newdata/train-* - config_name: atari-upndown_subset data_files: - split: train path: atari-upndown_subset/train-* - config_name: atari-venture_newdata data_files: - split: train path: atari-venture_newdata/train-* - config_name: atari-venture_subset data_files: - split: train path: atari-venture_subset/train-* - config_name: atari-videopinball_newdata data_files: - split: train path: atari-videopinball_newdata/train-* - config_name: atari-videopinball_subset data_files: - split: train path: atari-videopinball_subset/train-* - config_name: atari-wizardofwor_newdata data_files: - split: train path: atari-wizardofwor_newdata/train-* - config_name: atari-wizardofwor_subset data_files: - split: train path: atari-wizardofwor_subset/train-* - config_name: atari-yarsrevenge_newdata data_files: - split: train path: atari-yarsrevenge_newdata/train-* - config_name: atari-yarsrevenge_subset data_files: - split: train path: atari-yarsrevenge_subset/train-* - config_name: atari-zaxxon_newdata data_files: - split: train path: atari-zaxxon_newdata/train-* - config_name: atari-zaxxon_subset data_files: - split: train path: atari-zaxxon_subset/train-* - config_name: babyai-action-obj-door_newdata data_files: - split: train path: babyai-action-obj-door_newdata/train-* - config_name: babyai-action-obj-door_subset data_files: - split: train path: babyai-action-obj-door_subset/train-* - config_name: babyai-blocked-unlock-pickup_newdata data_files: - split: train path: babyai-blocked-unlock-pickup_newdata/train-* - config_name: babyai-blocked-unlock-pickup_subset data_files: - split: train path: babyai-blocked-unlock-pickup_subset/train-* - config_name: babyai-boss-level-no-unlock_newdata data_files: - split: train path: babyai-boss-level-no-unlock_newdata/train-* - config_name: babyai-boss-level-no-unlock_subset data_files: - split: train path: babyai-boss-level-no-unlock_subset/train-* - config_name: babyai-boss-level_newdata data_files: - split: train path: babyai-boss-level_newdata/train-* - config_name: babyai-boss-level_subset data_files: - split: train path: babyai-boss-level_subset/train-* - config_name: babyai-find-obj-s5_newdata data_files: - split: train path: babyai-find-obj-s5_newdata/train-* - config_name: babyai-find-obj-s5_subset data_files: - split: train path: babyai-find-obj-s5_subset/train-* - config_name: babyai-go-to-door_newdata data_files: - split: train path: babyai-go-to-door_newdata/train-* - config_name: babyai-go-to-door_subset data_files: - split: train path: babyai-go-to-door_subset/train-* - config_name: babyai-go-to-imp-unlock_newdata data_files: - split: train path: babyai-go-to-imp-unlock_newdata/train-* - config_name: babyai-go-to-imp-unlock_subset data_files: - split: train path: babyai-go-to-imp-unlock_subset/train-* - config_name: babyai-go-to-local_newdata data_files: - split: train path: babyai-go-to-local_newdata/train-* - config_name: babyai-go-to-local_subset data_files: - split: train path: babyai-go-to-local_subset/train-* - config_name: babyai-go-to-obj-door_newdata data_files: - split: train path: babyai-go-to-obj-door_newdata/train-* - config_name: babyai-go-to-obj-door_subset data_files: - split: train path: babyai-go-to-obj-door_subset/train-* - config_name: babyai-go-to-obj_newdata data_files: - split: train path: babyai-go-to-obj_newdata/train-* - config_name: babyai-go-to-obj_subset data_files: - split: train path: babyai-go-to-obj_subset/train-* - config_name: babyai-go-to-red-ball-grey_newdata data_files: - split: train path: babyai-go-to-red-ball-grey_newdata/train-* - config_name: babyai-go-to-red-ball-grey_subset data_files: - split: train path: babyai-go-to-red-ball-grey_subset/train-* - config_name: babyai-go-to-red-ball-no-dists_newdata data_files: - split: train path: babyai-go-to-red-ball-no-dists_newdata/train-* - config_name: babyai-go-to-red-ball-no-dists_subset data_files: - split: train path: babyai-go-to-red-ball-no-dists_subset/train-* - config_name: babyai-go-to-red-ball_newdata data_files: - split: train path: babyai-go-to-red-ball_newdata/train-* - config_name: babyai-go-to-red-ball_subset data_files: - split: train path: babyai-go-to-red-ball_subset/train-* - config_name: babyai-go-to-red-blue-ball_newdata data_files: - split: train path: babyai-go-to-red-blue-ball_newdata/train-* - config_name: babyai-go-to-red-blue-ball_subset data_files: - split: train path: babyai-go-to-red-blue-ball_subset/train-* - config_name: babyai-go-to-seq_newdata data_files: - split: train path: babyai-go-to-seq_newdata/train-* - config_name: babyai-go-to-seq_subset data_files: - split: train path: babyai-go-to-seq_subset/train-* - config_name: babyai-go-to_newdata data_files: - split: train path: babyai-go-to_newdata/train-* - config_name: babyai-go-to_subset data_files: - split: train path: babyai-go-to_subset/train-* - config_name: babyai-key-corridor_newdata data_files: - split: train path: babyai-key-corridor_newdata/train-* - config_name: babyai-key-corridor_subset data_files: - split: train path: babyai-key-corridor_subset/train-* - config_name: babyai-mini-boss-level_newdata data_files: - split: train path: babyai-mini-boss-level_newdata/train-* - config_name: babyai-mini-boss-level_subset data_files: - split: train path: babyai-mini-boss-level_subset/train-* - config_name: babyai-move-two-across-s8n9_newdata data_files: - split: train path: babyai-move-two-across-s8n9_newdata/train-* - config_name: babyai-move-two-across-s8n9_subset data_files: - split: train path: babyai-move-two-across-s8n9_subset/train-* - config_name: babyai-one-room-s8_newdata data_files: - split: train path: babyai-one-room-s8_newdata/train-* - config_name: babyai-one-room-s8_subset data_files: - split: train path: babyai-one-room-s8_subset/train-* - config_name: babyai-open-door_newdata data_files: - split: train path: babyai-open-door_newdata/train-* - config_name: babyai-open-door_subset data_files: - split: train path: babyai-open-door_subset/train-* - config_name: babyai-open-doors-order-n4_newdata data_files: - split: train path: babyai-open-doors-order-n4_newdata/train-* - config_name: babyai-open-doors-order-n4_subset data_files: - split: train path: babyai-open-doors-order-n4_subset/train-* - config_name: babyai-open-red-door_newdata data_files: - split: train path: babyai-open-red-door_newdata/train-* - config_name: babyai-open-red-door_subset data_files: - split: train path: babyai-open-red-door_subset/train-* - config_name: babyai-open-two-doors_newdata data_files: - split: train path: babyai-open-two-doors_newdata/train-* - config_name: babyai-open-two-doors_subset data_files: - split: train path: babyai-open-two-doors_subset/train-* - config_name: babyai-open_newdata data_files: - split: train path: babyai-open_newdata/train-* - config_name: babyai-open_subset data_files: - split: train path: babyai-open_subset/train-* - config_name: babyai-pickup-above_newdata data_files: - split: train path: babyai-pickup-above_newdata/train-* - config_name: babyai-pickup-above_subset data_files: - split: train path: babyai-pickup-above_subset/train-* - config_name: babyai-pickup-dist_newdata data_files: - split: train path: babyai-pickup-dist_newdata/train-* - config_name: babyai-pickup-dist_subset data_files: - split: train path: babyai-pickup-dist_subset/train-* - config_name: babyai-pickup-loc_newdata data_files: - split: train path: babyai-pickup-loc_newdata/train-* - config_name: babyai-pickup-loc_subset data_files: - split: train path: babyai-pickup-loc_subset/train-* - config_name: babyai-pickup_newdata data_files: - split: train path: babyai-pickup_newdata/train-* - config_name: babyai-pickup_subset data_files: - split: train path: babyai-pickup_subset/train-* - config_name: babyai-put-next-local_newdata data_files: - split: train path: babyai-put-next-local_newdata/train-* - config_name: babyai-put-next-local_subset data_files: - split: train path: babyai-put-next-local_subset/train-* - config_name: babyai-put-next_newdata data_files: - split: train path: babyai-put-next_newdata/train-* - config_name: babyai-put-next_subset data_files: - split: train path: babyai-put-next_subset/train-* - config_name: babyai-synth-loc_newdata data_files: - split: train path: babyai-synth-loc_newdata/train-* - config_name: babyai-synth-loc_subset data_files: - split: train path: babyai-synth-loc_subset/train-* - config_name: babyai-synth-seq_newdata data_files: - split: train path: babyai-synth-seq_newdata/train-* - config_name: babyai-synth-seq_subset data_files: - split: train path: babyai-synth-seq_subset/train-* - config_name: babyai-synth_newdata data_files: - split: train path: babyai-synth_newdata/train-* - config_name: babyai-synth_subset data_files: - split: train path: babyai-synth_subset/train-* - config_name: babyai-unblock-pickup_newdata data_files: - split: train path: babyai-unblock-pickup_newdata/train-* - config_name: babyai-unblock-pickup_subset data_files: - split: train path: babyai-unblock-pickup_subset/train-* - config_name: babyai-unlock-local_newdata data_files: - split: train path: babyai-unlock-local_newdata/train-* - config_name: babyai-unlock-local_subset data_files: - split: train path: babyai-unlock-local_subset/train-* - config_name: babyai-unlock-pickup_newdata data_files: - split: train path: babyai-unlock-pickup_newdata/train-* - config_name: babyai-unlock-pickup_subset data_files: - split: train path: babyai-unlock-pickup_subset/train-* - config_name: babyai-unlock-to-unlock_newdata data_files: - split: train path: babyai-unlock-to-unlock_newdata/train-* - config_name: babyai-unlock-to-unlock_subset data_files: - split: train path: babyai-unlock-to-unlock_subset/train-* - config_name: babyai-unlock_newdata data_files: - split: train path: babyai-unlock_newdata/train-* - config_name: babyai-unlock_subset data_files: - split: train path: babyai-unlock_subset/train-* - config_name: metaworld-assembly_newdata data_files: - split: train path: metaworld-assembly_newdata/train-* - config_name: metaworld-assembly_subset data_files: - split: train path: metaworld-assembly_subset/train-* - config_name: metaworld-basketball_newdata data_files: - split: train path: metaworld-basketball_newdata/train-* - config_name: metaworld-basketball_subset data_files: - split: train path: metaworld-basketball_subset/train-* - config_name: metaworld-bin-picking_newdata data_files: - split: train path: metaworld-bin-picking_newdata/train-* - config_name: metaworld-box-close_newdata data_files: - split: train path: metaworld-box-close_newdata/train-* - config_name: metaworld-button-press-topdown-wall_newdata data_files: - split: train path: metaworld-button-press-topdown-wall_newdata/train-* - config_name: metaworld-button-press-topdown-wall_subset data_files: - split: train path: metaworld-button-press-topdown-wall_subset/train-* - config_name: metaworld-button-press-topdown_newdata data_files: - split: train path: metaworld-button-press-topdown_newdata/train-* - config_name: metaworld-button-press-topdown_subset data_files: - split: train path: metaworld-button-press-topdown_subset/train-* - config_name: metaworld-button-press-wall_newdata data_files: - split: train path: metaworld-button-press-wall_newdata/train-* - config_name: metaworld-button-press-wall_subset data_files: - split: train path: metaworld-button-press-wall_subset/train-* - config_name: metaworld-button-press_newdata data_files: - split: train path: metaworld-button-press_newdata/train-* - config_name: metaworld-button-press_subset data_files: - split: train path: metaworld-button-press_subset/train-* - config_name: metaworld-coffee-button_newdata data_files: - split: train path: metaworld-coffee-button_newdata/train-* - config_name: metaworld-coffee-button_subset data_files: - split: train path: metaworld-coffee-button_subset/train-* - config_name: metaworld-coffee-pull_newdata data_files: - split: train path: metaworld-coffee-pull_newdata/train-* - config_name: metaworld-coffee-pull_subset data_files: - split: train path: metaworld-coffee-pull_subset/train-* - config_name: metaworld-coffee-push_newdata data_files: - split: train path: metaworld-coffee-push_newdata/train-* - config_name: metaworld-coffee-push_subset data_files: - split: train path: metaworld-coffee-push_subset/train-* - config_name: metaworld-dial-turn_newdata data_files: - split: train path: metaworld-dial-turn_newdata/train-* - config_name: metaworld-dial-turn_subset data_files: - split: train path: metaworld-dial-turn_subset/train-* - config_name: metaworld-disassemble_newdata data_files: - split: train path: metaworld-disassemble_newdata/train-* - config_name: metaworld-disassemble_subset data_files: - split: train path: metaworld-disassemble_subset/train-* - config_name: metaworld-door-close_newdata data_files: - split: train path: metaworld-door-close_newdata/train-* - config_name: metaworld-door-close_subset data_files: - split: train path: metaworld-door-close_subset/train-* - config_name: metaworld-door-lock_newdata data_files: - split: train path: metaworld-door-lock_newdata/train-* - config_name: metaworld-door-open_newdata data_files: - split: train path: metaworld-door-open_newdata/train-* - config_name: metaworld-door-open_subset data_files: - split: train path: metaworld-door-open_subset/train-* - config_name: metaworld-door-unlock_newdata data_files: - split: train path: metaworld-door-unlock_newdata/train-* - config_name: metaworld-drawer-close_newdata data_files: - split: train path: metaworld-drawer-close_newdata/train-* - config_name: metaworld-drawer-close_subset data_files: - split: train path: metaworld-drawer-close_subset/train-* - config_name: metaworld-drawer-open_newdata data_files: - split: train path: metaworld-drawer-open_newdata/train-* - config_name: metaworld-drawer-open_subset data_files: - split: train path: metaworld-drawer-open_subset/train-* - config_name: metaworld-faucet-close_newdata data_files: - split: train path: metaworld-faucet-close_newdata/train-* - config_name: metaworld-faucet-close_subset data_files: - split: train path: metaworld-faucet-close_subset/train-* - config_name: metaworld-faucet-open_newdata data_files: - split: train path: metaworld-faucet-open_newdata/train-* - config_name: metaworld-faucet-open_subset data_files: - split: train path: metaworld-faucet-open_subset/train-* - config_name: metaworld-hammer_newdata data_files: - split: train path: metaworld-hammer_newdata/train-* - config_name: metaworld-hammer_subset data_files: - split: train path: metaworld-hammer_subset/train-* - config_name: metaworld-handle-press-side_newdata data_files: - split: train path: metaworld-handle-press-side_newdata/train-* - config_name: metaworld-handle-press-side_subset data_files: - split: train path: metaworld-handle-press-side_subset/train-* - config_name: metaworld-handle-press_newdata data_files: - split: train path: metaworld-handle-press_newdata/train-* - config_name: metaworld-handle-press_subset data_files: - split: train path: metaworld-handle-press_subset/train-* - config_name: metaworld-handle-pull-side_newdata data_files: - split: train path: metaworld-handle-pull-side_newdata/train-* - config_name: metaworld-handle-pull-side_subset data_files: - split: train path: metaworld-handle-pull-side_subset/train-* - config_name: metaworld-handle-pull_newdata data_files: - split: train path: metaworld-handle-pull_newdata/train-* - config_name: metaworld-handle-pull_subset data_files: - split: train path: metaworld-handle-pull_subset/train-* - config_name: metaworld-lever-pull_newdata data_files: - split: train path: metaworld-lever-pull_newdata/train-* - config_name: metaworld-lever-pull_subset data_files: - split: train path: metaworld-lever-pull_subset/train-* - config_name: metaworld-peg-insert-side_newdata data_files: - split: train path: metaworld-peg-insert-side_newdata/train-* - config_name: metaworld-peg-insert-side_subset data_files: - split: train path: metaworld-peg-insert-side_subset/train-* - config_name: metaworld-peg-unplug-side_newdata data_files: - split: train path: metaworld-peg-unplug-side_newdata/train-* - config_name: metaworld-peg-unplug-side_subset data_files: - split: train path: metaworld-peg-unplug-side_subset/train-* - config_name: metaworld-pick-out-of-hole_newdata data_files: - split: train path: metaworld-pick-out-of-hole_newdata/train-* - config_name: metaworld-pick-out-of-hole_subset data_files: - split: train path: metaworld-pick-out-of-hole_subset/train-* - config_name: metaworld-pick-place-wall_newdata data_files: - split: train path: metaworld-pick-place-wall_newdata/train-* - config_name: metaworld-pick-place-wall_subset data_files: - split: train path: metaworld-pick-place-wall_subset/train-* - config_name: metaworld-pick-place_newdata data_files: - split: train path: metaworld-pick-place_newdata/train-* - config_name: metaworld-pick-place_subset data_files: - split: train path: metaworld-pick-place_subset/train-* - config_name: metaworld-plate-slide-back-side_newdata data_files: - split: train path: metaworld-plate-slide-back-side_newdata/train-* - config_name: metaworld-plate-slide-back-side_subset data_files: - split: train path: metaworld-plate-slide-back-side_subset/train-* - config_name: metaworld-plate-slide-back_newdata data_files: - split: train path: metaworld-plate-slide-back_newdata/train-* - config_name: metaworld-plate-slide-back_subset data_files: - split: train path: metaworld-plate-slide-back_subset/train-* - config_name: metaworld-plate-slide-side_newdata data_files: - split: train path: metaworld-plate-slide-side_newdata/train-* - config_name: metaworld-plate-slide-side_subset data_files: - split: train path: metaworld-plate-slide-side_subset/train-* - config_name: metaworld-plate-slide_newdata data_files: - split: train path: metaworld-plate-slide_newdata/train-* - config_name: metaworld-plate-slide_subset data_files: - split: train path: metaworld-plate-slide_subset/train-* - config_name: metaworld-push-back_newdata data_files: - split: train path: metaworld-push-back_newdata/train-* - config_name: metaworld-push-back_subset data_files: - split: train path: metaworld-push-back_subset/train-* - config_name: metaworld-push-wall_newdata data_files: - split: train path: metaworld-push-wall_newdata/train-* - config_name: metaworld-push-wall_subset data_files: - split: train path: metaworld-push-wall_subset/train-* - config_name: metaworld-push_newdata data_files: - split: train path: metaworld-push_newdata/train-* - config_name: metaworld-push_subset data_files: - split: train path: metaworld-push_subset/train-* - config_name: metaworld-reach-wall_newdata data_files: - split: train path: metaworld-reach-wall_newdata/train-* - config_name: metaworld-reach-wall_subset data_files: - split: train path: metaworld-reach-wall_subset/train-* - config_name: metaworld-reach_newdata data_files: - split: train path: metaworld-reach_newdata/train-* - config_name: metaworld-reach_subset data_files: - split: train path: metaworld-reach_subset/train-* - config_name: metaworld-shelf-place_newdata data_files: - split: train path: metaworld-shelf-place_newdata/train-* - config_name: metaworld-shelf-place_subset data_files: - split: train path: metaworld-shelf-place_subset/train-* - config_name: metaworld-soccer_newdata data_files: - split: train path: metaworld-soccer_newdata/train-* - config_name: metaworld-soccer_subset data_files: - split: train path: metaworld-soccer_subset/train-* - config_name: metaworld-stick-pull_newdata data_files: - split: train path: metaworld-stick-pull_newdata/train-* - config_name: metaworld-stick-pull_subset data_files: - split: train path: metaworld-stick-pull_subset/train-* - config_name: metaworld-stick-push_newdata data_files: - split: train path: metaworld-stick-push_newdata/train-* - config_name: metaworld-stick-push_subset data_files: - split: train path: metaworld-stick-push_subset/train-* - config_name: metaworld-sweep-into_newdata data_files: - split: train path: metaworld-sweep-into_newdata/train-* - config_name: metaworld-sweep-into_subset data_files: - split: train path: metaworld-sweep-into_subset/train-* - config_name: metaworld-sweep_newdata data_files: - split: train path: metaworld-sweep_newdata/train-* - config_name: metaworld-sweep_subset data_files: - split: train path: metaworld-sweep_subset/train-* - config_name: metaworld-window-close_newdata data_files: - split: train path: metaworld-window-close_newdata/train-* - config_name: metaworld-window-close_subset data_files: - split: train path: metaworld-window-close_subset/train-* - config_name: metaworld-window-open_newdata data_files: - split: train path: metaworld-window-open_newdata/train-* - config_name: metaworld-window-open_subset data_files: - split: train path: metaworld-window-open_subset/train-* - config_name: mujoco-ant_newdata data_files: - split: train path: mujoco-ant_newdata/train-* - config_name: mujoco-ant_subset data_files: - split: train path: mujoco-ant_subset/train-* - config_name: mujoco-doublependulum_newdata data_files: - split: train path: mujoco-doublependulum_newdata/train-* - config_name: mujoco-doublependulum_subset data_files: - split: train path: mujoco-doublependulum_subset/train-* - config_name: mujoco-halfcheetah_newdata data_files: - split: train path: mujoco-halfcheetah_newdata/train-* - config_name: mujoco-hopper_newdata data_files: - split: train path: mujoco-hopper_newdata/train-* - config_name: mujoco-humanoid_newdata data_files: - split: train path: mujoco-humanoid_newdata/train-* - config_name: mujoco-humanoid_subset data_files: - split: train path: mujoco-humanoid_subset/train-* - config_name: mujoco-pendulum_newdata data_files: - split: train path: mujoco-pendulum_newdata/train-* - config_name: mujoco-pendulum_subset data_files: - split: train path: mujoco-pendulum_subset/train-* - config_name: mujoco-pusher_newdata data_files: - split: train path: mujoco-pusher_newdata/train-* - config_name: mujoco-pusher_subset data_files: - split: train path: mujoco-pusher_subset/train-* - config_name: mujoco-reacher_newdata data_files: - split: train path: mujoco-reacher_newdata/train-* - config_name: mujoco-reacher_subset data_files: - split: train path: mujoco-reacher_subset/train-* - config_name: mujoco-standup_newdata data_files: - split: train path: mujoco-standup_newdata/train-* - config_name: mujoco-standup_subset data_files: - split: train path: mujoco-standup_subset/train-* - config_name: mujoco-swimmer_newdata data_files: - split: train path: mujoco-swimmer_newdata/train-* - config_name: mujoco-swimmer_subset data_files: - split: train path: mujoco-swimmer_subset/train-* - config_name: mujoco-walker_newdata data_files: - split: train path: mujoco-walker_newdata/train-* - config_name: mujoco-walker_subset data_files: - split: train path: mujoco-walker_subset/train-* ---

IGNF/PASTIS-HD

IGNF

--- license: etalab-2.0 task_categories: - image-classification - image-segmentation tags: - remote sensing - Agricultural size_categories: - 1K<n<10K --- # 🌱 PASTIS-HD 🌿 Panoptic Agricultural Satellite TIme Series : optical time series, radar time series and very high resolution image [PASTIS](https://github.com/VSainteuf/pastis-benchmark) is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite time series. It contains 2,433 patches within the French metropolitan territory with panoptic annotations (instance index + semantic label for each pixel). Each patch is a Sentinel-2 multispectral image time series of variable lentgh. This dataset have been extended in 2021 with aligned radar Sentinel-1 observations for all 2433 patches. For each patch, it constains approximately 70 observations of Sentinel-1 in ascending orbit, and 70 observations in descending orbit. Each each Sentinel1 observation is assembled into a 3-channel image: vertical polarization (VV), horizontal polarisation (VH), and the ratio vertical over horizontal polarization (VV/VH). This extension is named PASTIS-R. We extend PASTIS with aligned very high resolution satellite images from SPOT 6-7 constellation for all 2433 patches in addition to the Sentinel-1 and 2 time series. The image are resampled to a 1m resolution and converted to 8 bits. This enhancement significantly improves the dataset's spatial content, providing more granular information for agricultural parcel segmentation. **PASTIS-HD** can be used to evaluate multi-modal fusion methods (with optical time series, radar time series and VHR images) for parcel-based classification, semantic segmentation, and panoptic segmentation. ## Dataset in numbers 🛰️ Sentinel 2 | 🛰️ Sentinel 1 | 🛰️ **SPOT 6-7 VHR** | 🗻 Annotations :-------------------------------------------- | :-------------------------------------------------- | :------------------------------| :------------------------------ ➡️ 2,433 time series | ➡️ 2 time 2,433 time series | ➡️ **2,433 images** | 124,422 individual parcels ➡️ 10m / pixel | ➡️ 10m / pixel | ➡️ **1.5m / pixel** | covers ~4,000 km² ➡️ 128x128 pixels / images | ➡️ 128x128 pixels / images | ➡️ **1280x1280 pixels / images** | over 2B pixels ➡️ 38-61 acquisitions / series | ➡️ ~ 70 acquisitions / series | ➡️ **One observation** | 18 crop types ➡️ 10 spectral bands |➡️ 2 spectral bands | ➡️ **3 spectral bands** | ⚠️ The **SPOT data are natively 1.5m resolution**, but we over-sampled them at 1m to align them pixel-perfect with Sentinel data.  ## Data loading The Github repository associated to this dataset contains a PyTorch dataset class of [the OmniSat repository](https://github.com/gastruc/OmniSat/blob/main/src/data/Pastis.py) that can be readily used to load data for training models on PASTIS-HD. The time series contained in PASTIS have variable lengths. The Sentinel 1 and 2 time series are stored in numpy array. The SPOT images are in TIFF format. The annotations are in numpy array too. ⚠️ The S2 and S1 folders contains more than 2433 files on the contrary to the labels folder. Some patches are not labelled and not used for training. The relevant information can be find in the metadata.geojson file (with 2433 entries), which is used as an index by the dataloader. ### Remark about the folder names ⚠️ The **DATA_S1A** folder contains the Sentinel-1 **ascendent** images whereas the **DATA_S1D** folder contains the Sentinel-1 **descendant** images. ## Ground Truth Annotations The agricultural parcels are grouped into 18 different crop classes as shown in the table below. The backgroud class corresponds to non-agricultural land, and the void label for parcels that are mostly outside their patch.  Additional information about the dataset can be found in the documentation/pastis-documentation.pdf document. ## Credits - The Sentinel imagery used in PASTIS was retrieved from [THEIA](www.theia.land.fr): "Value-added data processed by the CNES for the Theia www.theia.land.fr data cluster using Copernicus data. The treatments use algorithms developed by Theia’s Scientific Expertise Centres. " - The annotations used in PASTIS stem from the French [land parcel identification system](https://www.data.gouv.fr/en/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/) produced by IGN. - The SPOT images are opendata thanks to the Dataterra Dinamis initiative in the case of the ["Couverture France DINAMIS"](https://dinamis.data-terra.org/opendata/) program. ## References If you use PASTIS please cite the [related paper](https://arxiv.org/abs/2107.07933): ``` @article{garnot2021panoptic, title={Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks}, author={Sainte Fare Garnot, Vivien and Landrieu, Loic}, journal={ICCV}, year={2021} } ``` For the PASTIS-R optical-radar fusion dataset, please also cite [this paper](https://arxiv.org/abs/2112.07558v1): ``` @article{garnot2021mmfusion, title = {Multi-modal temporal attention models for crop mapping from satellite time series}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, year = {2022}, doi = {https://doi.org/10.1016/j.isprsjprs.2022.03.012}, author = {Vivien {Sainte Fare Garnot} and Loic Landrieu and Nesrine Chehata}, } ``` For the PASTIS-HD with the 3 modalities optical-radar time series plus VHR images dataset, please also cite [this paper](https://arxiv.org/abs/2404.08351): ``` @article{astruc2024omnisat, title={Omni{S}at: {S}elf-Supervised Modality Fusion for {E}arth Observation}, author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic}, journal={ECCV}, year={2024} } ```

mteb/sickr-sts

mteb

--- language: - en ---

Jay-Rajput/DIS_IPL_Preds

Jay-Rajput

--- configs: - config_name: predictions data_files: predictions/*.json --- --- license: apache-2.0 ---

cfilt/IITB-IndicMonoDoc

cfilt

--- license: cc-by-4.0 task_categories: - text-generation language: - hi - mr - gu - sa - ta - te - ml - ne - as - bn - ks - or - pa - ur - sd - kn size_categories: - 10B<n<100B tags: - language-modeling - llm - clm viewer: false --- IITB Document level Monolingual Corpora for Indian languages. 22 scheduled languages of India + English (1) Assamese, (2) Bengali, (3) Gujarati, (4) Hindi, (5) Kannada, (6) Kashmiri, (7) Konkani, (8) Malayalam, (9) Manipuri, (10) Marathi, (11) Nepali, (12) Oriya, (13) Punjabi, (14) Sanskrit, (15) Sindhi, (16) Tamil, (17) Telugu, (18) Urdu (19) Bodo, (20) Santhali, (21) Maithili and (22) Dogri. | Language | Total (#Mil Tokens) | |:---------:|:--------------------:| | bn | 5258.47 | | en | 11986.53 | | gu | 887.18 | | hi | 11268.33 | | kn | 567.16 | | ml | 845.32 | | mr | 1066.76 | | ne | 1542.39 | | pa | 449.61 | | ta | 2171.92 | | te | 767.18 | | ur | 2391.79 | | as | 57.64 | | brx | 2.25 | | doi | 0.37 | | gom | 2.91 | | kas | 1.27 | | mai | 1.51 | | mni | 0.99 | | or | 81.96 | | sa | 80.09 | | sat | 3.05 | | sd | 83.81 | | Total= | 39518.51 | To cite this dataset: ``` @misc{doshi2024worry, title={Do Not Worry if You Do Not Have Data: Building Pretrained Language Models Using Translationese}, author={Meet Doshi and Raj Dabre and Pushpak Bhattacharyya}, year={2024}, eprint={2403.13638}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Zyphra/Zyda

Zyphra

--- dataset_info: config_name: default splits: - name: train num_examples: 1594197267 license: odc-by pretty_name: Zyda task_categories: - text-generation language: - en size_categories: - n>1T configs: - config_name: default data_files: - split: train path: data/*/*/* - config_name: zyda_no_starcoder data_files: - split: train path: data/zyda_no_starcoder/*/* - config_name: zyda_arxiv_only data_files: - split: train path: data/zyda_no_starcoder/zyda_arxiv/* - config_name: zyda_c4-en_only data_files: - split: train path: data/zyda_no_starcoder/c4_en/* - config_name: zyda_peS2o_only data_files: - split: train path: data/zyda_no_starcoder/zyda_peS2o/* - config_name: zyda_pile-uncopyrighted_only data_files: - split: train path: data/zyda_no_starcoder/zyda_pile-uncopyrighted/* - config_name: zyda_refinedweb_only data_files: - split: train path: data/zyda_no_starcoder/zyda_refinedweb/* - config_name: zyda_slimpajama_only data_files: - split: train path: data/zyda_no_starcoder/zyda_slimpajama/* - config_name: zyda_starcoder_only data_files: - split: train path: data/zyda_starcoder/*/* --- # Zyda <!-- Provide a quick summary of the dataset. --> Zyda is a 1.3T language modeling dataset created by collecting open and high quality datasets and combining them and performing a uniform filtering and deduplication step. We find that Zyda performs extremely well in ablations and is at least comparable and potentially better to the best openly available datasets available, due to our meticulous post-processing pipeline. We think the best use of Zyda is either as a standalone dataset for language model training up to the 1T scale, or in combination with Fineweb or Dolma for multi-trillion token training. An early version of Zyda was used as the primary dataset for phase 1 pretraining of [Zamba](https://arxiv.org/abs/2405.16712), a model which performs strongly on a per-token basis, testifying to the strength of Zyda as a pretraining dataset. Models trained on Zyda significantly outperform identical models of the Pythia suite trained on the [Pile](https://arxiv.org/abs/2101.00027) for 300B tokens. Zyda also outperforms Dolma, RefinedWeb, and Fineweb on 1.4B models trained on 50B tokens of each dataset. According to our evaluations, Zyda is the most performant per-token open dataset available in its non-starcoder variant on language tasks. The Zyda starcoder variant ties with fineweb. <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/VdrCqypZtTpjEs7bH1k9s.png" width="650" alt="Zyda performance across steps."> </center> These results are aggregate scores of classic language modeling evaluations (PIQA, WinoGrande, OpenBookQA, ARC-Easy, ARC-Challenge) across time for a 1.4B model trained on 50B tokens of each dataset. ## How to download Full dataset: ``` import datasets ds = datasets.load_dataset("Zyphra/Zyda", split="train") ``` Full dataset without StarCoder: ``` import datasets ds = datasets.load_dataset("Zyphra/Zyda", name="zyda_no_starcoder", split="train") ``` For downloading individual components put their name in the name arg of `load_dataset()`: - zyda_arxiv_only - zyda_c4-en_only - zyda_peS2o_only - zyda_pile-uncopyrighted_only - zyda_refinedweb_only - zyda_slimpajama_only - zyda_starcoder_only ## Breakdown by component | Component | Download size (parquet, GBs) | Documents (millions) | gpt-neox tokens (billions) | | --- | --- | --- | --- | | zyda_refinedweb_only | 1,712.4 | 920.5 | 564.8 | | zyda_c4-en_only | 366.7 | 254.5 | 117.5 | | zyda_slimpajama_only | 594.7 | 142.3 | 242.3 | | zyda_pile-uncopyrighted_only | 189.4 | 64.9 | 82.9 | | zyda_peS2o_only | 133.7 | 35.7 | 53.4 | | zyda_arxiv_only | 8.3 | 0.3 | 4.7 | | zyda_starcoder_only | 299.5 | 176.1 | 231.3 | | Total | 3,304.7 | 1,594.2 | 1,296.7 | ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** Zyphra - **Language(s) (NLP):** Primarily English - **License:** Open Data Commons License ## 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. --> Dataset fields: - `text`: contains actual text for training - `source`: component the text is coming from - `filtering_features`: precomputed values of different features that were used for filtering (converted to json string) - `source_other`: metadata from the source dataset (converted to json string) ### Source Data Zyda was drawn from seven component open datasets which are well-regarded in the community. These are: Pile Uncopyrighted: https://huggingface.co/datasets/monology/pile-uncopyrighted C4-en: https://huggingface.co/datasets/allenai/c4 peS2o: https://huggingface.co/datasets/allenai/peS2o RefinedWeb: https://huggingface.co/datasets/tiiuae/falcon-refinedweb SlimPajama: https://huggingface.co/datasets/cerebras/SlimPajama-627B arxiv_s2orc_parsed: https://huggingface.co/datasets/ArtifactAI/arxiv_s2orc_parsed StarCoder: https://huggingface.co/datasets/bigcode/starcoderdata <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/eCJWG3ZoA4fVk8bZZBHaG.png" width="650" alt="Composition of Zyda"> </center> <!--  --> <!--  --> #### 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. --> Zyda was created using a two stage post-processing pipeline consisting of *filtering* and *deduplication*. For the filtering stage, we utilized a set of hand-crafted and tuned filters derived from a number of sources such as C4, RedPajama, and Gopher, in addition to our own filters. For the deduplication stage, we used minhash approximate deduplication. We deduplicated on 13-grams and used a minhash signature size of 128 and filtered out documents above a Jaccard similarity of 0.4. For full details on our data processing, see the [Zyda technical report](https://arxiv.org/abs/2406.01981) and our [dataset processing code](https://github.com/Zyphra/Zyda_processing). #### Personal and Sensitive Information As a language modelling dataset, it likely contains PII which has not been filtered out of the component datasets and which may have been missed by our own filters. ## Bias, Risks, and Limitations As a dataset comprised of open web scrapes, it is likely that it contains biased and toxic content. ## Licensing Information We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this dataset, you are also bound by any license agreements and terms of use of the original data sources. ## Citation If you use our dataset to train a model, please cite us at: ``` @misc{tokpanov2024zyda, title={Zyda: A 1.3T Dataset for Open Language Modeling}, author={Yury Tokpanov and Beren Millidge and Paolo Glorioso and Jonathan Pilault and Adam Ibrahim and James Whittington and Quentin Anthony}, year={2024}, eprint={2406.01981}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Yelp/yelp_review_full

Yelp

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: YelpReviewFull license_details: yelp-licence dataset_info: config_name: yelp_review_full features: - name: label dtype: class_label: names: '0': 1 star '1': 2 star '2': 3 stars '3': 4 stars '4': 5 stars - name: text dtype: string splits: - name: train num_bytes: 483811554 num_examples: 650000 - name: test num_bytes: 37271188 num_examples: 50000 download_size: 322952369 dataset_size: 521082742 configs: - config_name: yelp_review_full data_files: - split: train path: yelp_review_full/train-* - split: test path: yelp_review_full/test-* default: true train-eval-index: - config: yelp_review_full task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- --- # Dataset Card for YelpReviewFull ## 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:** [Yelp](https://www.yelp.com/dataset) - **Repository:** [Crepe](https://github.com/zhangxiangxiao/Crepe) - **Paper:** [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626) - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu) ### Dataset Summary The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment. ### Languages The reviews were mainly written in english. ## Dataset Structure ### Data Instances A typical data point, comprises of a text and the corresponding label. An example from the YelpReviewFull test set looks as follows: ``` { 'label': 0, 'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!' } ``` ### Data Fields - 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n". - 'label': Corresponds to the score associated with the review (between 1 and 5). ### Data Splits The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5. In total there are 650,000 trainig samples and 50,000 testing samples. ## Dataset Creation ### Curation Rationale The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### 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 You can check the official [yelp-dataset-agreement](https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf). ### Citation Information Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.

Matthijs/cmu-arctic-xvectors

Matthijs

--- pretty_name: CMU ARCTIC X-Vectors task_categories: - text-to-speech - audio-to-audio license: mit --- # Speaker embeddings extracted from CMU ARCTIC There is one `.npy` file for each utterance in the dataset, 7931 files in total. The speaker embeddings are 512-element X-vectors. The [CMU ARCTIC](http://www.festvox.org/cmu_arctic/) dataset divides the utterances among the following speakers: - bdl (US male) - slt (US female) - jmk (Canadian male) - awb (Scottish male) - rms (US male) - clb (US female) - ksp (Indian male) The X-vectors were extracted using [this script](https://huggingface.co/mechanicalsea/speecht5-vc/blob/main/manifest/utils/prep_cmu_arctic_spkemb.py), which uses the `speechbrain/spkrec-xvect-voxceleb` model. Usage: ```python from datasets import load_dataset embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = embeddings_dataset[7306]["xvector"] speaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0) ```

hpprc/emb

hpprc

--- language: - ja license: other dataset_info: - config_name: auto-wiki-nli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 533673945 num_examples: 198895 download_size: 362814978 dataset_size: 533673945 - config_name: auto-wiki-qa-collection features: - name: text dtype: string splits: - name: train num_bytes: 5215705706 num_examples: 8215817 download_size: 3385038265 dataset_size: 5215705706 - config_name: auto-wiki-qa-dataset features: - name: passage_id dtype: int64 - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 30767957804 num_examples: 2377503 download_size: 21875194075 dataset_size: 30767957804 - config_name: auto-wiki-qa-nemotron-collection features: - name: text dtype: string splits: - name: train num_bytes: 4202532852 num_examples: 6354725 download_size: 2709124196 dataset_size: 4202532852 - config_name: auto-wiki-qa-nemotron-dataset features: - name: passage_id dtype: int64 - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 2034181294 num_examples: 156089 download_size: 1449231482 dataset_size: 2034181294 - config_name: baobab-wiki-retrieval-collection features: - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3771123469 num_examples: 5140862 download_size: 2463376300 dataset_size: 3771123469 - config_name: baobab-wiki-retrieval-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: 'null' - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: 'null' - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 7837529 num_examples: 838 download_size: 5661379 dataset_size: 7837529 - config_name: jagovfaqs-collection features: - name: text dtype: string splits: - name: train num_bytes: 13918890 num_examples: 22794 download_size: 5874592 dataset_size: 13918890 - config_name: jagovfaqs-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: 'null' - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: 'null' - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 205284001 num_examples: 22794 download_size: 93115345 dataset_size: 205284001 - config_name: janli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 14075833 num_examples: 13496 download_size: 3088881 dataset_size: 14075833 - config_name: jaquad-collection features: - name: text dtype: string splits: - name: train num_bytes: 4213318372 num_examples: 6364369 download_size: 2716125410 dataset_size: 4213318372 - config_name: jaquad-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 410758435 num_examples: 31748 download_size: 267846825 dataset_size: 410758435 - config_name: jcommonsenseqa-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: 'null' - name: neg_ids.original sequence: 'null' splits: - name: train num_bytes: 673948 num_examples: 8939 download_size: 381605 dataset_size: 673948 - config_name: jqara-collection features: - name: text dtype: string splits: - name: train num_bytes: 4267669475 num_examples: 6433384 download_size: 2751666583 dataset_size: 4267669475 - config_name: jqara-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: int64 - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 29789340 num_examples: 2235 download_size: 22310036 dataset_size: 29789340 - config_name: jsnli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 170593490 num_examples: 144190 download_size: 88629828 dataset_size: 170593490 - config_name: jsquad-collection features: - name: text dtype: string splits: - name: train num_bytes: 4210493031 num_examples: 6369790 download_size: 2714126867 dataset_size: 4210493031 - config_name: jsquad-dataset features: - name: passage_id dtype: int64 - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: int64 - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 812736672 num_examples: 62859 download_size: 514718047 dataset_size: 812736672 - config_name: miracl-collection features: - name: passage_id dtype: int64 - name: docid dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3026160577.0 num_examples: 6953614 download_size: 1682864613 dataset_size: 3026160577.0 - config_name: miracl-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: string - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: int64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 32393484 num_examples: 3477 download_size: 23431039 dataset_size: 32393484 - config_name: mkqa-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: 'null' - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 129900532 num_examples: 10000 download_size: 88793974 dataset_size: 129900532 - config_name: mkqa-triplet features: - name: idx dtype: string - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string splits: - name: train num_bytes: 7640649 num_examples: 10000 download_size: 4121496 dataset_size: 7640649 - config_name: mmarco-collection features: - name: text dtype: string splits: - name: train num_bytes: 3814117634 num_examples: 8829813 download_size: 2217976936 dataset_size: 3814117634 - config_name: mmarco-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: string - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: int64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 3548801103 num_examples: 391060 download_size: 2624355417 dataset_size: 3548801103 - config_name: mr-tydi-collection features: - name: passage_id dtype: int64 - name: docid dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 3061941618 num_examples: 7000027 download_size: 1702050239 dataset_size: 3061941618 - config_name: mr-tydi-dataset features: - name: anc dtype: string - name: pos_1st dtype: string - name: neg_1st.original dtype: string - name: neg_1st.me5-large dtype: string - name: sim_1st.me5-large dtype: float64 - name: neg_1st.bm25 dtype: string - name: sim_1st.bm25 dtype: float64 - name: pos_ids sequence: int64 - name: neg_ids.original sequence: int64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 35660240 num_examples: 3697 download_size: 25702000 dataset_size: 35660240 - config_name: niilc-qa-dataset features: - name: id dtype: string - name: anc dtype: string - name: answers sequence: string splits: - name: dev num_bytes: 94339 num_examples: 795 - name: test num_bytes: 24706 num_examples: 198 download_size: 69487 dataset_size: 119045 - config_name: nu-mnli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 145358014 num_examples: 77785 download_size: 90397670 dataset_size: 145358014 - config_name: nu-snli-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 133786645 num_examples: 109154 download_size: 68979487 dataset_size: 133786645 - config_name: paws-x-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string - name: neg.orig sequence: string splits: - name: train num_bytes: 124053741 num_examples: 49401 download_size: 75965630 dataset_size: 124053741 - config_name: qa-collection features: - name: text dtype: string splits: - name: train num_bytes: 4202542828.0 num_examples: 6354742 download_size: 2284295643 dataset_size: 4202542828.0 - config_name: quiz-no-mori-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: 'null' - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 340206118 num_examples: 25991 download_size: 241017142 dataset_size: 340206118 - config_name: quiz-works-dataset features: - name: anc dtype: string - name: answers sequence: string - name: pos_ids.original sequence: 'null' - name: neg_ids.original sequence: 'null' - name: pos_ids.me5-large sequence: int64 - name: pos_sims.me5-large sequence: float64 - name: pos_ids.bm25 sequence: int64 - name: pos_sims.bm25 sequence: float64 - name: neg_ids.me5-large sequence: int64 - name: neg_sims.me5-large sequence: float64 - name: neg_ids.bm25 sequence: int64 - name: neg_sims.bm25 sequence: float64 splits: - name: train num_bytes: 248971793 num_examples: 19073 download_size: 176241965 dataset_size: 248971793 - config_name: snow-triplet features: - name: anc dtype: string - name: pos sequence: string - name: neg sequence: string splits: - name: train num_bytes: 63640356 num_examples: 62758 download_size: 35752257 dataset_size: 63640356 configs: - config_name: auto-wiki-nli-triplet data_files: - split: train path: auto-wiki-nli-triplet/train-* - config_name: auto-wiki-qa-collection data_files: - split: train path: auto-wiki-qa-collection/train-* - config_name: auto-wiki-qa-dataset data_files: - split: train path: auto-wiki-qa-dataset/train-* - config_name: auto-wiki-qa-nemotron-collection data_files: - split: train path: auto-wiki-qa-nemotron-collection/train-* - config_name: auto-wiki-qa-nemotron-dataset data_files: - split: train path: auto-wiki-qa-nemotron-dataset/train-* - config_name: baobab-wiki-retrieval-collection data_files: - split: train path: baobab-wiki-retrieval-collection/train-* - config_name: baobab-wiki-retrieval-dataset data_files: - split: train path: baobab-wiki-retrieval-dataset/train-* - config_name: jagovfaqs-collection data_files: - split: train path: jagovfaqs-collection/train-* - config_name: jagovfaqs-dataset data_files: - split: train path: jagovfaqs-dataset/train-* - config_name: janli-triplet data_files: - split: train path: janli-triplet/train-* - config_name: jaquad-collection data_files: - split: train path: jaquad-collection/train-* - config_name: jaquad-dataset data_files: - split: train path: jaquad-dataset/train-* - config_name: jcommonsenseqa-dataset data_files: - split: train path: jcommonsenseqa-dataset/train-* - config_name: jqara-collection data_files: - split: train path: jqara-collection/train-* - config_name: jqara-dataset data_files: - split: train path: jqara-dataset/train-* - config_name: jsnli-triplet data_files: - split: train path: jsnli-triplet/train-* - config_name: jsquad-collection data_files: - split: train path: jsquad-collection/train-* - config_name: jsquad-dataset data_files: - split: train path: jsquad-dataset/train-* - config_name: miracl-collection data_files: - split: train path: miracl-collection/train-* - config_name: miracl-dataset data_files: - split: train path: miracl-dataset/train-* - config_name: mkqa-dataset data_files: - split: train path: mkqa-dataset/train-* - config_name: mkqa-triplet data_files: - split: train path: mkqa-triplet/train-* - config_name: mmarco-collection data_files: - split: train path: mmarco-collection/train-* - config_name: mmarco-dataset data_files: - split: train path: mmarco-dataset/train-* - config_name: mr-tydi-collection data_files: - split: train path: mr-tydi-collection/train-* - config_name: mr-tydi-dataset data_files: - split: train path: mr-tydi-dataset/train-* - config_name: niilc-qa-dataset data_files: - split: dev path: niilc-qa-dataset/dev-* - split: test path: niilc-qa-dataset/test-* - config_name: nu-mnli-triplet data_files: - split: train path: nu-mnli-triplet/train-* - config_name: nu-snli-triplet data_files: - split: train path: nu-snli-triplet/train-* - config_name: paws-x-triplet data_files: - split: train path: paws-x-triplet/train-* - config_name: qa-collection data_files: - split: train path: qa-collection/train-* - config_name: quiz-no-mori-dataset data_files: - split: train path: quiz-no-mori-dataset/train-* - config_name: quiz-works-dataset data_files: - split: train path: quiz-works-dataset/train-* - config_name: snow-triplet data_files: - split: train path: snow-triplet/train-* --- still WIP ## Dataset Description - **Paper:** https://arxiv.org/abs/2409.07737 - **Point of Contact:** [Hayato Tsukagoshi](mailto:tsukagoshi.hayato.r2@s.mail.nagoya-u.ac.jp) ## Information |Name|Type|License (根拠)| |-|-|-| |MMARCO|Retrieval|[Apache 2.0 (?)](https://huggingface.co/datasets/unicamp-dl/mmarco)| |Mr. TyDi|Retrieval|[Apache 2.0](https://huggingface.co/datasets/castorini/mr-tydi)| |MIRACL|Retrieval|[Apache 2.0](https://huggingface.co/datasets/miracl/miracl)| |JaGovFaqs|QA|[CC-BY-4.0](https://huggingface.co/datasets/matsuxr/JaGovFaqs-22k)| |Auto Wiki QA|QA & Retrieval|[CC-BY-SA-4.0](https://huggingface.co/datasets/cl-nagoya/auto-wiki-qa)| |Auto Wiki QA Nemotron|QA & Retrieval|[CC-BY-SA-4.0](https://huggingface.co/datasets/hpprc/auto-wiki-qa-nemotron)| |JCommonsenseQA|QA|[CC-BY-SA-4.0](https://github.com/yahoojapan/JGLUE)| |JSQuAD|QA & Retrieval|[CC-BY-SA-4.0](https://github.com/yahoojapan/JGLUE)| |Japanese Wikipedia Human Retrieval|QA & Retrieval|[Apache 2.0](https://huggingface.co/datasets/baobab-trees/wikipedia-human-retrieval-ja)| |JQaRA (dev, unused)|QA|[CC-BY-SA-4.0](https://huggingface.co/datasets/hotchpotch/JQaRA#:~:text=%E3%81%B0%E5%B9%B8%E3%81%84%E3%81%A7%E3%81%99%E3%80%82-,%E3%83%A9%E3%82%A4%E3%82%BB%E3%83%B3%E3%82%B9,%E3%81%A7%E3%81%82%E3%82%8B%20CC%20BY%2DSA%204.0%20%E3%81%BE%E3%81%9F%E3%81%AF%20GFDL%E3%81%A8%E3%81%97%E3%81%BE%E3%81%99%E3%80%82,-%E8%AC%9D%E8%BE%9E)| |JaQuAD|QA & Retrieval|[CC-BY-SA-3.0](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD)| |JSNLI|NLI|[CC-BY-SA-4.0](https://huggingface.co/datasets/shunk031/jsnli)| |Auto Wiki NLI|NLI|[CC-BY-SA-4.0](https://huggingface.co/datasets/hpprc/auto-wiki-nli-reward)| |NU-SNLI|NLI|[CC-BY-SA-4.0](https://huggingface.co/datasets/cl-nagoya/nu-snli)| |NU-MNLI|NLI|[CC-BY-SA-3.0, MIT, Others](https://huggingface.co/datasets/cl-nagoya/nu-mnli)| |PAWS-X|Paraphrase|[Free (二次利用自由)](https://github.com/google-research-datasets/paws?tab=License-1-ov-file#readme)| |SNOW|Paraphrase|[CC-BY-3.0](https://huggingface.co/datasets/SNOW-NLP/snow_simplified_japanese_corpus)| |MKQA|QA|[CC-BY-3.0](https://huggingface.co/datasets/apple/mkqa)| |Quiz Works|QA|[Free (二次利用自由)](https://quiz-works.com/about)| |Quiz No Mori|QA|[Free (二次利用自由)](https://quiz-schedule.info/quiz_no_mori/quizforestsecond.html)| |NIILC QA|QA|[CC-BY-SA](https://mynlp.is.s.u-tokyo.ac.jp/niilc-qa/)|

mlfoundations/dclm-pool-1b-5x

mlfoundations

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

Kaichengalex/YFCC15M

Kaichengalex

--- dataset_info: features: - name: images dtype: image - name: texts sequence: float32 splits: - name: train num_bytes: 748710703 num_examples: 10000 download_size: 746368611 dataset_size: 748710703 configs: - config_name: default data_files: - split: train path: data/train-* --- ## YFCC15M Recaption Dataset This YFCC15M Dataset is filtered by [DeCLIP](https://github.com/Sense-GVT/DeCLIP) and recaptioned utilize the diverse description generation framework proposed in [RWKV-CLIP](https://github.com/deepglint/RWKV-CLIP). The text is a list of text tokens with a length of 77, encoded using the CLIP tokenizer. You can use `from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer` to decode it back into the original text. ## Using Dataset You can easily download and use the arxiver dataset with Hugging Face's datasets library. ``` from datasets import load_dataset dataset = load_dataset("Kaichengalex/YFCC15M") ``` ## References If you find this dataset useful, please use the following BibTeX entry for citation. ``` @misc{gu2024rwkvclip, title={RWKV-CLIP: A Robust Vision-Language Representation Learner}, author={Tiancheng Gu and Kaicheng Yang and Xiang An and Ziyong Feng and Dongnan Liu and Weidong Cai and Jiankang Deng}, year={2024}, eprint={2406.06973}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

gsdf/EasyNegative

gsdf

--- license: other --- # Negative Embedding This is a Negative Embedding trained with Counterfeit. Please use it in the "\stable-diffusion-webui\embeddings" folder. It can be used with other models, but the effectiveness is not certain. # Counterfeit-V2.0.safetensors  # AbyssOrangeMix2_sfw.safetensors  # anything-v4.0-pruned.safetensors 

dair-ai/emotion

dair-ai

--- annotations_creators: - machine-generated language_creators: - machine-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: emotion pretty_name: Emotion tags: - emotion-classification dataset_info: - config_name: split features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 1741533 num_examples: 16000 - name: validation num_bytes: 214695 num_examples: 2000 - name: test num_bytes: 217173 num_examples: 2000 download_size: 1287193 dataset_size: 2173401 - config_name: unsplit features: - name: text dtype: string - name: label dtype: class_label: names: '0': sadness '1': joy '2': love '3': anger '4': fear '5': surprise splits: - name: train num_bytes: 45444017 num_examples: 416809 download_size: 26888538 dataset_size: 45444017 configs: - config_name: split data_files: - split: train path: split/train-* - split: validation path: split/validation-* - split: test path: split/test-* default: true - config_name: unsplit data_files: - split: train path: unsplit/train-* train-eval-index: - config: default task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "emotion" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **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:** 16.13 MB - **Size of the generated dataset:** 47.62 MB - **Total amount of disk used:** 63.75 MB ### Dataset Summary Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances An example looks as follows. ``` { "text": "im feeling quite sad and sorry for myself but ill snap out of it soon", "label": 0 } ``` ### Data Fields The data fields are: - `text`: a `string` feature. - `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5). ### Data Splits The dataset has 2 configurations: - split: with a total of 20_000 examples split into train, validation and split - unsplit: with a total of 416_809 examples in a single train split | name | train | validation | test | |---------|-------:|-----------:|-----:| | split | 16000 | 2000 | 2000 | | unsplit | 416809 | n/a | n/a | ## 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 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 should be used for educational and research purposes only. ### Citation Information If you use this dataset, please cite: ``` @inproceedings{saravia-etal-2018-carer, title = "{CARER}: Contextualized Affect Representations for Emotion Recognition", author = "Saravia, Elvis and Liu, Hsien-Chi Toby and Huang, Yen-Hao and Wu, Junlin and Chen, Yi-Shin", booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", month = oct # "-" # nov, year = "2018", address = "Brussels, Belgium", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D18-1404", doi = "10.18653/v1/D18-1404", pages = "3687--3697", abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.", } ``` ### Contributions Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

DL3DV/DL3DV-ALL-480P

DL3DV

--- tags: - 3D Vision - NeRF - 3D Gaussian - Dataset - Novel View Synthesis - Text to 3D - Image to 3D pretty_name: Dl3DV-Dataset size_categories: - 100B<n<1T --- # DL3DV-Dataset This repo has all the 480P frames with camera poses of DL3DV-10K Dataset. We are working hard to review all the dataset to avoid sensitive information. Thank you for your patience. # Download If you have enough space, you can use git to download a dataset from huggingface. See this [link](https://huggingface.co/docs/hub/en/datasets-downloading). [480P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P)/[960P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-960P) versions should satisfies most needs. If you do not have enough space, we further provide a [download script](https://github.com/DL3DV-10K/Dataset/blob/main/scripts/download.py) here to download a subset. The usage: ```Bash usage: download.py [-h] --odir ODIR --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} --resolution {4K,2K,960P,480P} --file_type {images+poses,video,colmap_cache} [--hash HASH] [--clean_cache] optional arguments: -h, --help show this help message and exit --odir ODIR output directory --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} The subset of the benchmark to download --resolution {4K,2K,960P,480P} The resolution to donwnload --file_type {images+poses,video,colmap_cache} The file type to download --hash HASH If set subset=hash, this is the hash code of the scene to download --clean_cache If set, will clean the huggingface cache to save space ``` Here are some examples: ```Bash # Make sure you have applied for the access. # Use this to download the download.py script wget https://raw.githubusercontent.com/DL3DV-10K/Dataset/main/scripts/download.py # Download 480P resolution images and poses, 0~1K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 1K --resolution 480P --file_type images+poses --clean_cache # Download 480P resolution images and poses, 1K~2K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 2K --resolution 480P --file_type images+poses --clean_cache ``` You can also download a specific scene with its hash. The scene-hash pair visualization can be found [here](https://htmlpreview.github.io/?https://github.com/DL3DV-10K/Dataset/blob/main/visualize/index.html). ```Bash # Download 480P resolution images and poses, 1K~2K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 2K --resolution 480P --file_type images+poses --hash e2cedefea8a0ed2d0ffbd5bdc08acbe7e1f85c96f72f7b790e9dfe1c98963047 --clean_cache ``` # News - [x] DL3DV-1K, 2K, 3K, 4K - [ ] DL3DV-5K ~ 10K

applied-ai-018/pretraining_v1-omega_books

applied-ai-018

--- dataset_info: config_name: CC-MAIN-2013-20 features: - name: text dtype: string - name: id dtype: string - name: dump dtype: string - name: url dtype: string - name: file_path dtype: string - name: language dtype: string - name: language_score dtype: float64 - name: token_count dtype: int64 - name: score dtype: float64 - name: int_score dtype: int64 splits: - name: train num_bytes: 235476901236 num_examples: 51901183 download_size: 138494178972 dataset_size: 235476901236 configs: - config_name: CC-MAIN-2013-20 data_files: - split: train path: CC-MAIN-2013-20/train-* ---

bigcode/the-stack-v2-train-smol-ids

bigcode

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: [] pretty_name: The-Stack-v2 extra_gated_prompt: "## Terms of Use for The Stack v2\n\nThe Stack v2 dataset is a\ \ collection of source code in over 600 programming languages. We ask that you read\ \ and acknowledge the following points before using the dataset:\n1. Downloading\ \ the dataset in bulk requires a an agreement with SoftwareHeritage and INRIA. Contact\ \ [datasets@softwareheritage.org](mailto:datasets@softwareheritage.org?subject=TheStackV2%20request%20for%20dataset%20access%20information)\ \ for more information.\n2. If you are using the dataset to train models you must\ \ adhere to the SoftwareHeritage [principles for language model training](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/).\n\ 3. The Stack v2 is a collection of source code from repositories with various licenses.\ \ Any use of all or part of the code gathered in The Stack v2 must abide by the\ \ terms of the original licenses, including attribution clauses when relevant. We\ \ facilitate this by providing provenance information for each data point.\n4. The\ \ Stack v2 is regularly updated to enact validated data removal requests. By clicking\ \ on \"Access repository\", you agree to update your own version of The Stack v2\ \ to the most recent usable version.\n\nBy clicking on \"Access repository\" below,\ \ you accept that your contact information (email address and username) can be shared\ \ with the dataset maintainers as well.\n " extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox dataset_info: features: - name: repo_name dtype: string - name: repo_url dtype: string - name: snapshot_id dtype: string - name: revision_id dtype: string - name: directory_id dtype: string - name: branch_name dtype: string - name: visit_date dtype: timestamp[ns] - name: revision_date dtype: timestamp[ns] - name: committer_date dtype: timestamp[ns] - name: github_id dtype: int64 - name: star_events_count dtype: int64 - name: fork_events_count dtype: int64 - name: gha_license_id dtype: string - name: gha_created_at dtype: timestamp[ns] - name: gha_updated_at dtype: timestamp[ns] - name: gha_pushed_at dtype: timestamp[ns] - name: gha_language dtype: string - name: files list: - name: blob_id dtype: string - name: path dtype: string - name: content_id dtype: string - name: language dtype: string - name: length_bytes dtype: int64 - name: detected_licenses sequence: string - name: license_type dtype: string - name: src_encoding dtype: string - name: is_vendor dtype: bool - name: is_generated dtype: bool - name: alphanum_fraction dtype: float32 - name: alpha_fraction dtype: float32 - name: num_lines dtype: int32 - name: avg_line_length dtype: float32 - name: max_line_length dtype: int32 - name: num_files dtype: int64 splits: - name: train num_bytes: 93623832913.11467 num_examples: 40138809 download_size: 59322439587 dataset_size: 93623832913.11467 configs: - config_name: default data_files: - split: train path: data/train-* --- # The Stack v2 <center> <img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/thestackv2_banner.png" alt="Stackv2" width="900" height="600"> </center> ## Dataset Description - **Homepage:** https://www.bigcode-project.org/ - **Repository:** https://github.com/bigcode-project - **Paper:** [Link](https://huggingface.co/papers/2402.19173) - **Point of Contact:** contact@bigcode-project.org The dataset consists of 4 versions: - [`bigcode/the-stack-v2`](https://huggingface.co/datasets/bigcode/the-stack-v2): the full "The Stack v2" dataset - [`bigcode/the-stack-v2-dedup`](https://huggingface.co/datasets/bigcode/the-stack-v2-dedup): based on the `bigcode/the-stack-v2` but further near-deduplicated - [`bigcode/the-stack-v2-train-full-ids`](https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids): based on the `bigcode/the-stack-v2-dedup` dataset but further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories. - [`bigcode/the-stack-v2-train-smol-ids`](https://huggingface.co/datasets/bigcode/the-stack-v2-train-smol-ids): based on the `bigcode/the-stack-v2-dedup` dataset but further filtered with heuristics and spanning 17 programming languages. The data is grouped into repositories. **<-- you are here** **These datasets only contain the SWHIDs to download the code files and not the content of the files itself. See examples below to see how to download content. We are working on making the training datasets available in the coming weeks.** The Stack v2 is significantly larger than v1: ||The Stack v1|The Stack v2| |-|-|-| | full | 6.4TB | 67.5TB | | dedup | 2.9TB | 32.1TB | | train (full) | ~200B tokens | ~900B tokens | ### Changelog |Release|Description| |-|-| | v2.1.0 | Removed repositories that opted out before 2024-04-09. Removed unreachable/private repositories (according to SWH) | | v2.0.1 | Version bump without modifications to the dataset. StarCoder2 was trained on this version | | v2.0 | Initial release of the Stack v2 | ### Dataset Summary The Stack v2 contains over 3B files in 600+ programming and markup languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets. This dataset is derived from the Software Heritage archive, the largest public archive of software source code and accompanying development history. Software Heritage is an open, non profit initiative to collect, preserve, and share the source code of all publicly available software, launched by Inria, in partnership with UNESCO. We acknowledge Software Heritage for providing access to this invaluable resource. For more details, visit the [Software Heritage website](https://www.softwareheritage.org). ### Languages The `smol` dataset contains 39 languages. ``` Ant Build System, AsciiDoc, C, C#, C++, CMake, Dockerfile, Go, Go Module, Gradle, Groovy, HTML, INI, Java, Java Properties, JavaScript, JSON, JSON with Comments, Kotlin, Lua, M4Sugar, Makefile, Markdown, Maven POM, PHP, Python, R, RDoc, reStructuredText, RMarkdown, Ruby, Rust, Shell, SQL, Swift, Text, TOML, TypeScript, YAML ``` ### How to use it ```python from datasets import load_dataset # full dataset (file IDs only) ds = load_dataset("bigcode/the-stack-v2-train-smol-ids", split="train") # dataset streaming (will only download the data as needed) ds = load_dataset("bigcode/the-stack-v2-train-smol-ids", streaming=True, split="train") for sample in iter(ds): print(sample) ``` #### Downloading the file contents The file contents are stored in the Software Heritage S3 bucket to ensure data compliance. Downloading data in bulk requires an agreement with SoftwareHeritage and INRIA as stated in the dataset agreement. Make sure to configure your environment with your [AWS credentials](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/configure/index.html#examples). ```bash pip install smart_open[s3] ``` ```python import os import boto3 from smart_open import open from datasets import load_dataset session = boto3.Session( aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"], aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"]) s3 = session.client("s3") def download_contents(files): for file in files: s3_url = f"s3://softwareheritage/content/{file['blob_id']}" with open(s3_url, "rb", compression=".gz", transport_params={"client": s3}) as fin: file["content"] = fin.read().decode(file["src_encoding"]) return {"files": files} ds = load_dataset("bigcode/the-stack-v2-train-smol-ids", split="train", streaming=True) ds = ds.map(lambda row: download_contents(row["files"])) for row in ds: for file in row["files"]: print(file["content"]) break ``` ## Dataset Structure ### Data Fields * `blob_id` (`string`): Software Heritage (SWH) ID of the file on AWS S3. * `directory_id` (`string`): SWH ID of the root directory of the repository. * `path` (`string`): The file path within the repository. * `content_id` (`string`): SWH content ID. * `detected_licenses` (`string[]`): List of licenses (SPDX) detected by ScanCode. * `license_type` (`string`): Inferred license type (`permissive` or `no_license`). * `repo_name` (`string`): Repository name on GitHub. * `snapshot_id` (`string`): SWH snapshot ID. * `revision_id` (`string`): SWH revision (commit) ID. * `branch_name` (`string`): Repository branch name. * `visit_date` (`timestamp[ns]`): SWH crawl (snapshot) timestamp. * `revision_date` (`timestamp[ns]`): SWH revision (commit) timestamp. * `committer_date` (`timestamp[ns]`): SWH revision (commit) timestamp reported by the committer. * `github_id` (`int64`): GitHub identifier for the repository. * `star_events_count` (`int64`): number of stars calculated from GHArchive events. * `fork_events_count` (`int64`): number of forks calculated from GHArchive events. * `gha_license_id` (`string`): GHArchive SPDX license identifier, `None` if the repo is missing. * `gha_event_created_at` (`timestamp[ns]`): Timestamp of the latest event on GHArchive for this repository. * `gha_created_at` (`timestamp[ns]`): Timestamp of repository creation on GitHub, `None` if the repo is missing. * `gha_language` (`string`): Repository's primary programming language on GitHub, `None` if the repo is missing. * `src_encoding` (`string`): Original encoding of the file content befre converting to UTF-8. * `language` (`string`): Programming language of the file, detected by `go-enry / linguist`. * `is_vendor` (`bool`): Indicator of vendor file (external library), detected by `go-enry`. * `is_generated` (`bool`): Indicator of generated file (external library), detected by `go-enry`. * `length_bytes` (`int64`): Length of the file content in UTF-8 bytes. * `extension` (`string`): File extension. ### Data Splits The dataset has no splits and all data is loaded as train split by default. If you want to setup a custom train-test split beware that dataset contains a lot of near-duplicates which can cause leakage into the test split. ## Dataset Creation For more information on the dataset creation pipeline please refer to the [technical report](https://huggingface.co/papers/2402.19173). ### Curation Rationale One of the challenges faced by researchers working on code LLMs is the lack of openness and transparency around the development of these systems. Most prior works described the high-level data collection process but did not release the training data. It is therefore difficult for other researchers to fully reproduce these models and understand what kind of pre-training data leads to high-performing code LLMs. By releasing an open large-scale code dataset we hope to make training of code LLMs more reproducible. ### Source Data #### Data Collection 3.28B unique files belonging to 104.2M github repositories were collected by traversing the Software Heritage [2023-09-06](https://docs.softwareheritage.org/devel/swh-dataset/graph/dataset.html#graph-dataset-2023-09-06) graph dataset. Additional repository-level metadata was collected from [GitHub Archive](https://www.gharchive.org/) data up to 2023-09-14. The total uncompressed size of all files is 67.53TB. Near-deduplication was implemented in the pre-processing pipeline on top of exact deduplication. Roughly 40% of permissively licensed files were (near-)duplicates. The following are not stored: * Files that cannot contribute to training code: binary, empty, could not be decoded * Files larger than 10MB **Training Datasets**: For the training datasets the programming languages were filtered further to 17 and 600+ for the `the-stack-v2-smol-ids` and `the-stack-v2-full-ids` dataset, respecively. In addition, heuristics were applied to further increase the quality of the dataset. The code files are also grouped into repositories to allow to pretrain with full repository context. For more details see the [technical report](https://huggingface.co/papers/2402.19173). ##### License detection We extract repository-level license information from [GH Archive](https://www.gharchive.org/) for all repositories with matching names in the SWH dataset. When the repo-level license is not available, i.e., for 96.93\% of repositories, we use the [ScanCode Toolkit](https://github.com/nexB/scancode-toolkit) to detect file-level licenses as follows: * Find all filenames that could contain a license (e.g., LICENSE, MIT.txt, Apache2.0) or contain a reference to the license (e.g., README.md, GUIDELINES); * Apply ScanCode's license detection to the matching files and gather the SPDX IDs of the detected licenses; * Propagate the detected licenses to all files that have the same base path within the repository as the license file. The licenses we consider permissive are listed [here](https://huggingface.co/datasets/bigcode/the-stack-v2/blob/main/license_stats.csv). This list was compiled from the licenses approved by the [Blue Oak Council](https://blueoakcouncil.org/list), as well as licenses categorized as "Permissive" or "Public Domain" by [ScanCode](https://scancode-licensedb.aboutcode.org/). #### Who are the source language producers? The source (code) language producers are users of GitHub that created unique repository names up until 2023-09-06 (cutoff date). ### Personal and Sensitive Information The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. Deduplication has helped to reduce the amount of sensitive data that may exist. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their [open-access](https://en.wikipedia.org/wiki/Open_access) research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. Complaints, removal requests, and "do not contact" requests can be sent to contact@bigcode-project.org. ### Opting out of The Stack v2 We are giving developers the ability to have their code removed from the dataset upon request. The process for submitting and enacting removal requests will keep evolving throughout the project as we receive feedback and build up more data governance tools. You can check if your code is in The Stack v2 with the following ["Am I In The Stack?" Space](https://huggingface.co/spaces/bigcode/in-the-stack). If you'd like to have your data removed from the dataset follow the [instructions on GitHub](https://github.com/bigcode-project/opt-out-v2). ## Considerations for Using the Data ### Social Impact of Dataset The Stack v2 is an output of the BigCode Project. BigCode aims to be responsible by design and by default. The project is conducted in the spirit of Open Science, focused on the responsible development of LLMs for code. With the release of The Stack v2, we aim to increase access, reproducibility, and transparency of code LLMs in the research community. Work to de-risk and improve on the implementation of ethical best practices of code LLMs is conducted in various BigCode working groups. The Legal, Ethics, and Governance working group has explored topics such as licensing (including copyleft and the intended use of permissively licensed code), attribution of generated code to original code, rights to restrict processing, the inclusion of Personally Identifiable Information (PII), and risks of malicious code, among other topics. This work is ongoing as of October 25th, 2022. We expect code LLMs to enable people from diverse backgrounds to write higher quality code and develop low-code applications. Mission-critical software could become easier to maintain as professional developers are guided by code-generating systems on how to write more robust and efficient code. While the social impact is intended to be positive, the increased accessibility of code LLMs comes with certain risks such as over-reliance on the generated code and long-term effects on the software development job market. A broader impact analysis relating to Code LLMs can be found in section 7 of this [paper](https://arxiv.org/abs/2107.03374). An in-depth risk assessments for Code LLMs can be found in section 4 of this [paper](https://arxiv.org/abs/2207.14157). ### Discussion of Biases The code collected from GitHub does not contain demographic information or proxy information about the demographics. However, it is not without risks, as the comments within the code may contain harmful or offensive language, which could be learned by the models. Widely adopted programming languages like C and Javascript are overrepresented compared to niche programming languages like Julia and Scala. Some programming languages such as SQL, Batchfile, TypeScript are less likely to be permissively licensed (4% vs the average 10%). This may result in a biased representation of those languages. Permissively licensed files also tend to be longer. The majority of natural language present in code from GitHub is English. ### Other Known Limitations One of the current limitations of The Stack v2 is that scraped HTML for websites may not be compliant with Web Content Accessibility Guidelines ([WCAG](https://www.w3.org/WAI/standards-guidelines/wcag/)). This could have an impact on HTML-generated code that may introduce web accessibility issues. The training dataset could contain malicious code and/or the model could be used to generate malware or ransomware. To the best of our knowledge, all files contained in the dataset are licensed with one of the permissive licenses (see list in [Licensing information](#licensing-information)) or no license. The accuracy of license attribution is limited by the accuracy of GHArchive and ScanCode Toolkit. Any mistakes should be reported to BigCode Project for review and follow-up as needed. ## Additional Information ### Dataset Curators 1. Harm de Vries, ServiceNow Research, harm.devries@servicenow.com 2. Leandro von Werra, Hugging Face, leandro@huggingface.co ### Licensing Information The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. The list of [SPDX license identifiers](https://spdx.org/licenses/) included in the dataset can be found [here](https://huggingface.co/datasets/bigcode/the-stack-v2/blob/main/license_stats.csv). ### Citation Information ```bash @misc{lozhkov2024starcoder, title={StarCoder 2 and The Stack v2: The Next Generation}, author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries}, year={2024}, eprint={2402.19173}, archivePrefix={arXiv}, primaryClass={cs.SE} } ```

ai4bharat/sangraha

ai4bharat

--- license: cc-by-4.0 task_categories: - text-generation language: - as - bn - gu - en - hi - kn - ks - ml - mr - ne - or - pa - sa - sd - ta - te - ur tags: - language-modeling - casual-lm - llm pretty_name: sangraha dataset_info: - config_name: verified features: - name: doc_id dtype: string - name: type dtype: string - name: text dtype: string splits: - name: asm - name: ben - name: brx - name: doi - name: eng - name: gom - name: guj - name: hin - name: kan - name: kas - name: mai - name: mal - name: mar - name: mni - name: nep - name: ori - name: pan - name: san - name: sat - name: snd - name: tam - name: tel - name: urd - config_name: unverified features: - name: doc_id dtype: string - name: text dtype: string splits: - name: asm - name: ben - name: guj - name: hin - name: kan - name: mal - name: mar - name: nep - name: ori - name: pan - name: san - name: tam - name: tel - name: urd - config_name: synthetic features: - name: doc_id dtype: string - name: text dtype: string splits: - name: asm_Beng - name: asm_Latn - name: ben_Beng - name: ben_Latn - name: guj_Gujr - name: guj_Latn - name: hin_Deva - name: hin_Latn - name: kan_Knda - name: kan_Latn - name: mal_Mlym - name: mal_Latn - name: mar_Deva - name: mar_Latn - name: npi_Deva - name: npi_Latn - name: ory_Orya - name: ory_Latn - name: pan_Guru - name: pan_Latn - name: san_Deva - name: san_Latn - name: tam_Taml - name: tam_Latn - name: tel_Telu - name: tel_Latn - name: urd_Arab - name: urd_Latn configs: - config_name: verified data_files: - split: asm path: verified/asm/*.parquet - split: ben path: verified/ben/*.parquet - split: brx path: verified/brx/*.parquet - split: doi path: verified/doi/*.parquet - split: eng path: verified/eng/*.parquet - split: gom path: verified/gom/*.parquet - split: guj path: verified/guj/*.parquet - split: hin path: verified/hin/*.parquet - split: kan path: verified/kan/*.parquet - split: kas path: verified/kas/*.parquet - split: mai path: verified/mai/*.parquet - split: mal path: verified/mal/*.parquet - split: mar path: verified/mar/*.parquet - split: mni path: verified/mni/*.parquet - split: nep path: verified/nep/*.parquet - split: ori path: verified/ori/*.parquet - split: pan path: verified/pan/*.parquet - split: san path: verified/san/*.parquet - split: sat path: verified/sat/*.parquet - split: snd path: verified/snd/*.parquet - split: tam path: verified/tam/*.parquet - split: tel path: verified/tel/*.parquet - split: urd path: verified/urd/*.parquet - config_name: unverified data_files: - split: asm path: unverified/asm/*.parquet - split: ben path: unverified/ben/*.parquet - split: guj path: unverified/guj/*.parquet - split: hin path: unverified/hin/*.parquet - split: kan path: unverified/kan/*.parquet - split: mal path: unverified/mal/*.parquet - split: mar path: unverified/mar/*.parquet - split: nep path: unverified/nep/*.parquet - split: ori path: unverified/ori/*.parquet - split: pan path: unverified/pan/*.parquet - split: san path: unverified/san/*.parquet - split: tam path: unverified/tam/*.parquet - split: tel path: unverified/tel/*.parquet - split: urd path: unverified/urd/*.parquet - config_name: synthetic data_files: - split: asm_Beng path: synthetic/asm_Beng/*.parquet - split: asm_Latn path: synthetic/asm_Latn/*.parquet - split: ben_Beng path: synthetic/ben_Beng/*.parquet - split: ben_Latn path: synthetic/ben_Latn/*.parquet - split: guj_Gujr path: synthetic/guj_Gujr/*.parquet - split: guj_Latn path: synthetic/guj_Latn/*.parquet - split: hin_Deva path: synthetic/hin_Deva/*.parquet - split: hin_Latn path: synthetic/hin_Latn/*.parquet - split: kan_Knda path: synthetic/kan_Knda/*.parquet - split: kan_Latn path: synthetic/kan_Latn/*.parquet - split: mal_Mlym path: synthetic/mal_Mlym/*.parquet - split: mal_Latn path: synthetic/mal_Latn/*.parquet - split: mar_Deva path: synthetic/mar_Deva/*.parquet - split: mar_Latn path: synthetic/mar_Latn/*.parquet - split: npi_Deva path: synthetic/npi_Deva/*.parquet - split: npi_Latn path: synthetic/npi_Latn/*.parquet - split: ory_Orya path: synthetic/ory_Orya/*.parquet - split: ory_Latn path: synthetic/ory_Latn/*.parquet - split: pan_Guru path: synthetic/pan_Guru/*.parquet - split: pan_Latn path: synthetic/pan_Latn/*.parquet - split: san_Deva path: synthetic/san_Deva/*.parquet - split: san_Latn path: synthetic/san_Latn/*.parquet - split: tam_Taml path: synthetic/tam_Taml/*.parquet - split: tam_Latn path: synthetic/tam_Latn/*.parquet - split: tel_Telu path: synthetic/tel_Telu/*.parquet - split: tel_Latn path: synthetic/tel_Latn/*.parquet - split: urd_Arab path: synthetic/urd_Arab/*.parquet - split: urd_Latn path: synthetic/urd_Latn/*.parquet size_categories: - 100B<n<1T --- # Sangraha <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63ef3cd11e695b35aa48bebc/nDnyidcqIOLAP9dTw9GrK.png" /> </p> Sangraha is the largest high-quality, cleaned Indic language pretraining data containing 251B tokens summed up over 22 languages, extracted from curated sources, existing multilingual corpora and large scale translations. **Coming Soon**: - Sangraha Synthetic - Translated and Romanised English Wikimedia data. - Sangraha Verified - Hindi YouTube transcribed data. **More information**: - For detailed information on the curation and cleaning process of Sangraha, please checkout our paper [on Arxiv](https://arxiv.org/abs/2403.06350); - Check out the scraping and cleaning pipelines used to curate Sangraha [on GitHub](https://github.com/AI4Bharat/IndicLLMSuite); ## Getting Started For downloading the entire Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha") ``` For downloading a subset (Verified/Unverified) of Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha", data_dir="<subset_name>") # for example: dataset = load_dataset("ai4bharat/sangraha", data_dir="verified") ``` For downloading one language from a subset of Sangraha: ```python from datasets import load_dataset dataset = load_dataset("ai4bharat/sangraha", data_dir="<subset_name>/<lang_code>") # for example: dataset = load_dataset("ai4bharat/sangraha", data_dir="verified/asm") ``` ## Background Sangraha contains three broad components: - **Sangraha Verified**: Containing scraped data from "human-verified" Websites, OCR-extracted data from high quality Indic language PDFs, transcribed data from various Indic language videos, podcasts, movies, courses, etc. - **Sangraha Unverfied**: High quality Indic language data extracted from existing multilingual corpora employing perplexity filtering using n-gram language models trained on Sangraha Verified. - **Sangraha Synthetic**: WikiMedia English translated to 14 Indic languages and further "romanised" from 14 languages by transliteration to English. ## Data Statistics | **Lang Code** | **Verified** | **Synthetic** | **Unverified** | **Total Tokens (in Millions)** | | ------------- | ------------ | ------------- | -------------- | ------------------------------ | | asm | 292.1 | 11,696.4 | 17.5 | 12,006.0 | | ben | 10,604.4 | 13,814.1 | 5,608.8 | 30,027.5 | | brx | 1.5 | - | - | 1.5 | | doi | 0.06 | - | - | 0.06 | | eng | 12,759.9 | - | - | 12,759.9 | | gom | 10.1 | - | - | 10.1 | | guj | 3,647.9 | 12,934.5 | 597.0 | 17,179.4 | | hin | 12,617.3 | 9,578.7 | 12,348.3 | 34,544.3 | | kan | 1,778.3 | 12,087.4 | 388.8 | 14,254.5 | | kas | 0.5 | - | - | 0.5 | | mai | 14.6 | - | - | 14.6 | | mal | 2,730.8 | 13,130.0 | 547.8 | 16,408.6 | | mar | 2,827.0 | 10,816.7 | 652.1 | 14,295.8 | | mni | 7.4 | - | - | 7.4 | | npi | 1,822.5 | 10,588.7 | 485.5 | 12,896.7 | | ori | 1,177.1 | 11,338.0 | 23.7 | 12,538.8 | | pan | 1,075.3 | 9,969.6 | 136.9 | 11,181.8 | | san | 1,329.0 | 13,553.5 | 9.8 | 14,892.3 | | sat | 0.3 | - | - | 0.3 | | snd | 258.2 | - | - | 258.2 | | tam | 3,985.1 | 11,859.3 | 1,515.9 | 17,360.3 | | urd | 3,658.1 | 9,415.8 | 1,328.2 | 14,402.1 | | tel | 3,706.8 | 11,924.5 | 647.4 | 16,278.7 | | **Total** | **64,306.1** | **162,707.9** | **24,307.7** | **251,321.0** | To cite Sangraha, please use: ``` @article{khan2024indicllmsuite, title = {IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages}, author = {Mohammed Safi Ur Rahman Khan and Priyam Mehta and Ananth Sankar and Umashankar Kumaravelan and Sumanth Doddapaneni and Suriyaprasaad G and Varun Balan G and Sparsh Jain and Anoop Kunchukuttan and Pratyush Kumar and Raj Dabre and Mitesh M. Khapra}, year = {2024}, journal = {arXiv preprint arXiv: 2403.06350} } ```

hendrycks/competition_math

hendrycks

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual pretty_name: Mathematics Aptitude Test of Heuristics (MATH) size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] tags: - explanation-generation dataset_info: features: - name: problem dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string splits: - name: train num_bytes: 5984788 num_examples: 7500 - name: test num_bytes: 3732575 num_examples: 5000 download_size: 20327424 dataset_size: 9717363 --- # Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/hendrycks/math - **Repository:** https://github.com/hendrycks/math - **Paper:** https://arxiv.org/pdf/2103.03874.pdf - **Leaderboard:** N/A - **Point of Contact:** Dan Hendrycks ### Dataset Summary The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's `\boxed` tag. An example from the dataset is: ``` {'problem': 'A board game spinner is divided into three parts labeled $A$, $B$ and $C$. The probability of the spinner landing on $A$ is $\\frac{1}{3}$ and the probability of the spinner landing on $B$ is $\\frac{5}{12}$. What is the probability of the spinner landing on $C$? Express your answer as a common fraction.', 'level': 'Level 1', 'type': 'Counting & Probability', 'solution': 'The spinner is guaranteed to land on exactly one of the three regions, so we know that the sum of the probabilities of it landing in each region will be 1. If we let the probability of it landing in region $C$ be $x$, we then have the equation $1 = \\frac{5}{12}+\\frac{1}{3}+x$, from which we have $x=\\boxed{\\frac{1}{4}}$.'} ``` ### Data Fields * `problem`: The competition math problem. * `solution`: The step-by-step solution. * `level`: The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'. * `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus. ### Data Splits * train: 7,500 examples * test: 5,000 examples ## 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 https://github.com/hendrycks/math/blob/main/LICENSE ### Citation Information ```bibtex @article{hendrycksmath2021, title={Measuring Mathematical Problem Solving With the MATH Dataset}, author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, journal={arXiv preprint arXiv:2103.03874}, year={2021} } ``` ### Contributions Thanks to [@hacobe](https://github.com/hacobe) for adding this dataset.

lmms-lab/Video-MME

lmms-lab

--- dataset_info: config_name: videomme features: - name: video_id dtype: string - name: duration dtype: string - name: domain dtype: string - name: sub_category dtype: string - name: url dtype: string - name: videoID dtype: string - name: question_id dtype: string - name: task_type dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string splits: - name: test num_bytes: 1003241.0 num_examples: 2700 download_size: 405167 dataset_size: 1003241.0 configs: - config_name: videomme data_files: - split: test path: videomme/test-* ---

agkphysics/AudioSet

agkphysics

--- language: - en license: cc-by-4.0 size_categories: - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - audio-classification paperswithcode_id: audioset pretty_name: AudioSet config_names: - balanced - unbalanced tags: - audio dataset_info: - config_name: balanced features: - name: video_id dtype: string - name: audio dtype: audio - name: labels sequence: string - name: human_labels sequence: string splits: - name: train num_bytes: 26016210987 num_examples: 18685 - name: test num_bytes: 23763682278 num_examples: 17142 download_size: 49805654900 dataset_size: 49779893265 - config_name: unbalanced features: - name: video_id dtype: string - name: audio dtype: audio - name: labels sequence: string - name: human_labels sequence: string splits: - name: train num_bytes: 2408656417541 num_examples: 1738788 - name: test num_bytes: 23763682278 num_examples: 17142 download_size: 2433673104977 dataset_size: 2432420099819 --- # Dataset Card for AudioSet ## Dataset Description - **Homepage**: https://research.google.com/audioset/index.html - **Paper**: https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/45857.pdf - **Leaderboard**: https://paperswithcode.com/sota/audio-classification-on-audioset ### Dataset Summary [AudioSet](https://research.google.com/audioset/dataset/index.html) is a dataset of 10-second clips from YouTube, annotated into one or more sound categories, following the AudioSet ontology. ### Supported Tasks and Leaderboards - `audio-classification`: Classify audio clips into categories. The leaderboard is available [here](https://paperswithcode.com/sota/audio-classification-on-audioset) ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances Example instance from the dataset: ```python { 'video_id': '--PJHxphWEs', 'audio': { 'path': 'audio/bal_train/--PJHxphWEs.flac', 'array': array([-0.04364824, -0.05268681, -0.0568949 , ..., 0.11446512, 0.14912748, 0.13409865]), 'sampling_rate': 48000 }, 'labels': ['/m/09x0r', '/t/dd00088'], 'human_labels': ['Speech', 'Gush'] } ``` ### Data Fields Instances have the following fields: - `video_id`: a `string` feature containing the original YouTube ID. - `audio`: an `Audio` feature containing the audio data and sample rate. - `labels`: a sequence of `string` features containing the labels associated with the audio clip. - `human_labels`: a sequence of `string` features containing the human-readable forms of the same labels as in `labels`. ### Data Splits The distribuion of audio clips is as follows: #### `balanced` configuration | |train|test | |-----------|----:|----:| |# instances|18685|17142| #### `unbalanced` configuration | |train |test | |-----------|------:|----:| |# instances|1738788|17142| ## 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? The labels are from the AudioSet ontology. Audio clips are from YouTube. ### 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 1. The YouTube videos in this copy of AudioSet were downloaded in March 2023, so not all of the original audios are available. The number of clips able to be downloaded is as follows: - Balanced train: 18685 audio clips out of 22160 originally. - Unbalanced train: 1738788 clips out of 2041789 originally. - Evaluation: 17142 audio clips out of 20371 originally. 2. Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at 44.1 kHz 24 bit. Audio files are stored in the FLAC format. ## 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 AudioSet data is licensed under CC-BY-4.0 ## Citation ```bibtex @inproceedings{jort_audioset_2017, title = {Audio Set: An ontology and human-labeled dataset for audio events}, author = {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter}, year = {2017}, booktitle = {Proc. IEEE ICASSP 2017}, address = {New Orleans, LA} } ```

kuroneko5943/amz20

kuroneko5943

--- annotations_creators: - found language: - en language_creators: - found license: - apache-2.0 multilinguality: - monolingual pretty_name: amz20 size_categories: - 1K<n<10K source_datasets: - extended|amazon_us_reviews tags: - amazon task_categories: - text-classification task_ids: - sentiment-classification ---

stanfordnlp/sst2

stanfordnlp

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: sst pretty_name: Stanford Sentiment Treebank v2 dataset_info: features: - name: idx dtype: int32 - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 4681603 num_examples: 67349 - name: validation num_bytes: 106252 num_examples: 872 - name: test num_bytes: 216640 num_examples: 1821 download_size: 3331058 dataset_size: 5004495 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://nlp.stanford.edu/sentiment/ - **Repository:** - **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges. Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary. ### Supported Tasks and Leaderboards - `sentiment-classification` ### Languages The text in the dataset is in English (`en`). ## Dataset Structure ### Data Instances ``` {'idx': 0, 'sentence': 'hide new secretions from the parental units ', 'label': 0} ``` ### Data Fields - `idx`: Monotonically increasing index ID. - `sentence`: Complete sentence expressing an opinion about a film. - `label`: Sentiment of the opinion, either "negative" (0) or positive (1). The test set labels are hidden (-1). ### Data Splits | | train | validation | test | |--------------------|---------:|-----------:|-----:| | Number of examples | 67349 | 872 | 1821 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? Rotten Tomatoes reviewers. ### 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 Unknown. ### Citation Information ```bibtex @inproceedings{socher-etal-2013-recursive, title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank", author = "Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher", booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", month = oct, year = "2013", address = "Seattle, Washington, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D13-1170", pages = "1631--1642", } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.

EuropeanParliament/Eurovoc

EuropeanParliament

--- license: eupl-1.1 configs: - config_name: 1996-03 data_files: "files/1996-03.jsonl.gz" - config_name: 1996-04 data_files: "files/1996-04.jsonl.gz" - config_name: 1996-05 data_files: "files/1996-05.jsonl.gz" - config_name: 1996-06 data_files: "files/1996-06.jsonl.gz" - config_name: 1996-07 data_files: "files/1996-07.jsonl.gz" - config_name: 1996-08 data_files: "files/1996-08.jsonl.gz" - config_name: 1996-09 data_files: "files/1996-09.jsonl.gz" - config_name: 1996-10 data_files: "files/1996-10.jsonl.gz" - config_name: 1996-11 data_files: "files/1996-11.jsonl.gz" - config_name: 1996-12 data_files: "files/1996-12.jsonl.gz" - config_name: 1997-01 data_files: "files/1997-01.jsonl.gz" - config_name: 1997-02 data_files: "files/1997-02.jsonl.gz" - config_name: 1997-03 data_files: "files/1997-03.jsonl.gz" - config_name: 1997-04 data_files: "files/1997-04.jsonl.gz" - config_name: 1997-05 data_files: "files/1997-05.jsonl.gz" - config_name: 1997-06 data_files: "files/1997-06.jsonl.gz" - config_name: 1997-07 data_files: "files/1997-07.jsonl.gz" - config_name: 1997-08 data_files: "files/1997-08.jsonl.gz" - config_name: 1997-09 data_files: "files/1997-09.jsonl.gz" - config_name: 1997-10 data_files: "files/1997-10.jsonl.gz" - config_name: 1997-11 data_files: "files/1997-11.jsonl.gz" - config_name: 1997-12 data_files: "files/1997-12.jsonl.gz" - config_name: 1998-01 data_files: "files/1998-01.jsonl.gz" - config_name: 1998-02 data_files: "files/1998-02.jsonl.gz" - config_name: 1998-03 data_files: "files/1998-03.jsonl.gz" - config_name: 1998-04 data_files: "files/1998-04.jsonl.gz" - config_name: 1998-05 data_files: "files/1998-05.jsonl.gz" - config_name: 1998-06 data_files: "files/1998-06.jsonl.gz" - config_name: 1998-07 data_files: "files/1998-07.jsonl.gz" - config_name: 1998-08 data_files: "files/1998-08.jsonl.gz" - config_name: 1998-09 data_files: "files/1998-09.jsonl.gz" - config_name: 1998-10 data_files: "files/1998-10.jsonl.gz" - config_name: 1998-11 data_files: "files/1998-11.jsonl.gz" - config_name: 1998-12 data_files: "files/1998-12.jsonl.gz" - config_name: 1999-01 data_files: "files/1999-01.jsonl.gz" - config_name: 1999-02 data_files: "files/1999-02.jsonl.gz" - config_name: 1999-03 data_files: "files/1999-03.jsonl.gz" - config_name: 1999-04 data_files: "files/1999-04.jsonl.gz" - config_name: 1999-05 data_files: "files/1999-05.jsonl.gz" - config_name: 1999-06 data_files: "files/1999-06.jsonl.gz" - config_name: 1999-07 data_files: "files/1999-07.jsonl.gz" - config_name: 1999-08 data_files: "files/1999-08.jsonl.gz" - config_name: 1999-09 data_files: "files/1999-09.jsonl.gz" - config_name: 1999-10 data_files: "files/1999-10.jsonl.gz" - config_name: 1999-11 data_files: "files/1999-11.jsonl.gz" - config_name: 1999-12 data_files: "files/1999-12.jsonl.gz" - config_name: 2000-01 data_files: "files/2000-01.jsonl.gz" - config_name: 2000-02 data_files: "files/2000-02.jsonl.gz" - config_name: 2000-03 data_files: "files/2000-03.jsonl.gz" - config_name: 2000-04 data_files: "files/2000-04.jsonl.gz" - config_name: 2000-05 data_files: "files/2000-05.jsonl.gz" - 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config_name: 2014-08 data_files: "files/2014-08.jsonl.gz" - config_name: 2014-09 data_files: "files/2014-09.jsonl.gz" - config_name: 2014-10 data_files: "files/2014-10.jsonl.gz" - config_name: 2014-11 data_files: "files/2014-11.jsonl.gz" - config_name: 2014-12 data_files: "files/2014-12.jsonl.gz" - config_name: 2015-01 data_files: "files/2015-01.jsonl.gz" - config_name: 2015-02 data_files: "files/2015-02.jsonl.gz" - config_name: 2015-03 data_files: "files/2015-03.jsonl.gz" - config_name: 2015-04 data_files: "files/2015-04.jsonl.gz" - config_name: 2015-05 data_files: "files/2015-05.jsonl.gz" - config_name: 2015-06 data_files: "files/2015-06.jsonl.gz" - config_name: 2015-07 data_files: "files/2015-07.jsonl.gz" - config_name: 2015-08 data_files: "files/2015-08.jsonl.gz" - config_name: 2015-09 data_files: "files/2015-09.jsonl.gz" - config_name: 2015-10 data_files: "files/2015-10.jsonl.gz" - config_name: 2015-11 data_files: "files/2015-11.jsonl.gz" - config_name: 2015-12 data_files: "files/2015-12.jsonl.gz" - 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config_name: 2017-06 data_files: "files/2017-06.jsonl.gz" - config_name: 2017-07 data_files: "files/2017-07.jsonl.gz" - config_name: 2017-08 data_files: "files/2017-08.jsonl.gz" - config_name: 2017-09 data_files: "files/2017-09.jsonl.gz" - config_name: 2017-10 data_files: "files/2017-10.jsonl.gz" - config_name: 2017-11 data_files: "files/2017-11.jsonl.gz" - config_name: 2017-12 data_files: "files/2017-12.jsonl.gz" - config_name: 2018-01 data_files: "files/2018-01.jsonl.gz" - config_name: 2018-02 data_files: "files/2018-02.jsonl.gz" - config_name: 2018-03 data_files: "files/2018-03.jsonl.gz" - config_name: 2018-04 data_files: "files/2018-04.jsonl.gz" - config_name: 2018-05 data_files: "files/2018-05.jsonl.gz" - config_name: 2018-06 data_files: "files/2018-06.jsonl.gz" - config_name: 2018-07 data_files: "files/2018-07.jsonl.gz" - config_name: 2018-08 data_files: "files/2018-08.jsonl.gz" - config_name: 2018-09 data_files: "files/2018-09.jsonl.gz" - config_name: 2018-10 data_files: "files/2018-10.jsonl.gz" - config_name: 2018-11 data_files: "files/2018-11.jsonl.gz" - config_name: 2018-12 data_files: "files/2018-12.jsonl.gz" - config_name: 2019-01 data_files: "files/2019-01.jsonl.gz" - config_name: 2019-02 data_files: "files/2019-02.jsonl.gz" - config_name: 2019-03 data_files: "files/2019-03.jsonl.gz" - config_name: 2019-04 data_files: "files/2019-04.jsonl.gz" - config_name: 2019-05 data_files: "files/2019-05.jsonl.gz" - config_name: 2019-06 data_files: "files/2019-06.jsonl.gz" - config_name: 2019-07 data_files: "files/2019-07.jsonl.gz" - config_name: 2019-08 data_files: "files/2019-08.jsonl.gz" - config_name: 2019-09 data_files: "files/2019-09.jsonl.gz" - config_name: 2019-10 data_files: "files/2019-10.jsonl.gz" - config_name: 2019-11 data_files: "files/2019-11.jsonl.gz" - config_name: 2019-12 data_files: "files/2019-12.jsonl.gz" - config_name: 2020-01 data_files: "files/2020-01.jsonl.gz" - config_name: 2020-02 data_files: "files/2020-02.jsonl.gz" - config_name: 2020-03 data_files: "files/2020-03.jsonl.gz" - config_name: 2020-04 data_files: "files/2020-04.jsonl.gz" - config_name: 2020-05 data_files: "files/2020-05.jsonl.gz" - config_name: 2020-06 data_files: "files/2020-06.jsonl.gz" - config_name: 2020-07 data_files: "files/2020-07.jsonl.gz" - config_name: 2020-08 data_files: "files/2020-08.jsonl.gz" - config_name: 2020-09 data_files: "files/2020-09.jsonl.gz" - config_name: 2020-10 data_files: "files/2020-10.jsonl.gz" - config_name: 2020-11 data_files: "files/2020-11.jsonl.gz" - config_name: 2020-12 data_files: "files/2020-12.jsonl.gz" - config_name: 2021-01 data_files: "files/2021-01.jsonl.gz" - config_name: 2021-02 data_files: "files/2021-02.jsonl.gz" - config_name: 2021-03 data_files: "files/2021-03.jsonl.gz" - config_name: 2021-04 data_files: "files/2021-04.jsonl.gz" - config_name: 2021-05 data_files: "files/2021-05.jsonl.gz" - config_name: 2021-06 data_files: "files/2021-06.jsonl.gz" - config_name: 2021-07 data_files: "files/2021-07.jsonl.gz" - config_name: 2021-08 data_files: "files/2021-08.jsonl.gz" - config_name: 2021-09 data_files: "files/2021-09.jsonl.gz" - config_name: 2021-10 data_files: "files/2021-10.jsonl.gz" - config_name: 2021-11 data_files: "files/2021-11.jsonl.gz" - config_name: 2021-12 data_files: "files/2021-12.jsonl.gz" - config_name: 2022-01 data_files: "files/2022-01.jsonl.gz" - config_name: 2022-02 data_files: "files/2022-02.jsonl.gz" - config_name: 2022-03 data_files: "files/2022-03.jsonl.gz" - config_name: 2022-04 data_files: "files/2022-04.jsonl.gz" - config_name: 2022-05 data_files: "files/2022-05.jsonl.gz" - config_name: 2022-06 data_files: "files/2022-06.jsonl.gz" - config_name: 2022-07 data_files: "files/2022-07.jsonl.gz" - config_name: 2022-08 data_files: "files/2022-08.jsonl.gz" - config_name: 2022-09 data_files: "files/2022-09.jsonl.gz" - config_name: 2022-10 data_files: "files/2022-10.jsonl.gz" - config_name: 2022-11 data_files: "files/2022-11.jsonl.gz" - config_name: 2022-12 data_files: "files/2022-12.jsonl.gz" - config_name: 2023-01 data_files: "files/2023-01.jsonl.gz" - config_name: 2023-02 data_files: "files/2023-02.jsonl.gz" - config_name: 2023-03 data_files: "files/2023-03.jsonl.gz" - config_name: 2023-04 data_files: "files/2023-04.jsonl.gz" - config_name: 2023-05 data_files: "files/2023-05.jsonl.gz" - config_name: 2023-06 data_files: "files/2023-06.jsonl.gz" - config_name: 2023-07 data_files: "files/2023-07.jsonl.gz" - config_name: 2023-08 data_files: "files/2023-08.jsonl.gz" - config_name: 2023-09 data_files: "files/2023-09.jsonl.gz" - config_name: 2023-10 data_files: "files/2023-10.jsonl.gz" - config_name: 2023-11 data_files: "files/2023-11.jsonl.gz" - config_name: 2023-12 data_files: "files/2023-12.jsonl.gz" --- # 🇪🇺 🏷️ EuroVoc dataset This dataset contains more that 3,700,000 documents in 39 languages with associated EuroVoc labels. ## What's Cellar ? Cellar is the common data repository of the Publications Office of the European Union. Digital publications and metadata are stored in and disseminated via Cellar, in order to be used by humans and machines. Aiming to transparently serve users, Cellar stores multilingual publications and metadata, it is open to all EU citizens and provides machine-readable data. https://op.europa.eu/fr/web/cellar ## Why was this dataset created ? "Extreme classification come with challenges of scalability due to large label spaces, data sparsity issues due to insufficient training samples." https://medium.com/datapy-ai/extreme-multi-label-classification-for-eurovoc-b51d74623820 ## How was dataset this created ? The source code is available, check `cellar.py` ## When this dataset was created ? 14 July 2023 ## What are the main characteristics of this dataset ? There are a total of 39 different languages present in this dataset, of which some are EU languages and some are not. As the following graph illustrates, most of the documents of the dataset are written in EU languages (English being the most present language in the dataset), and the non-EU languages are very poorly represented (for example Arabic, Japanese,...). Note that since the Irish language (`gle`) was granted full official and working status in the EU in 2022, there are very few documents in that language. Additionally, Croatian (`hrv`) is also less represented in the dataset as Croatia is the latest country to have joined the EU in 2013.  The lengths of the documents also varies depending on the language it is written in. The document lengths are quite variable, especially in English. There is therefore a quite large disparity in document lengths in this dataset. Note that this boxplot does not present the outliers, since the length of certain documents can contain up to 86 million characters. The red lines in the boxplot indicates the median length of the documents for each language.  We notice that the documents in Irish have a very wide variability in document lengths, due to the fact it has very few documents. Therefore, we present the same boxplot without the Irish language in order to visualize with more detail the document length distribution in the other languages.  ## How is the data structured ? An example of a sample of this dataset is the following : ```json { "title": "Commission information notice...", "date": "2023-09-29", "eurovoc_concepts": ["air transport", "intra-EU transport"], "url": "http://publications.europa.eu/resource/cellar/ec99987f-5e69-11ee-9220-01aa75ed71a1", "lang": "eng", "formats": ["fmx4", "pdfa2a", "xhtml"], "text": "To ensure ownership by the relevant actors,..." } ``` - `title` : title of the document - `date` : publication date of the document - `eurovoc_concepts` : list of the EuroVoc concepts related to this document - `url` : URL to access the document - `formats` : list of formats in which the original document is available - `text` : text content of the document ## Bibliography - Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2019. Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 78–87, Minneapolis, Minnesota. Association for Computational Linguistics. - I. Chalkidis, M. Fergadiotis, P. Malakasiotis and I. Androutsopoulos, Large-Scale Multi-Label Text Classification on EU Legislation. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, (short papers), 2019. - Andrei-Marius Avram, Vasile Pais, and Dan Ioan Tufis. 2021. PyEuroVoc: A Tool for Multilingual Legal Document Classification with EuroVoc Descriptors. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 92–101, Held Online. INCOMA Ltd.. - SHAHEEN, Zein, WOHLGENANNT, Gerhard, et FILTZ, Erwin. Large scale legal text classification using transformer models. arXiv preprint arXiv:2010.12871, 2020. ## Author(s) Sébastien Campion <sebastien.campion@europarl.europa.eu>

Fsoft-AIC/the-vault-function

Fsoft-AIC

--- language: - code - en multilinguality: - multiprogramming languages task_categories: - text-generation license: mit dataset_info: features: - name: identifier dtype: string - name: return_type dtype: string - name: repo dtype: string - name: path dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens dtype: string - name: original_docstring dtype: string - name: comment dtype: string - name: docstring_tokens dtype: string - name: docstring dtype: string - name: original_string dtype: string pretty_name: The Vault Function viewer: true --- ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Statistics](#dataset-statistics) - [Usage](#usage) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault) - **Paper:** [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156) - **Contact:** support.ailab@fpt.com - **Website:** https://www.fpt-aicenter.com/ai-residency/ <p align="center"> <img src="https://raw.githubusercontent.com/FSoft-AI4Code/TheVault/main/assets/the-vault-4-logo-png.png" width="300px" alt="logo"> </p> <div align="center"> # The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation </div> ## Dataset Summary The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset. We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility. ## Supported Tasks The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as *code summarization*, *text-to-code generation* and *code search*. ## Languages The natural language text (docstring) is in English. 10 programming languages are supported in The Vault: `Python`, `Java`, `JavaScript`, `PHP`, `C`, `C#`, `C++`, `Go`, `Ruby`, `Rust` ## Dataset Structure ### Data Instances ``` { "hexsha": "5c47f0b4c173a8fd03e4e633d9b3dd8211e67ad0", "repo": "neumanna94/beepboop", "path": "js/scripts.js", "license": [ "MIT" ], "language": "JavaScript", "identifier": "beepBoopSelector", "return_type": "<not_specific>", "original_string": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}", "original_docstring": "//Determines what beepBoop function to use", "docstring": "Determines what beepBoop function to use", "docstring_tokens": [ "Determines", "what", "beepBoop", "function", "to", "use" ], "code": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}", "code_tokens": [ "function", "beepBoopSelector", "(", "inputString", ",", "bbFunction", ")", "{", "if", "(", "bbFunction", "==", "1", ")", "{", "return", "beepBoop", "(", "inputString", ")", ";", "}", "else", "if", "(", "bbFunction", "==", "2", ")", "{", "return", "beepBoop2", "(", "inputString", ")", ";", "}", "else", "if", "(", "bbFunction", "==", "3", ")", "{", "return", "beepBoop3", "(", "inputString", ")", ";", "}", "else", "{", "}", "}" ], "short_docstring": "Determines what beepBoop function to use", "short_docstring_tokens": [ "Determines", "what", "beepBoop", "function", "to", "use" ], "comment": [], "parameters": [ { "param": "inputString", "type": null }, { "param": "bbFunction", "type": null } ], "docstring_params": { "returns": [], "raises": [], "params": [ { "identifier": "inputString", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "bbFunction", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] } } ``` ### Data Fields Data fields for function level: - **hexsha** (string): the unique git hash of file - **repo** (string): the owner/repo - **path** (string): the full path to the original file - **license** (list): licenses in the repo - **language** (string): the programming language - **identifier** (string): the function or method name - **return_type** (string): the type returned by the function - **original_string** (string): original version of function/class node - **original_docstring** (string): the raw string before tokenization or parsing - **code** (string): the part of the original that is code - **code_tokens** (list): tokenized version of `code` - **short_docstring** (string): short, brief summarization (first line of the docstring) - **short_docstring_tokens** (list): tokenized version of `short_docstring - **docstring** (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc) - **docstring_tokens** (list): tokenized version of docstring - **comment** (list): list of comments (line) inside the function/class - **parameters** (list): List of parameters and its type (type can be None) - **docstring_params** (dict): Dictionary of the parsed information from docstring See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) for more details and examples. ### Data Splits In this repo, The Vault is divided into 5 subsets, where three training versions are split based on size of the full training set, and the remains are validation set and test set (approximate 20,000 samples in each). The statistic for languages in each split set is illustrated in the following section. Before split, the dataset is deduplicated. There are 3 versions of training set that are small (5%), medium (20%) and large (100%). ## Dataset Statistics - Compare to other benchmarks | Dataset | #Language | #Code-text pair | |:--------------------------|----------:|-----------------:| | PyMT5 | 1 | ≈ 7,700,000 | | CoDesc | 1 | 4,211,516 | | CodeSearchNet | 6 | 2,326,976 | | CodeSearchNet (CodeXGLUE) | 6 | 1,005,474 | | Deepcom | 1 | 424,028 | | CONCODE | 1 | 2,184,310 | | Funcom | 1 | 2,149,121 | | CodeT5 | 8 | 3,158,313 | | **The Vault** | **10** | **34,098,775** | - Statistic for split sets | | train/small | train/medium | train/full | validation | test | total | |:-----------|------------:|-------------:|-----------:|-----------:|-------:|--------------:| |Python | 370,657 | 1,952,110 | 7,772,647 | 30,992 | 21,652 | 7,825,291 | |Java | 351,213 | 1,612,366 | 6,629,193 | 22,677 | 15,552 | 6,667,422 | |JavaScript | 82,931 | 404,729 | 1,640,416 | 22,044 | 21,108 | 1,683,568 | |PHP | 236,638 | 1,155,476 | 4,656,371 | 21,375 | 19,010 | 4,696,756 | |C | 105,978 | 381,207 | 1,639,319 | 27,525 | 19,122 | 1,685,966 | |C# | 141,090 | 783,166 | 3,305,891 | 24,787 | 19,638 | 3,350,316 | |C++ | 87,420 | 410,907 | 1,671,268 | 20,011 | 18,169 | 1,709,448 | |Go | 267,535 | 1,319,547 | 5,109,020 | 19,102 | 25,314 | 5,153,436 | |Ruby | 23,921 | 112,574 | 424,339 | 17,338 | 19,908 | 461,585 | |Rust | 35,367 | 224,015 | 825,130 | 16,716 | 23,141 | 864,987 | |TOTAL | 1,702,750 | 8,356,097 |33,673,594 |222,567 |202,614 |**34,098,775** | ## Usage You can load The Vault dataset using datasets library: ```pip install datasets``` ```python from datasets import load_dataset # Load full function level dataset (34M samples) dataset = load_dataset("Fsoft-AIC/the-vault-function") # Load function level train/validation/test set dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"]) # Load "small" (or "medium", "full") version of function level training set dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train/small"]) # specific language (e.g. Python) dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"], languages=['python']) # dataset streaming data = load_dataset("Fsoft-AIC/the-vault-function", split_set= ["train"], streaming= True) for sample in iter(data['train']): print(sample) ``` A back up dataset can be downloaded in azure storage. See [Download The Vault from Azure blob storage](https://github.com/FSoft-AI4Code/TheVault#download-via-link). ## Additional information ### Licensing Information MIT License ### Citation Information ``` @article{manh2023vault, title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation}, author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ}, journal={arXiv preprint arXiv:2305.06156}, year={2023} } ``` ### Contributions This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code).

opencsg/chinese-fineweb-edu-v2

opencsg

--- language: - zh pipeline_tag: text-generation license: apache-2.0 task_categories: - text-generation size_categories: - 10B<n<100B --- # **Chinese Fineweb Edu Dataset V2** [[中文]](#chinese) [[English]](#english) <a id="english"></a> <p align="center"> <img width="600px" alt="OpenCSG" src="./logo.png"> </p> <p align="center"><a href="https://opencsg.com/models">[OpenCSG Community]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a> <a href="https://twitter.com/OpenCsg">[Twitter]</a> </p> </div> <b>Chinese Fineweb Edu Dataset V2</b> is a comprehensive upgrade of the original Chinese Fineweb Edu, designed and optimized for natural language processing (NLP) tasks in the education sector. This high-quality Chinese pretraining dataset has undergone significant improvements and expansions, aimed at providing researchers and developers with more diverse and broadly applicable educational corpus resources. With a dataset size of 188 million entries (approximately 420 billion tokens), Fineweb Edu v2 not only increases the volume but also optimizes the data filtering methods and scoring models to ensure effectiveness and practicality in the educational domain. ## Enhanced Scoring Model In the Chinese Fineweb edu v2 version, the data selection scoring model has undergone a significant upgrade, utilizing the larger and more powerful OpenCSG csg-wukong-enterprise V2 model. The training data for this model has been increased to 1 million entries, covering a variety of text types such as books, news, blogs, and 25% English data. Compared to the previous version, the csg-wukong-enterprise V2 model boasts a larger parameter count and deeper semantic understanding, excelling particularly in Chinese text comprehension and processing. The model not only performs more detailed analysis of text structure and content but also captures deeper semantic and emotional nuances embedded in the language. This improvement means that during the data selection process, the model can more accurately assess the educational value, writing quality, and practical application of the text. Especially when dealing with high-demand texts in education and technology, the Fineweb2 scoring model ensures high quality and consistency in the selection results. This advancement significantly enhances the reliability of the data selection, providing stronger support for subsequent model training. # Prompt Improvements During the construction of the Fineweb2 dataset, the data filtering process was particularly crucial. To ensure that only text with real educational value and practicality was selected, we carefully optimized the design of the prompts used for data filtering. The new prompts more accurately evaluate the educational value, writing quality, and practicality of web content, refining the filtering process for better precision. The new prompts clearly define scoring standards for educational content and also set expectations for writing style, coherence, and thematic depth. The specific scoring criteria are as follows: Below is an excerpt from a web page. Please use the following 5-point rating system to assess the writing quality, educational value, and practicality of the webpage: ```Plain 以下是一段网页内容摘录。请使用以下5分制评分系统来评估该网页的写作水平、教育价值和实用性: 0分：如果网页没有提供任何教育价值,完全由无关信息(如广告、宣传材料、少儿不宜内容)组成。 1分：如果网页提供了一些可能有教育价值的基本信息,但包含较多的无关或非学术内容(如广告和宣传材料)。 2分：如果网页涉及某些与教育相关的元素,但与教育标准不太吻合。它可能将教育内容与非教育材料混杂,对潜在的有用的主题进行浅显概述,或以不连贯的写作风格呈现信息。 3分：如果网页适合教育使用,并介绍了与某些学校课程中可能学到的关键概念，或对个人发展有用的实用信息。它的内容连贯但可能不全面,或包含一些无关信息。它可能类似于教科书的一小段节选,可以学习但有明显局限,如涉及过于复杂的概念、过于具体的不重要事件。 4分：如果网页与教育高度相关，对个人学习发展有益,表现出清晰一致的写作风格。它可能类似于教科书的一个章节或教程,提供大量教育内容,极少包含无关信息,且概念对学生来说不会过于深奥。内容连贯、重点突出,对结构化学习有价值。 5分：如果网页摘录在教育价值上表现极好,完全适合小学、中学或大学教学或专业人士学习。它遵循详细的推理过程,写作风格易于理解,对主题提供深刻而全面的见解,不包含任何非教育性或无实用意义内容。 网页内容摘录: {} 在审查这段网页摘录后：请简要地为您的评分进行合理的解释，最多不超过100字，最后以“教育得分：<分数>”的格式结束。请根据所列出的标准系统地赋予分数。 ``` After reviewing this webpage excerpt, briefly explain the reasoning behind your score in no more than 100 words, ending with the format: "Educational Score: <score>." Please assign the score systematically based on the listed criteria. After merging all data, the sample score distribution was as follows: texts with scores of 3 and above were selected, totaling 188 million entries (about 420 billion tokens). These data, which are not only extensive but also carefully filtered and deduplicated, ensure the high quality and uniqueness of the dataset. These scored data will be used to train large-scale language models within the Fineweb2 dataset, helping them achieve superior performance in various tasks. <p align="center"> <img width="900px" alt="experiment" src="./distribution.png"> </p> # Expanded Data Sources The range of data sources for the Fineweb2 dataset has been further extended. Compared to the original Fineweb, Fineweb2 introduces massive datasets from various fields and sources, including Industry2, CCI3, michao, wanjuan1.0, wudao, and ChineseWebText. These datasets cover a broader range of industries and domains, enhancing the diversity and applicability of the dataset. <p align="center"> <img width="900px" alt="experiment" src="./datasource.png"> </p> In conclusion, the Fineweb2 dataset not only surpasses its predecessor in scale but also significantly improves the quality of data, content diversity, and precision of filtering. This lays a solid foundation for the further development of Chinese NLP applications and provides researchers with richer resources to explore and optimize various model training methods. **We warmly invite developers and researchers interested in this field to follow and engage with the community, working together to advance the technology. Stay tuned for the open-source release of the dataset!** ## License Agreement Usage of the Chinese Fineweb Edu dataset requires adherence to the OpenCSG Community License. The Chinese Fineweb Edu dataset supports commercial use. If you plan to use the OpenCSG model or its derivatives for commercial purposes, you must comply with the terms and conditions outlined in the OpenCSG Community License as well as the Apache 2.0 License. For commercial use, please send an email to lorraineg@opencsg.com and obtain permission. <a id="chinese"></a> <p> </p> # Chinese Fineweb Edu V2数据集介绍 <p align="center"> <img width="600px" alt="OpenCSG" src="./logo.png"> </p> <p align="center"><a href="https://opencsg.com/models">[OpenCSG 社区]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[微信]</a> <a href="https://twitter.com/OpenCsg">[推特]</a> </p> </div> <b>Chinese Fineweb Edu v2</b> 是Chinese Fineweb Edu的全新升级版，专为教育领域的自然语言处理（NLP）任务设计和优化的高质量中文预训练数据集。该数据集在前一版本的基础上进行了大规模的改进和扩展，致力于为研究人员和开发者提供更加多样化、广泛适用的教育类语料资源。Fineweb Edu v2 不仅数据量达到**188M条数据**，约**420B tokens**，还优化了数据的筛选方式和打分模型，以确保其在教育领域的有效性和实用性。 ## 更强的打分模型 在Chinese Fineweb edu v2版本中，数据筛选的打分模型进行了重大升级，采用了规模更大、性能更强的OpenCSG csg-wukong-enterprise V2模型。该模型的训练数据增加到100万条，涵盖了多种类型的文本，如书籍、新闻、博客，以及25%的英文数据。相比于上一版本的打分模型，csg-wukong-enterprise V2拥有更大的参数量和更深层次的语义理解能力，特别是在中文文本理解和处理方面表现出色。该模型不仅能对文本的结构、内容进行更细致的分析，还能有效捕捉隐藏在语言中的深层次语义和情感信息。 这种提升意味着在数据筛选过程中，模型能够更加精准地评估文本的教育价值、写作质量以及其对实际应用的价值。尤其是在处理教育类、技术类等高要求的文本时，Fineweb2的打分模型确保了筛选结果的高质量和高一致性。这一进步显著提高了数据筛选的可靠性，为后续的模型训练提供了更有力的保障。 ## Prompt改进 在Fineweb2数据集的构建过程中，数据筛选环节尤为重要。为确保筛选出真正具有教育价值和实用性的文本，我们对数据筛选的**Prompt设计**进行了细致的优化。新的Prompt能够更加准确地评估网页内容的**教育价值、写作水平和实用性**，从而使筛选过程更加细化和精确。 新的Prompt不仅明确了对教育内容的评分标准，还对文本的写作风格、连贯性以及主题深度提出了要求。具体评分标准如下： ```Plain 以下是一段网页内容摘录。请使用以下5分制评分系统来评估该网页的写作水平、教育价值和实用性: 0分：如果网页没有提供任何教育价值,完全由无关信息(如广告、宣传材料、少儿不宜内容)组成。 1分：如果网页提供了一些可能有教育价值的基本信息,但包含较多的无关或非学术内容(如广告和宣传材料)。 2分：如果网页涉及某些与教育相关的元素,但与教育标准不太吻合。它可能将教育内容与非教育材料混杂,对潜在的有用的主题进行浅显概述,或以不连贯的写作风格呈现信息。 3分：如果网页适合教育使用,并介绍了与某些学校课程中可能学到的关键概念，或对个人发展有用的实用信息。它的内容连贯但可能不全面,或包含一些无关信息。它可能类似于教科书的一小段节选,可以学习但有明显局限,如涉及过于复杂的概念、过于具体的不重要事件。 4分：如果网页与教育高度相关，对个人学习发展有益,表现出清晰一致的写作风格。它可能类似于教科书的一个章节或教程,提供大量教育内容,极少包含无关信息,且概念对学生来说不会过于深奥。内容连贯、重点突出,对结构化学习有价值。 5分：如果网页摘录在教育价值上表现极好,完全适合小学、中学或大学教学或专业人士学习。它遵循详细的推理过程,写作风格易于理解,对主题提供深刻而全面的见解,不包含任何非教育性或无实用意义内容。 网页内容摘录: {} 在审查这段网页摘录后：请简要地为您的评分进行合理的解释，最多不超过100字，最后以“教育得分：<分数>”的格式结束。请根据所列出的标准系统地赋予分数。 ``` 所有数据集合并后，样本的得分分布如下，通过csg-wukong-enterprise V2模型对这些数据进行评分后，最终选取了**3分以上**的文本，总计达到**188M条数据**，约**420B tokens**。这些数据不仅数量庞大，且经过了严格的筛选和去重处理，确保了数据集的**高质量和高独特性**。这些经过打分的数据将在Fineweb2的数据集中用于训练大规模语言模型，帮助其在各类任务中实现更高的性能表现。 <p align="center"> <img width="900px" alt="experiment" src="./distribution.png"> </p> ## 数据筛选范围扩大 Fineweb2数据集的数据来源进一步扩展。相较于初代Fineweb，Fineweb2引入了来自多个不同领域和来源的海量数据，新增了**Industry2、CCI3、michao、wanjuan1.0、wudao和ChineseWebText**等高质量数据集。这些数据集覆盖了更广泛的行业和领域，增加了数据集的多样性和广泛适用性。 <p align="center"> <img width="900px" alt="experiment" src="./datasource.png"> </p> 最终，Fineweb2的数据集不仅在规模上远超前作，还在数据的质量、内容的多样性、筛选的精确度等方面有了显著提升。这为未来中文NLP应用的进一步发展打下了坚实的基础，同时也为研究人员提供了更加丰富的资源去探索和优化各种模型训练方法。 **我们诚邀对这一领域感兴趣的开发者和研究者关注和联系社区，共同推动技术的进步。敬请期待数据集的开源发布！** ## 许可协议 使用 Chinese Fineweb Edu V2数据集需要遵循 OpenCSG 社区许可证。Chinese Fineweb Edu V2数据集支持商业用途。如果您计划将 OpenCSG 模型或其衍生产品用于商业目的，您必须遵守 OpenCSG 社区许可证以及 Apache 2.0 许可证中的条款和条件。如用于商业用途，需发送邮件至 lorraineg@opencsg.com，并获得许可。

lmms-lab/LLaVA-OneVision-Data

lmms-lab

--- language: - en - zh license: apache-2.0 pretty_name: llava-onevision-data dataset_info: - config_name: CLEVR-Math(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 791346970 num_examples: 5280 download_size: 441208499 dataset_size: 791346970 - config_name: FigureQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 463326576.625 num_examples: 17587 download_size: 258197193 dataset_size: 463326576.625 - config_name: GEOS(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1503641 num_examples: 498 download_size: 684471 dataset_size: 1503641 - config_name: GeoQA+(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 53579705.75 num_examples: 17162 download_size: 33480538 dataset_size: 53579705.75 - config_name: Geometry3K(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 218085473.5 num_examples: 9724 download_size: 125914780 dataset_size: 218085473.5 - config_name: IconQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 208430568.375 num_examples: 22589 download_size: 117222488 dataset_size: 208430568.375 - config_name: MapQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 384120915.875 num_examples: 5225 download_size: 215768443 dataset_size: 384120915.875 - config_name: PMC-VQA(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 571444866.5 num_examples: 35948 download_size: 326541003 dataset_size: 571444866.5 - config_name: Super-CLEVR(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2795082410.75 num_examples: 8642 download_size: 1580301917 dataset_size: 2795082410.75 - config_name: TabMWP(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 307726997.5 num_examples: 22452 download_size: 173938487 dataset_size: 307726997.5 - config_name: UniGeo(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 38296693.375 num_examples: 11949 download_size: 24170743 dataset_size: 38296693.375 - config_name: VisualWebInstruct(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 36317112275.0 num_examples: 263584 download_size: 36239916454 dataset_size: 36317112275.0 - config_name: VizWiz(MathV360K) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1170333936.5 num_examples: 6604 download_size: 660752297 dataset_size: 1170333936.5 - config_name: ai2d(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 438572782.375 num_examples: 2429 download_size: 437348514 dataset_size: 438572782.375 - config_name: ai2d(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 866076731 num_examples: 4864 download_size: 860306578 dataset_size: 866076731 - config_name: ai2d(internvl) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1832787249.625 num_examples: 12403 download_size: 527493895 dataset_size: 1832787249.625 - config_name: allava_instruct_laion4v features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5981767621.25 num_examples: 49990 download_size: 5873046236 dataset_size: 5981767621.25 - config_name: allava_instruct_vflan4v features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2680974558.25 num_examples: 19990 download_size: 2670088751 dataset_size: 2680974558.25 - config_name: aokvqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6896420844.25 num_examples: 16534 download_size: 6894236970 dataset_size: 6896420844.25 - config_name: chart2text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1145458729.5 num_examples: 26956 download_size: 1123681047 dataset_size: 1145458729.5 - config_name: chartqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 815335215.5 num_examples: 18260 download_size: 803084541 dataset_size: 815335215.5 - config_name: chrome_writting features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 44422597.875 num_examples: 8825 download_size: 39611257 dataset_size: 44422597.875 - config_name: clevr(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 10528974543.625 num_examples: 69995 download_size: 10460536445 dataset_size: 10528974543.625 - config_name: diagram_image_to_text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 18858266 num_examples: 295 download_size: 18659115 dataset_size: 18858266 - config_name: dvqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4487270615.625 num_examples: 199995 download_size: 4277056467 dataset_size: 4487270615.625 - config_name: figureqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2351194509.625 num_examples: 99995 download_size: 2222640639 dataset_size: 2351194509.625 - config_name: geo170k(align) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 204236256.75 num_examples: 60242 download_size: 58185410 dataset_size: 204236256.75 - config_name: geo170k(qa) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 266040519.125 num_examples: 67823 download_size: 160022430 dataset_size: 266040519.125 - config_name: geo3k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 42634333.625 num_examples: 2091 download_size: 41097851 dataset_size: 42634333.625 - config_name: geomverse(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2263893609.75 num_examples: 9298 download_size: 2211726352 dataset_size: 2263893609.75 - config_name: hateful_memes(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 3057252325.125 num_examples: 8495 download_size: 3055839880 dataset_size: 3057252325.125 - config_name: hitab(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 161706881.125 num_examples: 2495 download_size: 157871287 dataset_size: 161706881.125 - config_name: hme100k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 273229915.5 num_examples: 74492 download_size: 241005430 dataset_size: 273229915.5 - config_name: iam(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1131633206.75 num_examples: 5658 download_size: 1128371221 dataset_size: 1131633206.75 - config_name: iconqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 331284932.25 num_examples: 27302 download_size: 327005220 dataset_size: 331284932.25 - config_name: iiit5k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 21821437.25 num_examples: 1990 download_size: 21623116 dataset_size: 21821437.25 - config_name: image_textualization(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5218283253.375 num_examples: 99573 download_size: 5164176816 dataset_size: 5218283253.375 - config_name: infographic(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 713657496.25 num_examples: 1982 download_size: 656276080 dataset_size: 713657496.25 - config_name: infographic_vqa features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1528953078.75 num_examples: 4394 download_size: 1419340319 dataset_size: 1528953078.75 - config_name: infographic_vqa_llava_format features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1765315696.875 num_examples: 2113 download_size: 1764548536 dataset_size: 1765315696.875 - config_name: intergps(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 24973395.625 num_examples: 1275 download_size: 24736545 dataset_size: 24973395.625 - config_name: k12_printing features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1205153118.5 num_examples: 256636 download_size: 1108572712 dataset_size: 1205153118.5 - config_name: llavar_gpt4_20k features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 633833350.25 num_examples: 19790 download_size: 625365542 dataset_size: 633833350.25 - config_name: lrv_chart features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 99338686 num_examples: 1776 download_size: 97979446 dataset_size: 99338686 - config_name: lrv_normal(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 422589381.75 num_examples: 10490 download_size: 406958773 dataset_size: 422589381.75 - config_name: magpie_pro(l3_80b_mt) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1657129141 num_examples: 299988 download_size: 885893066 dataset_size: 1657129141 - config_name: magpie_pro(l3_80b_st) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1033666690 num_examples: 299990 download_size: 562771564 dataset_size: 1033666690 - config_name: magpie_pro(qwen2_72b_st) features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 703489344 num_examples: 299982 download_size: 361433408 dataset_size: 703489344 - config_name: mapqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 3355751195.5 num_examples: 37412 download_size: 3305639218 dataset_size: 3355751195.5 - config_name: mathqa features: - name: id dtype: string - name: image dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 18318538 num_examples: 29827 download_size: 7857130 dataset_size: 18318538 - config_name: mavis_math_metagen features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2304025372.5 num_examples: 87348 download_size: 322776224 dataset_size: 2304025372.5 - config_name: mavis_math_rule_geo features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 14313211512.25 num_examples: 99990 download_size: 5841283073 dataset_size: 14313211512.25 - config_name: multihiertt(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 300319803.25 num_examples: 7614 download_size: 295638314 dataset_size: 300319803.25 - config_name: orand_car_a features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 23602442.125 num_examples: 1999 download_size: 23333412 dataset_size: 23602442.125 - config_name: raven(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 1706160514.625 num_examples: 41995 download_size: 1693150088 dataset_size: 1706160514.625 - config_name: rendered_text(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11082594894.625 num_examples: 9995 download_size: 11081962044 dataset_size: 11082594894.625 - config_name: robut_sqa(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 685580779.375 num_examples: 8509 download_size: 678666263 dataset_size: 685580779.375 - config_name: robut_wikisql(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6200499653 num_examples: 74984 download_size: 6168399217 dataset_size: 6200499653 - config_name: robut_wtq(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4091776188.875 num_examples: 38241 download_size: 4062777449 dataset_size: 4091776188.875 - config_name: scienceqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 286843125.625 num_examples: 4971 download_size: 282896809 dataset_size: 286843125.625 - config_name: scienceqa(nona_context) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2111029055 num_examples: 19208 download_size: 2053942726 dataset_size: 2111029055 - config_name: screen2words(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 7977502095.375 num_examples: 15725 download_size: 7962327904 dataset_size: 7977502095.375 - config_name: sharegpt4o features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 6968025789.5 num_examples: 57284 download_size: 6772195470 dataset_size: 6968025789.5 - config_name: sharegpt4v(coco) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2620153362.875 num_examples: 50017 download_size: 2595583499 dataset_size: 2620153362.875 - config_name: sharegpt4v(knowledge) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 372100773.5 num_examples: 1988 download_size: 369799318 dataset_size: 372100773.5 - config_name: sharegpt4v(llava) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 781795487.25 num_examples: 29990 download_size: 400344187 dataset_size: 781795487.25 - config_name: sharegpt4v(sam) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4437405218.25 num_examples: 8990 download_size: 4428597081 dataset_size: 4437405218.25 - config_name: sroie features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 117810195 num_examples: 33616 download_size: 103647636 dataset_size: 117810195 - config_name: st_vqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 5771194098.75 num_examples: 17242 download_size: 5768888141 dataset_size: 5771194098.75 - config_name: tabmwp(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 311192518.375 num_examples: 22717 download_size: 306092255 dataset_size: 311192518.375 - config_name: tallyqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 35998988065.625 num_examples: 98675 download_size: 35982430394 dataset_size: 35998988065.625 - config_name: textcaps features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2222268476.25 num_examples: 21942 download_size: 2217838132 dataset_size: 2222268476.25 - config_name: textocr(gpt4v) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2581655353 num_examples: 25104 download_size: 2574418106 dataset_size: 2581655353 - config_name: tqa(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 331203026.25 num_examples: 27302 download_size: 326999466 dataset_size: 331203026.25 - config_name: ureader_cap features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 9269857109.75 num_examples: 91434 download_size: 2292099971 dataset_size: 9269857109.75 - config_name: ureader_ie features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11871457209.75 num_examples: 17322 download_size: 1999083115 dataset_size: 11871457209.75 - config_name: vision_flan(filtered) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 24847242604.5 num_examples: 186060 download_size: 24750561877 dataset_size: 24847242604.5 - config_name: vistext(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 550187184.5 num_examples: 9964 download_size: 452795103 dataset_size: 550187184.5 - config_name: visual7w(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 4451436523.875 num_examples: 14361 download_size: 4441971985 dataset_size: 4451436523.875 - config_name: visualmrc(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 2938154124.25 num_examples: 3022 download_size: 2909296079 dataset_size: 2938154124.25 - config_name: vqarad(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 95533417 num_examples: 308 download_size: 95410398 dataset_size: 95533417 - config_name: vsr(cauldron,llava_format) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 891981646 num_examples: 2152 download_size: 891572866 dataset_size: 891981646 - config_name: websight(cauldron) features: - name: id dtype: string - name: image dtype: image - name: conversations list: - name: from dtype: string - name: value dtype: string - name: data_source dtype: string splits: - name: train num_bytes: 11209715828.625 num_examples: 9995 download_size: 11144460985 dataset_size: 11209715828.625 configs: - config_name: CLEVR-Math(MathV360K) data_files: - split: train path: CLEVR-Math(MathV360K)/train-* - config_name: FigureQA(MathV360K) data_files: - split: train path: FigureQA(MathV360K)/train-* - config_name: GEOS(MathV360K) data_files: - split: train path: GEOS(MathV360K)/train-* - config_name: GeoQA+(MathV360K) data_files: - split: train path: GeoQA+(MathV360K)/train-* - config_name: Geometry3K(MathV360K) data_files: - split: train path: Geometry3K(MathV360K)/train-* - config_name: IconQA(MathV360K) data_files: - split: train path: IconQA(MathV360K)/train-* - config_name: MapQA(MathV360K) data_files: - split: train path: MapQA(MathV360K)/train-* - config_name: PMC-VQA(MathV360K) data_files: - split: train path: PMC-VQA(MathV360K)/train-* - config_name: Super-CLEVR(MathV360K) data_files: - split: train path: Super-CLEVR(MathV360K)/train-* - config_name: TabMWP(MathV360K) data_files: - split: train path: TabMWP(MathV360K)/train-* - config_name: UniGeo(MathV360K) data_files: - split: train path: UniGeo(MathV360K)/train-* - config_name: VisualWebInstruct(filtered) data_files: - split: train path: VisualWebInstruct(filtered)/train-* - config_name: VizWiz(MathV360K) data_files: - split: train path: VizWiz(MathV360K)/train-* - config_name: ai2d(cauldron,llava_format) data_files: - split: train path: ai2d(cauldron,llava_format)/train-* - config_name: ai2d(gpt4v) data_files: - split: train path: ai2d(gpt4v)/train-* - config_name: ai2d(internvl) data_files: - split: train path: ai2d(internvl)/train-* - config_name: allava_instruct_laion4v data_files: - split: train path: allava_instruct_laion4v/train-* - config_name: allava_instruct_vflan4v data_files: - split: train path: allava_instruct_vflan4v/train-* - config_name: aokvqa(cauldron,llava_format) data_files: - split: train path: aokvqa(cauldron,llava_format)/train-* - config_name: chart2text(cauldron) data_files: - split: train path: chart2text(cauldron)/train-* - config_name: chartqa(cauldron,llava_format) data_files: - split: train path: chartqa(cauldron,llava_format)/train-* - config_name: chrome_writting data_files: - split: train path: chrome_writting/train-* - config_name: clevr(cauldron,llava_format) data_files: - split: train path: clevr(cauldron,llava_format)/train-* - config_name: diagram_image_to_text(cauldron) data_files: - split: train path: diagram_image_to_text(cauldron)/train-* - config_name: dvqa(cauldron,llava_format) data_files: - split: train path: dvqa(cauldron,llava_format)/train-* - config_name: figureqa(cauldron,llava_format) data_files: - split: train path: figureqa(cauldron,llava_format)/train-* - config_name: geo170k(align) data_files: - split: train path: geo170k(align)/train-* - config_name: geo170k(qa) data_files: - split: train path: geo170k(qa)/train-* - config_name: geo3k data_files: - split: train path: geo3k/train-* - config_name: geomverse(cauldron) data_files: - split: train path: geomverse(cauldron)/train-* - config_name: hateful_memes(cauldron,llava_format) data_files: - split: train path: hateful_memes(cauldron,llava_format)/train-* - config_name: hitab(cauldron,llava_format) data_files: - split: train path: hitab(cauldron,llava_format)/train-* - config_name: hme100k data_files: - split: train path: hme100k/train-* - config_name: iam(cauldron) data_files: - split: train path: iam(cauldron)/train-* - config_name: iconqa(cauldron,llava_format) data_files: - split: train path: iconqa(cauldron,llava_format)/train-* - config_name: iiit5k data_files: - split: train path: iiit5k/train-* - config_name: image_textualization(filtered) data_files: - split: train path: image_textualization(filtered)/train-* - config_name: infographic(gpt4v) data_files: - split: train path: infographic(gpt4v)/train-* - config_name: infographic_vqa data_files: - split: train path: infographic_vqa/train-* - config_name: infographic_vqa_llava_format data_files: - split: train path: infographic_vqa_llava_format/train-* - config_name: intergps(cauldron,llava_format) data_files: - split: train path: intergps(cauldron,llava_format)/train-* - config_name: k12_printing data_files: - split: train path: k12_printing/train-* - config_name: llavar_gpt4_20k data_files: - split: train path: llavar_gpt4_20k/train-* - config_name: lrv_chart data_files: - split: train path: lrv_chart/train-* - config_name: lrv_normal(filtered) data_files: - split: train path: lrv_normal(filtered)/train-* - config_name: magpie_pro(l3_80b_mt) data_files: - split: train path: magpie_pro(l3_80b_mt)/train-* - config_name: magpie_pro(l3_80b_st) data_files: - split: train path: magpie_pro(l3_80b_st)/train-* - config_name: magpie_pro(qwen2_72b_st) data_files: - split: train path: magpie_pro(qwen2_72b_st)/train-* - config_name: mapqa(cauldron,llava_format) data_files: - split: train path: mapqa(cauldron,llava_format)/train-* - config_name: mathqa data_files: - split: train path: mathqa/train-* - config_name: mavis_math_metagen data_files: - split: train path: mavis_math_metagen/train-* - config_name: mavis_math_rule_geo data_files: - split: train path: mavis_math_rule_geo/train-* - config_name: multihiertt(cauldron) data_files: - split: train path: multihiertt(cauldron)/train-* - config_name: orand_car_a data_files: - split: train path: orand_car_a/train-* - config_name: raven(cauldron) data_files: - split: train path: raven(cauldron)/train-* - config_name: rendered_text(cauldron) data_files: - split: train path: rendered_text(cauldron)/train-* - config_name: robut_sqa(cauldron) data_files: - split: train path: robut_sqa(cauldron)/train-* - config_name: robut_wikisql(cauldron) data_files: - split: train path: robut_wikisql(cauldron)/train-* - config_name: robut_wtq(cauldron,llava_format) data_files: - split: train path: robut_wtq(cauldron,llava_format)/train-* - config_name: scienceqa(cauldron,llava_format) data_files: - split: train path: scienceqa(cauldron,llava_format)/train-* - config_name: scienceqa(nona_context) data_files: - split: train path: scienceqa(nona_context)/train-* - config_name: screen2words(cauldron) data_files: - split: train path: screen2words(cauldron)/train-* - config_name: sharegpt4o data_files: - split: train path: sharegpt4o/train-* - config_name: sharegpt4v(coco) data_files: - split: train path: sharegpt4v(coco)/train-* - config_name: sharegpt4v(knowledge) data_files: - split: train path: sharegpt4v(knowledge)/train-* - config_name: sharegpt4v(llava) data_files: - split: train path: sharegpt4v(llava)/train-* - config_name: sharegpt4v(sam) data_files: - split: train path: sharegpt4v(sam)/train-* - config_name: sroie data_files: - split: train path: sroie/train-* - config_name: st_vqa(cauldron,llava_format) data_files: - split: train path: st_vqa(cauldron,llava_format)/train-* - config_name: tabmwp(cauldron) data_files: - split: train path: tabmwp(cauldron)/train-* - config_name: tallyqa(cauldron,llava_format) data_files: - split: train path: tallyqa(cauldron,llava_format)/train-* - config_name: textcaps data_files: - split: train path: textcaps/train-* - config_name: textocr(gpt4v) data_files: - split: train path: textocr(gpt4v)/train-* - config_name: tqa(cauldron,llava_format) data_files: - split: train path: tqa(cauldron,llava_format)/train-* - config_name: ureader_cap data_files: - split: train path: ureader_cap/train-* - config_name: ureader_ie data_files: - split: train path: ureader_ie/train-* - config_name: vision_flan(filtered) data_files: - split: train path: vision_flan(filtered)/train-* - config_name: vistext(cauldron) data_files: - split: train path: vistext(cauldron)/train-* - config_name: visual7w(cauldron,llava_format) data_files: - split: train path: visual7w(cauldron,llava_format)/train-* - config_name: visualmrc(cauldron) data_files: - split: train path: visualmrc(cauldron)/train-* - config_name: vqarad(cauldron,llava_format) data_files: - split: train path: vqarad(cauldron,llava_format)/train-* - config_name: vsr(cauldron,llava_format) data_files: - split: train path: vsr(cauldron,llava_format)/train-* - config_name: websight(cauldron) data_files: - split: train path: websight(cauldron)/train-* --- # Dataset Card for LLaVA-OneVision **[2024-09-01]: Uploaded VisualWebInstruct(filtered), it's used in OneVision Stage** > almost all subsets are uploaded with HF's required format and you can use the recommended interface to download them and follow our code below to convert them. > the subset of `ureader_kg` and `ureader_qa` are uploaded with the processed jsons and tar.gz of image folders. > You may directly download them from the following url. > https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data/tree/main/ureader_kg In this dataset, we include the data splits used in the both final image stage and one-vision stage. For more details, please check our [paper](arxiv.org/abs/2408.03326) and our [training doc](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). ## Dataset Description - **Curated by:** Bo Li, Kaichen Zhang, Hao Zhang, Yuanhan Zhang, Renrui Zhang, Feng Li, Dong Guo - **Language(s) (NLP):** English, Chinese - **License:** Apache License 2.0 ## Dataset Sources <!-- Provide the basic links for the dataset. --> - **Dataset Collection:** We include a few subsets from existing dataset collection [Cambrian](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M), [Cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron), [UReader](https://arxiv.org/abs/2310.05126). Since we only used a few subsets from these datasets, and applied the cleaning and re-annotation process, we uploaded our processed version of these datasets into our own repository and thank the authors for providing the original datasets. - **Other Datasets:** For rest single source dataset, such as AI2D, OKVQA, we cite and link the original sources in our paper. ## Uses This dataset is used for the training of the LLaVA-OneVision model. We only allow the use of this dataset for academic research and education purpose. For OpenAI GPT-4 generated data, we recommend the users to check the [OpenAI Usage Policy](https://openai.com/policies/usage-policies/). ## Dataset Structure We expalin the data composition for mid-stage and final-stage at our repo in [**training doc**](https://github.com/LLaVA-VL/LLaVA-NeXT/tree/main/scripts/train#about-the-llava-onevision-data). ### Statistics We provide the statistics of the dataset in the following figures, and refer the audience to check our paper.   ### Code Guidance To help audience to better understand our dataest, we upload them into Hugging Face Dataset compatible format. During LLaVA-OneVision training, we use the `json` and `image/video` folder to store the data. > the subset of `ureader_kg` and `ureader_qa` are uploaded with the processed jsons and tar.gz of image folders. You may directly download them from the following url. > https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data/tree/main/ureader_kg Here we provide the code guidance to convert the dataset into the format of LLaVA-OneVision, and conduct the training of the LLaVA-OneVision model with converted dataset. ```python import os from datasets import load_dataset from tqdm import tqdm import json data = load_dataset("lmms-lab/LLaVA-OneVision-Data", split="train") image_folder = "<your_image_folder>" converted_data = [] for da in tqdm(data): json_data = {} json_data["id"] = da["id"] if da["image"] is not None: json_data["image"] = f"{da['id']}.jpg" da["image"].save(os.path.join(image_folder, json_data["image"])) json_data["conversations"] = da["conversations"] converted_data.append(json_data) with open("<your_json_file>.json", "w") as f: json.dump(converted_data, f, indent=4, ensure_ascii=False) ``` ## Citation **BibTeX:** [More Information Needed] ## Glossary The dataset collection process is conducted by all of the authors, we thank the Feng Li and Renrui Zhang for providing [LLaVA-M4-Instruct Data](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data) and Yuanhan for providing the [Video datasets](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K). After the dataset collection, the cleaning and re-annotation process, including final mixture of the dataset, is conducted by Bo Li and with the great help of Kaichen Zhang. ## Dataset Card Authors The dataset is curated by the following authors: Bo Li, Kaichen Zhang, Hao Zhang, Yuanhan Zhang, Renrui Zhang, Feng Li ## Dataset Card Contact [Bo Li](https://brianboli.com/): drluodian@gmail.com [Kaichen Zhang](https://www.linkedin.com/in/kaichen-zhang-014b17219/?originalSubdomain=sg)

DL3DV/DL3DV-ALL-960P

DL3DV

--- tags: - 3D Vision - NeRF - 3D Gaussian - Dataset - Novel View Synthesis - Text to 3D - Image to 3D pretty_name: Dl3DV-Dataset size_categories: - n>1T --- # DL3DV-Dataset This repo has all the 960P frames with camera poses of DL3DV-10K Dataset. We are working hard to review all the dataset to avoid sensitive information. Thank you for your patience. # Download If you have enough space, you can use git to download a dataset from huggingface. See this [link](https://huggingface.co/docs/hub/en/datasets-downloading). [480P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P)/[960P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-960P) versions should satisfies most needs. If you do not have enough space, we further provide a [download script](https://github.com/DL3DV-10K/Dataset/blob/main/scripts/download.py) here to download a subset. The usage: ```Bash usage: download.py [-h] --odir ODIR --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} --resolution {4K,2K,960P,480P} --file_type {images+poses,video,colmap_cache} [--hash HASH] [--clean_cache] optional arguments: -h, --help show this help message and exit --odir ODIR output directory --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} The subset of the benchmark to download --resolution {4K,2K,960P,480P} The resolution to donwnload --file_type {images+poses,video,colmap_cache} The file type to download --hash HASH If set subset=hash, this is the hash code of the scene to download --clean_cache If set, will clean the huggingface cache to save space ``` Here are some examples: ```Bash # Make sure you have applied for the access. # Use this to download the download.py script wget https://raw.githubusercontent.com/DL3DV-10K/Dataset/main/scripts/download.py # Download 960P resolution images and poses, 0~1K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 1K --resolution 960P --file_type images+poses --clean_cache # Download 960P resolution images and poses, 1K~2K subset, output to DL3DV-10K directory python download.py --odir DL3DV-10K --subset 2K --resolution 960P --file_type images+poses --clean_cache ``` You can also download a specific scene with its hash. The scene-hash pair visualization can be found [here](https://htmlpreview.github.io/?https://github.com/DL3DV-10K/Dataset/blob/main/visualize/index.html). ```Bash python download.py --odir DL3DV-10K --subset 2K --resolution 960P --file_type images+poses --hash e2cedefea8a0ed2d0ffbd5bdc08acbe7e1f85c96f72f7b790e9dfe1c98963047 --clean_cache ``` # News - [x] DL3DV-1K, 2K, 3K, 4K - [ ] DL3DV-5K ~ 10K

OpenGVLab/ShareGPT-4o

OpenGVLab

--- license: mit extra_gated_prompt: You agree to not use the dataset to conduct experiments that cause harm to human subjects. Please note that the data in this dataset may be subject to other agreements. Before using the data, be sure to read the relevant agreements carefully to ensure compliant use. Video copyrights belong to the original video creators or platforms and are for academic research use only. task_categories: - visual-question-answering - question-answering extra_gated_fields: Name: text Company/Organization: text Country: text E-Mail: text language: - en size_categories: - 100K<n<1M configs: - config_name: image_caption data_files: - split: images path: image_conversations/gpt-4o.jsonl - config_name: video_caption data_files: - split: ptest path: video_conversations/gpt4o.jsonl ---

BramVanroy/wikipedia_culturax_dutch

BramVanroy

--- language: - nl size_categories: - 10B<n<100B task_categories: - text-generation - text2text-generation pretty_name: Filtered CulturaX + Wikipedia for Dutch dataset_info: - config_name: 100M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 738455828.5851797 num_examples: 1018200 - name: test num_bytes: 7458534.414820259 num_examples: 10284 download_size: 411183119 dataset_size: 745914363.0 - config_name: 100k features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 745955.3074739829 num_examples: 1047 - name: test num_bytes: 7124.692526017029 num_examples: 10 download_size: 366788 dataset_size: 753080.0 - config_name: 10B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 66539945646.34457 num_examples: 40176566 - name: test num_bytes: 105996030.65543362 num_examples: 64000 download_size: 42132184504 dataset_size: 66645941677.0 - config_name: 10M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 76734151.72157606 num_examples: 139851 - name: test num_bytes: 774743.2784239326 num_examples: 1412 download_size: 37995388 dataset_size: 77508895.0 - config_name: 10k features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 72048.30379746835 num_examples: 78 - name: test num_bytes: 5896 num_examples: 1 download_size: 47197 dataset_size: 77944.30379746835 - config_name: 15B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 99730049355.25276 num_examples: 59584123 - name: test num_bytes: 107121206.74724333 num_examples: 64000 download_size: 63139415312 dataset_size: 99837170562.0 - config_name: 1B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 6797502496.392602 num_examples: 5102360 - name: test num_bytes: 68660322.60739774 num_examples: 51538 download_size: 4260450464 dataset_size: 6866162819.0 - config_name: 1M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 7442665.619329753 num_examples: 10694 - name: test num_bytes: 75164.38067024625 num_examples: 108 download_size: 3845466 dataset_size: 7517830.0 - config_name: 20B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 132920704365.75093 num_examples: 78991679 - name: test num_bytes: 107693939.24907027 num_examples: 64000 download_size: 84141456153 dataset_size: 133028398305.0 - config_name: 25B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 166111586295.01904 num_examples: 98399236 - name: test num_bytes: 108040894.98094498 num_examples: 64000 download_size: 105147418131 dataset_size: 166219627190.0 - config_name: 30B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 199302582477.5805 num_examples: 117806793 - name: test num_bytes: 108273597.41950662 num_examples: 64000 download_size: 126152714564 dataset_size: 199410856075.0 - config_name: 35B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 232493644456.181 num_examples: 137214350 - name: test num_bytes: 108440503.81899258 num_examples: 64000 download_size: 147149925109 dataset_size: 232602084960.0 - config_name: 40B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 265684747781.7734 num_examples: 156621907 - name: test num_bytes: 108566063.22660531 num_examples: 64000 download_size: 168152290262 dataset_size: 265793313845.0 - config_name: 45B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 298875877641.391 num_examples: 176029463 - name: test num_bytes: 108663946.60903454 num_examples: 64000 download_size: 189159571162 dataset_size: 298984541588.0 - config_name: 50B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 332067028077.12775 num_examples: 195437020 - name: test num_bytes: 108742395.87226707 num_examples: 64000 download_size: 210160621183 dataset_size: 332175770473.0 - config_name: 55B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 365258192681.75964 num_examples: 214844577 - name: test num_bytes: 108806676.24034382 num_examples: 64000 download_size: 231164757019 dataset_size: 365366999358.0 - config_name: 5B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 33351938314.309906 num_examples: 20769009 - name: test num_bytes: 102774477.69009268 num_examples: 64000 download_size: 21119808690 dataset_size: 33454712792.0 configs: - config_name: 100M data_files: - split: train path: 100M/train-* - split: test path: 100M/test-* - config_name: 100k data_files: - split: train path: 100k/train-* - split: test path: 100k/test-* - config_name: 10B data_files: - split: train path: 10B/train-* - split: test path: 10B/test-* - config_name: 10M data_files: - split: train path: 10M/train-* - split: test path: 10M/test-* - config_name: 10k data_files: - split: train path: 10k/train-* - split: test path: 10k/test-* - config_name: 15B data_files: - split: train path: 15B/train-* - split: test path: 15B/test-* - config_name: 1B data_files: - split: train path: 1B/train-* - split: test path: 1B/test-* - config_name: 1M data_files: - split: train path: 1M/train-* - split: test path: 1M/test-* - config_name: 20B data_files: - split: train path: 20B/train-* - split: test path: 20B/test-* - config_name: 25B data_files: - split: train path: 25B/train-* - split: test path: 25B/test-* - config_name: 30B data_files: - split: train path: 30B/train-* - split: test path: 30B/test-* - config_name: 35B data_files: - split: train path: 35B/train-* - split: test path: 35B/test-* - config_name: 40B data_files: - split: train path: 40B/train-* - split: test path: 40B/test-* - config_name: 45B data_files: - split: train path: 45B/train-* - split: test path: 45B/test-* - config_name: 50B data_files: - split: train path: 50B/train-* - split: test path: 50B/test-* - config_name: 55B data_files: - split: train path: 55B/train-* - split: test path: 55B/test-* - config_name: 5B data_files: - split: train path: 5B/train-* - split: test path: 5B/test-* --- # Filtered CulturaX + Wikipedia for Dutch This is a combined and filtered version of [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) and [Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia), only including Dutch. It is intended for the training of LLMs. Different configs are available based on the number of tokens (see a section below with an overview). This can be useful if you want to know exactly how many tokens you have. Great for using as a streaming dataset, too. Tokens are counted as white-space tokens, so depending on your tokenizer, you'll likely end up with more tokens than indicated here. Every config also has a test set (for validation) of 1% the total size of the dataset, minimally 1 max. 64k samples (~16M tokens). Wikipedia and CulturaX were suffled before merging and the teset set creation was also shuffled. Priority is given to Wikipedia to prioritize knowledge-content, so the smaller configs will consist exclusively of Wikipedia and for the larger configs we augment with CulturaX. Every config builds further on the previous, so this means that every config contains the same data as the smaller ones and more HOWEVER their train/test splits are not the same, so test set of one config may overlap with samples for another training set. This is usually not a problem but just be aware that you do not train on one config's training set and test with another config's test set. ## Configs ### `10k` -- 79 samples -- 10,087 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 10,087 - train_num_tokens: 9,205 - test_num_tokens: 882 - total_num_samples: 79 - train_num_samples: 78 - test_num_samples: 1 ### `100k` -- 1,057 samples -- 100,075 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 100,075 - train_num_tokens: 98,044 - test_num_tokens: 2,031 - total_num_samples: 1,057 - train_num_samples: 1,047 - test_num_samples: 10 ### `1M` -- 10,802 samples -- 1,000,239 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 1,000,239 - train_num_tokens: 991,119 - test_num_tokens: 9,120 - total_num_samples: 10,802 - train_num_samples: 10,694 - test_num_samples: 108 ### `10M` -- 141,263 samples -- 10,000,022 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 10,000,022 - train_num_tokens: 9,874,772 - test_num_tokens: 125,250 - total_num_samples: 141,263 - train_num_samples: 139,851 - test_num_samples: 1,412 ### `100M` -- 1,028,484 samples -- 100,000,047 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 100,000,047 - train_num_tokens: 99,013,372 - test_num_tokens: 986,675 - total_num_samples: 1,028,484 - train_num_samples: 1,018,200 - test_num_samples: 10,284 ### `1B` -- 5,153,898 samples -- 1,000,000,187 tokens - ratio_wikipedia: 61.21% - total_num_tokens: 1,000,000,187 - train_num_tokens: 989,990,190 - test_num_tokens: 10,009,997 - total_num_samples: 5,153,898 - train_num_samples: 5,102,360 - test_num_samples: 51,538 ### `5B` -- 20,833,009 samples -- 5,000,000,076 tokens - ratio_wikipedia: 25.35% - total_num_tokens: 5,000,000,076 - train_num_tokens: 4,984,493,654 - test_num_tokens: 15,506,422 - total_num_samples: 20,833,009 - train_num_samples: 20,769,009 - test_num_samples: 64,000 ### `10B` -- 40,240,566 samples -- 10,000,000,115 tokens - ratio_wikipedia: 18.41% - total_num_tokens: 10,000,000,115 - train_num_tokens: 9,984,156,828 - test_num_tokens: 15,843,287 - total_num_samples: 40,240,566 - train_num_samples: 40,176,566 - test_num_samples: 64,000 ### `15B` -- 59,648,123 samples -- 15,000,000,154 tokens - ratio_wikipedia: 15.98% - total_num_tokens: 15,000,000,154 - train_num_tokens: 14,983,970,518 - test_num_tokens: 16,029,636 - total_num_samples: 59,648,123 - train_num_samples: 59,584,123 - test_num_samples: 64,000 ### `20B` -- 79,055,679 samples -- 20,000,000,009 tokens - ratio_wikipedia: 14.75% - total_num_tokens: 20,000,000,009 - train_num_tokens: 19,983,799,357 - test_num_tokens: 16,200,652 - total_num_samples: 79,055,679 - train_num_samples: 78,991,679 - test_num_samples: 64,000 ### `25B` -- 98,463,236 samples -- 25,000,000,048 tokens - ratio_wikipedia: 14.00% - total_num_tokens: 25,000,000,048 - train_num_tokens: 24,983,765,326 - test_num_tokens: 16,234,722 - total_num_samples: 98,463,236 - train_num_samples: 98,399,236 - test_num_samples: 64,000 ### `30B` -- 117,870,793 samples -- 30,000,000,087 tokens - ratio_wikipedia: 13.50% - total_num_tokens: 30,000,000,087 - train_num_tokens: 29,983,707,932 - test_num_tokens: 16,292,155 - total_num_samples: 117,870,793 - train_num_samples: 117,806,793 - test_num_samples: 64,000 ### `35B` -- 137,278,350 samples -- 35,000,000,126 tokens - ratio_wikipedia: 13.14% - total_num_tokens: 35,000,000,126 - train_num_tokens: 34,983,914,739 - test_num_tokens: 16,085,387 - total_num_samples: 137,278,350 - train_num_samples: 137,214,350 - test_num_samples: 64,000 ### `40B` -- 156,685,907 samples -- 40,000,000,165 tokens - ratio_wikipedia: 12.87% - total_num_tokens: 40,000,000,165 - train_num_tokens: 39,983,508,625 - test_num_tokens: 16,491,540 - total_num_samples: 156,685,907 - train_num_samples: 156,621,907 - test_num_samples: 64,000 ### `45B` -- 176,093,463 samples -- 45,000,000,020 tokens - ratio_wikipedia: 12.66% - total_num_tokens: 45,000,000,020 - train_num_tokens: 44,983,608,118 - test_num_tokens: 16,391,902 - total_num_samples: 176,093,463 - train_num_samples: 176,029,463 - test_num_samples: 64,000 ### `50B` -- 195,501,020 samples -- 50,000,000,059 tokens - ratio_wikipedia: 12.49% - total_num_tokens: 50,000,000,059 - train_num_tokens: 49,983,567,461 - test_num_tokens: 16,432,598 - total_num_samples: 195,501,020 - train_num_samples: 195,437,020 - test_num_samples: 64,000 ### `55B` -- 214,908,577 samples -- 55,000,000,098 tokens - ratio_wikipedia: 12.35% - total_num_tokens: 55,000,000,098 - train_num_tokens: 54,983,723,278 - test_num_tokens: 16,276,820 - total_num_samples: 214,908,577 - train_num_samples: 214,844,577 - test_num_samples: 64,000 ## Filtering While CultruaX already has done a lot of filtering, some more filtering can be done to improve the quality of the corpus. These filters are described below. The baseline ratios (punctuation, uppercase, digits) were calculated on the SONAR-500 corpus (excluding WRPEA WRPED WRUEA WRUED WRUEB). **CulturaX**: - removed documents that contain the text "rechten voorbehouden" or "rights reserved" - remove documents whose URL contained "wikipedia.org" (because we include a cleaned version of Wikipedia ourselves) - removed documents that contain a "bad word" (see the section below) - removed documents that contain any non-latin characters. The idea is that "knowledge"-based information (e.g. original writing of a name) are allowed when the data comes from Wikipedia, but not from any other webcrawl, to avoid unsollicited noise. **CulturaX + Wikipedia**: - removed documents where ratio of punctuation marks vs. non-whitespace characters is higher than 0.2 - removed documents where ratio of uppercase vs. non-whitespace characters is higher than 0.22 - removed documents where ratio of digits vs. non-whitespace characters is higher than 0.16 - removed documents where the average token length is < 2 or > 20 ## Bad words ```python BAD_PHRASES_DOC_LEVEL = { # https://en.wikipedia.org/wiki/Dutch_profanity "achterlijk", "debiel", "downie", "idioot", "kankerlijer", "klere", "kolere", "minkukel", "pestkop", "pleuris", "pleuritis", "teringlijer", "tyfuslijer", "gadver", "getver", "godver", "godskolere", "godverork", "graftak", "kopvod", "verdomme", "anaalgeneraal", "bitch", "dikzak", "flikker", "fok", "fuck", "hoer", "klootzak", "klote", "kreng", "kringspiermusketier", "kut", "lamzak", "lul", "manwijf", "matennaai", "neuken", "neuker", "ouwehoer", "reet", "reetkever", "reetridder", "rotzak", "schijt", "shit", "slet", "slijmbal", "slons", "sodemieter", "stoephoer", "swaffel", "teef", "trut", "tut", "zak", "uilskuiken", "zeik", "bamivreter", "bosneger", "neger", "fransoos", "geitenneuker", "kaaskop", "kakker", "koelie", "lijp", "medelander", "mocro", "mof", "nikker", "poepchinees", "roetmop", "spaghettivreter", "loempiavouwer", "spanjool", "spleetoog", "tatta", "tokkie", "zandneger", "zwartzak", "halvezool", "kenau", "klootviool", "knuppel", "koekert", "koekwaus", "oelewapper", "smeerlap", "sukkel", "sul", "wappie", "wijf", "zooi", # xxx (a.o. https://gitlab.com/yhavinga/c4nlpreproc/-/blob/master/clean/badwords_ennl.py?ref_type=heads) "xxx", "anal", "blowjob", "buttplug", "cock", "cunt", "geil", "sex", # Standaardnederlands = seks, maybe we catch some porn or socialmedia sites with this misspelling "porn", # extra "nigger", "nigga", "hoerig", "klojo", } ``` ## Config details ## License information For CulturaX: https://huggingface.co/datasets/uonlp/CulturaX#license-information For Wikipedia: https://huggingface.co/datasets/wikimedia/wikipedia#licensing-information

allenai/social_i_qa

allenai

--- language: - en paperswithcode_id: social-iqa pretty_name: Social Interaction QA dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answerA dtype: string - name: answerB dtype: string - name: answerC dtype: string - name: label dtype: string splits: - name: train num_bytes: 6389954 num_examples: 33410 - name: validation num_bytes: 376508 num_examples: 1954 download_size: 2198056 dataset_size: 6766462 --- # Dataset Card for "social_i_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://leaderboard.allenai.org/socialiqa/submissions/get-started](https://leaderboard.allenai.org/socialiqa/submissions/get-started) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **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:** 2.20 MB - **Size of the generated dataset:** 6.76 MB - **Total amount of disk used:** 8.97 MB ### Dataset Summary We introduce Social IQa: Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people’s actions and their social implications. For example, given an action like "Jesse saw a concert" and a question like "Why did Jesse do this?", humans can easily infer that Jesse wanted "to see their favorite performer" or "to enjoy the music", and not "to see what's happening inside" or "to see if it works". The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models’ abilities to reason about the social implications of everyday events and situations. (Less) ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 2.20 MB - **Size of the generated dataset:** 6.76 MB - **Total amount of disk used:** 8.97 MB An example of 'validation' looks as follows. ``` { "answerA": "sympathetic", "answerB": "like a person who was unable to help", "answerC": "incredulous", "context": "Sydney walked past a homeless woman asking for change but did not have any money they could give to her. Sydney felt bad afterwards.", "label": "1", "question": "How would you describe Sydney?" } ``` ### Data Fields The data fields are the same among all splits. #### default - `context`: a `string` feature. - `question`: a `string` feature. - `answerA`: a `string` feature. - `answerB`: a `string` feature. - `answerC`: a `string` feature. - `label`: a `string` feature. ### Data Splits | name |train|validation| |-------|----:|---------:| |default|33410| 1954| ## 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` ``` ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

anon8231489123/ShareGPT_Vicuna_unfiltered

anon8231489123

--- license: apache-2.0 language: - en --- **Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.** **Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c** Two choices: - Removes instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json - Has instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split.json The choice is yours. The first dataset may go to far and remove valuable data. The second is better for when the AI asks for clarification, but it also may refuse to do stuff like browse the internet, which it actually may be able to do with certain langchain implementations. These are important things to think about before training. ~100k ShareGPT conversations narrowed down to 53k by: * Removing non-english conversations * Removing excessive unicode (indicative of Chinese or Korean text, usually) * Removing excessive repeated characters * Removing various instances "AI Moralizing". Conversations with these phrases were removed (and a few others that can't be mentioned here): "text-based AI language model", "domestic violence", "please refrain", "derogatory", "inappropriate", "offensive", "racism", "racist", "racial", "discriminate", "discriminatory", "discrimination", "sexist", "sexism", "unacceptable", "inclusive workplace", "lgbt", "morals", "ethics", "ethical", "legality", "illegal", "illegality", "hateful", "harmful", "it is never okay", "It is important to", "It's important to", "real-world consequences", "hate speech", "glorify", "not be appropriate", "supremacist", "extremist", "responsible AI", "AI principles", "AI assistant", "an AI language", "ableist", "hurtful", "gender stereotype", "gender inequality", "underrepresentation", "safe spaces", "gender-based", "inclusivity", "feminist", "feminism", "transgender", "empowerment", "communist", "capitalism", "stereotypes", "biases", "bias", "Microaggression", "prioritize human safety", "as a language model", "as an AI language model", "As a large language model", "As an AI", "ethical principles", "consensual", "it is not appropriate", "it's not appropriate", "I cannot fulfill your request", "harmful to human beings", "ethical guidelines", "my guidelines", "prioritize user safety", "adhere to ethical guidelines", "harmful consequences", "potentially harmful", "dangerous activities", "promote safety", "well-being of all users", "responsible information sharing", "jeopardize the safety", "illegal actions or intentions", "undermine the stability", "promote the well-being", "illegal activities or actions", "adherence to the law", "potentially be harmful", "illegal substances or activities", "committed to promoting", "safe information", "lawful information", "cannot provide guidance", "cannot provide information", "unable to offer assistance", "cannot engage in discussions", "programming prohibits", "follow ethical guidelines", "ensure the safety", "involves an illegal subject", "prioritize safety", "illegal subject", "prioritize user well-being", "cannot support or promote", "activities that could harm", "pose a risk to others", "against my programming", "activities that could undermine", "potentially dangerous", "not within the scope", "designed to prioritize safety", "not able to provide", "maintain user safety", "adhere to safety guidelines", "dangerous or harmful", "cannot provide any information", "focus on promoting safety" * Conversations split into 2048 token chunks as described here: https://github.com/lm-sys/FastChat/blob/main/docs/commands/data_cleaning.md This should be fully ready to train an unfiltered english Vicuna model based on the procedure here: https://github.com/lm-sys/FastChat/

legacy-datasets/mc4

legacy-datasets

--- pretty_name: mC4 annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - he - hi - hmn - ht - hu - hy - id - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - 'no' - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu language_bcp47: - bg-Latn - el-Latn - hi-Latn - ja-Latn - ru-Latn - zh-Latn license: - odc-by multilinguality: - multilingual size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M - 10M<n<100M - 100M<n<1B - 1B<n<10B source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: mc4 viewer: false --- <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "mc4" is deprecated and will be deleted. Use "<a href="https://huggingface.co/datasets/allenai/c4">allenai/c4</a>" instead.</p> </div> # Dataset Card for mC4 ## Table of Contents - [Dataset Card for mC4](#dataset-card-for-mc4) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://huggingface.co/datasets/allenai/c4 - **Paper:** https://arxiv.org/abs/1910.10683 ### Dataset Summary A multilingual colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the version prepared by AllenAI, hosted at this address: https://huggingface.co/datasets/allenai/c4 108 languages are available and are reported in the table below. Note that the languages that end with "-Latn" are simply romanized variants, i.e. written using the Latin script. | language code | language name | |:----------------|:---------------------| | af | Afrikaans | | am | Amharic | | ar | Arabic | | az | Azerbaijani | | be | Belarusian | | bg | Bulgarian | | bg-Latn | Bulgarian (Latin) | | bn | Bangla | | ca | Catalan | | ceb | Cebuano | | co | Corsican | | cs | Czech | | cy | Welsh | | da | Danish | | de | German | | el | Greek | | el-Latn | Greek (Latin) | | en | English | | eo | Esperanto | | es | Spanish | | et | Estonian | | eu | Basque | | fa | Persian | | fi | Finnish | | fil | Filipino | | fr | French | | fy | Western Frisian | | ga | Irish | | gd | Scottish Gaelic | | gl | Galician | | gu | Gujarati | | ha | Hausa | | haw | Hawaiian | | hi | Hindi | | hi-Latn | Hindi (Latin script) | | hmn | Hmong, Mong | | ht | Haitian | | hu | Hungarian | | hy | Armenian | | id | Indonesian | | ig | Igbo | | is | Icelandic | | it | Italian | | iw | former Hebrew | | ja | Japanese | | ja-Latn | Japanese (Latin) | | jv | Javanese | | ka | Georgian | | kk | Kazakh | | km | Khmer | | kn | Kannada | | ko | Korean | | ku | Kurdish | | ky | Kyrgyz | | la | Latin | | lb | Luxembourgish | | lo | Lao | | lt | Lithuanian | | lv | Latvian | | mg | Malagasy | | mi | Maori | | mk | Macedonian | | ml | Malayalam | | mn | Mongolian | | mr | Marathi | | ms | Malay | | mt | Maltese | | my | Burmese | | ne | Nepali | | nl | Dutch | | no | Norwegian | | ny | Nyanja | | pa | Punjabi | | pl | Polish | | ps | Pashto | | pt | Portuguese | | ro | Romanian | | ru | Russian | | ru-Latn | Russian (Latin) | | sd | Sindhi | | si | Sinhala | | sk | Slovak | | sl | Slovenian | | sm | Samoan | | sn | Shona | | so | Somali | | sq | Albanian | | sr | Serbian | | st | Southern Sotho | | su | Sundanese | | sv | Swedish | | sw | Swahili | | ta | Tamil | | te | Telugu | | tg | Tajik | | th | Thai | | tr | Turkish | | uk | Ukrainian | | und | Unknown language | | ur | Urdu | | uz | Uzbek | | vi | Vietnamese | | xh | Xhosa | | yi | Yiddish | | yo | Yoruba | | zh | Chinese | | zh-Latn | Chinese (Latin) | | zu | Zulu | You can load the mC4 subset of any language like this: ```python from datasets import load_dataset en_mc4 = load_dataset("mc4", "en") ``` And if you can even specify a list of languages: ```python from datasets import load_dataset mc4_subset_with_five_languages = load_dataset("mc4", languages=["en", "fr", "es", "de", "zh"]) ``` ### Supported Tasks and Leaderboards mC4 is mainly intended to pretrain language models and word representations. ### Languages The dataset supports 108 languages. ## Dataset Structure ### Data Instances An example form the `en` config is: ``` {'timestamp': '2018-06-24T01:32:39Z', 'text': 'Farm Resources in Plumas County\nShow Beginning Farmer Organizations & Professionals (304)\nThere are 304 resources serving Plumas County in the following categories:\nMap of Beginning Farmer Organizations & Professionals serving Plumas County\nVictoria Fisher - Office Manager - Loyalton, CA\nAmy Lynn Rasband - UCCE Plumas-Sierra Administrative Assistant II - Quincy , CA\nShow Farm Income Opportunities Organizations & Professionals (353)\nThere are 353 resources serving Plumas County in the following categories:\nFarm Ranch And Forest Retailers (18)\nMap of Farm Income Opportunities Organizations & Professionals serving Plumas County\nWarner Valley Wildlife Area - Plumas County\nShow Farm Resources Organizations & Professionals (297)\nThere are 297 resources serving Plumas County in the following categories:\nMap of Farm Resources Organizations & Professionals serving Plumas County\nThere are 57 resources serving Plumas County in the following categories:\nMap of Organic Certification Organizations & Professionals serving Plumas County', 'url': 'http://www.californialandcan.org/Plumas/Farm-Resources/'} ``` ### Data Fields The data have several fields: - `url`: url of the source as a string - `text`: text content as a string - `timestamp`: timestamp as a string ### Data Splits To build mC4, the authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. The resulting mC4 subsets for each language are reported in this table: | config | train | validation | |:---------|:--------|:-------------| | af | ? | ? | | am | ? | ? | | ar | ? | ? | | az | ? | ? | | be | ? | ? | | bg | ? | ? | | bg-Latn | ? | ? | | bn | ? | ? | | ca | ? | ? | | ceb | ? | ? | | co | ? | ? | | cs | ? | ? | | cy | ? | ? | | da | ? | ? | | de | ? | ? | | el | ? | ? | | el-Latn | ? | ? | | en | ? | ? | | eo | ? | ? | | es | ? | ? | | et | ? | ? | | eu | ? | ? | | fa | ? | ? | | fi | ? | ? | | fil | ? | ? | | fr | ? | ? | | fy | ? | ? | | ga | ? | ? | | gd | ? | ? | | gl | ? | ? | | gu | ? | ? | | ha | ? | ? | | haw | ? | ? | | hi | ? | ? | | hi-Latn | ? | ? | | hmn | ? | ? | | ht | ? | ? | | hu | ? | ? | | hy | ? | ? | | id | ? | ? | | ig | ? | ? | | is | ? | ? | | it | ? | ? | | iw | ? | ? | | ja | ? | ? | | ja-Latn | ? | ? | | jv | ? | ? | | ka | ? | ? | | kk | ? | ? | | km | ? | ? | | kn | ? | ? | | ko | ? | ? | | ku | ? | ? | | ky | ? | ? | | la | ? | ? | | lb | ? | ? | | lo | ? | ? | | lt | ? | ? | | lv | ? | ? | | mg | ? | ? | | mi | ? | ? | | mk | ? | ? | | ml | ? | ? | | mn | ? | ? | | mr | ? | ? | | ms | ? | ? | | mt | ? | ? | | my | ? | ? | | ne | ? | ? | | nl | ? | ? | | no | ? | ? | | ny | ? | ? | | pa | ? | ? | | pl | ? | ? | | ps | ? | ? | | pt | ? | ? | | ro | ? | ? | | ru | ? | ? | | ru-Latn | ? | ? | | sd | ? | ? | | si | ? | ? | | sk | ? | ? | | sl | ? | ? | | sm | ? | ? | | sn | ? | ? | | so | ? | ? | | sq | ? | ? | | sr | ? | ? | | st | ? | ? | | su | ? | ? | | sv | ? | ? | | sw | ? | ? | | ta | ? | ? | | te | ? | ? | | tg | ? | ? | | th | ? | ? | | tr | ? | ? | | uk | ? | ? | | und | ? | ? | | ur | ? | ? | | uz | ? | ? | | vi | ? | ? | | xh | ? | ? | | yi | ? | ? | | yo | ? | ? | | zh | ? | ? | | zh-Latn | ? | ? | | zu | ? | ? | ## 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 AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. ### Citation Information ``` @article{2019t5, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {arXiv e-prints}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.10683}, } ``` ### Contributions Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.

locuslab/TOFU

locuslab

--- annotations_creators: - machine-generated language: - en language_creators: - machine-generated license: mit multilinguality: - monolingual pretty_name: TOFU size_categories: - 1K<n<10K source_datasets: - original tags: - unlearning - question answering - TOFU - NLP - LLM task_categories: - question-answering task_ids: - closed-domain-qa configs: - config_name: full data_files: full.json default: true - config_name: forget01 data_files: forget01.json - config_name: forget05 data_files: forget05.json - config_name: forget10 data_files: forget10.json - config_name: retain90 data_files: retain90.json - config_name: retain95 data_files: retain95.json - config_name: retain99 data_files: retain99.json - config_name: world_facts data_files: world_facts.json - config_name: real_authors data_files: real_authors.json - config_name: forget01_perturbed data_files: forget01_perturbed.json - config_name: forget05_perturbed data_files: forget05_perturbed.json - config_name: forget10_perturbed data_files: forget10_perturbed.json - config_name: retain_perturbed data_files: retain_perturbed.json - config_name: world_facts_perturbed data_files: world_facts_perturbed.json - config_name: real_authors_perturbed data_files: real_authors_perturbed.json --- # TOFU: Task of Fictitious Unlearning 🍢 The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set. ## Quick Links - [**Website**](https://locuslab.github.io/tofu): The landing page for TOFU - [**arXiv Paper**](http://arxiv.org/abs/2401.06121): Detailed information about the TOFU dataset and its significance in unlearning tasks. - [**GitHub Repository**](https://github.com/locuslab/tofu): Access the source code, fine-tuning scripts, and additional resources for the TOFU dataset. - [**Dataset on Hugging Face**](https://huggingface.co/datasets/locuslab/TOFU): Direct link to download the TOFU dataset. - [**Leaderboard on Hugging Face Spaces**](https://huggingface.co/spaces/locuslab/tofu_leaderboard): Current rankings and submissions for the TOFU dataset challenges. - [**Summary on Twitter**](https://x.com/_akhaliq/status/1745643293839327268): A concise summary and key takeaways from the project. ## Applicability 🚀 The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 chat, and Phi-1.5 models, but can be easily adapted to other models. ## Loading the Dataset To load the dataset, use the following code: ```python from datasets import load_dataset dataset = load_dataset("locuslab/TOFU", "full") ``` ### Available forget sets are: - `forget01`: Forgetting 1% of the original dataset, all entries correspond to a single author. - `forget05`: Forgetting 5% of the original dataset, all entries correspond to a single author. - `forget10`: Forgetting 10% of the original dataset, all entries correspond to a single author. Retain sets corresponding to each forget set are also available, which can be used to train an Oracle model. ## Codebase The code for training the models and the availability of all fine-tuned models can be found at our [GitHub repository](https://github.com/locuslab/tofu). ## Citing Our Work If you find our codebase and dataset beneficial, please cite our work: ``` @misc{tofu2024, title={TOFU: A Task of Fictitious Unlearning for LLMs}, author={Pratyush Maini and Zhili Feng and Avi Schwarzschild and Zachary C. Lipton and J. Zico Kolter}, year={2024}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

ylacombe/cml-tts

ylacombe

--- language: - nl - fr - de - it - pl - pt - es license: cc-by-4.0 size_categories: - 1M<n<10M task_categories: - text-to-speech - text-to-audio pretty_name: CML-TTS dataset_info: - config_name: dutch features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 186374683541.98 num_examples: 309785 - name: dev num_bytes: 2912063172.928 num_examples: 4834 - name: test num_bytes: 2757891736.78 num_examples: 4570 download_size: 132987704971 dataset_size: 192044638451.68802 - config_name: french features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 64984002840.768 num_examples: 107598 - name: dev num_bytes: 2257393207.796 num_examples: 3739 - name: test num_bytes: 2281630546.306 num_examples: 3763 download_size: 48345998335 dataset_size: 69523026594.87 - config_name: german features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 369052038020.872 num_examples: 608296 - name: dev num_bytes: 3197115278.604 num_examples: 5314 - name: test num_bytes: 3288183839.092 num_examples: 5466 download_size: 280438261836 dataset_size: 375537337138.568 - config_name: italian features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 30242801015.92 num_examples: 50345 - name: dev num_bytes: 938644924.81 num_examples: 1765 - name: test num_bytes: 979116355.51 num_examples: 1835 download_size: 21996805791 dataset_size: 32160562296.239998 - config_name: polish features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 11127461686.356 num_examples: 18719 - name: dev num_bytes: 356048249 num_examples: 853 - name: test num_bytes: 367796887 num_examples: 814 download_size: 8114633186 dataset_size: 11851306822.356 - config_name: portuguese features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 20722423371.0 num_examples: 34265 - name: dev num_bytes: 622824524.224 num_examples: 1134 - name: test num_bytes: 673141068.9 num_examples: 1297 download_size: 14421097659 dataset_size: 22018388964.124 - config_name: spanish features: - name: audio dtype: audio - name: wav_filesize dtype: int64 - name: text dtype: string - name: transcript_wav2vec dtype: string - name: levenshtein dtype: float64 - name: duration dtype: float64 - name: num_words dtype: int64 - name: speaker_id dtype: int64 splits: - name: train num_bytes: 101377452063.176 num_examples: 168524 - name: dev num_bytes: 1882729515.184 num_examples: 3148 - name: test num_bytes: 1851592818.0 num_examples: 3080 download_size: 73687756096 dataset_size: 105111774396.36 configs: - config_name: dutch data_files: - split: train path: dutch/train-* - split: dev path: dutch/dev-* - split: test path: dutch/test-* - config_name: french data_files: - split: train path: french/train-* - split: dev path: french/dev-* - split: test path: french/test-* - config_name: german data_files: - split: train path: german/train-* - split: dev path: german/dev-* - split: test path: german/test-* - config_name: italian data_files: - split: train path: italian/train-* - split: dev path: italian/dev-* - split: test path: italian/test-* - config_name: polish data_files: - split: train path: polish/train-* - split: dev path: polish/dev-* - split: test path: polish/test-* - config_name: portuguese data_files: - split: train path: portuguese/train-* - split: dev path: portuguese/dev-* - split: test path: portuguese/test-* - config_name: spanish data_files: - split: train path: spanish/train-* - split: dev path: spanish/dev-* - split: test path: spanish/test-* --- # Dataset Card for CML-TTS ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Data Statistics](#data-statistics) - [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:** [MultiLingual LibriSpeech ASR corpus](https://www.openslr.org/146/) - **Repository:** [CML-TTS-Dataset](https://github.com/freds0/CML-TTS-Dataset) - **Paper:** [CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages](https://arxiv.org/abs/2306.10097) ### Dataset Summary CML-TTS is a recursive acronym for CML-Multi-Lingual-TTS, a Text-to-Speech (TTS) dataset developed at the Center of Excellence in Artificial Intelligence (CEIA) of the Federal University of Goias (UFG). CML-TTS is a dataset comprising audiobooks sourced from the public domain books of Project Gutenberg, read by volunteers from the LibriVox project. The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/146) to make it easier to stream. ### Supported Tasks - `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS). ### Languages The dataset includes recordings in Dutch, German, French, Italian, Polish, Portuguese, and Spanish, all at a sampling rate of 24kHz. ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German): ```python from datasets import load_dataset mls = load_dataset("ylacombe/cml-tts", "german", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True) print(next(iter(mls))) ``` #### *Bonus* You can create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). **Local:** ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler mls = load_dataset("ylacombe/cml-tts", "german", split="train") batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False) dataloader = DataLoader(mls, batch_sampler=batch_sampler) ``` **Streaming:** ```python from datasets import load_dataset from torch.utils.data import DataLoader mls = load_dataset("ylacombe/cml-tts", "german", split="train", streaming=True) dataloader = DataLoader(mls, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'audio': {'path': '6892_8912_000729.wav', 'array': array([-1.52587891e-...7344e-05]), 'sampling_rate': 24000}, 'wav_filesize': 601964, 'text': 'Proszę pana, tu pano... zdziwiony', 'transcript_wav2vec': 'proszę pana tu panow... zdziwiony', 'levenshtein': 0.96045197740113, 'duration': 13.648979591836737, 'num_words': 29, 'speaker_id': 6892} ``` ### Data Fields - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - transcript_wav2vec: the transcription of the audio file using the wav2vec model. Has been used to curate the dataset. - wav_filesize: The size of the audio waveform file. Has been used to curate the dataset. - levenshtein: The [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between the wav2vec transcription and the original transcription. Has been used to curate the dataset. - duration: The duration of the audio in seconds. - num_words: The number of words of the transcription. ### Data Splits | # Samples | Train | Dev | Test | |------------|--------|------|------| | german | 608296 | 5314 | 5466 | | dutch | 309785 | 4834 | 4570 | | french | 107598 | 3739 | 3763 | | spanish | 168524 | 3148 | 3080 | | italian | 50345 | 1765 | 1835 | | portuguese | 34265 | 1134 | 1297 | | polish | 18719 | 853 | 814 | ### Data Statistics | Language | Duration (Train) | Duration (Test) | Duration (Dev) | Speakers (Train) | Speakers (Test) | Speakers (Dev) | |------------|-------------------|------------------|----------------|------------------|-----------------|----------------| | | M | F | M | F | M | F | M | F | M | F | M | F | | Dutch | 482.82 | 162.17 | 2.46 | 1.29 | 2.24 | 1.67 | 8 | 27 | 3 | 3 | 2 | 4 | | French | 260.08 | 24.04 | 2.48 | 3.55 | 3.31 | 2.72 | 25 | 20 | 8 | 9 | 10 | 8 | | German | 1128.96 | 436.64 | 3.75 | 5.27 | 4.31 | 5.03 | 78 | 90 | 13 | 17 | 13 | 15 | | Italian | 73.78 | 57.51 | 1.47 | 0.85 | 0.40 | 1.52 | 23 | 38 | 5 | 5 | 4 | 6 | | Polish | 30.61 | 8.32 | 0.70 | 0.90 | 0.56 | 0.80 | 4 | 4 | 2 | 2 | 2 | 2 | | Portuguese | 23.14 | 44.81 | 0.28 | 0.24 | 0.68 | 0.20 | 20 | 10 | 5 | 4 | 6 | 3 | | Spanish | 279.15 | 164.08 | 2.77 | 2.06 | 3.40 | 2.34 | 35 | 42 | 10 | 8 | 11 | 9 | | Total | 3,176.13| | 28.11 | | 29.19 | | 424 | | 94 | | 95 | | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) ### Citation Information ``` @misc{oliveira2023cmltts, title={CML-TTS A Multilingual Dataset for Speech Synthesis in Low-Resource Languages}, author={Frederico S. Oliveira and Edresson Casanova and Arnaldo Cândido Júnior and Anderson S. Soares and Arlindo R. Galvão Filho}, year={2023}, eprint={2306.10097}, archivePrefix={arXiv}, primaryClass={eess.AS} } ``` ### Contributions Thanks to [@ylacombe](https://github.com/ylacombe) for adding this dataset.

mlfoundations/dclm-baseline-1.0-parquet

mlfoundations

--- language: - en license: cc-by-4.0 --- ## DCLM-baseline ***Note: this is an identical copy of https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0, where all the files have been mapped to a parquet format.*** DCLM-baseline is a 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks. Below are comparisions of model trained on DCLM-baseline with other models in the 7B regime. | Model | Params | Tokens | Open dataset? | CORE | MMLU | EXTENDED | |---------------|--------|--------|---------------|----------|----------|----------| | **Open weights, closed datasets** | | | | | | | | Llama2 | 7B | 2T | ✗ | 49.2 | 45.8 | 34.1 | | DeepSeek | 7B | 2T | ✗ | 50.7 | 48.5 | 35.3 | | Mistral-0.3 | 7B | ? | ✗ | 57.0 | 62.7 | 45.1 | | QWEN-2 | 7B | ? | ✗ | 57.5 | **71.9** | 50.5 | | Llama3 | 8B | 15T | ✗ | 57.6 | 66.2 | 46.3 | | Gemma | 8B | 6T | ✗ | 57.8 | 64.3 | 44.6 | | Phi-3 | 7B | ? | ✗ | **61.0** | 69.9 | **57.9** | | **Open weights, open datasets** | | | | | | | | Falcon | 7B | 1T | ✓ | 44.1 | 27.4 | 25.1 | | Amber | 7B | 1.2T | ✓ | 39.8 | 27.9 | 22.3 | | Crystal | 7B | 1.2T | ✓ | 48.0 | 48.2 | 33.2 | | OLMo-1.7 | 7B | 2.1T | ✓ | 47.0 | 54.0 | 34.2 | | MAP-Neo | 7B | 4.5T | ✓ | **50.2** | **57.1** | **40.4** | | **Models we trained** | | | | | | | | FineWeb edu | 7B | 0.14T | ✓ | 38.7 | 26.3 | 22.1 | | FineWeb edu | 7B | 0.28T | ✓ | 41.9 | 37.3 | 24.5 | | **DCLM-BASELINE** | 7B | 0.14T | ✓ | 44.1 | 38.3 | 25.0 | | **DCLM-BASELINE** | 7B | 0.28T | ✓ | 48.9 | 50.8 | 31.8 | | **DCLM-BASELINE** | 7B | 2.6T | ✓ | **57.1** | **63.7** | **45.4** | ## Dataset Details ### Dataset Description - **Curated by:** The DCLM Team - **Language(s) (NLP):** English - **License:** CC-by-4.0 ### Dataset Sources - **Repository:** https://datacomp.ai/dclm - **Paper:**: https://arxiv.org/abs/2406.11794 - **Construction Code**: https://github.com/mlfoundations/dclm ## Uses ### Direct Use DCLM-Baseline is intended to be used as a research baseline for the DCLM benchmark. It demonstrates the importance of data curation in training performant language models. ### Out-of-Scope Use DCLM-Baseline is not intended for training production-ready models or for specific domains such as code and math. It may not perform as well as domain-specific datasets for these tasks. Due to these limitations, the dataset is intended for research use only. DCLM-Baseline is a subset of the DCLM-Pool, which is a corpus of 240 trillion tokens derived from Common Crawl. The dataset is in plain text format. ## Dataset Creation ### Curation Rationale DCLM-Baseline was created to demonstrate the effectiveness of the DCLM testbed in developing high-quality training sets for language models. It serves as a proof of concept for the data curation strategies enabled by DCLM and is designed to be a research baseline for the benchmark. ### Source Data #### Data Collection and Processing DCLM-Baseline was created by applying a series of cleaning, filtering, and deduplication steps to the raw Common Crawl data (DCLM-Pool). The key steps include: 1. Heuristic cleaning and filtering (reproduction of RefinedWeb) 2. Deduplication using a Bloom filter 3. Model-based filtering using a fastText classifier trained on instruction-formatted data (OpenHermes 2.5 and r/ExplainLikeImFive) #### Who are the source data producers? The source data is from Common Crawl, which is a repository of web crawl data. ### Personal and Sensitive Information [More Information Needed] ## Bias, Risks, and Limitations The dataset may contain biases present in the Common Crawl data. The dataset's performance on code and math tasks is limited compared to its performance on language understanding tasks. DCLM-Baseline is designed for research purposes only. ### Recommendations Users should be aware of the potential biases and limitations of the dataset, especially when using it for specific domains like code and math. The dataset should only be used for research purposes in the context of the DCLM benchmark. ## Citation ```bibtex @misc{li2024datacomplm, title={DataComp-LM: In search of the next generation of training sets for language models}, author={Jeffrey Li and Alex Fang and Georgios Smyrnis and Maor Ivgi and Matt Jordan and Samir Gadre and Hritik Bansal and Etash Guha and Sedrick Keh and Kushal Arora and Saurabh Garg and Rui Xin and Niklas Muennighoff and Reinhard Heckel and Jean Mercat and Mayee Chen and Suchin Gururangan and Mitchell Wortsman and Alon Albalak and Yonatan Bitton and Marianna Nezhurina and Amro Abbas and Cheng-Yu Hsieh and Dhruba Ghosh and Josh Gardner and Maciej Kilian and Hanlin Zhang and Rulin Shao and Sarah Pratt and Sunny Sanyal and Gabriel Ilharco and Giannis Daras and Kalyani Marathe and Aaron Gokaslan and Jieyu Zhang and Khyathi Chandu and Thao Nguyen and Igor Vasiljevic and Sham Kakade and Shuran Song and Sujay Sanghavi and Fartash Faghri and Sewoong Oh and Luke Zettlemoyer and Kyle Lo and Alaaeldin El-Nouby and Hadi Pouransari and Alexander Toshev and Stephanie Wang and Dirk Groeneveld and Luca Soldaini and Pang Wei Koh and Jenia Jitsev and Thomas Kollar and Alexandros G. Dimakis and Yair Carmon and Achal Dave and Ludwig Schmidt and Vaishaal Shankar}, year={2024}, eprint={2406.11794}, archivePrefix={arXiv}, primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'} ```

open-rl-leaderboard/results_v2

open-rl-leaderboard

--- dataset_info: features: - name: user_id dtype: string - name: model_id dtype: string - name: sha dtype: string - name: status dtype: string - name: env_id dtype: string - name: episodic_returns sequence: float64 splits: - name: train num_bytes: 7120241 num_examples: 19092 download_size: 0 dataset_size: 7120241 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "results_v2" [Leaderboard](https://huggingface.co/spaces/open-rl-leaderboard/leaderboard) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AmazonScience/massive

AmazonScience

--- annotations_creators: - expert-generated language_creators: - found license: - cc-by-4.0 multilinguality: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - ca-ES - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - intent-classification - multi-class-classification paperswithcode_id: massive pretty_name: MASSIVE language_bcp47: - af-ZA - am-ET - ar-SA - az-AZ - bn-BD - ca-ES - cy-GB - da-DK - de-DE - el-GR - en-US - es-ES - fa-IR - fi-FI - fr-FR - he-IL - hi-IN - hu-HU - hy-AM - id-ID - is-IS - it-IT - ja-JP - jv-ID - ka-GE - km-KH - kn-IN - ko-KR - lv-LV - ml-IN - mn-MN - ms-MY - my-MM - nb-NO - nl-NL - pl-PL - pt-PT - ro-RO - ru-RU - sl-SL - sq-AL - sv-SE - sw-KE - ta-IN - te-IN - th-TH - tl-PH - tr-TR - ur-PK - vi-VN - zh-CN - zh-TW tags: - natural-language-understanding --- # MASSIVE 1.1: A 1M-Example Multilingual Natural Language Understanding Dataset with 52 Typologically-Diverse Languages ## Table of Contents - [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [No Warranty](#no-warranty) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://github.com/alexa/massive - **Repository:** https://github.com/alexa/massive - **Paper:** https://arxiv.org/abs/2204.08582 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/1697/overview - **Point of Contact:** [GitHub](https://github.com/alexa/massive/issues) ### Dataset Summary MASSIVE 1.1 is a parallel dataset of > 1M utterances across 52 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. | Name | Lang | Utt/Lang | Domains | Intents | Slots | |:-------------------------------------------------------------------------------:|:-------:|:--------------:|:-------:|:--------:|:------:| | MASSIVE 1.1 | 52 | 19,521 | 18 | 60 | 55 | | SLURP (Bastianelli et al., 2020) | 1 | 16,521 | 18 | 60 | 55 | | NLU Evaluation Data (Liu et al., 2019) | 1 | 25,716 | 18 | 54 | 56 | | Airline Travel Information System (ATIS) (Price, 1990) | 1 | 5,871 | 1 | 26 | 129 | | ATIS with Hindi and Turkish (Upadhyay et al., 2018) | 3 | 1,315-5,871 | 1 | 26 | 129 | | MultiATIS++ (Xu et al., 2020) | 9 | 1,422-5,897 | 1 | 21-26 | 99-140 | | Snips (Coucke et al., 2018) | 1 | 14,484 | - | 7 | 53 | | Snips with French (Saade et al., 2019) | 2 | 4,818 | 2 | 14-15 | 11-12 | | Task Oriented Parsing (TOP) (Gupta et al., 2018) | 1 | 44,873 | 2 | 25 | 36 | | Multilingual Task-Oriented Semantic Parsing (MTOP) (Li et al., 2021) | 6 | 15,195-22,288 | 11 | 104-113 | 72-75 | | Cross-Lingual Multilingual Task Oriented Dialog (Schuster et al., 2019) | 3 | 5,083-43,323 | 3 | 12 | 11 | | Microsoft Dialog Challenge (Li et al., 2018) | 1 | 38,276 | 3 | 11 | 29 | | Fluent Speech Commands (FSC) (Lugosch et al., 2019) | 1 | 30,043 | - | 31 | - | | Chinese Audio-Textual Spoken Language Understanding (CATSLU) (Zhu et al., 2019) | 1 | 16,258 | 4 | - | 94 | ### Supported Tasks and Leaderboards The dataset can be used to train a model for `natural-language-understanding` (NLU) : - `intent-classification` - `multi-class-classification` - `natural-language-understanding` ### Languages The MASSIVE 1.1 corpora consists of parallel sentences from 52 languages : - `Afrikaans - South Africa (af-ZA)` - `Amharic - Ethiopia (am-ET)` - `Arabic - Saudi Arabia (ar-SA)` - `Azeri - Azerbaijan (az-AZ)` - `Bengali - Bangladesh (bn-BD)` - `Catalan - Spain (ca-ES)` - `Chinese - China (zh-CN)` - `Chinese - Taiwan (zh-TW)` - `Danish - Denmark (da-DK)` - `German - Germany (de-DE)` - `Greek - Greece (el-GR)` - `English - United States (en-US)` - `Spanish - Spain (es-ES)` - `Farsi - Iran (fa-IR)` - `Finnish - Finland (fi-FI)` - `French - France (fr-FR)` - `Hebrew - Israel (he-IL)` - `Hungarian - Hungary (hu-HU)` - `Armenian - Armenia (hy-AM)` - `Indonesian - Indonesia (id-ID)` - `Icelandic - Iceland (is-IS)` - `Italian - Italy (it-IT)` - `Japanese - Japan (ja-JP)` - `Javanese - Indonesia (jv-ID)` - `Georgian - Georgia (ka-GE)` - `Khmer - Cambodia (km-KH)` - `Korean - Korea (ko-KR)` - `Latvian - Latvia (lv-LV)` - `Mongolian - Mongolia (mn-MN)` - `Malay - Malaysia (ms-MY)` - `Burmese - Myanmar (my-MM)` - `Norwegian - Norway (nb-NO)` - `Dutch - Netherlands (nl-NL)` - `Polish - Poland (pl-PL)` - `Portuguese - Portugal (pt-PT)` - `Romanian - Romania (ro-RO)` - `Russian - Russia (ru-RU)` - `Slovanian - Slovania (sl-SL)` - `Albanian - Albania (sq-AL)` - `Swedish - Sweden (sv-SE)` - `Swahili - Kenya (sw-KE)` - `Hindi - India (hi-IN)` - `Kannada - India (kn-IN)` - `Malayalam - India (ml-IN)` - `Tamil - India (ta-IN)` - `Telugu - India (te-IN)` - `Thai - Thailand (th-TH)` - `Tagalog - Philippines (tl-PH)` - `Turkish - Turkey (tr-TR)` - `Urdu - Pakistan (ur-PK)` - `Vietnamese - Vietnam (vi-VN)` - `Welsh - United Kingdom (cy-GB)` ## Load the dataset with HuggingFace ```python from datasets import load_dataset dataset = load_dataset("AmazonScience/massive", "en-US", split='train') print(dataset[0]) ``` ## Dataset Structure ### Data Instances ```json { "id": "0", "locale": "fr-FR", "partition": "test", "scenario": "alarm", "intent": "alarm_set", "utt": "réveille-moi à cinq heures du matin cette semaine", "annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]", "worker_id": "22", "slot_method": [ { "slot": "time", "method": "translation" }, { "slot": "date", "method": "translation" } ], "judgments": [ { "worker_id": "22", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" }, { "worker_id": "8", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" }, { "worker_id": "0", "intent_score": 1, "slots_score": 1, "grammar_score": 4, "spelling_score": 2, "language_identification": "target" } ] } ``` ### Data Fields `id`: maps to the original ID in the [SLURP](https://github.com/pswietojanski/slurp) collection. Mapping back to the SLURP en-US utterance, this utterance served as the basis for this localization. `locale`: is the language and country code accoring to ISO-639-1 and ISO-3166. `partition`: is either `train`, `dev`, or `test`, according to the original split in [SLURP](https://github.com/pswietojanski/slurp). `scenario`: is the general domain, aka "scenario" in SLURP terminology, of an utterance `intent`: is the specific intent of an utterance within a domain formatted as `{scenario}_{intent}` `utt`: the raw utterance text without annotations `annot_utt`: the text from `utt` with slot annotations formatted as `[{label} : {entity}]` `worker_id`: The obfuscated worker ID from MTurk of the worker completing the localization of the utterance. Worker IDs are specific to a locale and do *not* map across locales. `slot_method`: for each slot in the utterance, whether that slot was a `translation` (i.e., same expression just in the target language), `localization` (i.e., not the same expression but a different expression was chosen more suitable to the phrase in that locale), or `unchanged` (i.e., the original en-US slot value was copied over without modification). `judgments`: Each judgment collected for the localized utterance has 6 keys. `worker_id` is the obfuscated worker ID from MTurk of the worker completing the judgment. Worker IDs are specific to a locale and do *not* map across locales, but *are* consistent across the localization tasks and the judgment tasks, e.g., judgment worker ID 32 in the example above may appear as the localization worker ID for the localization of a different de-DE utterance, in which case it would be the same worker. ```plain intent_score : "Does the sentence match the intent?" 0: No 1: Yes 2: It is a reasonable interpretation of the goal slots_score : "Do all these terms match the categories in square brackets?" 0: No 1: Yes 2: There are no words in square brackets (utterance without a slot) grammar_score : "Read the sentence out loud. Ignore any spelling, punctuation, or capitalization errors. Does it sound natural?" 0: Completely unnatural (nonsensical, cannot be understood at all) 1: Severe errors (the meaning cannot be understood and doesn't sound natural in your language) 2: Some errors (the meaning can be understood but it doesn't sound natural in your language) 3: Good enough (easily understood and sounds almost natural in your language) 4: Perfect (sounds natural in your language) spelling_score : "Are all words spelled correctly? Ignore any spelling variances that may be due to differences in dialect. Missing spaces should be marked as a spelling error." 0: There are more than 2 spelling errors 1: There are 1-2 spelling errors 2: All words are spelled correctly language_identification : "The following sentence contains words in the following languages (check all that apply)" 1: target 2: english 3: other 4: target & english 5: target & other 6: english & other 7: target & english & other ``` ### Data Splits |Language|Train|Dev|Test| |:---:|:---:|:---:|:---:| |af-ZA|11514|2033|2974| |am-ET|11514|2033|2974| |ar-SA|11514|2033|2974| |az-AZ|11514|2033|2974| |bn-BD|11514|2033|2974| |ca-ES|11514|2033|2974| |cy-GB|11514|2033|2974| |da-DK|11514|2033|2974| |de-DE|11514|2033|2974| |el-GR|11514|2033|2974| |en-US|11514|2033|2974| |es-ES|11514|2033|2974| |fa-IR|11514|2033|2974| |fi-FI|11514|2033|2974| |fr-FR|11514|2033|2974| |he-IL|11514|2033|2974| |hi-IN|11514|2033|2974| |hu-HU|11514|2033|2974| |hy-AM|11514|2033|2974| |id-ID|11514|2033|2974| |is-IS|11514|2033|2974| |it-IT|11514|2033|2974| |ja-JP|11514|2033|2974| |jv-ID|11514|2033|2974| |ka-GE|11514|2033|2974| |km-KH|11514|2033|2974| |kn-IN|11514|2033|2974| |ko-KR|11514|2033|2974| |lv-LV|11514|2033|2974| |ml-IN|11514|2033|2974| |mn-MN|11514|2033|2974| |ms-MY|11514|2033|2974| |my-MM|11514|2033|2974| |nb-NO|11514|2033|2974| |nl-NL|11514|2033|2974| |pl-PL|11514|2033|2974| |pt-PT|11514|2033|2974| |ro-RO|11514|2033|2974| |ru-RU|11514|2033|2974| |sl-SL|11514|2033|2974| |sq-AL|11514|2033|2974| |sv-SE|11514|2033|2974| |sw-KE|11514|2033|2974| |ta-IN|11514|2033|2974| |te-IN|11514|2033|2974| |th-TH|11514|2033|2974| |tl-PH|11514|2033|2974| |tr-TR|11514|2033|2974| |ur-PK|11514|2033|2974| |vi-VN|11514|2033|2974| |zh-CN|11514|2033|2974| |zh-TW|11514|2033|2974| ### Personal and Sensitive Information The corpora is free of personal or sensitive information. ## Additional Information ### Dataset Curators __MASSIVE__: Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan. __SLURP__: Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena. __Hugging Face Upload and Integration__: Labrak Yanis (Not affiliated with the original corpus) ### Licensing Information ```plain Copyright Amazon.com Inc. or its affiliates. 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Creative Commons may be contacted at creativecommons.org. ``` ### Citation Information Please cite the following papers when using this dataset. ```latex @misc{fitzgerald2022massive, title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, year={2022}, eprint={2204.08582}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{bastianelli-etal-2020-slurp, title = "{SLURP}: A Spoken Language Understanding Resource Package", author = "Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.588", doi = "10.18653/v1/2020.emnlp-main.588", pages = "7252--7262", abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." } ```

Idavidrein/gpqa

Idavidrein

--- license: cc-by-4.0 viewer: true extra_gated_prompt: >- You agree to NOT reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model training corpora. extra_gated_fields: I accept these terms: checkbox configs: - config_name: gpqa_extended data_files: gpqa_extended.csv - config_name: gpqa_main data_files: gpqa_main.csv - config_name: gpqa_diamond data_files: gpqa_diamond.csv - config_name: gpqa_experts data_files: gpqa_experts.csv task_categories: - question-answering - text-generation language: - en tags: - open-domain-qa - open-book-qa - multiple-choice-qa pretty_name: GPQA size_categories: - n<1K --- # Dataset Card for GPQA <!-- Provide a quick summary of the dataset. --> GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google. We request that you **do not reveal examples from this dataset in plain text or images online**, to reduce the risk of leakage into foundation model training corpora. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are "Google-proof"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities. - **Curated by:** David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman - **License:** CC BY 4.0 ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** https://github.com/idavidrein/gpqa - **Paper:** https://arxiv.org/abs/2311.12022 ## Uses The dataset is primarily intended to be used for scalable oversight experiments, although it can also be used for more general LLM capabilities benchmarking. ## Dataset Card Contact David Rein: idavidrein@gmail.com --- Submit corrections to examples in GPQA via this form: https://forms.gle/iTY4zMETNsPhJq8R9 ---

cardiffnlp/tweet_eval

cardiffnlp

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - extended|other-tweet-datasets task_categories: - text-classification task_ids: - intent-classification - multi-class-classification - sentiment-classification paperswithcode_id: tweeteval pretty_name: TweetEval config_names: - emoji - emotion - hate - irony - offensive - sentiment - stance_abortion - stance_atheism - stance_climate - stance_feminist - stance_hillary dataset_info: - config_name: emoji features: - name: text dtype: string - name: label dtype: class_label: names: '0': ❤ '1': 😍 '2': 😂 '3': 💕 '4': 🔥 '5': 😊 '6': 😎 '7': ✨ '8': 💙 '9': 😘 '10': 📷 '11': 🇺🇸 '12': ☀ '13': 💜 '14': 😉 '15': 💯 '16': 😁 '17': 🎄 '18': 📸 '19': 😜 splits: - name: train num_bytes: 3803167 num_examples: 45000 - name: test num_bytes: 4255901 num_examples: 50000 - name: validation num_bytes: 396079 num_examples: 5000 download_size: 5939308 dataset_size: 8455147 - config_name: emotion features: - name: text dtype: string - name: label dtype: class_label: names: '0': anger '1': joy '2': optimism '3': sadness splits: - name: train num_bytes: 338871 num_examples: 3257 - name: test num_bytes: 146645 num_examples: 1421 - name: validation num_bytes: 38273 num_examples: 374 download_size: 367016 dataset_size: 523789 - config_name: hate features: - name: text dtype: string - name: label dtype: class_label: names: '0': non-hate '1': hate splits: - name: train num_bytes: 1223650 num_examples: 9000 - name: test num_bytes: 428934 num_examples: 2970 - name: validation num_bytes: 154144 num_examples: 1000 download_size: 1196346 dataset_size: 1806728 - config_name: irony features: - name: text dtype: string - name: label dtype: class_label: names: '0': non_irony '1': irony splits: - name: train num_bytes: 259187 num_examples: 2862 - name: test num_bytes: 75897 num_examples: 784 - name: validation num_bytes: 86017 num_examples: 955 download_size: 297647 dataset_size: 421101 - config_name: offensive features: - name: text dtype: string - name: label dtype: class_label: names: '0': non-offensive '1': offensive splits: - name: train num_bytes: 1648061 num_examples: 11916 - name: test num_bytes: 135473 num_examples: 860 - name: validation num_bytes: 192417 num_examples: 1324 download_size: 1234528 dataset_size: 1975951 - config_name: sentiment features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral '2': positive splits: - name: train num_bytes: 5425122 num_examples: 45615 - name: test num_bytes: 1279540 num_examples: 12284 - name: validation num_bytes: 239084 num_examples: 2000 download_size: 4849675 dataset_size: 6943746 - config_name: stance_abortion features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 68694 num_examples: 587 - name: test num_bytes: 33171 num_examples: 280 - name: validation num_bytes: 7657 num_examples: 66 download_size: 73517 dataset_size: 109522 - config_name: stance_atheism features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 54775 num_examples: 461 - name: test num_bytes: 25716 num_examples: 220 - name: validation num_bytes: 6320 num_examples: 52 download_size: 62265 dataset_size: 86811 - config_name: stance_climate features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 40249 num_examples: 355 - name: test num_bytes: 19925 num_examples: 169 - name: validation num_bytes: 4801 num_examples: 40 download_size: 48493 dataset_size: 64975 - config_name: stance_feminist features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 70509 num_examples: 597 - name: test num_bytes: 33305 num_examples: 285 - name: validation num_bytes: 8035 num_examples: 67 download_size: 76345 dataset_size: 111849 - config_name: stance_hillary features: - name: text dtype: string - name: label dtype: class_label: names: '0': none '1': against '2': favor splits: - name: train num_bytes: 69596 num_examples: 620 - name: test num_bytes: 34487 num_examples: 295 - name: validation num_bytes: 7532 num_examples: 69 download_size: 74057 dataset_size: 111615 configs: - config_name: emoji data_files: - split: train path: emoji/train-* - split: test path: emoji/test-* - split: validation path: emoji/validation-* - config_name: emotion data_files: - split: train path: emotion/train-* - split: test path: emotion/test-* - split: validation path: emotion/validation-* - config_name: hate data_files: - split: train path: hate/train-* - split: test path: hate/test-* - split: validation path: hate/validation-* - config_name: irony data_files: - split: train path: irony/train-* - split: test path: irony/test-* - split: validation path: irony/validation-* - config_name: offensive data_files: - split: train path: offensive/train-* - split: test path: offensive/test-* - split: validation path: offensive/validation-* - config_name: sentiment data_files: - split: train path: sentiment/train-* - split: test path: sentiment/test-* - split: validation path: sentiment/validation-* - config_name: stance_abortion data_files: - split: train path: stance_abortion/train-* - split: test path: stance_abortion/test-* - split: validation path: stance_abortion/validation-* - config_name: stance_atheism data_files: - split: train path: stance_atheism/train-* - split: test path: stance_atheism/test-* - split: validation path: stance_atheism/validation-* - config_name: stance_climate data_files: - split: train path: stance_climate/train-* - split: test path: stance_climate/test-* - split: validation path: stance_climate/validation-* - config_name: stance_feminist data_files: - split: train path: stance_feminist/train-* - split: test path: stance_feminist/test-* - split: validation path: stance_feminist/validation-* - config_name: stance_hillary data_files: - split: train path: stance_hillary/train-* - split: test path: stance_hillary/test-* - split: validation path: stance_hillary/validation-* train-eval-index: - config: emotion task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: hate task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: irony task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: offensive task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted - config: sentiment task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for tweet_eval ## 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:** [Needs More Information] - **Repository:** [GitHub](https://github.com/cardiffnlp/tweeteval) - **Paper:** [EMNLP Paper](https://arxiv.org/pdf/2010.12421.pdf) - **Leaderboard:** [GitHub Leaderboard](https://github.com/cardiffnlp/tweeteval) - **Point of Contact:** [Needs More Information] ### Dataset Summary TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. The tasks include - irony, hate, offensive, stance, emoji, emotion, and sentiment. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits. ### Supported Tasks and Leaderboards - `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers. ### Languages The text in the dataset is in English, as spoken by Twitter users. ## Dataset Structure ### Data Instances An instance from `emoji` config: ``` {'label': 12, 'text': 'Sunday afternoon walking through Venice in the sun with @user ️ ️ ️ @ Abbot Kinney, Venice'} ``` An instance from `emotion` config: ``` {'label': 2, 'text': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer. #motivation #leadership #worry"} ``` An instance from `hate` config: ``` {'label': 0, 'text': '@user nice new signage. Are you not concerned by Beatlemania -style hysterical crowds crongregating on you…'} ``` An instance from `irony` config: ``` {'label': 1, 'text': 'seeing ppl walking w/ crutches makes me really excited for the next 3 weeks of my life'} ``` An instance from `offensive` config: ``` {'label': 0, 'text': '@user Bono... who cares. Soon people will understand that they gain nothing from following a phony celebrity. Become a Leader of your people instead or help and support your fellow countrymen.'} ``` An instance from `sentiment` config: ``` {'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'} ``` An instance from `stance_abortion` config: ``` {'label': 1, 'text': 'we remind ourselves that love means to be willing to give until it hurts - Mother Teresa'} ``` An instance from `stance_atheism` config: ``` {'label': 1, 'text': '@user Bless Almighty God, Almighty Holy Spirit and the Messiah. #SemST'} ``` An instance from `stance_climate` config: ``` {'label': 0, 'text': 'Why Is The Pope Upset? via @user #UnzippedTruth #PopeFrancis #SemST'} ``` An instance from `stance_feminist` config: ``` {'label': 1, 'text': "@user @user is the UK's answer to @user and @user #GamerGate #SemST"} ``` An instance from `stance_hillary` config: ``` {'label': 1, 'text': "If a man demanded staff to get him an ice tea he'd be called a sexists elitist pig.. Oink oink #Hillary #SemST"} ``` ### Data Fields For `emoji` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: ❤ `1`: 😍 `2`: 😂 `3`: 💕 `4`: 🔥 `5`: 😊 `6`: 😎 `7`: ✨ `8`: 💙 `9`: 😘 `10`: 📷 `11`: 🇺🇸 `12`: ☀ `13`: 💜 `14`: 😉 `15`: 💯 `16`: 😁 `17`: 🎄 `18`: 📸 `19`: 😜 For `emotion` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: anger `1`: joy `2`: optimism `3`: sadness For `hate` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non-hate `1`: hate For `irony` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non_irony `1`: irony For `offensive` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: non-offensive `1`: offensive For `sentiment` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: negative `1`: neutral `2`: positive For `stance_abortion` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_atheism` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_climate` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_feminist` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor For `stance_hillary` config: - `text`: a `string` feature containing the tweet. - `label`: an `int` classification label with the following mapping: `0`: none `1`: against `2`: favor ### Data Splits | name | train | validation | test | | --------------- | ----- | ---------- | ----- | | emoji | 45000 | 5000 | 50000 | | emotion | 3257 | 374 | 1421 | | hate | 9000 | 1000 | 2970 | | irony | 2862 | 955 | 784 | | offensive | 11916 | 1324 | 860 | | sentiment | 45615 | 2000 | 12284 | | stance_abortion | 587 | 66 | 280 | | stance_atheism | 461 | 52 | 220 | | stance_climate | 355 | 40 | 169 | | stance_feminist | 597 | 67 | 285 | | stance_hillary | 620 | 69 | 295 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP. ### Licensing Information This is not a single dataset, therefore each subset has its own license (the collection itself does not have additional restrictions). All of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy) Additionally the license are: - emoji: Undefined - emotion(EmoInt): Undefined - hate (HateEval): Need permission [here](http://hatespeech.di.unito.it/hateval.html) - irony: Undefined - Offensive: Undefined - Sentiment: [Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ) - Stance: Undefined ### Citation Information ``` @inproceedings{barbieri2020tweeteval, title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}}, author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo}, booktitle={Proceedings of Findings of EMNLP}, year={2020} } ``` If you use any of the TweetEval datasets, please cite their original publications: #### Emotion Recognition: ``` @inproceedings{mohammad2018semeval, title={Semeval-2018 task 1: Affect in tweets}, author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana}, booktitle={Proceedings of the 12th international workshop on semantic evaluation}, pages={1--17}, year={2018} } ``` #### Emoji Prediction: ``` @inproceedings{barbieri2018semeval, title={Semeval 2018 task 2: Multilingual emoji prediction}, author={Barbieri, Francesco and Camacho-Collados, Jose and Ronzano, Francesco and Espinosa-Anke, Luis and Ballesteros, Miguel and Basile, Valerio and Patti, Viviana and Saggion, Horacio}, booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, pages={24--33}, year={2018} } ``` #### Irony Detection: ``` @inproceedings{van2018semeval, title={Semeval-2018 task 3: Irony detection in english tweets}, author={Van Hee, Cynthia and Lefever, Els and Hoste, V{\'e}ronique}, booktitle={Proceedings of The 12th International Workshop on Semantic Evaluation}, pages={39--50}, year={2018} } ``` #### Hate Speech Detection: ``` @inproceedings{basile-etal-2019-semeval, title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter", author = "Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and Rangel Pardo, Francisco Manuel and Rosso, Paolo and Sanguinetti, Manuela", booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation", year = "2019", address = "Minneapolis, Minnesota, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/S19-2007", doi = "10.18653/v1/S19-2007", pages = "54--63" } ``` #### Offensive Language Identification: ``` @inproceedings{zampieri2019semeval, title={SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)}, author={Zampieri, Marcos and Malmasi, Shervin and Nakov, Preslav and Rosenthal, Sara and Farra, Noura and Kumar, Ritesh}, booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation}, pages={75--86}, year={2019} } ``` #### Sentiment Analysis: ``` @inproceedings{rosenthal2017semeval, title={SemEval-2017 task 4: Sentiment analysis in Twitter}, author={Rosenthal, Sara and Farra, Noura and Nakov, Preslav}, booktitle={Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017)}, pages={502--518}, year={2017} } ``` #### Stance Detection: ``` @inproceedings{mohammad2016semeval, title={Semeval-2016 task 6: Detecting stance in tweets}, author={Mohammad, Saif and Kiritchenko, Svetlana and Sobhani, Parinaz and Zhu, Xiaodan and Cherry, Colin}, booktitle={Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)}, pages={31--41}, year={2016} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) and [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

BleachNick/UltraEdit

BleachNick

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name: FreeForm_1941 num_bytes: 767928606 num_examples: 2000 - name: FreeForm_1977 num_bytes: 780736929 num_examples: 2000 - name: FreeForm_1981 num_bytes: 775615890 num_examples: 2000 - name: FreeForm_1984 num_bytes: 769609649 num_examples: 2000 - name: FreeForm_1985 num_bytes: 770730441 num_examples: 2000 - name: FreeForm_1987 num_bytes: 768263066 num_examples: 2000 - name: FreeForm_1989 num_bytes: 780388977 num_examples: 2000 - name: FreeForm_1990 num_bytes: 772863509 num_examples: 2000 - name: FreeForm_1993 num_bytes: 773757340 num_examples: 2000 - name: FreeForm_1996 num_bytes: 770872885 num_examples: 2000 - name: FreeForm_2000 num_bytes: 32585530 num_examples: 83 - name: FreeForm_1205 num_bytes: 776134960.0 num_examples: 2000 download_size: 1182151585538 dataset_size: 1177371972678.0 configs: - config_name: default data_files: - split: FreeForm_0 path: data/FreeForm_0-* - split: FreeForm_1 path: data/FreeForm_1-* - split: FreeForm_2 path: data/FreeForm_2-* - split: FreeForm_3 path: data/FreeForm_3-* - split: FreeForm_4 path: data/FreeForm_4-* - split: FreeForm_5 path: data/FreeForm_5-* - split: FreeForm_6 path: data/FreeForm_6-* - split: FreeForm_7 path: data/FreeForm_7-* - split: FreeForm_8 path: data/FreeForm_8-* - split: FreeForm_9 path: data/FreeForm_9-* - split: FreeForm_10 path: data/FreeForm_10-* - split: FreeForm_11 path: data/FreeForm_11-* - split: FreeForm_12 path: data/FreeForm_12-* - split: FreeForm_13 path: data/FreeForm_13-* - split: FreeForm_14 path: data/FreeForm_14-* - split: FreeForm_15 path: data/FreeForm_15-* - split: FreeForm_16 path: data/FreeForm_16-* - split: FreeForm_17 path: data/FreeForm_17-* - split: FreeForm_18 path: data/FreeForm_18-* - split: FreeForm_19 path: data/FreeForm_19-* - split: FreeForm_20 path: data/FreeForm_20-* - split: FreeForm_21 path: data/FreeForm_21-* - split: FreeForm_22 path: data/FreeForm_22-* - split: FreeForm_23 path: data/FreeForm_23-* - split: FreeForm_24 path: data/FreeForm_24-* - split: FreeForm_25 path: data/FreeForm_25-* - split: FreeForm_26 path: data/FreeForm_26-* - split: FreeForm_27 path: data/FreeForm_27-* - split: FreeForm_28 path: data/FreeForm_28-* - split: FreeForm_29 path: data/FreeForm_29-* - split: FreeForm_30 path: data/FreeForm_30-* - split: FreeForm_31 path: data/FreeForm_31-* - split: FreeForm_32 path: data/FreeForm_32-* - split: FreeForm_33 path: data/FreeForm_33-* - split: FreeForm_34 path: data/FreeForm_34-* - split: FreeForm_35 path: data/FreeForm_35-* - split: FreeForm_36 path: data/FreeForm_36-* - split: FreeForm_37 path: data/FreeForm_37-* - split: FreeForm_38 path: data/FreeForm_38-* - split: FreeForm_39 path: data/FreeForm_39-* - split: FreeForm_40 path: data/FreeForm_40-* - split: FreeForm_41 path: data/FreeForm_41-* - split: FreeForm_42 path: data/FreeForm_42-* - split: FreeForm_43 path: data/FreeForm_43-* - split: FreeForm_44 path: data/FreeForm_44-* - split: FreeForm_45 path: data/FreeForm_45-* - split: FreeForm_46 path: data/FreeForm_46-* - split: FreeForm_47 path: data/FreeForm_47-* - split: FreeForm_48 path: data/FreeForm_48-* - split: FreeForm_49 path: data/FreeForm_49-* - split: FreeForm_50 path: data/FreeForm_50-* - split: FreeForm_51 path: data/FreeForm_51-* - split: FreeForm_52 path: data/FreeForm_52-* - split: FreeForm_53 path: data/FreeForm_53-* - split: FreeForm_54 path: data/FreeForm_54-* - split: FreeForm_55 path: data/FreeForm_55-* - split: FreeForm_56 path: data/FreeForm_56-* - split: FreeForm_57 path: data/FreeForm_57-* - split: FreeForm_58 path: data/FreeForm_58-* - split: FreeForm_59 path: data/FreeForm_59-* - split: FreeForm_60 path: data/FreeForm_60-* - split: FreeForm_61 path: data/FreeForm_61-* - split: FreeForm_62 path: data/FreeForm_62-* - split: FreeForm_63 path: data/FreeForm_63-* - split: FreeForm_64 path: data/FreeForm_64-* - split: FreeForm_65 path: data/FreeForm_65-* - split: FreeForm_66 path: data/FreeForm_66-* - split: FreeForm_67 path: data/FreeForm_67-* - split: FreeForm_68 path: data/FreeForm_68-* - split: FreeForm_69 path: data/FreeForm_69-* - split: FreeForm_70 path: data/FreeForm_70-* - split: FreeForm_71 path: data/FreeForm_71-* - split: FreeForm_72 path: data/FreeForm_72-* - split: FreeForm_73 path: data/FreeForm_73-* - split: FreeForm_74 path: data/FreeForm_74-* - split: FreeForm_75 path: data/FreeForm_75-* - split: FreeForm_76 path: data/FreeForm_76-* - split: FreeForm_77 path: data/FreeForm_77-* - split: FreeForm_78 path: data/FreeForm_78-* - split: FreeForm_79 path: data/FreeForm_79-* - split: FreeForm_80 path: data/FreeForm_80-* - split: FreeForm_81 path: data/FreeForm_81-* - split: FreeForm_82 path: data/FreeForm_82-* - split: FreeForm_83 path: data/FreeForm_83-* - split: FreeForm_84 path: data/FreeForm_84-* - split: FreeForm_85 path: data/FreeForm_85-* - split: FreeForm_86 path: data/FreeForm_86-* - split: FreeForm_87 path: data/FreeForm_87-* - split: FreeForm_88 path: data/FreeForm_88-* - split: FreeForm_89 path: data/FreeForm_89-* - split: FreeForm_90 path: data/FreeForm_90-* - split: FreeForm_91 path: data/FreeForm_91-* - split: FreeForm_92 path: data/FreeForm_92-* - split: FreeForm_93 path: data/FreeForm_93-* - split: FreeForm_94 path: data/FreeForm_94-* - split: FreeForm_95 path: data/FreeForm_95-* - split: FreeForm_96 path: data/FreeForm_96-* - split: FreeForm_97 path: data/FreeForm_97-* - split: FreeForm_98 path: data/FreeForm_98-* - split: FreeForm_99 path: data/FreeForm_99-* - split: FreeForm_100 path: data/FreeForm_100-* - split: FreeForm_101 path: data/FreeForm_101-* - split: FreeForm_102 path: data/FreeForm_102-* - split: FreeForm_103 path: data/FreeForm_103-* - split: FreeForm_104 path: data/FreeForm_104-* - split: FreeForm_105 path: data/FreeForm_105-* - split: FreeForm_106 path: data/FreeForm_106-* - split: FreeForm_107 path: data/FreeForm_107-* - split: FreeForm_108 path: data/FreeForm_108-* - split: FreeForm_109 path: data/FreeForm_109-* - split: FreeForm_110 path: data/FreeForm_110-* - split: FreeForm_111 path: data/FreeForm_111-* - split: FreeForm_112 path: data/FreeForm_112-* - split: FreeForm_113 path: data/FreeForm_113-* - split: FreeForm_114 path: data/FreeForm_114-* - split: FreeForm_115 path: data/FreeForm_115-* - split: FreeForm_116 path: data/FreeForm_116-* - split: FreeForm_117 path: data/FreeForm_117-* - split: FreeForm_118 path: data/FreeForm_118-* - split: FreeForm_119 path: data/FreeForm_119-* - split: FreeForm_120 path: data/FreeForm_120-* - split: FreeForm_121 path: data/FreeForm_121-* - split: FreeForm_122 path: data/FreeForm_122-* - split: FreeForm_123 path: data/FreeForm_123-* - split: FreeForm_124 path: data/FreeForm_124-* - split: FreeForm_125 path: data/FreeForm_125-* - split: FreeForm_126 path: data/FreeForm_126-* - split: FreeForm_127 path: data/FreeForm_127-* - split: FreeForm_128 path: data/FreeForm_128-* - split: FreeForm_129 path: data/FreeForm_129-* - split: FreeForm_130 path: data/FreeForm_130-* - split: FreeForm_131 path: data/FreeForm_131-* - split: FreeForm_132 path: data/FreeForm_132-* - split: FreeForm_133 path: data/FreeForm_133-* - split: FreeForm_134 path: data/FreeForm_134-* - split: FreeForm_135 path: data/FreeForm_135-* - split: FreeForm_136 path: data/FreeForm_136-* - split: FreeForm_137 path: data/FreeForm_137-* - split: FreeForm_138 path: data/FreeForm_138-* - split: FreeForm_139 path: data/FreeForm_139-* - split: FreeForm_140 path: data/FreeForm_140-* - split: FreeForm_141 path: data/FreeForm_141-* - split: FreeForm_142 path: data/FreeForm_142-* - split: FreeForm_143 path: data/FreeForm_143-* - split: FreeForm_144 path: data/FreeForm_144-* - split: FreeForm_145 path: data/FreeForm_145-* - split: FreeForm_146 path: data/FreeForm_146-* - split: FreeForm_147 path: data/FreeForm_147-* - split: FreeForm_148 path: data/FreeForm_148-* - split: FreeForm_149 path: data/FreeForm_149-* - split: FreeForm_150 path: data/FreeForm_150-* - split: FreeForm_151 path: data/FreeForm_151-* - split: FreeForm_152 path: data/FreeForm_152-* - split: FreeForm_153 path: data/FreeForm_153-* - split: FreeForm_154 path: data/FreeForm_154-* - split: FreeForm_155 path: data/FreeForm_155-* - split: FreeForm_156 path: data/FreeForm_156-* - split: FreeForm_157 path: data/FreeForm_157-* - split: FreeForm_158 path: data/FreeForm_158-* - split: FreeForm_159 path: data/FreeForm_159-* - split: FreeForm_160 path: data/FreeForm_160-* - split: FreeForm_161 path: data/FreeForm_161-* - split: FreeForm_162 path: data/FreeForm_162-* - split: FreeForm_163 path: data/FreeForm_163-* - split: FreeForm_164 path: data/FreeForm_164-* - split: FreeForm_165 path: data/FreeForm_165-* - split: FreeForm_166 path: data/FreeForm_166-* - split: FreeForm_167 path: data/FreeForm_167-* - split: FreeForm_168 path: data/FreeForm_168-* - split: FreeForm_169 path: data/FreeForm_169-* - split: FreeForm_170 path: data/FreeForm_170-* - split: FreeForm_171 path: data/FreeForm_171-* - split: FreeForm_172 path: data/FreeForm_172-* - split: FreeForm_173 path: data/FreeForm_173-* - split: FreeForm_174 path: data/FreeForm_174-* - split: FreeForm_175 path: data/FreeForm_175-* - split: FreeForm_176 path: data/FreeForm_176-* - split: FreeForm_177 path: data/FreeForm_177-* - split: FreeForm_178 path: data/FreeForm_178-* - split: FreeForm_179 path: data/FreeForm_179-* - split: FreeForm_180 path: data/FreeForm_180-* - split: FreeForm_181 path: data/FreeForm_181-* - split: FreeForm_182 path: data/FreeForm_182-* - split: FreeForm_183 path: data/FreeForm_183-* - split: FreeForm_184 path: data/FreeForm_184-* - split: FreeForm_185 path: data/FreeForm_185-* - split: FreeForm_186 path: data/FreeForm_186-* - split: FreeForm_187 path: data/FreeForm_187-* - split: FreeForm_188 path: data/FreeForm_188-* - split: FreeForm_189 path: data/FreeForm_189-* - split: FreeForm_190 path: data/FreeForm_190-* - split: FreeForm_191 path: data/FreeForm_191-* - split: FreeForm_192 path: data/FreeForm_192-* - split: FreeForm_193 path: data/FreeForm_193-* - split: FreeForm_194 path: data/FreeForm_194-* - split: FreeForm_195 path: data/FreeForm_195-* - split: FreeForm_196 path: data/FreeForm_196-* - split: FreeForm_197 path: data/FreeForm_197-* - split: FreeForm_198 path: data/FreeForm_198-* - split: FreeForm_199 path: data/FreeForm_199-* - split: FreeForm_200 path: data/FreeForm_200-* - split: FreeForm_201 path: data/FreeForm_201-* - split: FreeForm_202 path: data/FreeForm_202-* - split: FreeForm_203 path: data/FreeForm_203-* - split: FreeForm_204 path: data/FreeForm_204-* - split: FreeForm_205 path: data/FreeForm_205-* - split: FreeForm_206 path: data/FreeForm_206-* - split: FreeForm_207 path: data/FreeForm_207-* - split: FreeForm_208 path: data/FreeForm_208-* - split: FreeForm_209 path: data/FreeForm_209-* - split: FreeForm_210 path: data/FreeForm_210-* - split: FreeForm_211 path: data/FreeForm_211-* - split: FreeForm_212 path: data/FreeForm_212-* - split: FreeForm_213 path: data/FreeForm_213-* - split: FreeForm_214 path: data/FreeForm_214-* - split: FreeForm_215 path: data/FreeForm_215-* - split: FreeForm_216 path: data/FreeForm_216-* - split: FreeForm_217 path: data/FreeForm_217-* - split: FreeForm_218 path: data/FreeForm_218-* - split: FreeForm_219 path: data/FreeForm_219-* - split: FreeForm_220 path: data/FreeForm_220-* - split: FreeForm_221 path: data/FreeForm_221-* - split: FreeForm_222 path: data/FreeForm_222-* - split: FreeForm_223 path: data/FreeForm_223-* - split: FreeForm_224 path: data/FreeForm_224-* - split: FreeForm_225 path: data/FreeForm_225-* - split: FreeForm_226 path: data/FreeForm_226-* - split: FreeForm_227 path: data/FreeForm_227-* - split: FreeForm_228 path: data/FreeForm_228-* - split: FreeForm_229 path: data/FreeForm_229-* - split: FreeForm_230 path: data/FreeForm_230-* - split: FreeForm_231 path: data/FreeForm_231-* - split: FreeForm_232 path: data/FreeForm_232-* - split: FreeForm_233 path: data/FreeForm_233-* - split: FreeForm_234 path: data/FreeForm_234-* - split: FreeForm_235 path: data/FreeForm_235-* - split: FreeForm_236 path: data/FreeForm_236-* - split: FreeForm_237 path: data/FreeForm_237-* - split: FreeForm_238 path: data/FreeForm_238-* - split: FreeForm_239 path: data/FreeForm_239-* - split: FreeForm_240 path: data/FreeForm_240-* - split: FreeForm_241 path: data/FreeForm_241-* - split: FreeForm_242 path: data/FreeForm_242-* - split: FreeForm_243 path: data/FreeForm_243-* - split: FreeForm_244 path: data/FreeForm_244-* - split: FreeForm_245 path: data/FreeForm_245-* - split: FreeForm_246 path: data/FreeForm_246-* - split: FreeForm_247 path: data/FreeForm_247-* - split: FreeForm_248 path: data/FreeForm_248-* - split: FreeForm_249 path: data/FreeForm_249-* - split: FreeForm_250 path: data/FreeForm_250-* - split: FreeForm_251 path: data/FreeForm_251-* - split: FreeForm_252 path: data/FreeForm_252-* - split: FreeForm_253 path: data/FreeForm_253-* - split: FreeForm_254 path: data/FreeForm_254-* - split: FreeForm_255 path: data/FreeForm_255-* - split: FreeForm_256 path: data/FreeForm_256-* - split: FreeForm_257 path: data/FreeForm_257-* - split: FreeForm_258 path: data/FreeForm_258-* - split: FreeForm_259 path: data/FreeForm_259-* - split: FreeForm_260 path: data/FreeForm_260-* - split: FreeForm_261 path: data/FreeForm_261-* - split: FreeForm_262 path: data/FreeForm_262-* - split: FreeForm_263 path: data/FreeForm_263-* - split: FreeForm_264 path: data/FreeForm_264-* - split: FreeForm_265 path: data/FreeForm_265-* - split: FreeForm_266 path: data/FreeForm_266-* - split: FreeForm_267 path: data/FreeForm_267-* - split: FreeForm_268 path: data/FreeForm_268-* - split: FreeForm_269 path: data/FreeForm_269-* - split: FreeForm_270 path: data/FreeForm_270-* - split: FreeForm_271 path: data/FreeForm_271-* - split: FreeForm_272 path: data/FreeForm_272-* - split: FreeForm_273 path: data/FreeForm_273-* - split: FreeForm_274 path: data/FreeForm_274-* - split: FreeForm_275 path: data/FreeForm_275-* - split: FreeForm_276 path: data/FreeForm_276-* - split: FreeForm_277 path: data/FreeForm_277-* - split: FreeForm_278 path: data/FreeForm_278-* - split: FreeForm_279 path: data/FreeForm_279-* - split: FreeForm_280 path: data/FreeForm_280-* - split: FreeForm_281 path: data/FreeForm_281-* - split: FreeForm_282 path: data/FreeForm_282-* - split: FreeForm_283 path: data/FreeForm_283-* - split: FreeForm_284 path: data/FreeForm_284-* - split: FreeForm_285 path: data/FreeForm_285-* - split: FreeForm_286 path: data/FreeForm_286-* - split: FreeForm_287 path: data/FreeForm_287-* - split: FreeForm_288 path: data/FreeForm_288-* - split: FreeForm_289 path: data/FreeForm_289-* - split: FreeForm_290 path: data/FreeForm_290-* - split: FreeForm_291 path: data/FreeForm_291-* - split: FreeForm_292 path: data/FreeForm_292-* - split: FreeForm_293 path: data/FreeForm_293-* - split: FreeForm_294 path: data/FreeForm_294-* - split: FreeForm_295 path: data/FreeForm_295-* - split: FreeForm_296 path: data/FreeForm_296-* - split: FreeForm_297 path: data/FreeForm_297-* - split: FreeForm_298 path: data/FreeForm_298-* - split: FreeForm_299 path: data/FreeForm_299-* - split: FreeForm_300 path: data/FreeForm_300-* - split: FreeForm_301 path: data/FreeForm_301-* - split: FreeForm_302 path: data/FreeForm_302-* - split: FreeForm_303 path: data/FreeForm_303-* - split: FreeForm_304 path: data/FreeForm_304-* - split: FreeForm_305 path: data/FreeForm_305-* - split: FreeForm_306 path: data/FreeForm_306-* - split: FreeForm_307 path: data/FreeForm_307-* - split: FreeForm_308 path: data/FreeForm_308-* - split: FreeForm_309 path: data/FreeForm_309-* - split: FreeForm_310 path: data/FreeForm_310-* - split: FreeForm_311 path: data/FreeForm_311-* - split: FreeForm_312 path: data/FreeForm_312-* - split: FreeForm_313 path: data/FreeForm_313-* - split: FreeForm_314 path: data/FreeForm_314-* - split: FreeForm_315 path: data/FreeForm_315-* - split: FreeForm_316 path: data/FreeForm_316-* - split: FreeForm_317 path: data/FreeForm_317-* - split: FreeForm_318 path: data/FreeForm_318-* - split: FreeForm_319 path: data/FreeForm_319-* - split: FreeForm_320 path: data/FreeForm_320-* - split: FreeForm_321 path: data/FreeForm_321-* - split: FreeForm_322 path: data/FreeForm_322-* - split: FreeForm_323 path: data/FreeForm_323-* - split: FreeForm_324 path: data/FreeForm_324-* - split: FreeForm_325 path: data/FreeForm_325-* - split: FreeForm_326 path: data/FreeForm_326-* - split: FreeForm_327 path: data/FreeForm_327-* - split: FreeForm_328 path: data/FreeForm_328-* - split: FreeForm_329 path: data/FreeForm_329-* - split: FreeForm_330 path: data/FreeForm_330-* - split: FreeForm_331 path: data/FreeForm_331-* - split: FreeForm_332 path: data/FreeForm_332-* - split: FreeForm_333 path: data/FreeForm_333-* - split: FreeForm_334 path: data/FreeForm_334-* - split: FreeForm_335 path: data/FreeForm_335-* - split: FreeForm_336 path: data/FreeForm_336-* - split: FreeForm_337 path: data/FreeForm_337-* - split: FreeForm_338 path: data/FreeForm_338-* - split: FreeForm_339 path: data/FreeForm_339-* - split: FreeForm_340 path: data/FreeForm_340-* - split: FreeForm_341 path: data/FreeForm_341-* - split: FreeForm_342 path: data/FreeForm_342-* - split: FreeForm_343 path: data/FreeForm_343-* - split: FreeForm_344 path: data/FreeForm_344-* - split: FreeForm_345 path: data/FreeForm_345-* - split: FreeForm_346 path: data/FreeForm_346-* - split: FreeForm_347 path: data/FreeForm_347-* - split: FreeForm_348 path: data/FreeForm_348-* - split: FreeForm_349 path: data/FreeForm_349-* - split: FreeForm_350 path: data/FreeForm_350-* - split: FreeForm_351 path: data/FreeForm_351-* - split: FreeForm_352 path: data/FreeForm_352-* - split: FreeForm_353 path: data/FreeForm_353-* - split: FreeForm_354 path: data/FreeForm_354-* - split: FreeForm_355 path: data/FreeForm_355-* - split: FreeForm_356 path: data/FreeForm_356-* - split: FreeForm_357 path: data/FreeForm_357-* - split: FreeForm_358 path: data/FreeForm_358-* - split: FreeForm_359 path: data/FreeForm_359-* - split: FreeForm_360 path: data/FreeForm_360-* - split: FreeForm_361 path: data/FreeForm_361-* - split: FreeForm_362 path: data/FreeForm_362-* - split: FreeForm_363 path: data/FreeForm_363-* - split: FreeForm_364 path: data/FreeForm_364-* - split: FreeForm_365 path: data/FreeForm_365-* - split: FreeForm_366 path: data/FreeForm_366-* - split: FreeForm_367 path: data/FreeForm_367-* - split: FreeForm_368 path: data/FreeForm_368-* - split: FreeForm_369 path: data/FreeForm_369-* - split: FreeForm_370 path: data/FreeForm_370-* - split: FreeForm_371 path: data/FreeForm_371-* - split: FreeForm_372 path: data/FreeForm_372-* - split: FreeForm_373 path: data/FreeForm_373-* - split: FreeForm_374 path: data/FreeForm_374-* - split: FreeForm_375 path: data/FreeForm_375-* - split: FreeForm_376 path: data/FreeForm_376-* - split: FreeForm_377 path: data/FreeForm_377-* - split: FreeForm_378 path: data/FreeForm_378-* - split: FreeForm_379 path: data/FreeForm_379-* - split: FreeForm_380 path: data/FreeForm_380-* - split: FreeForm_381 path: data/FreeForm_381-* - split: FreeForm_382 path: data/FreeForm_382-* - split: FreeForm_383 path: data/FreeForm_383-* - split: FreeForm_384 path: data/FreeForm_384-* - split: FreeForm_385 path: data/FreeForm_385-* - split: FreeForm_386 path: data/FreeForm_386-* - split: FreeForm_387 path: data/FreeForm_387-* - split: FreeForm_388 path: data/FreeForm_388-* - split: FreeForm_389 path: data/FreeForm_389-* - split: FreeForm_390 path: data/FreeForm_390-* - split: FreeForm_391 path: data/FreeForm_391-* - split: FreeForm_392 path: data/FreeForm_392-* - split: FreeForm_393 path: data/FreeForm_393-* - split: FreeForm_394 path: data/FreeForm_394-* - split: FreeForm_395 path: data/FreeForm_395-* - split: FreeForm_396 path: data/FreeForm_396-* - split: FreeForm_397 path: data/FreeForm_397-* - split: FreeForm_398 path: data/FreeForm_398-* - split: FreeForm_399 path: data/FreeForm_399-* - split: FreeForm_400 path: data/FreeForm_400-* - split: FreeForm_401 path: data/FreeForm_401-* - split: FreeForm_402 path: data/FreeForm_402-* - split: FreeForm_403 path: data/FreeForm_403-* - split: FreeForm_404 path: data/FreeForm_404-* - split: FreeForm_405 path: data/FreeForm_405-* - split: FreeForm_406 path: data/FreeForm_406-* - split: FreeForm_407 path: data/FreeForm_407-* - split: FreeForm_408 path: data/FreeForm_408-* - split: FreeForm_409 path: data/FreeForm_409-* - split: FreeForm_410 path: data/FreeForm_410-* - split: FreeForm_411 path: data/FreeForm_411-* - split: FreeForm_412 path: data/FreeForm_412-* - split: FreeForm_413 path: data/FreeForm_413-* - split: FreeForm_414 path: data/FreeForm_414-* - split: FreeForm_415 path: data/FreeForm_415-* - split: FreeForm_416 path: data/FreeForm_416-* - split: FreeForm_417 path: data/FreeForm_417-* - split: FreeForm_418 path: data/FreeForm_418-* - split: FreeForm_419 path: data/FreeForm_419-* - split: FreeForm_420 path: data/FreeForm_420-* - split: FreeForm_421 path: data/FreeForm_421-* - split: FreeForm_422 path: data/FreeForm_422-* - split: FreeForm_423 path: data/FreeForm_423-* - split: FreeForm_424 path: data/FreeForm_424-* - split: FreeForm_425 path: data/FreeForm_425-* - split: FreeForm_426 path: data/FreeForm_426-* - split: FreeForm_427 path: data/FreeForm_427-* - split: FreeForm_428 path: data/FreeForm_428-* - 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split: FreeForm_1675 path: data/FreeForm_1675-* - split: FreeForm_1962 path: data/FreeForm_1962-* - split: FreeForm_1792 path: data/FreeForm_1792-* - split: FreeForm_1615 path: data/FreeForm_1615-* - split: FreeForm_1846 path: data/FreeForm_1846-* - split: FreeForm_1616 path: data/FreeForm_1616-* - split: FreeForm_1850 path: data/FreeForm_1850-* - split: FreeForm_1964 path: data/FreeForm_1964-* - split: FreeForm_1801 path: data/FreeForm_1801-* - split: FreeForm_1851 path: data/FreeForm_1851-* - split: FreeForm_1965 path: data/FreeForm_1965-* - split: FreeForm_1626 path: data/FreeForm_1626-* - split: FreeForm_1853 path: data/FreeForm_1853-* - split: FreeForm_1967 path: data/FreeForm_1967-* - split: FreeForm_1692 path: data/FreeForm_1692-* - split: FreeForm_1854 path: data/FreeForm_1854-* - split: FreeForm_1975 path: data/FreeForm_1975-* - split: FreeForm_1699 path: data/FreeForm_1699-* - split: FreeForm_1755 path: data/FreeForm_1755-* - split: FreeForm_1757 path: data/FreeForm_1757-* - split: FreeForm_1763 path: data/FreeForm_1763-* - split: FreeForm_1814 path: data/FreeForm_1814-* - split: FreeForm_1816 path: data/FreeForm_1816-* - split: FreeForm_1821 path: data/FreeForm_1821-* - split: FreeForm_1856 path: data/FreeForm_1856-* - split: FreeForm_1862 path: data/FreeForm_1862-* - split: FreeForm_1873 path: data/FreeForm_1873-* - split: FreeForm_1875 path: data/FreeForm_1875-* - split: FreeForm_1877 path: data/FreeForm_1877-* - split: FreeForm_1935 path: data/FreeForm_1935-* - split: FreeForm_1936 path: data/FreeForm_1936-* - split: FreeForm_1938 path: data/FreeForm_1938-* - split: FreeForm_1939 path: data/FreeForm_1939-* - split: FreeForm_1941 path: data/FreeForm_1941-* - split: FreeForm_1977 path: data/FreeForm_1977-* - split: FreeForm_1981 path: data/FreeForm_1981-* - split: FreeForm_1984 path: data/FreeForm_1984-* - split: FreeForm_1985 path: data/FreeForm_1985-* - split: FreeForm_1987 path: data/FreeForm_1987-* - split: FreeForm_1989 path: data/FreeForm_1989-* - split: FreeForm_1990 path: data/FreeForm_1990-* - split: FreeForm_1993 path: data/FreeForm_1993-* - split: FreeForm_1996 path: data/FreeForm_1996-* - split: FreeForm_2000 path: data/FreeForm_2000-* tags: - art --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> 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). ## 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] ## Bibtex citation ```bibtex @misc{zhao2024ultraeditinstructionbasedfinegrainedimage, title={UltraEdit: Instruction-based Fine-Grained Image Editing at Scale}, author={Haozhe Zhao and Xiaojian Ma and Liang Chen and Shuzheng Si and Rujie Wu and Kaikai An and Peiyu Yu and Minjia Zhang and Qing Li and Baobao Chang}, year={2024}, eprint={2407.05282}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.05282}, } ```

nguha/legalbench

nguha

--- language: - en license: other size_categories: - 10K<n<100K task_categories: - text-classification - question-answering - text-generation tags: - legal - law - finance dataset_info: - config_name: abercrombie features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 307 num_examples: 5 - name: test num_bytes: 6240 num_examples: 95 download_size: 19558988 dataset_size: 6547 - config_name: canada_tax_court_outcomes features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2975 num_examples: 6 - name: test num_bytes: 157411 num_examples: 244 download_size: 19558988 dataset_size: 160386 - config_name: citation_prediction_classification features: - name: answer dtype: string - name: citation dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 660 num_examples: 2 - name: test num_bytes: 26112 num_examples: 108 download_size: 19558988 dataset_size: 26772 - config_name: citation_prediction_open features: - name: answer dtype: string - name: circuit dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 555 num_examples: 2 - name: test num_bytes: 13460 num_examples: 53 download_size: 19558988 dataset_size: 14015 - config_name: consumer_contracts_qa features: - name: answer dtype: string - name: contract dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 9941 num_examples: 4 - name: test num_bytes: 1221320 num_examples: 396 download_size: 19558988 dataset_size: 1231261 - config_name: contract_nli_confidentiality_of_agreement features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4070 num_examples: 8 - name: test num_bytes: 43818 num_examples: 82 download_size: 19558988 dataset_size: 47888 - config_name: contract_nli_explicit_identification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3615 num_examples: 8 - name: test num_bytes: 62133 num_examples: 109 download_size: 19558988 dataset_size: 65748 - config_name: contract_nli_inclusion_of_verbally_conveyed_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3817 num_examples: 8 - name: test num_bytes: 81933 num_examples: 139 download_size: 19558988 dataset_size: 85750 - config_name: contract_nli_limited_use features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4855 num_examples: 8 - name: test num_bytes: 98534 num_examples: 208 download_size: 19558988 dataset_size: 103389 - config_name: contract_nli_no_licensing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2591 num_examples: 8 - name: test num_bytes: 78173 num_examples: 162 download_size: 19558988 dataset_size: 80764 - config_name: contract_nli_notice_on_compelled_disclosure features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3907 num_examples: 8 - name: test num_bytes: 80470 num_examples: 142 download_size: 19558988 dataset_size: 84377 - config_name: contract_nli_permissible_acquirement_of_similar_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2736 num_examples: 8 - name: test num_bytes: 87469 num_examples: 178 download_size: 19558988 dataset_size: 90205 - config_name: contract_nli_permissible_copy features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3480 num_examples: 8 - name: test num_bytes: 39015 num_examples: 87 download_size: 19558988 dataset_size: 42495 - config_name: contract_nli_permissible_development_of_similar_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3921 num_examples: 8 - name: test num_bytes: 62603 num_examples: 136 download_size: 19558988 dataset_size: 66524 - config_name: contract_nli_permissible_post-agreement_possession features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4608 num_examples: 8 - name: test num_bytes: 65932 num_examples: 111 download_size: 19558988 dataset_size: 70540 - config_name: contract_nli_return_of_confidential_information features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3499 num_examples: 8 - name: test num_bytes: 35672 num_examples: 66 download_size: 19558988 dataset_size: 39171 - config_name: contract_nli_sharing_with_employees features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3173 num_examples: 8 - name: test num_bytes: 104240 num_examples: 170 download_size: 19558988 dataset_size: 107413 - config_name: contract_nli_sharing_with_third-parties features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3249 num_examples: 8 - name: test num_bytes: 104822 num_examples: 180 download_size: 19558988 dataset_size: 108071 - config_name: contract_nli_survival_of_obligations features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2272 num_examples: 8 - name: test num_bytes: 75450 num_examples: 157 download_size: 19558988 dataset_size: 77722 - config_name: contract_qa features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 2408 num_examples: 8 - name: test num_bytes: 26370 num_examples: 80 download_size: 19558988 dataset_size: 28778 - config_name: corporate_lobbying features: - name: answer dtype: string - name: bill_summary dtype: string - name: bill_title dtype: string - name: company_description dtype: string - name: company_name dtype: string - name: index dtype: string splits: - name: train num_bytes: 54334 num_examples: 10 - name: test num_bytes: 2974813 num_examples: 490 download_size: 19558988 dataset_size: 3029147 - config_name: cuad_affiliate_license-licensee features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4067 num_examples: 6 - name: test num_bytes: 115798 num_examples: 198 download_size: 19558988 dataset_size: 119865 - config_name: cuad_affiliate_license-licensor features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4247 num_examples: 6 - name: test num_bytes: 64931 num_examples: 88 download_size: 19558988 dataset_size: 69178 - config_name: cuad_anti-assignment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2070 num_examples: 6 - name: test num_bytes: 513026 num_examples: 1172 download_size: 19558988 dataset_size: 515096 - config_name: cuad_audit_rights features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2555 num_examples: 6 - name: test num_bytes: 526977 num_examples: 1216 download_size: 19558988 dataset_size: 529532 - config_name: cuad_cap_on_liability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2621 num_examples: 6 - name: test num_bytes: 587220 num_examples: 1246 download_size: 19558988 dataset_size: 589841 - config_name: cuad_change_of_control features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2231 num_examples: 6 - name: test num_bytes: 203823 num_examples: 416 download_size: 19558988 dataset_size: 206054 - config_name: cuad_competitive_restriction_exception features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2774 num_examples: 6 - name: test num_bytes: 115844 num_examples: 220 download_size: 19558988 dataset_size: 118618 - config_name: cuad_covenant_not_to_sue features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2581 num_examples: 6 - name: test num_bytes: 153799 num_examples: 308 download_size: 19558988 dataset_size: 156380 - config_name: cuad_effective_date features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2080 num_examples: 6 - name: test num_bytes: 87802 num_examples: 236 download_size: 19558988 dataset_size: 89882 - config_name: cuad_exclusivity features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1897 num_examples: 6 - name: test num_bytes: 355097 num_examples: 762 download_size: 19558988 dataset_size: 356994 - config_name: cuad_expiration_date features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1638 num_examples: 6 - name: test num_bytes: 354232 num_examples: 876 download_size: 19558988 dataset_size: 355870 - config_name: cuad_governing_law features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2420 num_examples: 6 - name: test num_bytes: 337322 num_examples: 876 download_size: 19558988 dataset_size: 339742 - config_name: cuad_insurance features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2537 num_examples: 6 - name: test num_bytes: 475827 num_examples: 1030 download_size: 19558988 dataset_size: 478364 - config_name: cuad_ip_ownership_assignment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4756 num_examples: 6 - name: test num_bytes: 294749 num_examples: 576 download_size: 19558988 dataset_size: 299505 - config_name: cuad_irrevocable_or_perpetual_license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 5328 num_examples: 6 - name: test num_bytes: 160279 num_examples: 280 download_size: 19558988 dataset_size: 165607 - config_name: cuad_joint_ip_ownership features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 5011 num_examples: 6 - name: test num_bytes: 90592 num_examples: 192 download_size: 19558988 dataset_size: 95603 - config_name: cuad_license_grant features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3690 num_examples: 6 - name: test num_bytes: 709331 num_examples: 1396 download_size: 19558988 dataset_size: 713021 - config_name: cuad_liquidated_damages features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3579 num_examples: 6 - name: test num_bytes: 97839 num_examples: 220 download_size: 19558988 dataset_size: 101418 - config_name: cuad_minimum_commitment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2874 num_examples: 6 - name: test num_bytes: 354078 num_examples: 772 download_size: 19558988 dataset_size: 356952 - config_name: cuad_most_favored_nation features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2103 num_examples: 6 - name: test num_bytes: 32800 num_examples: 64 download_size: 19558988 dataset_size: 34903 - config_name: cuad_no-solicit_of_customers features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3310 num_examples: 6 - name: test num_bytes: 40828 num_examples: 84 download_size: 19558988 dataset_size: 44138 - config_name: cuad_no-solicit_of_employees features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3619 num_examples: 6 - name: test num_bytes: 72661 num_examples: 142 download_size: 19558988 dataset_size: 76280 - config_name: cuad_non-compete features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3675 num_examples: 6 - name: test num_bytes: 211272 num_examples: 442 download_size: 19558988 dataset_size: 214947 - config_name: cuad_non-disparagement features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2168 num_examples: 6 - name: test num_bytes: 49850 num_examples: 100 download_size: 19558988 dataset_size: 52018 - config_name: cuad_non-transferable_license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3643 num_examples: 6 - name: test num_bytes: 269505 num_examples: 542 download_size: 19558988 dataset_size: 273148 - config_name: cuad_notice_period_to_terminate_renewal features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 4166 num_examples: 6 - name: test num_bytes: 100014 num_examples: 222 download_size: 19558988 dataset_size: 104180 - config_name: cuad_post-termination_services features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 3349 num_examples: 6 - name: test num_bytes: 419477 num_examples: 808 download_size: 19558988 dataset_size: 422826 - config_name: cuad_price_restrictions features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2945 num_examples: 6 - name: test num_bytes: 19430 num_examples: 46 download_size: 19558988 dataset_size: 22375 - config_name: cuad_renewal_term features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2163 num_examples: 6 - name: test num_bytes: 168528 num_examples: 386 download_size: 19558988 dataset_size: 170691 - config_name: cuad_revenue-profit_sharing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2581 num_examples: 6 - name: test num_bytes: 363594 num_examples: 774 download_size: 19558988 dataset_size: 366175 - config_name: cuad_rofr-rofo-rofn features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2817 num_examples: 6 - name: test num_bytes: 338243 num_examples: 690 download_size: 19558988 dataset_size: 341060 - config_name: cuad_source_code_escrow features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2696 num_examples: 6 - name: test num_bytes: 58125 num_examples: 118 download_size: 19558988 dataset_size: 60821 - config_name: cuad_termination_for_convenience features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1506 num_examples: 6 - name: test num_bytes: 181164 num_examples: 430 download_size: 19558988 dataset_size: 182670 - config_name: cuad_third_party_beneficiary features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2378 num_examples: 6 - name: test num_bytes: 24106 num_examples: 68 download_size: 19558988 dataset_size: 26484 - config_name: cuad_uncapped_liability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2570 num_examples: 6 - name: test num_bytes: 158009 num_examples: 294 download_size: 19558988 dataset_size: 160579 - config_name: cuad_unlimited-all-you-can-eat-license features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 2414 num_examples: 6 - name: test num_bytes: 22347 num_examples: 48 download_size: 19558988 dataset_size: 24761 - config_name: cuad_volume_restriction features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1397 num_examples: 6 - name: test num_bytes: 129456 num_examples: 322 download_size: 19558988 dataset_size: 130853 - config_name: cuad_warranty_duration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string - name: document_name dtype: string splits: - name: train num_bytes: 1815 num_examples: 6 - name: test num_bytes: 142580 num_examples: 320 download_size: 19558988 dataset_size: 144395 - config_name: definition_classification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1826 num_examples: 8 - name: test num_bytes: 371743 num_examples: 1337 download_size: 19558988 dataset_size: 373569 - config_name: definition_extraction features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2731 num_examples: 8 - name: test num_bytes: 254689 num_examples: 687 download_size: 19558988 dataset_size: 257420 - config_name: diversity_1 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 803 num_examples: 6 - name: test num_bytes: 41135 num_examples: 300 download_size: 19558988 dataset_size: 41938 - config_name: diversity_2 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1041 num_examples: 6 - name: test num_bytes: 53537 num_examples: 300 download_size: 19558988 dataset_size: 54578 - config_name: diversity_3 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 992 num_examples: 6 - name: test num_bytes: 50744 num_examples: 300 download_size: 19558988 dataset_size: 51736 - config_name: diversity_4 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1070 num_examples: 6 - name: test num_bytes: 53464 num_examples: 300 download_size: 19558988 dataset_size: 54534 - config_name: diversity_5 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 1232 num_examples: 6 - name: test num_bytes: 62550 num_examples: 300 download_size: 19558988 dataset_size: 63782 - config_name: diversity_6 features: - name: aic_is_met dtype: string - name: answer dtype: string - name: index dtype: string - name: parties_are_diverse dtype: string - name: text dtype: string splits: - name: train num_bytes: 2016 num_examples: 6 - name: test num_bytes: 100411 num_examples: 300 download_size: 19558988 dataset_size: 102427 - config_name: function_of_decision_section features: - name: Citation dtype: string - name: Paragraph dtype: string - name: answer dtype: string - name: index dtype: string splits: - name: train num_bytes: 1547 num_examples: 7 - name: test num_bytes: 210419 num_examples: 367 download_size: 19558988 dataset_size: 211966 - config_name: hearsay features: - name: answer dtype: string - name: index dtype: string - name: slice dtype: string - name: text dtype: string splits: - name: train num_bytes: 788 num_examples: 5 - name: test num_bytes: 17150 num_examples: 94 download_size: 19558988 dataset_size: 17938 - config_name: insurance_policy_interpretation features: - name: answer dtype: string - name: claim dtype: string - name: index dtype: string - name: policy dtype: string splits: - name: train num_bytes: 3119 num_examples: 5 - name: test num_bytes: 70764 num_examples: 133 download_size: 19558988 dataset_size: 73883 - config_name: international_citizenship_questions features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 832 num_examples: 4 - name: test num_bytes: 2089107 num_examples: 9306 download_size: 19558988 dataset_size: 2089939 - config_name: jcrew_blocker features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 7352 num_examples: 6 - name: test num_bytes: 59879 num_examples: 54 download_size: 19558988 dataset_size: 67231 - config_name: learned_hands_benefits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 8267 num_examples: 6 - name: test num_bytes: 87512 num_examples: 66 download_size: 19558988 dataset_size: 95779 - config_name: learned_hands_business features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6075 num_examples: 6 - name: test num_bytes: 202116 num_examples: 174 download_size: 19558988 dataset_size: 208191 - config_name: learned_hands_consumer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6355 num_examples: 6 - name: test num_bytes: 795463 num_examples: 614 download_size: 19558988 dataset_size: 801818 - config_name: learned_hands_courts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 10693 num_examples: 6 - name: test num_bytes: 228204 num_examples: 192 download_size: 19558988 dataset_size: 238897 - config_name: learned_hands_crime features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 7322 num_examples: 6 - name: test num_bytes: 846597 num_examples: 688 download_size: 19558988 dataset_size: 853919 - config_name: learned_hands_divorce features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 10651 num_examples: 6 - name: test num_bytes: 189279 num_examples: 150 download_size: 19558988 dataset_size: 199930 - config_name: learned_hands_domestic_violence features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 11170 num_examples: 6 - name: test num_bytes: 239797 num_examples: 174 download_size: 19558988 dataset_size: 250967 - config_name: learned_hands_education features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6992 num_examples: 6 - name: test num_bytes: 79184 num_examples: 56 download_size: 19558988 dataset_size: 86176 - config_name: learned_hands_employment features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 11223 num_examples: 6 - name: test num_bytes: 909220 num_examples: 710 download_size: 19558988 dataset_size: 920443 - config_name: learned_hands_estates features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5970 num_examples: 6 - name: test num_bytes: 216836 num_examples: 178 download_size: 19558988 dataset_size: 222806 - config_name: learned_hands_family features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 8714 num_examples: 6 - name: test num_bytes: 3073508 num_examples: 2265 download_size: 19558988 dataset_size: 3082222 - config_name: learned_hands_health features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6155 num_examples: 6 - name: test num_bytes: 336934 num_examples: 226 download_size: 19558988 dataset_size: 343089 - config_name: learned_hands_housing features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 9726 num_examples: 6 - name: test num_bytes: 6028612 num_examples: 4494 download_size: 19558988 dataset_size: 6038338 - config_name: learned_hands_immigration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3955 num_examples: 6 - name: test num_bytes: 165352 num_examples: 134 download_size: 19558988 dataset_size: 169307 - config_name: learned_hands_torts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4484 num_examples: 6 - name: test num_bytes: 615649 num_examples: 432 download_size: 19558988 dataset_size: 620133 - config_name: learned_hands_traffic features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6250 num_examples: 6 - name: test num_bytes: 667539 num_examples: 556 download_size: 19558988 dataset_size: 673789 - config_name: legal_reasoning_causality features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4688 num_examples: 4 - name: test num_bytes: 87007 num_examples: 55 download_size: 19558988 dataset_size: 91695 - config_name: maud_ability_to_consummate_concept_is_subject_to_mae_carveouts features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5322 num_examples: 1 - name: test num_bytes: 304051 num_examples: 69 download_size: 19558988 dataset_size: 309373 - config_name: maud_accuracy_of_fundamental_target_rws_bringdown_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 271 num_examples: 1 - name: test num_bytes: 148869 num_examples: 175 download_size: 19558988 dataset_size: 149140 - config_name: maud_accuracy_of_target_capitalization_rw_(outstanding_shares)_bringdown_standard_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1493 num_examples: 1 - name: test num_bytes: 152224 num_examples: 181 download_size: 19558988 dataset_size: 153717 - config_name: maud_accuracy_of_target_general_rw_bringdown_timing_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1000 num_examples: 1 - name: test num_bytes: 152717 num_examples: 181 download_size: 19558988 dataset_size: 153717 - config_name: maud_additional_matching_rights_period_for_modifications_(cor) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2170 num_examples: 1 - name: test num_bytes: 312632 num_examples: 158 download_size: 19558988 dataset_size: 314802 - config_name: maud_application_of_buyer_consent_requirement_(negative_interim_covenant) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 558 num_examples: 1 - name: test num_bytes: 96990 num_examples: 180 download_size: 19558988 dataset_size: 97548 - config_name: maud_buyer_consent_requirement_(ordinary_course) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2620 num_examples: 1 - name: test num_bytes: 138668 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_change_in_law__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6000 num_examples: 1 - name: test num_bytes: 448666 num_examples: 99 download_size: 19558988 dataset_size: 454666 - config_name: maud_changes_in_gaap_or_other_accounting_principles__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5998 num_examples: 1 - name: test num_bytes: 444442 num_examples: 98 download_size: 19558988 dataset_size: 450440 - config_name: maud_cor_permitted_in_response_to_intervening_event features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2631 num_examples: 1 - name: test num_bytes: 195447 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_cor_permitted_with_board_fiduciary_determination_only features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3970 num_examples: 1 - name: test num_bytes: 194108 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_cor_standard_(intervening_event) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 727 num_examples: 1 - name: test num_bytes: 175140 num_examples: 84 download_size: 19558988 dataset_size: 175867 - config_name: maud_cor_standard_(superior_offer) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1173 num_examples: 1 - name: test num_bytes: 196905 num_examples: 100 download_size: 19558988 dataset_size: 198078 - config_name: maud_definition_contains_knowledge_requirement_-_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1899 num_examples: 1 - name: test num_bytes: 231405 num_examples: 147 download_size: 19558988 dataset_size: 233304 - config_name: maud_definition_includes_asset_deals features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 614 num_examples: 1 - name: test num_bytes: 289644 num_examples: 146 download_size: 19558988 dataset_size: 290258 - config_name: maud_definition_includes_stock_deals features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 683 num_examples: 1 - name: test num_bytes: 292466 num_examples: 148 download_size: 19558988 dataset_size: 293149 - config_name: maud_fiduciary_exception__board_determination_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1594 num_examples: 1 - name: test num_bytes: 288180 num_examples: 179 download_size: 19558988 dataset_size: 289774 - config_name: maud_fiduciary_exception_board_determination_trigger_(no_shop) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3538 num_examples: 1 - name: test num_bytes: 286236 num_examples: 179 download_size: 19558988 dataset_size: 289774 - config_name: maud_financial_point_of_view_is_the_sole_consideration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3290 num_examples: 1 - name: test num_bytes: 217048 num_examples: 112 download_size: 19558988 dataset_size: 220338 - config_name: maud_fls_(mae)_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4669 num_examples: 1 - name: test num_bytes: 349856 num_examples: 77 download_size: 19558988 dataset_size: 354525 - config_name: maud_general_economic_and_financial_conditions_subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5998 num_examples: 1 - name: test num_bytes: 445306 num_examples: 98 download_size: 19558988 dataset_size: 451304 - config_name: maud_includes_consistent_with_past_practice features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1127 num_examples: 1 - name: test num_bytes: 140161 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_initial_matching_rights_period_(cor) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3041 num_examples: 1 - name: test num_bytes: 311761 num_examples: 158 download_size: 19558988 dataset_size: 314802 - config_name: maud_initial_matching_rights_period_(ftr) features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1850 num_examples: 1 - name: test num_bytes: 279202 num_examples: 132 download_size: 19558988 dataset_size: 281052 - config_name: maud_intervening_event_-_required_to_occur_after_signing_-_answer features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3055 num_examples: 1 - name: test num_bytes: 230249 num_examples: 147 download_size: 19558988 dataset_size: 233304 - config_name: maud_knowledge_definition features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 240 num_examples: 1 - name: test num_bytes: 359730 num_examples: 167 download_size: 19558988 dataset_size: 359970 - config_name: maud_liability_standard_for_no-shop_breach_by_target_non-do_representatives features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 154 num_examples: 1 - name: test num_bytes: 40946 num_examples: 156 download_size: 19558988 dataset_size: 41100 - config_name: maud_ordinary_course_efforts_standard features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1037 num_examples: 1 - name: test num_bytes: 140251 num_examples: 181 download_size: 19558988 dataset_size: 141288 - config_name: maud_pandemic_or_other_public_health_event__subject_to_disproportionate_impact_modifier features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3728 num_examples: 1 - name: test num_bytes: 447053 num_examples: 98 download_size: 19558988 dataset_size: 450781 - config_name: maud_pandemic_or_other_public_health_event_specific_reference_to_pandemic-related_governmental_responses_or_measures features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3728 num_examples: 1 - name: test num_bytes: 447053 num_examples: 98 download_size: 19558988 dataset_size: 450781 - config_name: maud_relational_language_(mae)_applies_to features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4948 num_examples: 1 - name: test num_bytes: 409477 num_examples: 90 download_size: 19558988 dataset_size: 414425 - config_name: maud_specific_performance features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 771 num_examples: 1 - name: test num_bytes: 107392 num_examples: 178 download_size: 19558988 dataset_size: 108163 - config_name: maud_tail_period_length features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 406 num_examples: 1 - name: test num_bytes: 108632 num_examples: 179 download_size: 19558988 dataset_size: 109038 - config_name: maud_type_of_consideration features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 258 num_examples: 1 - name: test num_bytes: 139270 num_examples: 172 download_size: 19558988 dataset_size: 139528 - config_name: nys_judicial_ethics features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: year dtype: string splits: - name: train num_bytes: 1697 num_examples: 8 - name: test num_bytes: 53974 num_examples: 292 download_size: 19558988 dataset_size: 55671 - config_name: opp115_data_retention features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1791 num_examples: 8 - name: test num_bytes: 18620 num_examples: 88 download_size: 19558988 dataset_size: 20411 - config_name: opp115_data_security features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2123 num_examples: 8 - name: test num_bytes: 352667 num_examples: 1334 download_size: 19558988 dataset_size: 354790 - config_name: opp115_do_not_track features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2507 num_examples: 8 - name: test num_bytes: 26363 num_examples: 110 download_size: 19558988 dataset_size: 28870 - config_name: opp115_first_party_collection_use features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 2227 num_examples: 8 - name: test num_bytes: 463566 num_examples: 2086 download_size: 19558988 dataset_size: 465793 - config_name: opp115_international_and_specific_audiences features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1643 num_examples: 8 - name: test num_bytes: 338196 num_examples: 980 download_size: 19558988 dataset_size: 339839 - config_name: opp115_policy_change features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1201 num_examples: 8 - name: test num_bytes: 94060 num_examples: 431 download_size: 19558988 dataset_size: 95261 - config_name: opp115_third_party_sharing_collection features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1217 num_examples: 8 - name: test num_bytes: 383909 num_examples: 1590 download_size: 19558988 dataset_size: 385126 - config_name: opp115_user_access,_edit_and_deletion features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1251 num_examples: 8 - name: test num_bytes: 108969 num_examples: 462 download_size: 19558988 dataset_size: 110220 - config_name: opp115_user_choice_control features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1695 num_examples: 8 - name: test num_bytes: 353113 num_examples: 1546 download_size: 19558988 dataset_size: 354808 - config_name: oral_argument_question_purpose features: - name: Docket No. dtype: string - name: answer dtype: string - name: index dtype: string - name: question dtype: string splits: - name: train num_bytes: 2415 num_examples: 7 - name: test num_bytes: 95262 num_examples: 312 download_size: 19558988 dataset_size: 97677 - config_name: overruling features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 629 num_examples: 6 - name: test num_bytes: 443484 num_examples: 2394 download_size: 19558988 dataset_size: 444113 - config_name: personal_jurisdiction features: - name: answer dtype: string - name: index dtype: string - name: slice dtype: string - name: text dtype: string splits: - name: train num_bytes: 1660 num_examples: 4 - name: test num_bytes: 21089 num_examples: 50 download_size: 19558988 dataset_size: 22749 - config_name: privacy_policy_entailment features: - name: answer dtype: string - name: description dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 6282 num_examples: 8 - name: test num_bytes: 3174950 num_examples: 4335 download_size: 19558988 dataset_size: 3181232 - config_name: privacy_policy_qa features: - name: answer dtype: string - name: index dtype: string - name: question dtype: string - name: text dtype: string splits: - name: train num_bytes: 2231 num_examples: 8 - name: test num_bytes: 2817986 num_examples: 10923 download_size: 19558988 dataset_size: 2820217 - config_name: proa features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1057 num_examples: 5 - name: test num_bytes: 25475 num_examples: 95 download_size: 19558988 dataset_size: 26532 - config_name: rule_qa features: - name: answer dtype: string - name: doctrine dtype: string - name: index dtype: string - name: text dtype: string splits: - name: test num_bytes: 12665 num_examples: 50 download_size: 19558988 dataset_size: 12665 - config_name: sara_entailment features: - name: answer dtype: string - name: case id dtype: string - name: description dtype: string - name: index dtype: string - name: question dtype: string - name: statute dtype: string - name: text dtype: string splits: - name: train num_bytes: 2528 num_examples: 4 - name: test num_bytes: 225560 num_examples: 272 download_size: 19558988 dataset_size: 228088 - config_name: sara_numeric features: - name: answer dtype: string - name: case id dtype: string - name: description dtype: string - name: index dtype: string - name: question dtype: string - name: statute dtype: string - name: text dtype: string splits: - name: train num_bytes: 238363 num_examples: 4 - name: test num_bytes: 5725392 num_examples: 96 download_size: 19558988 dataset_size: 5963755 - config_name: scalr features: - name: answer dtype: string - name: choice_0 dtype: string - name: choice_1 dtype: string - name: choice_2 dtype: string - name: choice_3 dtype: string - name: choice_4 dtype: string - name: index dtype: string - name: question dtype: string splits: - name: test num_bytes: 1026740 num_examples: 571 download_size: 19558988 dataset_size: 1026740 - config_name: ssla_company_defendants features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5847 num_examples: 3 - name: test num_bytes: 2313039 num_examples: 1228 download_size: 19558988 dataset_size: 2318886 - config_name: ssla_individual_defendants features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5962 num_examples: 3 - name: test num_bytes: 2002620 num_examples: 1012 download_size: 19558988 dataset_size: 2008582 - config_name: ssla_plaintiff features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 5831 num_examples: 3 - name: test num_bytes: 1926518 num_examples: 1033 download_size: 19558988 dataset_size: 1932349 - config_name: successor_liability features: - name: answer dtype: string - name: index dtype: string - name: issue dtype: string - name: text dtype: string splits: - name: train num_bytes: 1734 num_examples: 3 - name: test num_bytes: 26490 num_examples: 47 download_size: 19558988 dataset_size: 28224 - config_name: supply_chain_disclosure_best_practice_accountability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 18987 num_examples: 8 - name: test num_bytes: 1347025 num_examples: 379 download_size: 19558988 dataset_size: 1366012 - config_name: supply_chain_disclosure_best_practice_audits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 23879 num_examples: 8 - name: test num_bytes: 1342065 num_examples: 379 download_size: 19558988 dataset_size: 1365944 - config_name: supply_chain_disclosure_best_practice_certification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 22058 num_examples: 8 - name: test num_bytes: 1338516 num_examples: 378 download_size: 19558988 dataset_size: 1360574 - config_name: supply_chain_disclosure_best_practice_training features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 24071 num_examples: 8 - name: test num_bytes: 1341885 num_examples: 379 download_size: 19558988 dataset_size: 1365956 - config_name: supply_chain_disclosure_best_practice_verification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 27158 num_examples: 8 - name: test num_bytes: 1338739 num_examples: 379 download_size: 19558988 dataset_size: 1365897 - config_name: supply_chain_disclosure_disclosed_accountability features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 18902 num_examples: 8 - name: test num_bytes: 1344444 num_examples: 378 download_size: 19558988 dataset_size: 1363346 - config_name: supply_chain_disclosure_disclosed_audits features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 24404 num_examples: 8 - name: test num_bytes: 1341624 num_examples: 379 download_size: 19558988 dataset_size: 1366028 - config_name: supply_chain_disclosure_disclosed_certification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 17987 num_examples: 8 - name: test num_bytes: 1342646 num_examples: 378 download_size: 19558988 dataset_size: 1360633 - config_name: supply_chain_disclosure_disclosed_training features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 27093 num_examples: 8 - name: test num_bytes: 1338919 num_examples: 379 download_size: 19558988 dataset_size: 1366012 - config_name: supply_chain_disclosure_disclosed_verification features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 25387 num_examples: 8 - name: test num_bytes: 1340578 num_examples: 379 download_size: 19558988 dataset_size: 1365965 - config_name: telemarketing_sales_rule features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 1230 num_examples: 4 - name: test num_bytes: 17140 num_examples: 47 download_size: 19558988 dataset_size: 18370 - config_name: textualism_tool_dictionaries features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 4842 num_examples: 4 - name: test num_bytes: 102644 num_examples: 107 download_size: 19558988 dataset_size: 107486 - config_name: textualism_tool_plain features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3338 num_examples: 4 - name: test num_bytes: 167428 num_examples: 165 download_size: 19558988 dataset_size: 170766 - config_name: ucc_v_common_law features: - name: answer dtype: string - name: contract dtype: string - name: index dtype: string splits: - name: train num_bytes: 904 num_examples: 6 - name: test num_bytes: 12694 num_examples: 94 download_size: 19558988 dataset_size: 13598 - config_name: unfair_tos features: - name: answer dtype: string - name: index dtype: string - name: text dtype: string splits: - name: train num_bytes: 3308 num_examples: 9 - name: test num_bytes: 787108 num_examples: 3813 download_size: 19558988 dataset_size: 790416 --- # Dataset Card for Dataset Name - **Homepage: https://hazyresearch.stanford.edu/legalbench/** - **Repository: https://github.com/HazyResearch/legalbench/** - **Paper: https://arxiv.org/abs/2308.11462** ## Dataset Description ### Dataset Summary The LegalBench project is an ongoing open science effort to collaboratively curate tasks for evaluating legal reasoning in English large language models (LLMs). The benchmark currently consists of 162 tasks gathered from 40 contributors. Note: Because LegalBench is intended to test zero and few-shot reasoning, the available "train" splits are small. However, if you are interested in finetuning models or studying model performance in a more traditional train/test regime, you can combine and re-partition train and test data. If you have questions about the project or would like to get involved, please see the website for more information. ### Supported Tasks and Leaderboards LegalBench tasks span multiple types (binary classification, multi-class classification, extraction, generation, entailment), multiple types of text (statutes, judicial opinions, contracts, etc.), and multiple areas of law (evidence, contracts, civil procedure, etc.). For more information on tasks, we recommend visiting the website, where you can search through task descriptions, or the Github repository, which contains more granular task descriptions. We also recommend reading the paper, which provides more background on task significance and construction process. ### Languages All LegalBench tasks are in English. ## Dataset Structure ### Data Instances Detailed descriptions of the instances for each task can be found on the Github. An example of an instance, for the `abercrombie` task, is provided below: ``` { "text": "The mark "Ivory" for a product made of elephant tusks.", "label": "generic" "idx": 0 } ``` A substantial number of LegalBench tasks are binary classification tasks, which require the LLM to determine if a piece of text has some legal attribute. Because these are framed as Yes/No questions, the label space is "Yes" or "No". ### Data Fields Detailed descriptions of the instances for each task can be found on the Github. ### Data Splits Each task (except for `rule_qa` and `scalr`) has both a training and evaluation split. Following [RAFT](https://huggingface.co/datasets/ought/raft), train splits only consists of a few-labeled instances, reflecting the few-shot nature of most LLMs. ## Dataset Creation ### Curation Rationale LegalBench was created to enable researchers to better benchmark the legal reasoning capabilities of LLMs. ### Source Data #### Initial Data Collection and Normalization Broadly, LegalBench tasks are drawn from three sources. The first source of tasks are existing available datasets and corpora. Most of these were originally released for non-LLM evaluation settings. In creating tasks for LegalBench from these sources, we often significantly reformatted data and restructured the prediction objective. For instance, the original [CUAD dataset](https://github.com/TheAtticusProject/cuad) contains annotations on long-documents and is intended for evaluating extraction with span-prediction models. We restructure this corpora to generate a binary classification task for each type of contractual clause. While the original corpus emphasized the long-document aspects of contracts, our restructured tasks emphasize whether LLMs can identify the distinguishing features of different types of clauses. The second source of tasks are datasets that were previously constructed by legal professionals but never released. This primarily includes datasets hand-coded by legal scholars as part of prior empirical legal projects. The last category of tasks are those that were developed specifically for \name, by the authors of this paper. Overall, tasks are drawn from 36 distinct corpora. Please see the Appendix of the paper for more details. #### Who are the source language producers? LegalBench data was created by humans. Demographic information for these individuals is not available. ### Annotations #### Annotation process Please see the paper for more information on the annotation process used in the creation of each task. #### Who are the annotators? Please see the paper for more information on the identity of annotators for each task. ### Personal and Sensitive Information Data in this benchmark has either been synthetically generated, or derived from an already public source (e.g., contracts from the EDGAR database). Several tasks have been derived from the LearnedHands corpus, which consists of public posts on /r/LegalAdvice. Some posts may discuss sensitive issues. ## Considerations for Using the Data ### Social Impact of Dataset Please see the original paper for a discussion of social impact. ### Discussion of Biases Please see the original paper for a discussion of social impact. ### Other Known Limitations LegalBench primarily contains tasks corresponding to American law. ## Additional Information ### Dataset Curators Please see the website for a full list of participants in the LegalBench project. ### Licensing Information LegalBench tasks are subject to different licenses. Please see the paper for a description of the licenses. ### Citation Information If you intend to reference LegalBench broadly, please use the citation below. If you are working with a particular task, please use the citation below in addition to the task specific citation (which can be found on the task page on the website or Github). ``` @misc{guha2023legalbench, title={LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models}, author={Neel Guha and Julian Nyarko and Daniel E. Ho and Christopher Ré and Adam Chilton and Aditya Narayana and Alex Chohlas-Wood and Austin Peters and Brandon Waldon and Daniel N. Rockmore and Diego Zambrano and Dmitry Talisman and Enam Hoque and Faiz Surani and Frank Fagan and Galit Sarfaty and Gregory M. Dickinson and Haggai Porat and Jason Hegland and Jessica Wu and Joe Nudell and Joel Niklaus and John Nay and Jonathan H. Choi and Kevin Tobia and Margaret Hagan and Megan Ma and Michael Livermore and Nikon Rasumov-Rahe and Nils Holzenberger and Noam Kolt and Peter Henderson and Sean Rehaag and Sharad Goel and Shang Gao and Spencer Williams and Sunny Gandhi and Tom Zur and Varun Iyer and Zehua Li}, year={2023}, eprint={2308.11462}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{koreeda2021contractnli, title={ContractNLI: A dataset for document-level natural language inference for contracts}, author={Koreeda, Yuta and Manning, Christopher D}, journal={arXiv preprint arXiv:2110.01799}, year={2021} } @article{hendrycks2021cuad, title={Cuad: An expert-annotated nlp dataset for legal contract review}, author={Hendrycks, Dan and Burns, Collin and Chen, Anya and Ball, Spencer}, journal={arXiv preprint arXiv:2103.06268}, year={2021} } @article{wang2023maud, title={MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding}, author={Wang, Steven H and Scardigli, Antoine and Tang, Leonard and Chen, Wei and Levkin, Dimitry and Chen, Anya and Ball, Spencer and Woodside, Thomas and Zhang, Oliver and Hendrycks, Dan}, journal={arXiv preprint arXiv:2301.00876}, year={2023} } @inproceedings{wilson2016creation, title={The creation and analysis of a website privacy policy corpus}, author={Wilson, Shomir and Schaub, Florian and Dara, Aswarth Abhilash and Liu, Frederick and Cherivirala, Sushain and Leon, Pedro Giovanni and Andersen, Mads Schaarup and Zimmeck, Sebastian and Sathyendra, Kanthashree Mysore and Russell, N Cameron and others}, booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={1330--1340}, year={2016} } @inproceedings{zheng2021does, title={When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings}, author={Zheng, Lucia and Guha, Neel and Anderson, Brandon R and Henderson, Peter and Ho, Daniel E}, booktitle={Proceedings of the eighteenth international conference on artificial intelligence and law}, pages={159--168}, year={2021} } @article{zimmeck2019maps, title={Maps: Scaling privacy compliance analysis to a million apps}, author={Zimmeck, Sebastian and Story, Peter and Smullen, Daniel and Ravichander, Abhilasha and Wang, Ziqi and Reidenberg, Joel R and Russell, N Cameron and Sadeh, Norman}, journal={Proc. Priv. Enhancing Tech.}, volume={2019}, pages={66}, year={2019} } @article{ravichander2019question, title={Question answering for privacy policies: Combining computational and legal perspectives}, author={Ravichander, Abhilasha and Black, Alan W and Wilson, Shomir and Norton, Thomas and Sadeh, Norman}, journal={arXiv preprint arXiv:1911.00841}, year={2019} } @article{holzenberger2021factoring, title={Factoring statutory reasoning as language understanding challenges}, author={Holzenberger, Nils and Van Durme, Benjamin}, journal={arXiv preprint arXiv:2105.07903}, year={2021} } @article{lippi2019claudette, title={CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service}, author={Lippi, Marco and Pa{\l}ka, Przemys{\l}aw and Contissa, Giuseppe and Lagioia, Francesca and Micklitz, Hans-Wolfgang and Sartor, Giovanni and Torroni, Paolo}, journal={Artificial Intelligence and Law}, volume={27}, pages={117--139}, year={2019}, publisher={Springer} } ```

lithium0003/findtextCenterNet_dataset

lithium0003

--- license: mit ---

mteb/emotion

mteb

--- language: - en --- ** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)**

allenai/sciq

allenai

--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-nc-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - closed-domain-qa paperswithcode_id: sciq pretty_name: SciQ dataset_info: features: - name: question dtype: string - name: distractor3 dtype: string - name: distractor1 dtype: string - name: distractor2 dtype: string - name: correct_answer dtype: string - name: support dtype: string splits: - name: train num_bytes: 6546183 num_examples: 11679 - name: validation num_bytes: 554120 num_examples: 1000 - name: test num_bytes: 563927 num_examples: 1000 download_size: 4674410 dataset_size: 7664230 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "sciq" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://allenai.org/data/sciq](https://allenai.org/data/sciq) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **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:** 2.82 MB - **Size of the generated dataset:** 7.68 MB - **Total amount of disk used:** 10.50 MB ### Dataset Summary The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 2.82 MB - **Size of the generated dataset:** 7.68 MB - **Total amount of disk used:** 10.50 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "correct_answer": "coriolis effect", "distractor1": "muon effect", "distractor2": "centrifugal effect", "distractor3": "tropical effect", "question": "What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere?", "support": "\"Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to..." } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `distractor3`: a `string` feature. - `distractor1`: a `string` feature. - `distractor2`: a `string` feature. - `correct_answer`: a `string` feature. - `support`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|11679| 1000|1000| ## 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 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 the [Creative Commons Attribution-NonCommercial 3.0 Unported License](http://creativecommons.org/licenses/by-nc/3.0/). ### Citation Information ``` @inproceedings{SciQ, title={Crowdsourcing Multiple Choice Science Questions}, author={Johannes Welbl, Nelson F. Liu, Matt Gardner}, year={2017}, journal={arXiv:1707.06209v1} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

facebook/voxpopuli

facebook

--- annotations_creators: [] language: - en - de - fr - es - pl - it - ro - hu - cs - nl - fi - hr - sk - sl - et - lt language_creators: [] license: - cc0-1.0 - other multilinguality: - multilingual pretty_name: VoxPopuli size_categories: [] source_datasets: [] tags: [] task_categories: - automatic-speech-recognition task_ids: [] --- # Dataset Card for Voxpopuli ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/facebookresearch/voxpopuli - **Repository:** https://github.com/facebookresearch/voxpopuli - **Paper:** https://arxiv.org/abs/2101.00390 - **Point of Contact:** [changhan@fb.com](mailto:changhan@fb.com), [mriviere@fb.com](mailto:mriviere@fb.com), [annl@fb.com](mailto:annl@fb.com) ### Dataset Summary VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home). We acknowledge the European Parliament for creating and sharing these materials. This implementation contains transcribed speech data for 18 languages. It also contains 29 hours of transcribed speech data of non-native English intended for research in ASR for accented speech (15 L2 accents) ### Example usage VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name: ```python from datasets import load_dataset voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr") ``` To load all the languages in a single dataset use "multilang" config name: ```python voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang") ``` To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter: ```python voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"]) ``` To load accented English data, use "en_accented" config name: ```python voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented") ``` **Note that L2 English subset contains only `test` split.** ### Supported Tasks and Leaderboards * automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). Accented English subset can also be used for research in ASR for accented speech (15 L2 accents) ### Languages VoxPopuli contains labelled (transcribed) data for 18 languages: | Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens | |:---:|:---:|:---:|:---:|:---:| | English | En | 543 | 1313 | 4.8M | | German | De | 282 | 531 | 2.3M | | French | Fr | 211 | 534 | 2.1M | | Spanish | Es | 166 | 305 | 1.6M | | Polish | Pl | 111 | 282 | 802K | | Italian | It | 91 | 306 | 757K | | Romanian | Ro | 89 | 164 | 739K | | Hungarian | Hu | 63 | 143 | 431K | | Czech | Cs | 62 | 138 | 461K | | Dutch | Nl | 53 | 221 | 488K | | Finnish | Fi | 27 | 84 | 160K | | Croatian | Hr | 43 | 83 | 337K | | Slovak | Sk | 35 | 96 | 270K | | Slovene | Sl | 10 | 45 | 76K | | Estonian | Et | 3 | 29 | 18K | | Lithuanian | Lt | 2 | 21 | 10K | | Total | | 1791 | 4295 | 15M | Accented speech transcribed data has 15 various L2 accents: | Accent | Code | Transcribed Hours | Transcribed Speakers | |:---:|:---:|:---:|:---:| | Dutch | en_nl | 3.52 | 45 | | German | en_de | 3.52 | 84 | | Czech | en_cs | 3.30 | 26 | | Polish | en_pl | 3.23 | 33 | | French | en_fr | 2.56 | 27 | | Hungarian | en_hu | 2.33 | 23 | | Finnish | en_fi | 2.18 | 20 | | Romanian | en_ro | 1.85 | 27 | | Slovak | en_sk | 1.46 | 17 | | Spanish | en_es | 1.42 | 18 | | Italian | en_it | 1.11 | 15 | | Estonian | en_et | 1.08 | 6 | | Lithuanian | en_lt | 0.65 | 7 | | Croatian | en_hr | 0.42 | 9 | | Slovene | en_sl | 0.25 | 7 | ## Dataset Structure ### Data Instances ```python { 'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5', 'language': 11, # "hr" 'audio': { 'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav', 'array': array([-0.01434326, -0.01055908, 0.00106812, ..., 0.00646973], dtype=float32), 'sampling_rate': 16000 }, 'raw_text': '', 'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.', 'gender': 'female', 'speaker_id': '119431', 'is_gold_transcript': True, 'accent': 'None' } ``` ### Data Fields * `audio_id` (string) - id of audio segment * `language` (datasets.ClassLabel) - numerical id of audio segment * `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally). * `raw_text` (string) - original (orthographic) audio segment text * `normalized_text` (string) - normalized audio segment transcription * `gender` (string) - gender of speaker * `speaker_id` (string) - id of speaker * `is_gold_transcript` (bool) - ? * `accent` (string) - type of accent, for example "en_lt", if applicable, else "None". ### Data Splits All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home) #### Initial Data Collection and Normalization The VoxPopuli transcribed set comes from aligning the full-event source speech audio with the transcripts for plenary sessions. Official timestamps are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps, we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation. Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available. The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts. The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data. The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment. We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER). #### Who are the source language producers? Speakers are participants of the European Parliament events, many of them are EU officials. ### 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 Gender speakers distribution is imbalanced, percentage of female speakers is mostly lower than 50% across languages, with the minimum of 15% for the Lithuanian language data. VoxPopuli includes all available speeches from the 2009-2020 EP events without any selections on the topics or speakers. The speech contents represent the standpoints of the speakers in the EP events, many of which are EU officials. ### Other Known Limitations ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset is distributet under CC0 license, see also [European Parliament's legal notice](https://www.europarl.europa.eu/legal-notice/en/) for the raw data. ### Citation Information Please cite this paper: ```bibtex @inproceedings{wang-etal-2021-voxpopuli, title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation", author = "Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.80", pages = "993--1003", } ``` ### Contributions Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

EpicPinkPenguin/procgen

EpicPinkPenguin

--- language: - en license: apache-2.0 size_categories: - 10M<n<100M task_categories: - reinforcement-learning pretty_name: Procgen Benchmark Dataset dataset_info: - config_name: bigfish features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 129932068797 dataset_size: 289372500000 - config_name: bossfight features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 198057598671 dataset_size: 289372500000 - config_name: caveflyer features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 149023406845 dataset_size: 289372500000 - config_name: chaser features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 63831099402 dataset_size: 289372500000 - config_name: climber features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 63990304413 dataset_size: 289372500000 - config_name: coinrun features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 76990220716 dataset_size: 289372500000 - config_name: dodgeball features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 104691253324 dataset_size: 289372500000 - config_name: fruitbot features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 271549939959 dataset_size: 289372500000 - config_name: heist features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 74316944819 dataset_size: 289372500000 - config_name: jumper features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 101573987650 dataset_size: 289372500000 - config_name: leaper features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 66796546658 dataset_size: 289372500000 - config_name: maze features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 75397896559 dataset_size: 289372500000 - config_name: miner features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 57170722948 dataset_size: 289372500000 - config_name: ninja features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 99759972643 dataset_size: 289372500000 - config_name: plunder features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 103307437365 dataset_size: 289372500000 - config_name: starpilot features: - name: observation dtype: array3_d: shape: - 64 - 64 - 3 dtype: uint8 - name: action dtype: uint8 - name: reward dtype: float32 - name: done dtype: bool - name: truncated dtype: bool splits: - name: train num_bytes: 260435250000 num_examples: 9000000 - name: test num_bytes: 28937250000 num_examples: 1000000 download_size: 170031712117 dataset_size: 289372500000 configs: - config_name: bigfish data_files: - split: train path: bigfish/train-* - split: test path: bigfish/test-* - config_name: bossfight data_files: - split: train path: bossfight/train-* - split: test path: bossfight/test-* - config_name: caveflyer data_files: - split: train path: caveflyer/train-* - split: test path: caveflyer/test-* - config_name: chaser data_files: - split: train path: chaser/train-* - split: test path: chaser/test-* - config_name: climber data_files: - split: train path: climber/train-* - split: test path: climber/test-* - config_name: coinrun data_files: - split: train path: coinrun/train-* - split: test path: coinrun/test-* - config_name: dodgeball data_files: - split: train path: dodgeball/train-* - split: test path: dodgeball/test-* - config_name: fruitbot data_files: - split: train path: fruitbot/train-* - split: test path: fruitbot/test-* - config_name: heist data_files: - split: train path: heist/train-* - split: test path: heist/test-* - config_name: jumper data_files: - split: train path: jumper/train-* - split: test path: jumper/test-* - config_name: leaper data_files: - split: train path: leaper/train-* - split: test path: leaper/test-* - config_name: maze data_files: - split: train path: maze/train-* - split: test path: maze/test-* - config_name: miner data_files: - split: train path: miner/train-* - split: test path: miner/test-* - config_name: ninja data_files: - split: train path: ninja/train-* - split: test path: ninja/test-* - config_name: plunder data_files: - split: train path: plunder/train-* - split: test path: plunder/test-* - config_name: starpilot data_files: - split: train path: starpilot/train-* - split: test path: starpilot/test-* tags: - procgen - bigfish - benchmark - openai - bossfight - caveflyer - chaser - climber - dodgeball - fruitbot - heist - jumper - leaper - maze - miner - ninja - plunder - starpilot --- # Procgen Benchmark This dataset contains expert trajectories generated by a [PPO](https://arxiv.org/abs/1707.06347) reinforcement learning agent trained on each of the 16 procedurally-generated gym environments from the [Procgen Benchmark](https://openai.com/index/procgen-benchmark/). The environments were created on `distribution_mode=easy` and with unlimited levels. Disclaimer: This is not an official repository from OpenAI. ## Dataset Usage Regular usage (for environment bigfish): ```python from datasets import load_dataset train_dataset = load_dataset("EpicPinkPenguin/procgen", name="bigfish", split="train") test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bigfish", split="test") ``` Usage with PyTorch (for environment bossfight): ```python from datasets import load_dataset train_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="train").with_format("torch") test_dataset = load_dataset("EpicPinkPenguin/procgen", name="bossfight", split="test").with_format("torch") ``` ## Agent Performance The PPO RL agent was trained for 25M steps on each environment and obtained the following final performance metrics on the evaluation environment. These values are attain or surpass the performance described in "Easy Difficulty Baseline Results" in Appendix I of the paper. | Environment | Steps (Train) | Steps (Test) | Return | Observation | |:------------|:----------------|:---------------|:-------|:------------| | bigfish | 9,000,000 | 1,000,000 | 29.72 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/lHQXBqLdoWicXlt68I9QX.mp4"></video> | | bossfight | 9,000,000 | 1,000,000 | 11.13 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/LPoafGi4YBWqqkuFlEN_l.mp4"></video> | | caveflyer | 9,000,000 | 1,000,000 | 08.95 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XVqRwu_9yfX4ECQc4At4G.mp4"></video> | | chaser | 9,000,000 | 1,000,000 | 10.98 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/FIKVv48SThqiC1Z2PYQ7U.mp4"></video> | | climber | 9,000,000 | 1,000,000 | 11.66 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/XJQlA7IyF9_gwUiw-FkND.mp4"></video> | | coinrun | 9,000,000 | 1,000,000 | 09.61 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/Ucv3HZttewMRQzTL8r_Tw.mp4"></video> | | dodgeball | 9,000,000 | 1,000,000 | 11.07 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/5HetbKuXBpO-v1jcVyLTU.mp4"></video> | | fruitbot | 9,000,000 | 1,000,000 | 32.49 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/zKCyxXvauXjUac-5kEAWz.mp4"></video> | | heist | 9,000,000 | 1,000,000 | 08.37 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/AdZ6XNmUN5_00BKd9BN8R.mp4"></video> | | jumper | 9,000,000 | 1,000,000 | 08.46 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/s5k31gWK2Vc6Lp6QVzQXA.mp4"></video> | | leaper | 9,000,000 | 1,000,000 | 07.11 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/_hDMocxjmzutc0t5FfoTX.mp4"></video> | | maze | 9,000,000 | 1,000,000 | 09.95 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/uhNdDPuNhZpxVns91Ba-9.mp4"></video> | | miner | 9,000,000 | 1,000,000 | 12.21 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/ElpJ8l2WHJGrprZ3-giHU.mp4"></video> | | ninja | 9,000,000 | 1,000,000 | 08.88 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/b9i-fb2Twh8XmBBNf2DRG.mp4"></video> | | plunder | 9,000,000 | 1,000,000 | 22.19 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/JPeGNOVzrotuYUjfzZj40.mp4"></video> | | starpilot | 9,000,000 | 1,000,000 | 49.94 | <video controls autoplay loop src="https://cdn-uploads.huggingface.co/production/uploads/633c1daf31c06121a58f2df9/wY9lZgkw5tor19hCWmm6A.mp4"></video> | ## Dataset Structure ### Data Instances Each data instance represents a single step consisting of tuples of the form (observation, action, reward, done, truncated) = (o_t, a_t, r_{t+1}, done_{t+1}, trunc_{t+1}). ```json {'action': 1, 'done': False, 'observation': [[[0, 166, 253], [0, 174, 255], [0, 170, 251], [0, 191, 255], [0, 191, 255], [0, 221, 255], [0, 243, 255], [0, 248, 255], [0, 243, 255], [10, 239, 255], [25, 255, 255], [0, 241, 255], [0, 235, 255], [17, 240, 255], [10, 243, 255], [27, 253, 255], [39, 255, 255], [58, 255, 255], [85, 255, 255], [111, 255, 255], [135, 255, 255], [151, 255, 255], [173, 255, 255], ... [0, 0, 37], [0, 0, 39]]], 'reward': 0.0, 'truncated': False} ``` ### Data Fields - `observation`: The current RGB observation from the environment. - `action`: The action predicted by the agent for the current observation. - `reward`: The received reward from stepping the environment with the current action. - `done`: If the new observation is the start of a new episode. Obtained after stepping the environment with the current action. - `truncated`: If the new observation is the start of a new episode due to truncation. Obtained after stepping the environment with the current action. ### Data Splits The dataset is divided into a `train` (90%) and `test` (10%) split. Each environment-dataset has in sum 10M steps (data points). ## Dataset Creation The dataset was created by training an RL agent with [PPO](https://arxiv.org/abs/1707.06347) for 25M steps in each environment. The trajectories where generated by sampling from the predicted action distribution at each step (not taking the argmax). The environments were created on `distribution_mode=easy` and with unlimited levels. ## Procgen Benchmark The [Procgen Benchmark](https://openai.com/index/procgen-benchmark/), released by OpenAI, consists of 16 procedurally-generated environments designed to measure how quickly reinforcement learning (RL) agents learn generalizable skills. It emphasizes experimental convenience, high diversity within and across environments, and is ideal for evaluating both sample efficiency and generalization. The benchmark allows for distinct training and test sets in each environment, making it a standard research platform for the OpenAI RL team. It aims to address the need for more diverse RL benchmarks compared to complex environments like Dota and StarCraft.

mteb/stsbenchmark-sts

mteb

--- language: - en ---

BAAI/CCI3-HQ

BAAI

--- task_categories: - text-generation language: - zh dataset_info: features: - name: id dtype: string - name: text dtype: string - name: score dtype: float splits: - name: train configs: - config_name: default data_files: - split: train path: data/part_* extra_gated_prompt: "You agree to not use the dataset to conduct experiments that cause harm to human subjects." extra_gated_fields: Company/Organization: text Country: country --- ## Data Description To address the scarcity of high-quality safety datasets in the Chinese, we open-sourced the [CCI](https://huggingface.co/datasets/BAAI/CCI-Data) (Chinese Corpora Internet) dataset on November 29, 2023. Building on this foundation, we continue to expand the data source, adopt stricter data cleaning methods, and complete the construction of the CCI 3.0 dataset. This dataset is composed of high-quality, reliable Internet data from trusted sources. And then with more stricter filtering, The CCI 3.0 HQ corpus released is about 500GB in size. ## Update - Oct 25, 2024, CCI 3.0 HQ [Tech Report](./tech_report.pdf) released! - Sep 20, 2024, CCI 3.0 HQ released! ## Data Format | Field | Type | Meaning | | :-------: | :----: | :--------------------------: | | id | String | Document ID, globally unique | | text | String | Content of the document | | score | String | Meta Info of the document | ## Sample ```json { "id": "02301a3477ca2b5434ab29dfc32f95d853abc", "text": "《农村财政与财务》杂志创办于1996，是中国农村财政研究会主管的国家重点学术期刊，国家级期刊，影响因子0.163，现被万方收录(中)等权威机构收录，主要方向：研究报告、文献综述、简报、专题研究\n《农村财政与财务》以宣传党和国家财政政策、推动税收体制改革、研究财税理论、指导基层财政和涉农工作,传播理财知识为宗旨,融政策性、指导性、权威性、实用性和知识性为一体。\n《农村财政与财务》是贯彻国家方针、政策、探索财税理论和有关难点、热点问题,交流财政科学化、精细化管理经验,帮助读者提高综合素质和政策水平不可或缺的理想媒体。\n中共中央办公厅国务院办公厅印发《关于加快构建政策体系培育新型农业经营主体的意见》\n9月5号投的，15号就给了初审结果，给出的修改意见，主要是篇幅过长，以及图片格式的问题。修改后过了一周，就发录用通知了。皇天不负有心人啊，继续努力。\n两个意见，总体来看属于一个大修，一个小修，编辑要求修改后复审。但是意见真的给的很中肯，用了一个星期时间认真修改。提交修改稿后，编辑部很快送出外审，当天外审专家就完成了复审工作，然后在第二天立马显示接收了。这个复审速度吓得我惊人，不敢相信是被录用了，后来打电话确认已被录用，等待后续排版工作。\n两个审稿人，审理比较负责，给出了几点小建议，属于小修，修改后录用，编辑对全文进行了细致标注，对格式要求、图表制作规范较为严格，杂志效率挺高，尤其是编辑部反应神速，必须赞一个。\n农村财政与财务杂志的编辑和审稿人都非常专业，两个审稿人分别提出了3条和5条审稿意见，而且有些意见颇有意义,但是对我的文章还是非常肯定的，不到一个月消息回复审稿人分别要求大修和小修，要求比较严谨，数据比较足够，就能中。祝好运。\n农村财政与财务杂志速度还是很快的，而且是我见过的回复字数最多最多的编辑信，投稿一个月，反馈结果。修改后，递交编辑部，审稿人很心细，改的很认真。连标点居然都帮我改……修改两次后录用。\n编辑的工作十分点赞，态度也是很友善，审稿专家也是非常专业，虽然历经的时间比较长才录用，但是也情有可原，毕竟投稿量太大，而且期间加上放假，难免时间较长，进入编辑加工阶段后才进行了咨询，编辑也进行了详细的回复，希望对各位投稿有所帮助。\n农村财政与财务杂志编辑很负责，整个投稿流程节奏非常快。个人感觉这个杂志还是不错的。2位审稿人都比较专业，有个审稿人的一些意见还是非常有帮助，非常有针对性。速度也比较快。推荐大家投稿！\n第二年来订阅杂志了，客服的态度很好哦，杂志的寄送也还及时，希望以后对老顾客有一定的优惠。\n农村财政与财务杂志的审稿速度还是值得肯定的。综合来说，审稿人还是比较认真的，给修改的也比较仔细，对创新性要求还算比较高吧，编辑老师也非常的平易近人。虽然是第一次投稿，但是还是很幸运被收录了。个人建议文章比较注重自主创新，思维清晰。希望能对大家有帮助！\n农村财政与财务杂志效率很高的，也觉得自己蛮幸运的。当时看到外审两三天回来了，以为要被拒了呢，结果给修改意见了。两周后提交修改稿，两三天后显示录用了。整个下来小一个月吧，第一次投稿，还是感觉蛮幸运的。\n该刊审稿较快，出刊也快前后跨度就半年左右，编辑老师态度很好，最好使用邮箱投稿，外审一般会告知你，里面文章质量感觉都挺好的，良心杂志，介意普刊的同仁可以投投看！！\n农村财政与财务杂志质量不错,审稿较严格,录用较快。属于很规范的中文杂志。编辑很负责，处理也很快、工作规范，相当满意。审稿专家很认真细致，意见提的很详细，对论文提高很有帮助！相当愉快的一次投稿经历~\n总的来说，审稿专家还是蛮认真的，对待问题都很细致。另外，编辑也相当赞，经常打电话去咨询状态，一直很要是有创意，内容丰富，应该就没有问题。\neleme**:杂志工作人员的处理速度相当不错哦，审稿专家很负责。\nfazhi**:投稿后编辑态度不错，邮件联系均有及时回复。\n15年11月16日投稿，修改了两次，第一次对文章创新性提出了意见，第二次是格式方面的修改，12月15日通知正刊录用。算是比较快的了。该刊给人的第一感觉就是正规，对论文内容、格式等要求也很严格，应该认真对待。祝大家成功！\nxiajia**:很开心。总体来说，审稿速度很快，比较满意；可以试试。\n9月初投稿，一直没有消息，月底打电话问，还在外审。10月初收到退修通知，修改后返回，编辑回复很快，让修改了格式，然后通知录用。编辑很负责。等待校稿和版费通知。\njince**:感觉给出的意见很诚恳，很有建设性。\n初审大概一周左右，进入外审程序。8月底左右还是正在二审中，我打电话问了下，才告诉我需要修改，网上的状态变成“二审已审回”；按照修改意见修改后以电子邮件形式提交，大概一周后收到录用通知。\nsansui**:审稿速度还是相当神速，编辑部老师很好，很负责任。\n农村财政与财务速度蛮快的，编辑部也很负责，很有主见。审稿人信息反馈很快，20多天就有消息了，录用消息也第一时间通知，很及时、速度、高效，一点也不耽误时间。\n编辑非常认真负责，邮件联系回复也非常快，稿件开始本来有些问题，考虑不用的，但是编辑又给了一次修改的机会，说是修改好了还可能录用，就花心思修，修改后一个月不到就说录用了，还有一些小问题后面陆续解决了。\n用了两个月的时候，才被录用。审稿周期不短，可能也是自己写的不好一再返修的原因。觉得审稿人给的身高意见比较细致、对问题的提出比较准确。农村财政与财务的档次也很高。写的有点多所以相对的版面费也就要多一些。\nsusu**:个人感觉该期刊对文章的选题热点、创新点、写作水平都比较注重。\n个人感觉还不错。第一篇中的论文，还是很开心的。5月28号投稿7月15号通知录用。修改意见中，只有文中的格式问题以及图标中的，字体，单位问题。修改后就成功录用啦。\n农村财政与财务杂志的审稿速度飞快，貌似一个月左右就拟录用了，然后改了两次格式，缩小篇幅，大概也就一个半月搞掂。编辑部人员服务态度很好！很有耐心！大家可以尝试下这个杂志。", "score": 2.3 } ``` ## Download The CCI 3.0 HQ dataset is simultaneously open-sourced on the [BAAI DataHub](https://data.baai.ac.cn/details/BAAI-CCI3-HQ) and Huggingface. ### BAAI DataHub Users can click the link [CCI 3.0 HQ Dataset](https://data.baai.ac.cn/details/BAAI-CCI3-HQ) to view the data files, and click to download. Note that users need to register on BAAI DataHub to use the data, and filling out a survey questionnaire is required before their first download. ### Huggingface To use the data, you can load it using the following code: ```python from datasets import load_dataset dataset = load_dataset("BAAI/CCI3-HQ") ``` ### Evaluation #### Setup Due to the mixed Chinese and English datasets, we chose Qwen2-0.5B model for datasets evaluation, each experiment with 100B tokens training. We follow the same evaluation setup for all models using [FineWeb setup](https://github.com/huggingface/cosmopedia/tree/main/evaluation) with [lighteval](https://github.com/huggingface/lighteval) library. You can checkout the [evaluation script](./lighteval_tasks_v2.py) here. #### Results We conducted two types of experiments: 1. Mixed Dataset Experiment: The ratio of English, code, and Chinese is 60% : 10% : 30%. 2. Chinese Dataset Experiment: The Chinese ratio is 100%. For English datasets, we uniformly used [FineWeb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu/tree/main/sample/100BT). For code data, we used [StarCoder](https://huggingface.co/bigcode/starcoder). For Chinese datasets, we selected [wanjuan-v1](https://github.com/opendatalab/WanJuan1.0), [skypile](https://huggingface.co/datasets/Skywork/SkyPile-150B), and [cci3.0](https://huggingface.co/datasets/BAAI/CCI3-Data). For Mixed Dataset Experiment all evaluation metrics are averaged and for Chinese Dataset Experiment only chinese evaluation metrics are averaged.  All evaluation metrics across training are depicted in . ## Citation Information You can cite [our paper](https://arxiv.org/abs/2410.18505) or this dataset: ``` @misc{wang2024cci30hqlargescalechinesedataset, title={CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models}, author={Liangdong Wang and Bo-Wen Zhang and Chengwei Wu and Hanyu Zhao and Xiaofeng Shi and Shuhao Gu and Jijie Li and Quanyue Ma and TengFei Pan and Guang Liu}, year={2024}, eprint={2410.18505}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2410.18505}, } ``` ## User Agreement Users need to comply with the usage agreement of the CCI 3.0 HQ dataset. You can view the agreement by clicking on the following link: （[View Usage Agreement](https://data.baai.ac.cn/resources/agreement/cci_usage_aggrement.pdf)）.

trl-internal-testing/zen

trl-internal-testing

--- dataset_info: - config_name: conversational_implicit_prompt_preference features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2755 num_examples: 17 - name: test num_bytes: 386 num_examples: 2 download_size: 6623 dataset_size: 3141 - config_name: conversational_language_modeling features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1399 num_examples: 17 - name: test num_bytes: 210 num_examples: 2 download_size: 3723 dataset_size: 1609 - config_name: conversational_preference features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2070 num_examples: 17 - name: test num_bytes: 295 num_examples: 2 download_size: 8123 dataset_size: 2365 - config_name: conversational_prompt_completion features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1467 num_examples: 17 - name: test num_bytes: 218 num_examples: 2 download_size: 5796 dataset_size: 1685 - config_name: conversational_prompt_only features: - name: prompt list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 821 num_examples: 17 - name: test num_bytes: 107 num_examples: 2 download_size: 3326 dataset_size: 928 - config_name: conversational_unpaired_preference features: - name: prompt list: - name: content dtype: string - name: role dtype: string - name: completion list: - name: content dtype: string - name: role dtype: string - name: label dtype: bool splits: - name: train num_bytes: 1441 num_examples: 17 - name: test num_bytes: 219 num_examples: 2 download_size: 6421 dataset_size: 1660 - config_name: standard_implicit_prompt_preference features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 1537 num_examples: 17 - name: test num_bytes: 258 num_examples: 2 download_size: 4330 dataset_size: 1795 - config_name: standard_language_modeling features: - name: text dtype: string splits: - name: train num_bytes: 744 num_examples: 17 - name: test num_bytes: 136 num_examples: 2 download_size: 2457 dataset_size: 880 - config_name: standard_preference features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 1213 num_examples: 17 - name: test num_bytes: 205 num_examples: 2 download_size: 4466 dataset_size: 1418 - config_name: standard_prompt_completion features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 812 num_examples: 17 - name: test num_bytes: 144 num_examples: 2 download_size: 3231 dataset_size: 956 - config_name: standard_prompt_only features: - name: prompt dtype: string splits: - name: train num_bytes: 460 num_examples: 17 - name: test num_bytes: 69 num_examples: 2 download_size: 2044 dataset_size: 529 - config_name: standard_stepwise features: - name: prompt dtype: string - name: completions sequence: string - name: label sequence: bool splits: - name: train num_bytes: 1402.9473684210527 num_examples: 17 - name: test num_bytes: 165.05263157894737 num_examples: 2 download_size: 5033 dataset_size: 1568.0 - config_name: standard_stepwise_supervision features: - name: prompt dtype: string - name: completions sequence: string - name: labels sequence: bool splits: - name: train num_bytes: 1382 num_examples: 17 - name: test num_bytes: 187 num_examples: 2 download_size: 5039 dataset_size: 1569 - config_name: standard_unpaired_preference features: - name: prompt dtype: string - name: completion dtype: string - name: label dtype: bool splits: - name: train num_bytes: 840 num_examples: 17 - name: test num_bytes: 131 num_examples: 2 download_size: 3861 dataset_size: 971 configs: - config_name: conversational_implicit_prompt_preference data_files: - split: train path: conversational_implicit_prompt_preference/train-* - split: test path: conversational_implicit_prompt_preference/test-* - config_name: conversational_language_modeling data_files: - split: train path: conversational_language_modeling/train-* - split: test path: conversational_language_modeling/test-* - config_name: conversational_preference data_files: - split: train path: conversational_preference/train-* - split: test path: conversational_preference/test-* - config_name: conversational_prompt_completion data_files: - split: train path: conversational_prompt_completion/train-* - split: test path: conversational_prompt_completion/test-* - config_name: conversational_prompt_only data_files: - split: train path: conversational_prompt_only/train-* - split: test path: conversational_prompt_only/test-* - config_name: conversational_unpaired_preference data_files: - split: train path: conversational_unpaired_preference/train-* - split: test path: conversational_unpaired_preference/test-* - config_name: standard_implicit_prompt_preference data_files: - split: train path: standard_implicit_prompt_preference/train-* - split: test path: standard_implicit_prompt_preference/test-* - config_name: standard_language_modeling data_files: - split: train path: standard_language_modeling/train-* - split: test path: standard_language_modeling/test-* - config_name: standard_preference data_files: - split: train path: standard_preference/train-* - split: test path: standard_preference/test-* - config_name: standard_prompt_completion data_files: - split: train path: standard_prompt_completion/train-* - split: test path: standard_prompt_completion/test-* - config_name: standard_prompt_only data_files: - split: train path: standard_prompt_only/train-* - split: test path: standard_prompt_only/test-* - config_name: standard_stepwise data_files: - split: train path: standard_stepwise/train-* - split: test path: standard_stepwise/test-* - config_name: standard_stepwise_supervision data_files: - split: train path: standard_stepwise_supervision/train-* - split: test path: standard_stepwise_supervision/test-* - config_name: standard_unpaired_preference data_files: - split: train path: standard_unpaired_preference/train-* - split: test path: standard_unpaired_preference/test-* ---

faur-ai/fulg

faur-ai

--- license: odc-by viewer: true task_categories: - text-generation language: - ro tags: - language-modeling - casual-lm - llm pretty_name: FuLG size_categories: - 100B<n<1T --- # ❄️FuLG The FuLG dataset is a comprehensive Romanian language corpus comprising 150 billion tokens, carefully extracted from Common Crawl. This extensive dataset is the result of rigorous filtering and deduplication processes applied to 95 Common Crawl snapshots. The compressed dataset has 289 GB. For more details, check the [arXiv preprint](https://arxiv.org/abs/2407.13657). ### How do I download this? ##### Using 🤗 Datasets ```python from datasets import load_dataset # Full dataset dataset = load_dataset("faur-ai/fulg") # To load the data from a specific CC snapshot dataset = load_dataset("faur-ai/fulg", data_dir='2018-05') ``` ##### Using Git ```bash git clone https://huggingface.co/datasets/faur-ai/fulg ``` ### Data Fields The data have several fields: - `url`: url of the source as a string - `date_download`: date of crawl - `digest`: hash of content - `length`: length of content - `nlines`: number of lines - `source_domain`: domain of document - `title`: title of document - `raw_content`: text content as a string - `cc_segment`: source CommonCrawl segment - `original_nlines`: original number of lines before processing - `original_length`: original length before processing - `language`: language (ro) - `language_score`: score for language ### Licensing Information We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this dataset, you are also bound any license agreements and terms of use of the original data sources. ## Bibtex If you use our dataset, please cite us at: ```bibtex @misc{fulg150bromaniancorpus, title={FuLG: 150B Romanian Corpus for Language Model Pretraining}, author={Vlad-Andrei Bădoiu and Mihai-Valentin Dumitru and Alexandru M. Gherghescu and Alexandru Agache and Costin Raiciu}, year={2024}, eprint={2407.13657}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.13657}, } ```

fixie-ai/common_voice_17_0

fixie-ai

--- dataset_info: - config_name: ar features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 300234489.0 num_examples: 10470 - name: test num_bytes: 311234035.0 num_examples: 10480 - name: train num_bytes: 718845895.0 num_examples: 28369 download_size: 1250028526 dataset_size: 1330314419.0 - config_name: de features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 23759438592.6 num_examples: 589100 - name: test num_bytes: 715601886.0 num_examples: 16183 - name: validation num_bytes: 710830645.0 num_examples: 16183 download_size: 24582787064 dataset_size: 25185871123.6 - config_name: en features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: test num_bytes: 9329520290.338 num_examples: 16393 - name: validation num_bytes: 9434608798.338 num_examples: 16393 - name: train num_bytes: 44987747251.6 num_examples: 1101170 - name: validated num_bytes: 68921650062.024 num_examples: 1799288 download_size: 128219063641 dataset_size: 132673526402.3 - config_name: es features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 13216214878.31 num_examples: 336846 - name: test num_bytes: 748084507.0 num_examples: 15857 - name: validation num_bytes: 770184703.0 num_examples: 15857 download_size: 14415677901 dataset_size: 14734484088.309998 - config_name: fr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 20630346378.228 num_examples: 558054 - name: test num_bytes: 684908439.0 num_examples: 16159 - name: validation num_bytes: 703910244.0 num_examples: 16159 download_size: 21981003249 dataset_size: 22019165061.228 - config_name: frold features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 20616364930.228 num_examples: 558054 - name: test num_bytes: 674959025.258 num_examples: 16159 - name: validation num_bytes: 703829746.38 num_examples: 16159 download_size: 21972606682 dataset_size: 21995153701.866 - config_name: hi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 275394930.996 num_examples: 9378 - name: validation num_bytes: 145392985.176 num_examples: 4856 - name: test num_bytes: 220164125.264 num_examples: 6308 - name: other num_bytes: 253400896.056 num_examples: 8088 - name: invalidated num_bytes: 53706876.0 num_examples: 1550 - name: validated num_bytes: 721036368.28 num_examples: 20658 download_size: 1481543483 dataset_size: 1669096181.7719998 - config_name: it features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 6137402083.638 num_examples: 169771 - name: validation num_bytes: 701042124.0 num_examples: 15149 - name: test num_bytes: 741163579.0 num_examples: 15155 download_size: 7600033249 dataset_size: 7579607786.638 - config_name: ja features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 186515137.0 num_examples: 6261 - name: test num_bytes: 199063298.0 num_examples: 6261 - name: train num_bytes: 307772889.0 num_examples: 10039 download_size: 684220424 dataset_size: 693351324.0 - config_name: pt features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 290319070.0 num_examples: 9464 - name: test num_bytes: 304560776.0 num_examples: 9467 - name: train num_bytes: 624494986.0 num_examples: 21968 download_size: 1188978689 dataset_size: 1219374832.0 - config_name: ru features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: validation num_bytes: 393037777.0 num_examples: 10203 - name: test num_bytes: 397099376.0 num_examples: 10203 - name: train num_bytes: 977625337.0 num_examples: 26377 download_size: 1734268016 dataset_size: 1767762490.0 - config_name: sv-SE features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 201604157.344 num_examples: 7744 - name: validation num_bytes: 145407584.16 num_examples: 5210 - name: test num_bytes: 168456898.744 num_examples: 5259 - name: other num_bytes: 182626841.121 num_examples: 6759 - name: invalidated num_bytes: 43666692.56 num_examples: 1428 - name: validated num_bytes: 1302439008.81 num_examples: 40770 download_size: 1772780355 dataset_size: 2044201182.7389998 - config_name: tr features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 854586956.976 num_examples: 35147 - name: validation num_bytes: 265450510.268 num_examples: 11258 - name: test num_bytes: 363424742.28 num_examples: 11290 - name: other num_bytes: 4238883.0 num_examples: 117 - name: invalidated num_bytes: 152949072.07 num_examples: 4530 - name: validated num_bytes: 2694662410.926 num_examples: 114056 download_size: 4038924157 dataset_size: 4335312575.5199995 - config_name: uk features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: continuation dtype: string splits: - name: train num_bytes: 824014245.552 num_examples: 25137 - name: validation num_bytes: 338351263.068 num_examples: 10007 - name: test num_bytes: 363575667.839 num_examples: 10011 - name: other num_bytes: 211123163.846 num_examples: 7851 - name: invalidated num_bytes: 141986802.304 num_examples: 3204 - name: validated num_bytes: 2579348540.4549994 num_examples: 75489 download_size: 4037277320 dataset_size: 4458399683.063999 configs: - config_name: ar data_files: - split: validation path: ar/validation-* - split: test path: ar/test-* - split: train path: ar/train-* - config_name: de data_files: - split: validation path: de/validation-* - split: test path: de/test-* - split: train path: de/train-* - config_name: en data_files: - split: test path: en/test-* - split: validation path: en/validation-* - split: train path: en/train-* - split: validated path: en/validated-* - config_name: es data_files: - split: validation path: es/validation-* - split: test path: es/test-* - split: train path: es/train-* - config_name: fr data_files: - split: validation path: fr/validation-* - split: train path: frnew/train-* - split: test path: fr/test-* - config_name: frold data_files: - split: train path: fr/train-* - split: test path: fr/test-* - split: validation path: fr/validation-* - config_name: hi data_files: - split: train path: hi/train/** - split: validation path: hi/validation/** - split: test path: hi/test/** - split: other path: hi/other/** - split: invalidated path: hi/invalidated/** - split: validated path: hi/validated/** - config_name: it data_files: - split: validation path: it/validation-* - split: test path: it/test-* - split: train path: it/train-* - config_name: ja data_files: - split: validation path: ja/validation-* - split: test path: ja/test-* - split: train path: ja/train-* - config_name: pt data_files: - split: validation path: pt/validation-* - split: test path: pt/test-* - split: train path: pt/train-* - config_name: ru data_files: - split: validation path: ru/validation-* - split: test path: ru/test-* - split: train path: ru/train-* - config_name: sv-SE data_files: - split: train path: sv-SE/train/** - split: validation path: sv-SE/validation/** - split: test path: sv-SE/test/** - split: other path: sv-SE/other/** - split: invalidated path: sv-SE/invalidated/** - split: validated path: sv-SE/validated/** - config_name: tr data_files: - split: train path: tr/train/** - split: validation path: tr/validation/** - split: test path: tr/test/** - split: other path: tr/other/** - split: invalidated path: tr/invalidated/** - split: validated path: tr/validated/** - config_name: uk data_files: - split: train path: uk/train/** - split: validation path: uk/validation/** - split: test path: uk/test/** - split: other path: uk/other/** - split: invalidated path: uk/invalidated/** - split: validated path: uk/validated/** ---

ceval/ceval-exam

ceval

--- license: cc-by-nc-sa-4.0 task_categories: - text-classification - multiple-choice - question-answering language: - zh pretty_name: C-Eval size_categories: - 10K<n<100K --- C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https://arxiv.org/abs/2305.08322) for more details. Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/SJTU-LIT/ceval/tree/main#how-to-submit) ### Load the data ```python from datasets import load_dataset dataset=load_dataset(r"ceval/ceval-exam",name="computer_network") print(dataset['val'][0]) # {'id': 0, 'question': '使用位填充方法，以01111110为位首flag，数据为011011111111111111110010，求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ''} ``` More details on loading and using the data are at our [github page](https://github.com/SJTU-LIT/ceval#data). Please cite our paper if you use our dataset. ``` @article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, journal={arXiv preprint arXiv:2305.08322}, year={2023} } ```

mlfoundations/MINT-1T-PDF-CC-2023-23

mlfoundations

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2023-23`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c).  ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

shuaishuaicdp/GUI-World

shuaishuaicdp

--- task_categories: - question-answering - text-generation language: - en pretty_name: GUI-World size_categories: - 10K<n<100K --- <div align="center"> <h1>GUI-World: A Dataset for GUI-Orientated Multimodal Large Language Models [](https://arxiv.org/abs/2406.10819) [](https://huggingface.co/shuaishuaicdp/GUI-Vid) [](https://gui-world.github.io/) <img src="figures/GUI_overview.png"> <img src="figures/radar.jpg"> <p align="center"> </p> </div> ## Dataset: GUI-World ### Overview GUI-World introduces a comprehensive benchmark for evaluating MLLMs in dynamic and complex GUI environments. It features extensive annotations covering six GUI scenarios and eight types of GUI-oriented questions. The dataset assesses state-of-the-art ImageLLMs and VideoLLMs, highlighting their limitations in handling dynamic and multi-step tasks. It provides valuable insights and a foundation for future research in enhancing the understanding and interaction capabilities of MLLMs with dynamic GUI content. This dataset aims to advance the development of robust GUI agents capable of perceiving and interacting with both static and dynamic GUI elements. ### How to use GUI-World See [Github](https://github.com/Dongping-Chen/GUI-World) for further details. Based on GUI-World, we train the first VideoLLM [**GUI-Vid**](https://huggingface.co/shuaishuaicdp/GUI-Vid) with powerful GUI understanding capability. ## License This work is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). ## Citation ``` @article{chen2024gui, title={GUI-WORLD: A Dataset for GUI-Orientated Multimodal Large Language Models}, author={GUI-World Team}, year={2024} } ```

datablations/oscar-filter

datablations

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: meta struct: - name: warc_headers struct: - name: warc-record-id dtype: string - name: warc-date dtype: string - name: content-type dtype: string - name: content-length dtype: int32 - name: warc-type dtype: string - name: warc-identified-content-language dtype: string - name: warc-refers-to dtype: string - name: warc-target-uri dtype: string - name: warc-block-digest dtype: string - name: identification struct: - name: label dtype: string - name: prob dtype: float32 - name: annotations sequence: string - name: line_identifications list: - name: label dtype: string - name: prob dtype: float32 - name: perplexity_score dtype: float64 - name: text_length dtype: int64 - name: url dtype: string - name: domain dtype: string - name: dup_ratio dtype: float64 - name: pairs sequence: sequence: int64 - name: repetitions sequence: binary - name: included_in_dedup dtype: bool - name: cluster sequence: int64 splits: - name: train num_bytes: 3188486875748 num_examples: 431992659 download_size: 419397499659 dataset_size: 3188486875748 --- this is the one where we build the suffix array for 25% Oscar and only deduplicate that part - by deduplication I mean removing any document which has an at least 100-char span overlapping with another document in the 25% chunk. This is very strict and preserves only about 20 million documents, so less then 5% of the full Oscar.

indolem/IndoMMLU

indolem

--- license: mit task_categories: - question-answering language: - id tags: - knowledge pretty_name: IndoMMLU size_categories: - 10K<n<100K --- # IndoMMLU <!--- [](https://github.com/internLM/OpenCompass/) [](https://github.com/EleutherAI/lm-evaluation-harness) --> <p align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/IndoMMLU-Bar.png" style="width: 100%;" id="title-icon"> </p> <p align="center"> <a href="http://www.fajrikoto.com" target="_blank">Fajri Koto</a>, <a href="https://www.linkedin.com/in/nuaisyah/" target="_blank">Nurul Aisyah</a>, <a href="https://haonan-li.github.io/" target="_blank">Haonan Li</a>, <a href="https://people.eng.unimelb.edu.au/tbaldwin/" target="_blank">Timothy Baldwin</a> </p> <h4 align="center"> <p align="center" style="display: flex; flex-direction: row; justify-content: center; align-items: center"> 📄 <a href="https://arxiv.org/abs/2310.04928" target="_blank" style="margin-right: 15px; margin-left: 10px">Paper</a> • 🏆 <a href="https://github.com/fajri91/IndoMMLU/blob/main/README_EN.md#evaluation" target="_blank" style="margin-left: 10px">Leaderboard</a> • 🤗 <a href="https://huggingface.co/datasets/indolem/indommlu" target="_blank" style="margin-left: 10px">Dataset</a> </p> </h4> ## Introduction We introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia. By employing professional teachers, we obtain 14,906 questions across 63 tasks and education levels, with 46\% of the questions focusing on assessing proficiency in the Indonesian language and knowledge of nine local languages and cultures in Indonesia. <p align="left"> <img src="https://github.com/fajri91/eval_picts/blob/master/IndoMMLU-dist.png?raw=true" style="width: 500px;" id="title-icon"> </p> ## Subjects | Level | Subjects | |-----------|------------------------------------| | SD (Primary School) | Science, Social science, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Dayak Ngaju, Minangkabau culture, Art, Sports, Islam religion, Christian religion, Hindu religion | | SMP (Junior High School) | Science, Social science, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Minangkabau culture, Art, Sports, Islam religion, Christian religion, Hindu religion | | SMA (Senior High School) | Physics, Chemistry, Biology, Geography, Sociology, Economics, History, Civics, Indonesian Language, Balinese, Makassarese, Banjarese, Lampungic, Madurese, Sundanese, Javanese, Art, Sports, Islam religion, Christian religion, Hindu religion | University Entrance Test | Chemistry, Biology, Geography, Sociology, Economics, History, Indonesian Language | We categorize the collected questions into different subject areas, including: (1) STEM (Science, Technology, Engineering, and Mathematics); (2) Social Science; (3) Humanities; (4) Indonesian Language; and (5) Local Languages and Cultures. ## Examples These questions are written in Indonesian. For local language subjects, some are written in the local languages. The English version is for illustrative purposes only. <p align="left"> <img src="https://github.com/fajri91/eval_picts/blob/master/min_example.png?raw=true" style="width: 400px;" id="title-icon"> </p> ## Evaluation We evaluate 24 multilingual LLMs of different sizes in zero-shot and few-shot settings. This includes [GPT-3.5 (ChatGPT)](https://chat.openai.com/), [XGLM](https://arxiv.org/abs/2112.10668), [Falcon](https://falconllm.tii.ae/), [BLOOMZ](https://huggingface.co/bigscience/bloomz), [mT0](https://huggingface.co/bigscience/bloomz), [LLaMA](https://arxiv.org/abs/2302.13971), and [Bactrian-X](https://github.com/mbzuai-nlp/bactrian-x). Prior to the question and multiple-choice options, we add a simple prompt in the Indonesian language: ``` Ini adalah soal [subject] untuk [level]. Pilihlah salah satu jawaban yang dianggap benar! English Translation: This is a [subject] question for [level]. Please choose the correct answer! ``` #### Zero-shot Evaluation | Model (#param) | STEM | Social Science | Humanities | Indonesian Lang. | Local L. Culture | Average | |---------------------|------|----------|-------------|---------|----------|---------| | Random | 21.9 | 23.4 | 23.5 | 24.4 | 26.6 | 24.4 | | [GPT-3.5 (175B)](https://chat.openai.com/) | **54.3** | **62.5** | **64.0** | **62.2** | 39.3 | **53.2** | | [XGLM (564M)](https://huggingface.co/facebook/xglm-564M) | 22.1 | 23.0 | 25.6 | 25.6 | 27.5 | 25.2 | | [XGLM (1.7B)](https://huggingface.co/facebook/xglm-1.7B) | 20.9 | 23.0 | 24.6 | 24.8 | 26.6 | 24.4 | | [XGLM (2.9B)](https://huggingface.co/facebook/xglm-2.9B) | 22.9 | 23.2 | 25.4 | 26.3 | 27.2 | 25.2 | | [XGLM (4.5B)](https://huggingface.co/facebook/xglm-4.5B) | 21.8 | 23.1 | 25.6 | 25.8 | 27.1 | 25.0 | | [XGLM (7.5B)](https://huggingface.co/facebook/xglm-7.5B) | 22.7 | 21.7 | 23.6 | 24.5 | 27.5 | 24.5 | | [Falcon (7B)](https://huggingface.co/tiiuae/falcon-7b) | 22.1 | 22.9 | 25.5 | 25.7 | 27.5 | 25.1 | | [Falcon (40B)](https://huggingface.co/tiiuae/falcon-40b) | 30.2 | 34.8 | 34.8 | 34.9 | 29.2 | 32.1 | | [BLOOMZ (560M)](https://huggingface.co/bigscience/bloomz-560m) | 22.9 | 23.6 | 23.2 | 24.2 | 25.1 | 24.0 | | [BLOOMZ (1.1B)](https://huggingface.co/bigscience/bloomz-1b1) | 20.4 | 21.4 | 21.1 | 23.5 | 24.7 | 22.4 | | [BLOOMZ (1.7B)](https://huggingface.co/bigscience/bloomz-1b7) | 31.5 | 39.3 | 38.3 | 42.8 | 29.4 | 34.4 | | [BLOOMZ (3B)](https://huggingface.co/bigscience/bloomz-3b) | 33.5 | 44.5 | 39.7 | 46.7 | 29.8 | 36.4 | | [BLOOMZ (7.1B)](https://huggingface.co/bigscience/bloomz-7b1) | 37.1 | 46.7 | 44.0 | 49.1 | 28.2 | 38.0 | | [mT0_small (300M)](https://huggingface.co/bigscience/mt0-small) | 21.8 | 21.4 | 25.7 | 25.1 | 27.6 | 24.9 | | [mT0_base (580M)](https://huggingface.co/bigscience/mt0-base) | 22.6 | 22.6 | 25.7 | 25.6 | 26.9 | 25.0 | | [mT0_large (1.2B)](https://huggingface.co/bigscience/mt0-large) | 22.0 | 23.4 | 25.1 | 27.3 | 27.6 | 25.2 | | [mT0_xl (3.7B)](https://huggingface.co/bigscience/mt0-xl) | 31.4 | 42.9 | 41.0 | 47.8 | 35.7 | 38.2 | | [mT0_xxl (13B)](https://huggingface.co/bigscience/mt0-xxl) | 33.5 | 46.2 | 47.9 | 52.6 | **39.6** | 42.5 | | [LLaMA (7B)](https://arxiv.org/abs/2302.13971) | 22.8 | 23.1 | 25.1 | 26.7 | 27.6 | 25.3 | | [LLaMA (13B)](https://arxiv.org/abs/2302.13971) | 24.1 | 23.0 | 24.4 | 29.5 | 26.7 | 25.3 | | [LLaMA (30B)](https://arxiv.org/abs/2302.13971) | 25.4 | 23.5 | 25.9 | 28.4 | 28.7 | 26.5 | | [LLaMA (65B)](https://arxiv.org/abs/2302.13971) | 33.0 | 37.7 | 40.8 | 41.4 | 32.1 | 35.8 | | [Bactrian-X-LLaMA (7B)](https://github.com/mbzuai-nlp/bactrian-x) | 23.3 | 24.0 | 26.0 | 26.1 | 27.5 | 25.7 | | [Bactrian-X-LLaMA (13B)](https://github.com/mbzuai-nlp/bactrian-x) | 28.3 | 29.9 | 32.8 | 35.2 | 29.2 | 30.3 | #### GPT-3.5 performance (% accuracy) across different education levels <p align="left"> <img src="https://github.com/fajri91/eval_picts/blob/master/IndoMMLU-result.png?raw=true" style="width: 370px;" id="title-icon"> </p> Red indicates that the score is below the minimum passing threshold of 65, while green signifies a score at or above this minimum. We can observe that ChatGPT mostly passes a score of 65 in Indonesian primary school exams. #### Few-shot Evaluation <p align="left"> <img src="https://github.com/fajri91/eval_picts/blob/master/plot_fewshot.png?raw=true" style="width: 380px;" id="title-icon"> </p> ## Data Each question in the dataset is a multiple-choice question with up to 5 choices and only one choice as the correct answer. We provide our dataset according to each subject in [data](data) folder. You can also access our dataset via [Hugging Face](https://huggingface.co/datasets/indolem/indommlu). <!-- #### Quick Use Our dataset has been added to [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and [OpenCompass](https://github.com/InternLM/opencompass), you can evaluate your model via these open-source tools. --> #### Evaluation The code for the evaluation of each model we used is in `evaluate.py`, and the code to run them is listed in `run.sh`. ## Citation ``` @inproceedings{koto-etal-2023-indommlu, title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}", author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = December, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", } ``` ## License The IndoMMLU dataset is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).

ShareGPT4Video/ShareGPT4Video

ShareGPT4Video

--- license: cc-by-nc-4.0 task_categories: - visual-question-answering - question-answering language: - en pretty_name: ShareGPT4Video Captions Dataset Card size_categories: - 1M<n configs: - config_name: ShareGPT4Video data_files: sharegpt4video_40k.jsonl --- # ShareGPT4Video 4.8M Dataset Card ## Dataset details **Dataset type:** ShareGPT4Video Captions 4.8M is a set of GPT4-Vision-powered multi-modal captions data of videos. It is constructed to enhance modality alignment and fine-grained visual concept perception in Large Video-Language Models (LVLMs) and Text-to-Video Models (T2VMs). This advancement aims to bring LVLMs and T2VMs towards the capabilities of GPT4V and Sora. * sharegpt4video_40k.jsonl is generated by GPT4-Vision (ShareGPT4Video). * share-captioner-video_mixkit-pexels-pixabay_4814k_0417.json is generated by our ShareCaptioner-Video trained on GPT4-Vision-generated video-caption pairs. * sharegpt4video_mix181k_vqa-153k_share-cap-28k.json is curated from sharegpt4video_instruct_gpt4-vision_cap40k.json for the supervised fine-tuning stage of LVLMs. * llava_v1_5_mix665k_with_video_chatgpt72k_share4video28k.json has replaced 28K detailed-caption-related data in VideoChatGPT with 28K high-quality captions from ShareGPT4Video. This file is utilized to validate the effectiveness of high-quality captions under the VideoLLaVA and LLaMA-VID models. **Dataset date:** ShareGPT4Video Captions 4.8M was collected in 4.17 2024. **Paper or resources for more information:** [[Project](https://ShareGPT4Video.github.io/)] [[Paper](https://arxiv.org/abs/2406.04325v1)] [[Code](https://github.com/ShareGPT4Omni/ShareGPT4Video)] [[ShareGPT4Video-8B](https://huggingface.co/Lin-Chen/sharegpt4video-8b)] **License:** Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use ## Intended use **Primary intended uses:** The primary use of ShareGPT4Video Captions 4.8M is research on large multimodal models and text-to-video models. **Primary intended users:** The primary intended users of this dataset are researchers and hobbyists in computer vision, natural language processing, machine learning, AIGC, and artificial intelligence. ## Paper arxiv.org/abs/2406.04325

bop-benchmark/datasets

bop-benchmark

--- task_categories: - image-segmentation - object-detection - robotics - zero-shot-object-detection size_categories: - n>1T configs: - config_name: MegaPose-ShapeNetCore data_files: MegaPose-ShapeNetCore/*.tar - config_name: MegaPose-GSO data_files: MegaPose-GSO/*.tar --- # BOP: Benchmark for 6D Object Pose Estimation The goal of BOP is to capture the state of the art in estimating the 6D pose, i.e. 3D translation and 3D rotation, of rigid objects from RGB/RGB-D images. An accurate, fast, robust, scalable and easy-to-train method that solves this task will have a big impact in application fields such as robotics or augmented reality.  Homepage: https://bop.felk.cvut.cz/home/ Toolkit: https://github.com/thodan/bop_toolkit ## Downloading datasets #### Option 1: Using `huggingface_hub`: <details><summary>Click to expand</summary> a. Install the library: ``` pip install --upgrade huggingface_hub ``` b. Download the dataset: ``` from huggingface_hub import snapshot_download dataset_name = "hope" local_dir = "./datasets" snapshot_download(repo_id="bop-benchmark/datasets", allow_patterns=f"{dataset_name}/*zip", repo_type="dataset", local_dir=local_dir) ``` If you want to download the entire BOP datasets (~3TB), please remove the `allow_patterns` argument. More options are available in the [official documentation](https://huggingface.co/docs/huggingface_hub/main/en/guides/download). </details> #### Option 2: Using `huggingface_hub[cli]`: <details><summary>Click to expand</summary> a. Install the library: ``` pip install -U "huggingface_hub[cli]" ``` b. Download the dataset: ``` export LOCAL_DIR=./datasets export DATASET_NAME=hope huggingface-cli download bop-benchmark/datasets --include "$DATASET_NAME/*.zip" --local-dir $LOCAL_DIR --repo-type=dataset ``` Please remove this argument `--include "$DATASET_NAME/*.zip"` to download entire BOP datasets (~3TB). More options are available in the [official documentation](https://huggingface.co/docs/huggingface_hub/main/en/guides/download). </details> #### Option 3: Using `wget`: <details><summary>Click to expand</summary> Similar `wget` command as in [BOP website](https://bop.felk.cvut.cz/datasets/) can be used to download the dataset from huggingface hub: ``` export SRC=https://huggingface.co/datasets/bop-benchmark/datasets/resolve/main wget $SRC/lm/lm_base.zip # Base archive wget $SRC/lm/lm_models.zip # 3D object models wget $SRC/lm/lm_test_all.zip # All test images ("_bop19" for a subset) wget $SRC/lm/lm_train_pbr.zip # PBR training images ``` </details> Datasets are stored in `.zip` format. You can extract them using the following command: ``` bash scripts/extract_bop.sh ``` If you are running on a machine with high bandwidth, you can increase your download speed by adding the following environment variable: ``` pip install huggingface_hub[hf_transfer] export HF_HUB_ENABLE_HF_TRANSFER=1 ``` ## Uploading datasets You create a new dataset and want to share it with BOP community. Here is a step-by-step guide to upload the dataset and create a pull request to [our huggingface hub](https://huggingface.co/datasets/bop-benchmark/datasets/). Feel free to reach out to vanngn.nguyen@gmail.com if you have any questions. Similar to the download process, you can upload the dataset using the `huggingface_hub` library or `huggingface_hub[cli]`. We recommend using `huggingface_hub[cli]` for its simplicity. #### Option 1: Using `huggingface_hub[cli]`: <details><summary>Click to expand</summary> a. Install the library: ``` pip install -U "huggingface_hub[cli]" ``` b. Log-in and create a token ``` huggingface-cli login ``` Then go to [this link](https://huggingface.co/settings/tokens) and generate a token. IMPORTANT: the token should have write access as shown below: <img src="./media/token_hf.png" alt="image" width="300"> Make sure you are in the bop-benchmark group by running: ``` huggingface-cli whoami ``` c. Upload dataset: The command is applied for both folders and specific files: ``` # Usage: huggingface-cli upload bop-benchmark/datasets [local_path] [path_in_repo] --repo-type=dataset --create-pr ``` For example, to upload hope dataset: ``` export LOCAL_FOLDER=./datasets/hope export HF_FOLDER=/hope huggingface-cli upload bop-benchmark/datasets $LOCAL_FOLDER $HF_FOLDER --repo-type=dataset --create-pr ``` </details> #### Option 2: Using `huggingface_hub`: <details><summary>Click to expand</summary> a. Install the library: ``` pip install --upgrade huggingface_hub ``` b. Creating a pull-request: We recommend organizing the dataset in a folder and then uploading it to the huggingface hub. For example, to upload `lmo`: ``` from huggingface_hub import HfApi, CommitOperationAdd dataset_name = "lmo" local_dir = "./datasets/lmo" operations = [] for file in local_dir.glob("*"): add_commit = CommitOperationAdd( path_in_repo=f"/{dataset_name}", path_or_fileobj=local_dir, ) operations.append(add_commit) api = HfApi() MY_TOKEN = # get from https://huggingface.co/settings/tokens api.create_commit(repo_id="bop-benchmark/datasets", repo_type="dataset", commit_message=f"adding {dataset_name} dataset", token=MY_TOKEN, operations=operations, create_pr=True) ``` If your dataset is large (> 500 GB), you can upload it in chunks by adding the `multi_commits=True, multi_commits_verbose=True,` argument. More options are available in the [official documentation](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/package_reference/hf_api#huggingface_hub.HfApi.create_pull_request). </details> ## FAQ #### 1. How to upload a large file > 50 GB? Note that HuggingFace limits the size of each file to 50 GB. If your dataset is larger, you can split it into smaller files: ``` zip -s 50g input.zip --out output.zip ``` This command will split the `input.zip` into multiple files of 50GB size `output.zip`, `output.z01`, `output.z01`, ... You can then extract them using one of the following commands: ``` # option 1: combine zip -s0 output.zip --out input.zip # option 2: using 7z to unzip directly 7z x output.zip ``` #### 2. How to increase download speed? If you are running on a machine with high bandwidth, you can increase your download speed by adding the following environment variable: ``` pip install huggingface_hub[hf_transfer] export HF_HUB_ENABLE_HF_TRANSFER=1 ``` ## Publications - [**BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects**](https://arxiv.org/pdf/2403.09799.pdf) - T. Hodaň, M. Sundermeyer, Y. Labbé, V. N. Nguyen, G. Wang, E. Brachmann, B. Drost, V. Lepetit, C. Rother, J. Matas - IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW, [CV4MR workshop](https://cv4mr.github.io/)) 2024, Seattle - [PDF](https://arxiv.org/pdf/2403.09799.pdf), [SLIDES](https://cmp.felk.cvut.cz/sixd/workshop_2023/slides/bop_challenge_2023_results.pdf), [VIDEO](https://www.youtube.com/watch?v=PcDszFANcDQ), [BIB](https://cmp.felk.cvut.cz/~hodanto2/data/hodan2023bop.bib) - [**BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects**](https://arxiv.org/pdf/2302.13075.pdf) - M. Sundermeyer, T. Hodaň, Y. Labbé, G. Wang, E. Brachmann, B. Drost, C. Rother, J. Matas - IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW, [CV4MR workshop](https://cv4mr.github.io/)) 2023, Vancouver - [PDF](https://arxiv.org/pdf/2302.13075.pdf), [SLIDES](https://cmp.felk.cvut.cz/sixd/workshop_2022/slides/bop_challenge_2022_results.pdf), [VIDEO 1](https://vimeo.com/showcase/9946695/video/768457697), [VIDEO 2](https://vimeo.com/showcase/9946695/video/768458355), [BIB](https://cmp.felk.cvut.cz/~hodanto2/data/sundermeyer2022bop.bib) - [**BOP Challenge 2020 on 6D Object Localization**](https://arxiv.org/pdf/2009.07378.pdf) - T. Hodaň, M. Sundermeyer, B. Drost, Y. Labbé, E. Brachmann, F. Michel, C. Rother, J. Matas - European Conference on Computer Vision Workshops (ECCVW) 2020, Glasgow - [PDF](https://arxiv.org/pdf/2009.07378.pdf), [SLIDES](https://bop.felk.cvut.cz/media/bop_challenge_2020_results.pdf), [BIB](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2020bop.bib) - [**BOP: Benchmark for 6D Object Pose Estimation**](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop.pdf) - T. Hodaň, F. Michel, E. Brachmann, W. Kehl, A. G. Buch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X. Zabulis, C. Sahin, F. Manhardt, F. Tombari, T.-K. Kim, J. Matas, C. Rother - European Conference on Computer Vision (ECCV) 2018, Munich - [PDF](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop.pdf), [SLIDES](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop_slides_eccv.pdf), [POSTER](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop_poster.pdf), [BIB](http://cmp.felk.cvut.cz/~hodanto2/data/hodan2018bop.bib) The online evaluation system has been developed by [T. Hodaň](http://www.hodan.xyz) and [A. Melenovský](https://www.linkedin.com/in/anton%C3%ADn-melenovsk%C3%BD-09907b151/).

cschell/xr-motion-dataset-catalogue

cschell

--- language: - en tags: - kinematic research - XR user motions - VR user motions - AR user motions - motions pretty_name: XR Motion Dataset Catalogue --- # XR Motion Dataset Catalogue ## Overview The XR Motion Dataset Catalogue, accompanying our paper "Navigating the Kinematic Maze: A Comprehensive Guide to XR Motion Dataset Standards," standardizes and simplifies access to Extended Reality (XR) motion datasets. The catalogue represents our initiative to streamline the usage of kinematic data in XR research by aligning various datasets to a consistent format and structure. ### Dataset Specifications All datasets in this catalogue have been standardized with the following specifications: - **Coordinate System:** X (Right), Y (Up), Z (Forward) - **Rotation Representation:** Quaternions - **Units of Measurement:** Centimeters for spatial data - **Time Encoding:** Milliseconds for time-related data These specifications ensure uniformity and comparability across all datasets in the catalogue. ### Conversion Scripts Repository The alignment of datasets was facilitated by a series of conversion scripts, which are available in our GitHub repository: [XR Motion Dataset Conversion Scripts](https://github.com/cschell/xr-motion-dataset-conversion-scripts). These scripts detail the process of aligning attribute names, coordinate systems, rotation representations, units of measurement, and time encoding. ### Included Datasets The catalogue includes the following datasets: 1. [LiebersBeatSaber23](https://doi.org/10.1145/3611659.3615696) 2. [Boxrr23](https://doi.org/10.25350/B5NP4V) – *edit 2024-05-04: we are still working on providing the aligned version – in the meantime you find the original version [here](https://huggingface.co/datasets/cschell/boxrr-23/)* 3. BOXRR24 – *WIP: we are currently working on the next version of the BOXRR-23 dataset, which will include significantly more user – we do our best to make it available later this year* 4. [LiebersHand22](https://doi.org/10.1080/10447318.2022.2120845) 5. [LiebersLabStudy21](https://doi.org/10.1145/3411764.3445528) 6. [MooreCrossDomain23](https://doi.org/10.1109/ISMAR59233.2023.00054) 7. <del>[RMillerBall22](https://github.com/Terascale-All-sensing-Research-Studio/VR-Biometric-Authentication)</del> *request for permissions pending* 8. [VrNet](http://arxiv.org/abs/2306.03381) 9. [WhoIsAlyx](https://doi.org/10.3389/frvir.2023.1272234) ## Installation and Usage ### Loading the Dataset with Hugging Face `datasets` Library To load a dataset from the catalogue, use the `datasets` library in Python. For example, to load the `WhoIsAlyx` dataset: ```python from datasets import load_dataset dataset = load_dataset("cschell/xr-motion-dataset-catalogue", "who_is_alyx", trust_remote_code=True) ``` ### Loading Individual Recordings with Pandas To load individual recordings, you can use `pandas`. Here's an example: ```python import pandas as pd file_url_path = "hf://datasets/cschell/xr-motion-dataset-catalogue/who_is_alyx/player_02/2022-01-07.parquet" recording = pd.read_parquet(file_url_path) ``` ## Contributing and Feedback Contributions and feedback are welcome to enhance the XR Motion Dataset Catalogue. Feel free to open a pull request or contact us directly. <!-- ## Citation If you use the XR Motion Dataset Catalogue in your research, please cite our paper: ``` @article{your_paper_identifier, title={Navigating the Kinematic Maze: A Comprehensive Guide to XR Motion Dataset Standards}, author={Your Name and Other Authors}, journal={Journal Name}, year={Year} } ``` -->

kamilakesbi/transformers_image_doc

kamilakesbi

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 406434.0 num_examples: 2 download_size: 381914 dataset_size: 406434.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

lmms-lab/MME

lmms-lab

--- size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answer dtype: string - name: category dtype: string splits: - name: test num_bytes: 1733070098.024 num_examples: 2374 download_size: 864018279 dataset_size: 1733070098.024 --- # Evaluation Dataset for MME

bigscience/xP3all

bigscience

--- annotations_creators: - expert-generated - crowdsourced language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3 size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?", "targets": "Yes" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. |Language|Kilobytes|%|Samples|%| |--------|------:|-:|---:|-:| |tw|106288|0.11|265071|0.33| |bm|107056|0.11|265180|0.33| |ak|108096|0.11|265071|0.33| |ca|110608|0.11|271191|0.33| |eu|113008|0.11|281199|0.35| |fon|113072|0.11|265063|0.33| |st|114080|0.11|265063|0.33| |ki|115040|0.12|265180|0.33| |tum|116032|0.12|265063|0.33| |wo|122560|0.12|365063|0.45| |ln|126304|0.13|365060|0.45| |as|156256|0.16|265063|0.33| |or|161472|0.16|265063|0.33| |kn|165456|0.17|265063|0.33| |ml|175040|0.18|265864|0.33| |rn|192992|0.19|318189|0.39| |nso|229712|0.23|915051|1.13| |tn|235536|0.24|915054|1.13| |lg|235936|0.24|915021|1.13| |rw|249360|0.25|915043|1.13| |ts|250256|0.25|915044|1.13| |sn|252496|0.25|865056|1.07| |xh|254672|0.26|915058|1.13| |zu|263712|0.26|915061|1.13| |ny|272128|0.27|915063|1.13| |ig|325232|0.33|950097|1.17| |yo|352784|0.35|918416|1.13| |ne|393680|0.39|315754|0.39| |pa|523248|0.52|339210|0.42| |gu|560688|0.56|347499|0.43| |sw|566656|0.57|1130481|1.4| |mr|666240|0.67|417269|0.52| |bn|832720|0.83|428843|0.53| |ta|926912|0.93|415433|0.51| |te|1343232|1.35|584590|0.72| |ur|1918272|1.92|855756|1.06| |vi|3102512|3.11|1672106|2.07| |code|4330752|4.34|2707724|3.34| |hi|4403568|4.41|1554667|1.92| |zh|4599440|4.61|3589234|4.43| |id|4612256|4.62|2643418|3.27| |ar|4683456|4.69|2160181|2.67| |fr|6591120|6.6|5316403|6.57| |pt|6886800|6.9|3752156|4.63| |es|8587920|8.6|5413205|6.69| |en|39252528|39.33|32740750|40.44| |total|99807184|100.0|80956089|100.0| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for HumanEval) - Natural Language Inference - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) #### Additional [xP3all](https://huggingface.co/datasets/bigscience/xP3all) datasets - Coreference Resolution - [WSC (Fixed)](https://huggingface.co/datasets/super_glue) - Sentence Completion - [HellaSwag](https://huggingface.co/datasets/hellaswag) - Translation - [MultiEurlex](https://huggingface.co/datasets/multi_eurlex) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.

nyu-visionx/Cambrian-10M

nyu-visionx

--- task_categories: - visual-question-answering - question-answering language: - en size_categories: - 1M<n<10M license: apache-2.0 --- # Cambrian-10M Dataset **Please see paper & website for more information:** - https://cambrian-mllm.github.io/ - https://arxiv.org/abs/2406.16860 ## Overview Cambrian-10M is a comprehensive dataset designed for instruction tuning, particularly in multimodal settings involving visual interaction data. The dataset is crafted to address the scarcity of high-quality multimodal instruction-tuning data and to maintain the language abilities of multimodal large language models (LLMs). ## Data Collection ### Multimodal Data Sources Unlike language data, multimodal instruction-tuning data is much rarer and harder to collect. To address this, we leverage existing multimodal benchmarks and datasets involving visual interaction data, such as Visual Question Answering (VQA) and Optical Character Recognition (OCR) data. This approach helps mitigate the catastrophic forgetting commonly observed when fine-tuning multimodal LLMs. ### Language-Only Instruction-Following Data To ensure the preservation of language capabilities, we also collect a small volume of high-quality language-only instruction-following data from the community. ### Targeted Internet Data Collection Engine We introduce a data engine designed to create large-scale, reliable, high-quality knowledge-based multimodal instruction tuning data. The engine works as follows: 1. **Field and Subfield Selection**: The engine selects a target field and subfield, such as “Physics”. 2. **Topic Identification**: An LLM like GPT-4 identifies topics within the field (e.g., “Newton’s Laws”). 3. **Reliable Source Search**: The engine searches reliable sources like Wikipedia for each topic. 4. **Text-Image Association Extraction**: The parser extracts image-caption-text tuples from the sources. 5. **Q&A Pair Generation**: The caption-text is fed to an LLM, such as GPT-3.5, to generate instruction-type Q&A pairs about the image. These Q&A pairs, along with the images, form our VQA dataset. ### GPT Rewriting We also incorporate recent MLLMs such as GPT-4v and GPT-4o to generate extended responses and free-form instruction tuning data. To play with gpt generated data, use [gpt4v_77k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4v_77k.jsonl), Curated [gpt4o_60k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4o_60k.jsonl) - [gpt4v_77k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4v_77k.jsonl) contains more extended responses from Cambrian-10M. - [gpt4o_60k](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/gpt4o_60k.jsonl) contains more creative data in visual interactions. ## Cambrian-10M Composition The Cambrian-10M dataset consists of approximately 9.784 million data points, offering a diverse range of data for various research applications. The composition of the dataset is visualized in Fig. 9. ## Cambrian-7M We make an initial effort to study data curation. In particular, we find the following data ratio to perform most optimally - **Language**: 21.00% - **General**: 34.52% - **OCR**: 27.22% - **Counting**: 8.71% - **Math**: 7.20% - **Code**: 0.87% - **Science**: 0.88%  ## Getting Started with Cambrian Data Before you start, ensure you have sufficient storage space to download and process the data. Cambrian-10M contains a total of 10 million images collected from previous datasets, an internet data engine, and GPT-generated instruction tuning data. Follow these steps to get started: 1. **Download the Data Repository** Download the data repository. Note that due to Hugging Face policy constraints, the data folder is archived into tar files. We also split the `allava` and `data_engine` data into smaller tar files because they exceed the 50 GB size limit. 2. **Merge Tar Files** To explore the Cambrian-10M dataset, first merge the different parts of `allava` and `data_engine` together: ```bash python merge_tars.py ``` 3. **Extract Tar Files** Then, extract all the tar files into the current directory: ```bash python extract.py ``` 4. **Training with Cambrian** You can train with the raw [Cambrian10M](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/Cambrian10M.jsonl), Curated [Cambrian7M](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/resolve/main/jsons/Cambrian7M.jsonl). We recommend using the Curated [Cambrian7M with system prompt](https://huggingface.co/datasets/nyu-visionx/Cambrian-10M/blob/main/jsons/Cambrian7M_withsystemprompt.jsonl) that also alleviates 'answer machine' problem.

OpenGVLab/OmniCorpus-CC

OpenGVLab

--- language: - en license: cc-by-4.0 size_categories: - 100M<n<1B task_categories: - image-to-text - visual-question-answering dataset_info: - config_name: CC-MAIN-2013-20 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 19908676196 num_examples: 3878063 download_size: 9303464923 dataset_size: 19908676196 - config_name: CC-MAIN-2013-48 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 15282078925 num_examples: 3091537 download_size: 6965036866 dataset_size: 15282078925 - config_name: CC-MAIN-2014-10 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7227087609 num_examples: 1390034 download_size: 3259239561 dataset_size: 7227087609 - config_name: CC-MAIN-2014-15 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 10106913108 num_examples: 1968361 download_size: 4567738362 dataset_size: 10106913108 - config_name: CC-MAIN-2014-23 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7997621043 num_examples: 1455331 download_size: 3468852905 dataset_size: 7997621043 - config_name: CC-MAIN-2014-35 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 6228103779 num_examples: 1219200 download_size: 2849584613 dataset_size: 6228103779 - config_name: CC-MAIN-2014-41 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 8321822952 num_examples: 1573955 download_size: 3775989970 dataset_size: 8321822952 - config_name: CC-MAIN-2014-42 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7732679416 num_examples: 1511931 download_size: 3505766162 dataset_size: 7732679416 - config_name: CC-MAIN-2014-49 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 4473311810 num_examples: 837735 download_size: 1982728919 dataset_size: 4473311810 - config_name: CC-MAIN-2014-52 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7292722888 num_examples: 1304730 download_size: 2957626766 dataset_size: 7292722888 - config_name: CC-MAIN-2015-06 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 5775826679 num_examples: 1061940 download_size: 2462379667 dataset_size: 5775826679 - config_name: CC-MAIN-2015-11 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 6263650452 num_examples: 1129411 download_size: 2528026633 dataset_size: 6263650452 - config_name: CC-MAIN-2015-14 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 4524425019 num_examples: 885221 download_size: 1939222111 dataset_size: 4524425019 - config_name: CC-MAIN-2015-18 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - 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name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 3414418701 num_examples: 627995 download_size: 1403890884 dataset_size: 3414418701 - config_name: CC-MAIN-2016-30 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 7244342539 num_examples: 1183776 download_size: 2913394840 dataset_size: 7244342539 - config_name: CC-MAIN-2016-36 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - 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name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 54814836003 num_examples: 9930553 download_size: 27458854931 dataset_size: 54814836003 - config_name: CC-MAIN-2019-09 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 54426174385 num_examples: 8897510 download_size: 28125345656 dataset_size: 54426174385 - config_name: CC-MAIN-2019-13 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 48712051219 num_examples: 7803004 download_size: 25156014252 dataset_size: 48712051219 - config_name: CC-MAIN-2019-18 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 48203751852 num_examples: 7532171 download_size: 24844412087 dataset_size: 48203751852 - config_name: CC-MAIN-2019-22 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 51674379059 num_examples: 8339842 download_size: 26257475492 dataset_size: 51674379059 - config_name: CC-MAIN-2019-26 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 43336967638 num_examples: 7320268 download_size: 21900316910 dataset_size: 43336967638 - config_name: CC-MAIN-2019-30 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 46313133200 num_examples: 7682281 download_size: 23262218065 dataset_size: 46313133200 - config_name: CC-MAIN-2019-35 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 49570657315 num_examples: 8098108 download_size: 24938729240 dataset_size: 49570657315 - config_name: CC-MAIN-2019-39 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 43538081906 num_examples: 7102645 download_size: 21728983014 dataset_size: 43538081906 - config_name: CC-MAIN-2019-43 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 52817470138 num_examples: 8567061 download_size: 26105523209 dataset_size: 52817470138 - config_name: CC-MAIN-2019-47 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 42252827792 num_examples: 6775943 download_size: 21228532199 dataset_size: 42252827792 - config_name: CC-MAIN-2019-51 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 38926356094 num_examples: 6415558 download_size: 19510339598 dataset_size: 38926356094 - config_name: CC-MAIN-2020-05 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 48189844491 num_examples: 7921372 download_size: 24235687030 dataset_size: 48189844491 - config_name: CC-MAIN-2020-10 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 48904133840 num_examples: 8211791 download_size: 24576159189 dataset_size: 48904133840 - config_name: CC-MAIN-2020-16 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 51243682770 num_examples: 8578633 download_size: 25485035979 dataset_size: 51243682770 - config_name: CC-MAIN-2020-24 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 59424939072 num_examples: 10438139 download_size: 29827361603 dataset_size: 59424939072 - config_name: CC-MAIN-2020-29 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 66229730938 num_examples: 11475631 download_size: 33030161773 dataset_size: 66229730938 - config_name: CC-MAIN-2020-34 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 54287690582 num_examples: 9495610 download_size: 27018821467 dataset_size: 54287690582 - config_name: CC-MAIN-2020-40 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 71587907978 num_examples: 12058149 download_size: 35795677487 dataset_size: 71587907978 - config_name: CC-MAIN-2020-45 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - 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name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 58557861606 num_examples: 9539918 download_size: 29083801775 dataset_size: 58557861606 - config_name: CC-MAIN-2021-04 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - 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name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 60802783945 num_examples: 10176190 download_size: 30326513365 dataset_size: 60802783945 - config_name: CC-MAIN-2021-17 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 75061494488 num_examples: 12343366 download_size: 37345114890 dataset_size: 75061494488 - config_name: CC-MAIN-2021-21 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 70036417178 num_examples: 11584034 download_size: 34806730527 dataset_size: 70036417178 - 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name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 126436062118 num_examples: 21495687 download_size: 63067252044 dataset_size: 126436062118 - config_name: CC-MAIN-2022-40 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 123806739937 num_examples: 20206120 download_size: 61929035270 dataset_size: 123806739937 - config_name: CC-MAIN-2022-49 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 152577158166 num_examples: 24634059 download_size: 76529854484 dataset_size: 152577158166 - config_name: CC-MAIN-2023-06 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 174815301023 num_examples: 28962355 download_size: 87301203013 dataset_size: 174815301023 - config_name: CC-MAIN-2023-14 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 227631152876 num_examples: 37223376 download_size: 114188282465 dataset_size: 227631152876 - config_name: CC-MAIN-2023-23 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 321036722459 num_examples: 52119692 download_size: 161491274249 dataset_size: 321036722459 - config_name: CC-MAIN-2023-40 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 648032999611 num_examples: 101292016 download_size: 317965522325 dataset_size: 648032999611 - config_name: CC-MAIN-2023-50 features: - name: general_metadata struct: - name: domain sequence: string - name: fluency_prob dtype: float64 - name: id dtype: string - name: non_advertisement_prob dtype: float64 - name: politics_prob dtype: float64 - name: porn_prob dtype: float64 - name: toxic_prob dtype: float64 - name: url dtype: string - name: images sequence: string - name: texts sequence: string - name: metadata list: - name: aesthetic_prob dtype: float64 - name: bytes dtype: int64 - name: d_hash dtype: string - name: d_hash_dup_count dtype: int64 - name: height dtype: int64 - name: img_url_sha dtype: string - name: p_hash dtype: string - name: p_hash_dup_count dtype: int64 - name: unsafe_prob dtype: float64 - name: width dtype: int64 splits: - name: train num_bytes: 744768384551 num_examples: 117073004 download_size: 365332295606 dataset_size: 744768384551 configs: - config_name: CC-MAIN-2013-20 data_files: - split: train path: CC-MAIN-2013-20/train-* - config_name: CC-MAIN-2013-48 data_files: - split: train path: CC-MAIN-2013-48/train-* - config_name: CC-MAIN-2014-10 data_files: - split: train path: CC-MAIN-2014-10/train-* - 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split: train path: CC-MAIN-2019-35/train-* - config_name: CC-MAIN-2019-39 data_files: - split: train path: CC-MAIN-2019-39/train-* - config_name: CC-MAIN-2019-43 data_files: - split: train path: CC-MAIN-2019-43/train-* - config_name: CC-MAIN-2019-47 data_files: - split: train path: CC-MAIN-2019-47/train-* - config_name: CC-MAIN-2019-51 data_files: - split: train path: CC-MAIN-2019-51/train-* - config_name: CC-MAIN-2020-05 data_files: - split: train path: CC-MAIN-2020-05/train-* - config_name: CC-MAIN-2020-10 data_files: - split: train path: CC-MAIN-2020-10/train-* - config_name: CC-MAIN-2020-16 data_files: - split: train path: CC-MAIN-2020-16/train-* - config_name: CC-MAIN-2020-24 data_files: - split: train path: CC-MAIN-2020-24/train-* - config_name: CC-MAIN-2020-29 data_files: - split: train path: CC-MAIN-2020-29/train-* - config_name: CC-MAIN-2020-34 data_files: - split: train path: CC-MAIN-2020-34/train-* - config_name: CC-MAIN-2020-40 data_files: - split: train path: CC-MAIN-2020-40/train-* - config_name: CC-MAIN-2020-45 data_files: - split: train path: CC-MAIN-2020-45/train-* - config_name: CC-MAIN-2020-50 data_files: - split: train path: CC-MAIN-2020-50/train-* - config_name: CC-MAIN-2021-04 data_files: - split: train path: CC-MAIN-2021-04/train-* - config_name: CC-MAIN-2021-10 data_files: - split: train path: CC-MAIN-2021-10/train-* - config_name: CC-MAIN-2021-17 data_files: - split: train path: CC-MAIN-2021-17/train-* - config_name: CC-MAIN-2021-21 data_files: - split: train path: CC-MAIN-2021-21/train-* - config_name: CC-MAIN-2021-25 data_files: - split: train path: CC-MAIN-2021-25/train-* - config_name: CC-MAIN-2021-31 data_files: - split: train path: CC-MAIN-2021-31/train-* - config_name: CC-MAIN-2021-39 data_files: - split: train path: CC-MAIN-2021-39/train-* - config_name: CC-MAIN-2021-43 data_files: - split: train path: CC-MAIN-2021-43/train-* - config_name: CC-MAIN-2021-49 data_files: - split: train path: CC-MAIN-2021-49/train-* - config_name: CC-MAIN-2022-05 data_files: - split: train path: CC-MAIN-2022-05/train-* - config_name: CC-MAIN-2022-21 data_files: - split: train path: CC-MAIN-2022-21/train-* - config_name: CC-MAIN-2022-27 data_files: - split: train path: CC-MAIN-2022-27/train-* - config_name: CC-MAIN-2022-33 data_files: - split: train path: CC-MAIN-2022-33/train-* - config_name: CC-MAIN-2022-40 data_files: - split: train path: CC-MAIN-2022-40/train-* - config_name: CC-MAIN-2022-49 data_files: - split: train path: CC-MAIN-2022-49/train-* - config_name: CC-MAIN-2023-06 data_files: - split: train path: CC-MAIN-2023-06/train-* - config_name: CC-MAIN-2023-14 data_files: - split: train path: CC-MAIN-2023-14/train-* - config_name: CC-MAIN-2023-23 data_files: - split: train path: CC-MAIN-2023-23/train-* - config_name: CC-MAIN-2023-40 data_files: - split: train path: CC-MAIN-2023-40/train-* - config_name: CC-MAIN-2023-50 data_files: - split: train path: CC-MAIN-2023-50/train-* --- ⭐️ **NOTE:** Several parquet files were marked unsafe (viruses) by official scaning of hf, while they are reported safe by ClamAV and Virustotal. We found [many false positive cases](https://discuss.huggingface.co/u/mcpotato/summary) of the hf automatic scanning in hf discussions and raise [one discussion](https://discuss.huggingface.co/t/one-parquet-file-of-my-dataset-was-marked-unsafe/113745) to ask for a re-scanning. # OmniCorpus-CC This is the repository of OmniCorpus-CC, which contains 988 million image-text interleaved documents collected from [Common Crawl](https://commoncrawl.org/). - Repository: https://github.com/OpenGVLab/OmniCorpus - Paper: https://arxiv.org/abs/2406.08418 OmniCorpus dataset is a large-scale image-text interleaved dataset, which pushes the boundaries of scale and diversity by encompassing **8.6 billion images** interleaved with **1,696 text tokens** from diverse sources, significantly surpassing previous datasets. This dataset demonstrates several advantages over its counterparts: 1. **Larger data scale:** Our dataset is 1.7 times larger in images and 12.5 times larger in texts compared to the previously largest multimodal dataset, LAION-5B, while maintaining excellent data quality. 2. **Richer data diversity:** Drawing from a broader range of data sources, our dataset is more diverse than other image-text interleaved datasets. It includes bilingual multimodal data in both Chinese and English, and encompasses text-centric and vision-centric documents extracted from common websites and video platforms. 3. **More flexible format:** The streaming data format of our dataset offers exceptional flexibility, allowing adaptation to various data structures, including pure text corpora, image-text pairs, and interleaved data formats. <img width="578" alt="image" src="https://github.com/OpenGVLab/OmniCorpus/assets/47669167/641a6427-ba50-41e6-8634-8810113fd803"> The OmniCorpus contains three sections: - **OmniCorpus-CC**: processed from dumps in Common Crawl from 2013 to Nov./Dec. 2023. - **OmniCorpus-CW**: sourced from Chinese internet resources, will be availiable in [OpenDataLab](https://opendatalab.com/) platform. - **OmniCorpus-YT**: samples Youtube video frames as images and collects subtitles as texts. Code for pre-training, evaluating, main body extracting, and filtering have been released in the official [repository](https://github.com/OpenGVLab/OmniCorpus). A pre-trained model is availiable [here](https://huggingface.co/Qingyun/OmniCorpus-InternVL). # Data Pipeline Our data pipeline consists of five key stages: main body extraction, preliminary text filtering, document deduplication, image downloading \& filtering, and detailed text filtering. Each stage efficiently reduces the dataset to retain only high-quality data. Please refer to our paper for more details about the data pipeline. <img width="723" alt="image" src="https://github.com/OpenGVLab/OmniCorpus/assets/47669167/a6de8928-58fb-4ff4-8ef9-4bd90e9ada5f"> # Usages The image-text interleaved documents are recommanded for the following usages: - Pre-training multimodal large language model (MLLM): Recent MLLMs (such as Flamingo series, EMU series, IDEFICS series, MM1, Cambrian-1, and xGen-MM) have shown that image-text interleaved data aids multimodal in-context learning and maintains the capabilities of large language models during multimodal fine-tuning. - Long text-image retrieval: We provide image-text similarities calculated with CLIP, which can convert the documents to image-text retrieval dataset with longer text. A retrieval model pre-trained on such data can retrieval images based on longer text, which can be used for multimodal RAG, converting pure text to multimodal sample, etc. - Source for futher dataset research: Our data is large-scale, which can serve as the source for researches for data curation strategies. We provide many useful attributes as metadata for each document, which can enrich the filtering strategy and reduce the cost. - ...... # Data Format Following common practices, the data is organized into Parquet file format. You might encounter errors when using `pandas.read_parquet` (because the data structure contains nested elements). We recommend using fastparquet to load the parquet files. ```Python import fastparquet df = fastparquet.ParquetFile(parquet_file_path).to_pandas() # You can also use iter_batches parquet_file = pq.ParquetFile(filepath) for batch in parquet_file.iter_batches(): df = batch.to_pandas() ``` You can convert the i-th document and convert it into a dictionary. ```Python doc_dict = df.iloc[i].to_dict() ``` The document format is as follow: ```json { 'images': [ <str: image_1_url>, None, <str: image_2_url>, None, ], 'texts': [ None, <str: text_paragraph_1_content> None, <str: text_paragraph_2_content>, ] 'metadata': [ <dict: image_1_metadata>, None, <dict: image_2_metadata>, None ], 'general_metadata': { "url": <str: document url>, "id": <str: document id>, "domain": <list[str]: domains extracted from document url>, "fluency_prob": <float: the probability of fluency>, "non_advertisement_prob": <float: the probability of non-advertisement>, "porn_prob": <float: the probability of porn content>, "politics_prob": <float: the probability of politics content>, "toxic_prob": <float: the probability of toxic content>, } } ``` Each image metadata is as follow: ```json { "img_url_sha": <str: sha code of image url>, "width": <int: image width>, "height": <int: image height>, "bytes": <int: byte number of the image file>, "d_hash": <str: d_hash code of the image, used for image deduplication>, "p_hash": <str: p_hash code of the image, used for image deduplication>, "d_hash_dup_count": <int: duplicated times detected by d_hash code>, "p_hash_dup_count": <int: duplicated times detected by p_hash code>, "aesthetic prob": <float: aesthetic probility>, "unsafe prob": <float: NSFW probility>, } ``` # License OmniCorpus is released under a [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/deed.en) license, with the primary intent of supporting research activities. # Citation ``` @article{li2024omnicorpus, title={OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text}, author={Li, Qingyun and Chen, Zhe and Wang, Weiyun and Wang, Wenhai and Ye, Shenglong and Jin, Zhenjiang and others}, journal={arXiv preprint arXiv:2406.08418}, year={2024} } ```

SPRIGHT-T2I/spright

SPRIGHT-T2I

--- language: - en size_categories: - 1M<n<10M license: - other license_name: intel-research-use-license license_link: LICENSE --- # <u>Dataset Description</u> SPRIGHT (**SP**atially **RIGHT**) is the first spatially focused, large scale vision-language dataset. It was built by re-captioning ∼6 million images from 4 widely-used datasets: * [CC12M](https://arxiv.org/abs/2102.08981) * [Segment Anything](https://arxiv.org/abs/2304.02643) * [COCO Validation](https://arxiv.org/abs/1405.0312) * [LAION Aesthetics](https://laion.ai/blog/laion-aesthetics/) This repository contains the re-captioned data from CC12M and Segment Anything, while the COCO data is present [here](https://huggingface.co/datasets/SPRIGHT-T2I/spright_coco). We do not release images from LAION, as the parent images are currently private. Below are some illustrative examples from the SPRIGHT dataset, where the captions are correct in its entirety; both in capturing the spatial relationships and overall description of the image.  We also share some illustrative examples from the SPRIGHT dataset, where the captions are not completely correct.  ## <u>Dataset Sources</u> ### CC-12M We re-caption a total of 2.3 million images from the CC-12M data taset, filtering out images of resolution less than 768. ### Segment Anything We re-caption 3.5 million images as part of our process. Since SA has all human faces blurred, we filter out images which contain blurring i.e. we filter our images where humans are detected (using the Owl-V2 object detector). Since SA does not have ground-truth captions, we also generate its general captions using the CoCa captioning model. ## <u>Dataset Structure</u> ### Samples Each tar file contains 10k samples. Each sample is composed of: - an image - "{idx}.jpg" - related captions (general caption and spatial caption) - "{idx}.json" - metadata (image width and height, original dataset the image was taken from and its original id) - "{idx}.metadata.json" ### How to use it In order to load the data, you can use the [`load_data.py`](./load_data.py) script. The metadata.json file contains the size and the split for each tar file. We also provide a script [`robust_upload.py`](robust_upload.py) used to efficiently upload the data to Hugging Face Hub. Note: filenames inside each .tar partition do NOT contain leading zeroes, which may confound some sorting mechanism (eg: python's sort() function); users that download and extract data or filenames from the .tar partions should be aware of this and use a "natural sort" style function to accomodate this convention. ## <u>Dataset Creation</u> #### Data Generation We leverage [LLaVA-1.5-13B](https://github.com/haotian-liu/LLaVA) to produce synthetic spatial captions, and use the following prompt to create the SPRIGHT dataset: > "Using 2 sentences, describe the spatial relationships seen in the image. You can use words like left/right, above/below, front/behind, far/near/adjacent, inside/outside. Also describe relative sizes of objects seen in the image." #### Dataset validation - Using [FAITHScore](https://arxiv.org/abs/2311.01477): We leverage a large language model to deconstruct generated captions into atomic (simple) claims that can be individually and independently verified in VQA format. The captions are on average 88.9% correct. - Using [GPT4(V)](https://cdn.openai.com/papers/GPTV_System_Card.pdf_): We perform a small-scale study on 100 images to evaluate our captions with GPT-4(V). Specifically, we prompt GPT-4(V) to rate each caption between a score of 1 to 10, especially focusing on the correctness of the spatial relationships captured. We achieve a mean and median rating of 6.41 and 7.0. - Human annotation: We also annotate a total of 3000 images through a crowd-sourced human study, where each participant annotates a maximum of 30 image-text pairs. Most captions in SPRIGHT have >1 sentences. Therefore, for a fine-grained evaluation, we randomly select 1 sentence, from a caption in SPRIGHT and evaluate its correctness for a given image. Across 149 responses, we get an accuracy of 66.57%. # <u>Acknowledgements</u> We thank [Lucain](https://fr.linkedin.com/in/lucainpouget) from the Hugging Face team for helping us with the `robust_upload.py` script. ## <u>Citation</u> ```bibtex @misc{chatterjee2024getting, title={Getting it Right: Improving Spatial Consistency in Text-to-Image Models}, author={Agneet Chatterjee and Gabriela Ben Melech Stan and Estelle Aflalo and Sayak Paul and Dhruba Ghosh and Tejas Gokhale and Ludwig Schmidt and Hannaneh Hajishirzi and Vasudev Lal and Chitta Baral and Yezhou Yang}, year={2024}, eprint={2404.01197}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## License SPRIGHT-T2I/spright is licensed under the [Intel Research License](./LICENSE). All Rights Reserved. Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

lowercaseonly/cghd

lowercaseonly

--- license: cc-by-3.0 pretty_name: A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images size_categories: - 1K<n<10K task_categories: - object-detection - image-segmentation language: - en - de --- # Public Ground-Truth Dataset for Handwritten Circuit Diagrams (GTDB-HD) This repository contains images of hand-drawn electrical circuit diagrams as well as accompanying bounding box annotation for object detection as well as segmentation ground truth files. This dataset is intended to train (e.g. neural network) models for the purpose of the extraction of electrical graphs from raster graphics. ## Structure The folder structure is made up as follows: ``` gtdh-hd │ README.md # This File │ classes.json # Classes List │ classes_color.json # Classes to Color Map │ classes_discontinuous.json # Classes Morphology Info │ classes_ports.json # Electrical Port Descriptions for Classes │ consistency.py # Dataset Statistics and Consistency Check | loader.py # Simple Dataset Loader and Storage Functions │ segmentation.py # Multiclass Segmentation Generation │ utils.py # Helper Functions │ requirements.txt # Requirements for Scripts └───drafter_D │ └───annotations # Bounding Box Annotations │ │ │ CX_DY_PZ.xml │ │ │ ... │ │ │ └───images # Raw Images │ │ │ CX_DY_PZ.jpg │ │ │ ... │ │ │ └───instances # Instance Segmentation Polygons │ │ │ CX_DY_PZ.json │ │ │ ... │ │ │ └───segmentation # Binary Segmentation Maps (Strokes vs. Background) │ │ │ CX_DY_PZ.jpg │ │ │ ... ... ``` Where: - `D` is the (globally) running number of a drafter - `X` is the (globally) running number of the circuit (12 Circuits per Drafter) - `Y` is the Local Number of the Circuit's Drawings (2 Drawings per Circuit) - `Z` is the Local Number of the Drawing's Image (4 Pictures per Drawing) ### Image Files Every image is RGB-colored and either stored as `jpg`, `jpeg` or `png` (both uppercase and lowercase suffixes exist). ### Bounding Box Annotations A complete list of class labels including a suggested mapping table to integer numbers for training and prediction purposes can be found in `classes.json`. The annotations contains **BB**s (Bounding Boxes) of **RoI**s (Regions of Interest) like electrical symbols or texts within the raw images and are stored in the [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) format. Please note: *For every Raw image in the dataset, there is an accompanying bounding box annotation file.* #### Known Labeled Issues - C25_D1_P4 cuts off a text - C27 cuts of some texts - C29_D1_P1 has one additional text - C31_D2_P4 has a text less - C33_D1_P4 has a text less - C46_D2_P2 cuts of a text ### Instance Segmentation For every binary segmentation map, there is an accompanying polygonal annotation file for instance segmentation purposes, which is stored in the [labelme](https://github.com/wkentaro/labelme) format. Note that the contained polygons are quite coarse, intended to be used in conjunction with the binary segmentation maps for connection extraction and to tell individual instances with overlapping BBs apart. ### Segmentation Maps Binary Segmentation images are available for some samples and bear the same resolution as the respective image files. They are considered to contain only black and white pixels indicating areas of drawings strokes and background respectively. ### Netlists For some images, there are also netlist files available, which are stored in the [ASC](http://ltwiki.org/LTspiceHelp/LTspiceHelp/Spice_Netlist.htm) format. ### Consistency and Statistics This repository comes with a stand-alone script to: - Obtain Statistics on - Class Distribution - BB Sizes - Check the BB Consistency - Classes with Regards to the `classes.json` - Counts between Pictures of the same Drawing - Ensure a uniform writing style of the Annotation Files (indent) The respective script is called without arguments to operate on the **entire** dataset: ``` $ python3 consistency.py ``` Note that due to a complete re-write of the annotation data, the script takes several seconds to finish. A drafter can be specified as CLI argument to restrict the evaluation (for example drafter 15): ``` $ python3 consistency.py 15 ``` ### Multi-Class (Instance) Segmentation Processing This dataset comes with a script to process both new and existing (instance) segmentation files. It is invoked as follows: ``` $ python3 segmentation.py <command> <drafter_id> <target> <source> ``` Where: - `<command>` has to be one of: - `transform` - Converts existing BB Annotations to Polygon Annotations - Default target folder: `instances` - Existing polygon files will not be overridden in the default settings, hence this command will take no effect in an completely populated dataset. - Intended to be invoked after adding new binary segmentation maps - **This step has to be performed before all other commands** - `wire` - Generates Wire Describing Polygons - Default target folder: `wires` - `keypoint` - Generates Keypoints for Component Terminals - Default target folder: `keypoints` - `create` - Generates Multi-Class segmentation Maps - Default target folder: `segmentation_multi_class` - `refine` - Refines Coarse Polygon Annotations to precisely match the annotated objects - Default target folder: `instances_refined` - For instance segmentation purposes - `pipeline` - executes `wire`,`keypoint` and `refine` stacked, with one common `source` and `target` folder - Default target folder: `instances_refined` - `assign` - Connector Point to Port Type Assignment by Geometric Transformation Matching - `<drafter_id>` **optionally** restricts the process to one of the drafters - `<target>` **optionally** specifies a divergent target folder for results to be placed in - `<source>` **optionally** specifies a divergent source folder to read from Please note that source and target forlders are **always** subfolder inside the individual drafter folders. Specifying source and target folders allow to stack the results of individual processing steps. For example, to perform the entire pipeline for drafter 20 manually, use: ``` python3 segmentation.py wire 20 instances_processed instances python3 segmentation.py keypoint 20 instances_processed instances_processed python3 segmentation.py refine 20 instances_processed instances_processed ``` ### Dataset Loader This dataset is also shipped with a set of loader and writer functions, which are internally used by the segmentation and consistency scripts and can be used for training. The dataset loader is simple, framework-agnostic and has been prepared to be callable from any location in the file system. Basic usage: ``` from loader import read_dataset db_bb = read_dataset() # Read all BB Annotations db_seg = read_dataset(segmentation=True) # Read all Polygon Annotations db_bb_val = read_dataset(drafter=12) # Read Drafter 12 BB Annotations len(db_bb) # Get The Amount of Samples db_bb[5] # Get an Arbitrary Sample db = read_images(drafter=12) # Returns a list of (Image, Annotation) pairs db = read_snippets(drafter=12) # Returns a list of (Image, Annotation) pairs ``` ## Citation If you use this dataset for scientific publications, please consider citing us as follows: ``` @inproceedings{thoma2021public, title={A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images}, author={Thoma, Felix and Bayer, Johannes and Li, Yakun and Dengel, Andreas}, booktitle={International Conference on Document Analysis and Recognition}, pages={20--27}, year={2021}, organization={Springer} } ``` ## How to Contribute If you want to contribute to the dataset as a drafter or in case of any further questions, please send an email to: <johannes.bayer@dfki.de> (corresponding author), <yakun.li@dfki.de>, <andreas.dengel@dfki.de> ## Guidelines These guidelines are used throughout the generation of the dataset. They can be used as an instruction for participants and data providers. ### Drafter Guidelines - 12 Circuits should be drawn, each of them twice (24 drawings in total) - Most important: The drawing should be as natural to the drafter as possible - Free-Hand sketches are preferred, using rulers and drawing Template stencils should be avoided unless it appears unnatural to the drafter - Different types of pens/pencils should be used for different drawings - Different kinds of (colored, structured, ruled, lined) paper should be used - One symbol set (European/American) should be used throughout one drawing (consistency) - It is recommended to use the symbol set that the drafter is most familiar with - It is **strongly** recommended to share the first one or two circuits for review by the dataset organizers before drawing the rest to avoid problems (complete redrawing in worst case) ### Image Capturing Guidelines - For each drawing, 4 images should be taken (96 images in total per drafter) - Angle should vary - Lighting should vary - Moderate (e.g. motion) blur is allowed - All circuit-related aspects of the drawing must be _human-recognicable_ - The drawing should be the main part of the image, but _naturally_ occurring objects from the environment are welcomed - The first image should be _clean_, i.e. ideal capturing conditions - Kinks and Buckling can be applied to the drawing between individual image capturing - Try to use the file name convention (`CX_DY_PZ.jpg`) as early as possible - The circuit range `X` will be given to you - `Y` should be `1` or `2` for the drawing - `Z` should be `1`,`2`,`3` or `4` for the picture ### Object Annotation Guidelines - General Placement - A **RoI** must be **completely** surrounded by its **BB** - A **BB** should be as tight as possible to the **RoI** - In case of connecting lines not completely touching the symbol, the BB should extended (only by a small margin) to enclose those gaps (epecially considering junctions) - Characters that are part of the **essential symbol definition** should be included in the BB (e.g. the `+` of a polarized capacitor should be included in its BB) - **Junction** annotations - Used for actual junction points (Connection of three or more wire segments with a small solid circle) - Used for connection of three or more sraight line wire segements where a physical connection can be inferred by context (i.e. can be distinuished from **crossover**) - Used for wire line corners - Redundant Junction Points should **not** be annotated (small solid circle in the middle of a straight line segment) - Should not be used for corners or junctions that are part of the symbol definition (e.g. Transistors) - **Crossover** Annotations - If dashed/dotted line: BB should cover the two next dots/dashes - **Text** annotations - Individual Text Lines should be annotated Individually - Text Blocks should only be annotated If Related to Circuit or Circuit's Components - Semantically meaningful chunks of information should be annotated Individually - component characteristics enclosed in a single annotation (e.g. __100Ohms__, __10%__ tolerance, __5V__ max voltage) - Component Names and Types (e.g. __C1__, __R5__, __ATTINY2313__) - Custom Component Terminal Labels (i.e. __Integrated Circuit__ Pins) - Circuit Descriptor (e.g. "Radio Amplifier") - Texts not related to the Circuit should be ignored - e.g. Brief paper, Company Logos - Drafters auxiliary markings for internal organization like "D12" - Texts on Surrounding or Background Papers - Characters which are part of the essential symbol definition should __not__ be annotated as Text dedicatedly - e.g. Schmitt Trigger __S__, , and gate __&__, motor __M__, Polarized capacitor __+__ - Only add terminal text annotation if the terminal is not part of the essential symbol definition - **Table** cells should be annotated independently - **Operation Amplifiers** - Both the triangular US symbols and the european IC-like symbols symbols for OpAmps should be labeled `operational_amplifier` - The `+` and `-` signs at the OpAmp's input terminals are considered essential and should therefore not be annotated as texts - **Complex Components** - Both the entire Component and its sub-Components and internal connections should be annotated: | Complex Component | Annotation | | ----------------- | ------------------------------------------------------ | | Optocoupler | 0. `optocoupler` as Overall Annotation | | | 1. `diode.light_emitting` | | | 2. `transistor.photo` (or `resistor.photo`) | | | 3. `optical` if LED and Photo-Sensor arrows are shared | | | Then the arrows area should be includes in all | | Relay | 0. `relay` as Overall Annotation | | (also for | 1. `inductor` | | coupled switches) | 2. `switch` | | | 3. `mechanical` for the dashed line between them | | Transformer | 0. `transformer` as Overall Annotation | | | 1. `inductor` or `inductor.coupled` (watch the dot) | | | 3. `magnetic` for the core | #### Rotation Annotations The Rotation (integer in degree) should capture the overall rotation of the symbol shape. However, the position of the terminals should also be taked into consideration. Under idealized circumstances (no perspective distorion and accurately drawn symbols according to the symbol library), these two requirements equal each other. For pathological cases however, in which shape and the set of terminals (or even individual terminals) are conflicting, the rotation should compromise between all factors. Rotation annotations are currently work in progress. They should be provided for at least the following classes: - "voltage.dc" - "resistor" - "capacitor.unpolarized" - "diode" - "transistor.bjt" #### Text Annotations - The Character Sequence in the Text Label Annotations should describe the actual Characters depicted in the respective Bounding Box as Precisely as Possible - Bounding Box Annotations of class `text` - Bear an additional `<text>` tag in which their content is given as string - The `Omega` and `Mikro` Symbols are escaped respectively - Currently Work in Progress - The utils script allows for migrating text annotations from one annotation file to another: `python3 utils.py source target` ### Segmentation Map Guidelines - Areas of __Intended__ drawing strokes (ink and pencil abrasion respectively) should be marked black, all other pixels (background) should be white - shining through the paper (from the rear side or other sheets) should be considered background ### Polygon Annotation Guidelines 0. Before starting, make sure the respective files exist for the image sample to be polygon-annotated: - BB Annotations (Pascal VOC XML File) - (Binary) Segmentation Map 1. Transform the BB annotations into raw polygons - Use: `python3 segmentation.py transform` 2. Refine the Polygons - **To Avoid Embedding Image Data into the resulting JSON**, use: `labelme --nodata` - Just make sure there are no overlaps between instances - Especially take care about overlaps with structural elements like junctions and crossovers 3. Generate Multi-Class Segmentation Maps from the refined polygons - Use: `python3 segmentation.py create` - Use the generated images for a visual inspection - After spotting problems, continue with Step 2 ### Terminal Annotation Guidelines ``` labelme --labels "connector" --config "{shift_auto_shape_color: 1}" --nodata ``` ## Licence The entire content of this repository, including all image files, annotation files as well as has sourcecode, metadata and documentation has been published under the [Creative Commons Attribution Share Alike Licence 3.0](https://creativecommons.org/licenses/by-sa/3.0/).

fixie-ai/covost2

fixie-ai

--- dataset_info: - config_name: ar_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 61607709.192 num_examples: 2283 - name: validation num_bytes: 56223234.024 num_examples: 1758 - name: test num_bytes: 54650910.41 num_examples: 1695 download_size: 160468333 dataset_size: 172481853.626 - config_name: ca_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 4397026262.322 num_examples: 95854 - name: validation num_bytes: 544108371.96 num_examples: 12730 - name: test num_bytes: 604755238.63 num_examples: 12730 download_size: 4957773433 dataset_size: 5545889872.912 - config_name: cy_en features: - name: client_id dtype: string - 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name: validation num_bytes: 718057559.808 num_examples: 15531 - name: test num_bytes: 728710692.301 num_examples: 15531 download_size: 13813410823 dataset_size: 15543645797.289001 - config_name: en_fa features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14108661241.18 num_examples: 289430 - name: validation num_bytes: 718670909.808 num_examples: 15531 - name: test num_bytes: 729271000.301 num_examples: 15531 download_size: 13816798013 dataset_size: 15556603151.289001 - config_name: en_id features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 14098627451.18 num_examples: 289430 - name: validation num_bytes: 718144327.808 num_examples: 15531 - 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name: id dtype: string splits: - name: validation num_bytes: 630615357.241 num_examples: 13221 - name: test num_bytes: 666447063.067 num_examples: 13221 - name: train num_bytes: 3769457359.8 num_examples: 79015 download_size: 4531969416 dataset_size: 5066519780.108 - config_name: et_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 97124727.544 num_examples: 1782 - name: validation num_bytes: 80290798.168 num_examples: 1576 - name: test num_bytes: 81970364.51 num_examples: 1571 download_size: 257604448 dataset_size: 259385890.222 - config_name: fa_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 1872724297.149 num_examples: 53949 - name: validation num_bytes: 140067911.23 num_examples: 3445 - name: test num_bytes: 149319550.35 num_examples: 3445 download_size: 1679853440 dataset_size: 2162111758.729 - config_name: fr_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 632191608.84 num_examples: 14760 - name: test num_bytes: 698178059.08 num_examples: 14760 - name: train num_bytes: 8128016830.77 num_examples: 207374 download_size: 8900934523 dataset_size: 9458386498.69 - config_name: id_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 36136135.768 num_examples: 1243 - name: validation num_bytes: 25058845.0 num_examples: 792 - name: test num_bytes: 26577467.0 num_examples: 844 download_size: 86110062 dataset_size: 87772447.768 - config_name: it_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 1517510665.568 num_examples: 31698 - name: validation num_bytes: 422409218.1 num_examples: 8940 - name: test num_bytes: 454569171.595 num_examples: 8951 download_size: 2125529183 dataset_size: 2394489055.2630005 - config_name: ja_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 38181610.624 num_examples: 1119 - name: validation num_bytes: 24623052.0 num_examples: 635 - name: test num_bytes: 25558787.0 num_examples: 684 download_size: 88228548 dataset_size: 88363449.624 - config_name: lv_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 66152116.328 num_examples: 2337 - name: validation num_bytes: 32655276.0 num_examples: 1125 - name: test num_bytes: 50997551.638 num_examples: 1629 download_size: 137700207 dataset_size: 149804943.96600002 - config_name: mn_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 87891433.547 num_examples: 2067 - name: validation num_bytes: 77519039.943 num_examples: 1761 - name: test num_bytes: 83667460.167 num_examples: 1759 download_size: 242638800 dataset_size: 249077933.657 - config_name: nl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 216102081.4 num_examples: 7108 - name: validation num_bytes: 55386349.319 num_examples: 1699 - name: test num_bytes: 60219179.711 num_examples: 1699 download_size: 320267264 dataset_size: 331707610.43 - config_name: pt_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 274723273.528 num_examples: 9158 - name: validation num_bytes: 118345891.704 num_examples: 3318 - name: test num_bytes: 166247624.001 num_examples: 4023 download_size: 540891735 dataset_size: 559316789.233 - config_name: ru_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 557219472.672 num_examples: 12112 - name: validation num_bytes: 290218427.6 num_examples: 6110 - name: test num_bytes: 312622838.0 num_examples: 6300 download_size: 1112848246 dataset_size: 1160060738.272 - config_name: sl_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 55992153.0 num_examples: 1843 - name: validation num_bytes: 15074155.0 num_examples: 509 - name: test num_bytes: 10209711.0 num_examples: 360 download_size: 83863293 dataset_size: 81276019.0 - config_name: sv-SE_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 48298330.64 num_examples: 2160 - name: validation num_bytes: 32544646.416 num_examples: 1349 - name: test num_bytes: 46894324.615 num_examples: 1595 download_size: 121860373 dataset_size: 127737301.671 - config_name: ta_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 47757197.616 num_examples: 1358 - name: validation num_bytes: 13670695.0 num_examples: 384 - name: test num_bytes: 29891516.0 num_examples: 786 download_size: 87791516 dataset_size: 91319408.616 - config_name: tr_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: train num_bytes: 119299427.798 num_examples: 3966 - name: validation num_bytes: 52552534.232 num_examples: 1624 - name: test num_bytes: 59106253.862 num_examples: 1629 download_size: 224018260 dataset_size: 230958215.89200002 - config_name: zh-CN_en features: - name: client_id dtype: string - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: translation dtype: string - name: id dtype: string splits: - name: validation num_bytes: 231018998.33 num_examples: 4843 - name: test num_bytes: 243850956.45 num_examples: 4898 - name: train num_bytes: 341425113.6 num_examples: 7085 download_size: 766660661 dataset_size: 816295068.38 configs: - config_name: ar_en data_files: - split: train path: ar_en/train-* - split: validation path: ar_en/validation-* - split: test path: ar_en/test-* - config_name: ca_en data_files: - split: train path: ca_en/train-* - split: validation path: ca_en/validation-* - split: test path: ca_en/test-* - config_name: cy_en data_files: - split: train path: cy_en/train-* - split: validation path: cy_en/validation-* - split: test path: cy_en/test-* - config_name: de_en data_files: - split: train path: de_en/train-* - split: validation path: de_en/validation-* - split: test path: de_en/test-* - config_name: en_ar data_files: - split: train path: en_ar/train-* - split: validation path: en_ar/validation-* - split: test path: en_ar/test-* - config_name: en_ca data_files: - split: train path: en_ca/train-* - split: validation path: en_ca/validation-* - split: test path: en_ca/test-* - config_name: en_cy data_files: - split: train path: en_cy/train-* - split: validation path: en_cy/validation-* - split: test path: en_cy/test-* - config_name: en_de data_files: - split: train path: en_de/train-* - split: validation path: en_de/validation-* - split: test path: en_de/test-* - config_name: en_et data_files: - split: train path: en_et/train-* - split: validation path: en_et/validation-* - split: test path: en_et/test-* - config_name: en_fa data_files: - split: train path: en_fa/train-* - split: validation path: en_fa/validation-* - split: test path: en_fa/test-* - config_name: en_id data_files: - split: train path: en_id/train-* - split: validation path: en_id/validation-* - split: test path: en_id/test-* - config_name: en_ja data_files: - split: train path: en_ja/train-* - split: validation path: en_ja/validation-* - split: test path: en_ja/test-* - config_name: en_lv data_files: - split: train path: en_lv/train-* - split: validation path: en_lv/validation-* - split: test path: en_lv/test-* - config_name: en_mn data_files: - split: train path: en_mn/train-* - split: validation path: en_mn/validation-* - split: test path: en_mn/test-* - config_name: en_sl data_files: - split: train path: en_sl/train-* - split: validation path: en_sl/validation-* - split: test path: en_sl/test-* - config_name: en_sv-SE data_files: - split: train path: en_sv-SE/train-* - split: validation path: en_sv-SE/validation-* - split: test path: en_sv-SE/test-* - config_name: en_ta data_files: - split: train path: en_ta/train-* - split: validation path: en_ta/validation-* - split: test path: en_ta/test-* - config_name: en_tr data_files: - split: train path: en_tr/train-* - split: validation path: en_tr/validation-* - split: test path: en_tr/test-* - config_name: en_zh-CN data_files: - split: train path: en_zh-CN/train-* - split: validation path: en_zh-CN/validation-* - split: test path: en_zh-CN/test-* - config_name: es_en data_files: - split: validation path: es_en/validation-* - split: test path: es_en/test-* - split: train path: es_en/train-* - config_name: et_en data_files: - split: train path: et_en/train-* - split: validation path: et_en/validation-* - split: test path: et_en/test-* - config_name: fa_en data_files: - split: train path: fa_en/train-* - split: validation path: fa_en/validation-* - split: test path: fa_en/test-* - config_name: fr_en data_files: - split: validation path: fr_en/validation-* - split: test path: fr_en/test-* - split: train path: fr_en/train-* - config_name: id_en data_files: - split: train path: id_en/train-* - split: validation path: id_en/validation-* - split: test path: id_en/test-* - config_name: it_en data_files: - split: train path: it_en/train-* - split: validation path: it_en/validation-* - split: test path: it_en/test-* - config_name: ja_en data_files: - split: train path: ja_en/train-* - split: validation path: ja_en/validation-* - split: test path: ja_en/test-* - config_name: lv_en data_files: - split: train path: lv_en/train-* - split: validation path: lv_en/validation-* - split: test path: lv_en/test-* - config_name: mn_en data_files: - split: train path: mn_en/train-* - split: validation path: mn_en/validation-* - split: test path: mn_en/test-* - config_name: nl_en data_files: - split: train path: nl_en/train-* - split: validation path: nl_en/validation-* - split: test path: nl_en/test-* - config_name: pt_en data_files: - split: train path: pt_en/train-* - split: validation path: pt_en/validation-* - split: test path: pt_en/test-* - config_name: ru_en data_files: - split: train path: ru_en/train-* - split: validation path: ru_en/validation-* - split: test path: ru_en/test-* - config_name: sl_en data_files: - split: train path: sl_en/train-* - split: validation path: sl_en/validation-* - split: test path: sl_en/test-* - config_name: sv-SE_en data_files: - split: train path: sv-SE_en/train-* - split: validation path: sv-SE_en/validation-* - split: test path: sv-SE_en/test-* - config_name: ta_en data_files: - split: train path: ta_en/train-* - split: validation path: ta_en/validation-* - split: test path: ta_en/test-* - config_name: tr_en data_files: - split: train path: tr_en/train-* - split: validation path: tr_en/validation-* - split: test path: tr_en/test-* - config_name: zh-CN_en data_files: - split: validation path: zh-CN_en/validation-* - split: test path: zh-CN_en/test-* - split: train path: zh-CN_en/train-* --- This is a partial copy of [CoVoST2](https://huggingface.co/datasets/facebook/covost2) dataset. The main difference is that the audio data is included in the dataset, which makes usage easier and allows browsing the samples using HF Dataset Viewer. The limitation of this method is that all audio samples of the `EN_XX` subsets are duplicated, as such the size of the dataset is larger. As such, not all the data is included: Only the `validation` and `test` subsets are available. From the `XX_EN` subsets, only `fr`, `es`, and `zh-CN` are included.

databricks/databricks-dolly-15k

databricks

--- license: cc-by-sa-3.0 task_categories: - question-answering - summarization language: - en size_categories: - 10K<n<100K --- # 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 # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # 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.

TempoFunk/tempofunk-sdance

TempoFunk

--- task_categories: - text-to-video - text-to-image - video-classification - image-classification language: - en size_categories: - 1K<n<10K license: agpl-3.0 --- # TempoFunk S(mall)Dance 10k samples of metadata and encoded latents & prompts of videos themed around **dance**. ## Data format - Video frame latents - Numpy arrays - 120 frames, 512x512 source size - Encoded shape (120, 4, 64, 64) - CLIP (openai) encoded prompts - Video description (as seen in metadata) - Encoded shape (77,768) - Video metadata as JSON (description, tags, categories, source URLs, etc.)

MMMU/MMMU

MMMU

--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering - visual-question-answering - multiple-choice pretty_name: mmmu dataset_info: - config_name: Accounting features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262599.0 num_examples: 5 - name: validation num_bytes: 1598285.0 num_examples: 30 - name: test num_bytes: 22135625.0 num_examples: 380 download_size: 37363379 dataset_size: 23996509.0 - config_name: Agriculture features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 22082656.0 num_examples: 5 - name: validation num_bytes: 119217558.0 num_examples: 30 - name: test num_bytes: 993664077.0 num_examples: 287 download_size: 1158036990 dataset_size: 1134964291.0 - config_name: Architecture_and_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 137750.0 num_examples: 5 - name: validation num_bytes: 721378.0 num_examples: 30 - name: test num_bytes: 16054607.0 num_examples: 551 download_size: 48763955 dataset_size: 16913735.0 - config_name: Art features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 6241184.0 num_examples: 5 - name: validation num_bytes: 29934534.0 num_examples: 30 - name: test num_bytes: 237801390.0 num_examples: 231 download_size: 585798641 dataset_size: 273977108.0 - config_name: Art_Theory features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 7435106.0 num_examples: 5 - name: validation num_bytes: 33481558.0 num_examples: 30 - name: test num_bytes: 553174647.0 num_examples: 429 download_size: 930525695 dataset_size: 594091311.0 - config_name: Basic_Medical_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 814310.0 num_examples: 5 - name: validation num_bytes: 4125930.0 num_examples: 30 - name: test num_bytes: 48125891.0 num_examples: 326 download_size: 84666454 dataset_size: 53066131.0 - config_name: Biology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 574342.0 num_examples: 5 - name: validation num_bytes: 8491863.0 num_examples: 30 - name: test num_bytes: 132966151.0 num_examples: 345 download_size: 410242502 dataset_size: 142032356.0 - config_name: Chemistry features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262397.0 num_examples: 5 - name: validation num_bytes: 1518573.0 num_examples: 30 - name: test num_bytes: 37219529.0 num_examples: 603 download_size: 108345562 dataset_size: 39000499.0 - config_name: Clinical_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1467945.0 num_examples: 5 - name: validation num_bytes: 10882484.0 num_examples: 30 - name: test num_bytes: 98201863.0 num_examples: 325 download_size: 160611488 dataset_size: 110552292.0 - config_name: Computer_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 440523.0 num_examples: 5 - name: validation num_bytes: 2072018.0 num_examples: 30 - name: test num_bytes: 32047381.0 num_examples: 371 download_size: 55640991 dataset_size: 34559922.0 - config_name: Design features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2259873.0 num_examples: 5 - name: validation num_bytes: 17923120.0 num_examples: 30 - name: test num_bytes: 77676331.0 num_examples: 169 download_size: 142866617 dataset_size: 97859324.0 - config_name: Diagnostics_and_Laboratory_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2056117.0 num_examples: 5 - name: validation num_bytes: 37106233.0 num_examples: 30 - name: test num_bytes: 157003069.0 num_examples: 162 download_size: 603957093 dataset_size: 196165419.0 - config_name: Economics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 171434.0 num_examples: 5 - name: validation num_bytes: 1487048.0 num_examples: 30 - name: test num_bytes: 11852300.0 num_examples: 267 download_size: 20777635 dataset_size: 13510782.0 - config_name: Electronics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 123632.0 num_examples: 5 - name: validation num_bytes: 641377.0 num_examples: 30 - name: test num_bytes: 5717686.0 num_examples: 256 download_size: 11602832 dataset_size: 6482695.0 - config_name: Energy_and_Power features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 105006.0 num_examples: 5 - name: validation num_bytes: 1641935.0 num_examples: 30 - name: test num_bytes: 14748428.0 num_examples: 432 download_size: 35246567 dataset_size: 16495369.0 - config_name: Finance features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 296124.0 num_examples: 5 - name: validation num_bytes: 1071060.0 num_examples: 30 - name: test num_bytes: 12065803.0 num_examples: 355 download_size: 29551521 dataset_size: 13432987.0 - config_name: Geography features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1494060.0 num_examples: 5 - name: validation num_bytes: 6671316.0 num_examples: 30 - name: test num_bytes: 137218400.0 num_examples: 565 download_size: 374766631 dataset_size: 145383776.0 - config_name: History features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1444231.0 num_examples: 5 - name: validation num_bytes: 8819857.0 num_examples: 30 - name: test num_bytes: 115228815.0 num_examples: 278 download_size: 232549641 dataset_size: 125492903.0 - config_name: Literature features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2451201.0 num_examples: 5 - name: validation num_bytes: 14241046.0 num_examples: 30 - name: test num_bytes: 50301541.0 num_examples: 112 download_size: 132145895 dataset_size: 66993788.0 - config_name: Manage features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 449514.0 num_examples: 5 - name: validation num_bytes: 3277436.0 num_examples: 30 - name: test num_bytes: 29963963.0 num_examples: 245 download_size: 51186888 dataset_size: 33690913.0 - config_name: Marketing features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 116960.0 num_examples: 5 - name: validation num_bytes: 1472981.0 num_examples: 30 - name: test num_bytes: 7732976.0 num_examples: 181 download_size: 13146078 dataset_size: 9322917.0 - config_name: Materials features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 239632.0 num_examples: 5 - name: validation num_bytes: 2305223.0 num_examples: 30 - name: test num_bytes: 25256854.0 num_examples: 458 download_size: 105773156 dataset_size: 27801709.0 - config_name: Math features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 175839.0 num_examples: 5 - name: validation num_bytes: 1444496.0 num_examples: 30 - name: test num_bytes: 27701845.0 num_examples: 505 download_size: 174098418 dataset_size: 29322180.0 - config_name: Mechanical_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 152542.0 num_examples: 5 - name: validation num_bytes: 874988.0 num_examples: 30 - name: test num_bytes: 15093746.0 num_examples: 429 download_size: 30450114 dataset_size: 16121276.0 - config_name: Music features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1417615.0 num_examples: 5 - name: validation num_bytes: 9359372.0 num_examples: 30 - name: test num_bytes: 134096770.0 num_examples: 334 download_size: 174725052 dataset_size: 144873757.0 - config_name: Pharmacy features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 207924.0 num_examples: 5 - name: validation num_bytes: 1656342.0 num_examples: 30 - name: test num_bytes: 31866248.0 num_examples: 430 download_size: 62721263 dataset_size: 33730514.0 - config_name: Physics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 233734.0 num_examples: 5 - name: validation num_bytes: 1114130.0 num_examples: 30 - name: test num_bytes: 15905705.0 num_examples: 408 download_size: 35238571 dataset_size: 17253569.0 - config_name: Psychology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 600864.0 num_examples: 5 - name: validation num_bytes: 4403886.0 num_examples: 30 - name: test num_bytes: 53813915.0 num_examples: 305 download_size: 102466671 dataset_size: 58818665.0 - config_name: Public_Health features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 234781.0 num_examples: 5 - name: validation num_bytes: 1508761.0 num_examples: 30 - name: test num_bytes: 32150088.0 num_examples: 509 download_size: 48231609 dataset_size: 33893630.0 - config_name: Sociology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 3769220.0 num_examples: 5 - name: validation num_bytes: 18455336.0 num_examples: 30 - name: test num_bytes: 144301123.0 num_examples: 252 download_size: 310313826 dataset_size: 166525679.0 configs: - config_name: Accounting data_files: - split: dev path: Accounting/dev-* - split: validation path: Accounting/validation-* - split: test path: Accounting/test-* - config_name: Agriculture data_files: - split: dev path: Agriculture/dev-* - split: validation path: Agriculture/validation-* - split: test path: Agriculture/test-* - config_name: Architecture_and_Engineering data_files: - split: dev path: Architecture_and_Engineering/dev-* - split: validation path: Architecture_and_Engineering/validation-* - split: test path: Architecture_and_Engineering/test-* - config_name: Art data_files: - split: dev path: Art/dev-* - split: validation path: Art/validation-* - split: test path: Art/test-* - config_name: Art_Theory data_files: - split: dev path: Art_Theory/dev-* - split: validation path: Art_Theory/validation-* - split: test path: Art_Theory/test-* - config_name: Basic_Medical_Science data_files: - split: dev path: Basic_Medical_Science/dev-* - split: validation path: Basic_Medical_Science/validation-* - split: test path: Basic_Medical_Science/test-* - config_name: Biology data_files: - split: dev path: Biology/dev-* - split: validation path: Biology/validation-* - split: test path: Biology/test-* - config_name: Chemistry data_files: - split: dev path: Chemistry/dev-* - split: validation path: Chemistry/validation-* - split: test path: Chemistry/test-* - config_name: Clinical_Medicine data_files: - split: dev path: Clinical_Medicine/dev-* - split: validation path: Clinical_Medicine/validation-* - split: test path: Clinical_Medicine/test-* - config_name: Computer_Science data_files: - split: dev path: Computer_Science/dev-* - split: validation path: Computer_Science/validation-* - split: test path: Computer_Science/test-* - config_name: Design data_files: - split: dev path: Design/dev-* - split: validation path: Design/validation-* - split: test path: Design/test-* - config_name: Diagnostics_and_Laboratory_Medicine data_files: - split: dev path: Diagnostics_and_Laboratory_Medicine/dev-* - split: validation path: Diagnostics_and_Laboratory_Medicine/validation-* - split: test path: Diagnostics_and_Laboratory_Medicine/test-* - config_name: Economics data_files: - split: dev path: Economics/dev-* - split: validation path: Economics/validation-* - split: test path: Economics/test-* - config_name: Electronics data_files: - split: dev path: Electronics/dev-* - split: validation path: Electronics/validation-* - split: test path: Electronics/test-* - config_name: Energy_and_Power data_files: - split: dev path: Energy_and_Power/dev-* - split: validation path: Energy_and_Power/validation-* - split: test path: Energy_and_Power/test-* - config_name: Finance data_files: - split: dev path: Finance/dev-* - split: validation path: Finance/validation-* - split: test path: Finance/test-* - config_name: Geography data_files: - split: dev path: Geography/dev-* - split: validation path: Geography/validation-* - split: test path: Geography/test-* - config_name: History data_files: - split: dev path: History/dev-* - split: validation path: History/validation-* - split: test path: History/test-* - config_name: Literature data_files: - split: dev path: Literature/dev-* - split: validation path: Literature/validation-* - split: test path: Literature/test-* - config_name: Manage data_files: - split: dev path: Manage/dev-* - split: validation path: Manage/validation-* - split: test path: Manage/test-* - config_name: Marketing data_files: - split: dev path: Marketing/dev-* - split: validation path: Marketing/validation-* - split: test path: Marketing/test-* - config_name: Materials data_files: - split: dev path: Materials/dev-* - split: validation path: Materials/validation-* - split: test path: Materials/test-* - config_name: Math data_files: - split: dev path: Math/dev-* - split: validation path: Math/validation-* - split: test path: Math/test-* - config_name: Mechanical_Engineering data_files: - split: dev path: Mechanical_Engineering/dev-* - split: validation path: Mechanical_Engineering/validation-* - split: test path: Mechanical_Engineering/test-* - config_name: Music data_files: - split: dev path: Music/dev-* - split: validation path: Music/validation-* - split: test path: Music/test-* - config_name: Pharmacy data_files: - split: dev path: Pharmacy/dev-* - split: validation path: Pharmacy/validation-* - split: test path: Pharmacy/test-* - config_name: Physics data_files: - split: dev path: Physics/dev-* - split: validation path: Physics/validation-* - split: test path: Physics/test-* - config_name: Psychology data_files: - split: dev path: Psychology/dev-* - split: validation path: Psychology/validation-* - split: test path: Psychology/test-* - config_name: Public_Health data_files: - split: dev path: Public_Health/dev-* - split: validation path: Public_Health/validation-* - split: test path: Public_Health/test-* - config_name: Sociology data_files: - split: dev path: Sociology/dev-* - split: validation path: Sociology/validation-* - split: test path: Sociology/test-* tags: - biology - medical - finance - chemistry - music - art - art_theory - design - music - business - accounting - economics - finance - manage - marketing - health - medicine - basic_medical_science - clinical - pharmacy - public_health - humanities - social_science - history - literature - sociology - psychology - science - biology - chemistry - geography - math - physics - engineering - agriculture - architecture - computer_science - electronics - energy_and_power - materials - mechanical_engineering --- # MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI) [**🌐 Homepage**](https://mmmu-benchmark.github.io/) | [**🏆 Leaderboard**](https://mmmu-benchmark.github.io/#leaderboard) | [**🤗 Dataset**](https://huggingface.co/datasets/MMMU/MMMU/) | [**🤗 Paper**](https://huggingface.co/papers/2311.16502) | [**📖 arXiv**](https://arxiv.org/abs/2311.16502) | [**GitHub**](https://github.com/MMMU-Benchmark/MMMU) ## 🔔News - **🛠️[2024-05-30]: Fixed duplicate option issues in Materials dataset items (validation_Materials_25; test_Materials_17, 242) and content error in validation_Materials_25.** - **🛠️[2024-04-30]: Fixed missing "-" or "^" signs in Math dataset items (dev_Math_2, validation_Math_11, 12, 16; test_Math_8, 23, 43, 113, 164, 223, 236, 287, 329, 402, 498) and corrected option errors in validation_Math_2. If you encounter any issues with the dataset, please contact us promptly!** - **🚀[2024-01-31]: We added Human Expert performance on the [Leaderboard](https://mmmu-benchmark.github.io/#leaderboard)!🌟** - **🔥[2023-12-04]: Our evaluation server for test set is now availble on [EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview). We welcome all submissions and look forward to your participation! 😆** ## Dataset Details ### Dataset Description We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes **11.5K meticulously collected multimodal questions** from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span **30 subjects** and **183 subfields**, comprising **30 highly heterogeneous image types**, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI). 🎯 **We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.** The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the **test set** on **[EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview)**.  ### Dataset Creation MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning. The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials. - **Content:** The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects. - **Image Types:** The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text.  ## 🏆 Mini-Leaderboard We show a mini-leaderboard here and please find more information in our paper or [**homepage**](https://mmmu-benchmark.github.io/). | Model | Val (900) | Test (10.5K) | |--------------------------------|:---------:|:------------:| | Expert (Best) | 88.6 | - | | Expert (Medium) | 82.6 | - | | Expert (Worst) | 76.2 | - | | GPT-4o* | **69.1** | - | | Gemini 1.5 Pro* | 62.2 | - | | InternVL2-Pro* | 62.0 | **55.7** | | Gemini 1.0 Ultra* | 59.4 | - | | Claude 3 Opus* | 59.4 | - | | GPT-4V(ision) (Playground) | 56.8 | **55.7** | | Reka Core* | 56.3 | - | | Gemini 1.5 Flash* | 56.1 | - | | SenseChat-Vision-0423-Preview* | 54.6 | 50.3 | | Reka Flash* | 53.3 | - | | Claude 3 Sonnet* | 53.1 | - | | HPT Pro* | 52.0 | - | | VILA1.5* | 51.9 | 46.9 | | Qwen-VL-MAX* | 51.4 | 46.8 | | InternVL-Chat-V1.2* | 51.6 | 46.2 | | Skywork-VL* | 51.4 | 46.2 | | LLaVA-1.6-34B* | 51.1 | 44.7 | | Claude 3 Haiku* | 50.2 | - | | Adept Fuyu-Heavy* | 48.3 | - | | Gemini 1.0 Pro* | 47.9 | - | | Marco-VL-Plus* | 46.2 | 44.3 | | Yi-VL-34B* | 45.9 | 41.6 | | Qwen-VL-PLUS* | 45.2 | 40.8 | | HPT Air* | 44.0 | - | | Reka Edge* | 42.8 | - | | Marco-VL* | 41.2 | 40.4 | | OmniLMM-12B* | 41.1 | 40.4 | | Bunny-8B* | 43.3 | 39.0 | | Bunny-4B* | 41.4 | 38.4 | | Weitu-VL-1.0-15B* | - | 38.4 | | InternLM-XComposer2-VL* | 43.0 | 38.2 | | Yi-VL-6B* | 39.1 | 37.8 | | InfiMM-Zephyr-7B* | 39.4 | 35.5 | | InternVL-Chat-V1.1* | 39.1 | 35.3 | | Math-LLaVA-13B* | 38.3 | 34.6 | | SVIT* | 38.0 | 34.1 | | MiniCPM-V* | 37.2 | 34.1 | | MiniCPM-V-2* | 37.1 | - | | Emu2-Chat* | 36.3 | 34.1 | | BLIP-2 FLAN-T5-XXL | 35.4 | 34.0 | | InstructBLIP-T5-XXL | 35.7 | 33.8 | | LLaVA-1.5-13B | 36.4 | 33.6 | | Bunny-3B* | 38.2 | 33.0 | | Qwen-VL-7B-Chat | 35.9 | 32.9 | | SPHINX* | 32.9 | 32.9 | | mPLUG-OWL2* | 32.7 | 32.1 | | BLIP-2 FLAN-T5-XL | 34.4 | 31.0 | | InstructBLIP-T5-XL | 32.9 | 30.6 | | Gemini Nano2* | 32.6 | - | | CogVLM | 32.1 | 30.1 | | Otter | 32.2 | 29.1 | | LLaMA-Adapter2-7B | 29.8 | 27.7 | | MiniGPT4-Vicuna-13B | 26.8 | 27.6 | | Adept Fuyu-8B | 27.9 | 27.4 | | Kosmos2 | 24.4 | 26.6 | | OpenFlamingo2-9B | 28.7 | 26.3 | | Frequent Choice | 22.1 | 23.9 | | Random Choice | 26.8 | 25.8 | *: results provided by the authors. ## Limitations Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases. And the focus on college-level subjects might not fully be a sufficient test for Expert AGI. However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities. In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI. ## Disclaimers The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution. Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed. ## Contact - Xiang Yue: xiangyue.work@gmail.com - Yu Su: su.809@osu.edu - Wenhu Chen: wenhuchen@uwaterloo.ca ## Citation **BibTeX:** ```bibtex @inproceedings{yue2023mmmu, title={MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI}, author={Xiang Yue and Yuansheng Ni and Kai Zhang and Tianyu Zheng and Ruoqi Liu and Ge Zhang and Samuel Stevens and Dongfu Jiang and Weiming Ren and Yuxuan Sun and Cong Wei and Botao Yu and Ruibin Yuan and Renliang Sun and Ming Yin and Boyuan Zheng and Zhenzhu Yang and Yibo Liu and Wenhao Huang and Huan Sun and Yu Su and Wenhu Chen}, booktitle={Proceedings of CVPR}, year={2024}, } ```

mlfoundations/MINT-1T-PDF-CC-2024-10

mlfoundations

--- license: cc-by-4.0 task_categories: - image-to-text - text-generation language: - en tags: - multimodal pretty_name: MINT-1T size_categories: - 100B<n<1T --- <h1 align="center"> 🍃 MINT-1T:<br>Scaling Open-Source Multimodal Data by 10x:<br> A Multimodal Dataset with One Trillion Tokens </h1> 🍃 MINT-1T is an open-source **M**ultimodal **INT**erleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing a subset of the PDF portion of 🍃 MINT-1T associated with CommonCrawl dump `CC-2024-10`. For other PDF, HTML, and ArXiv subsets, refer to the [🍃 MINT-1T collection](https://huggingface.co/collections/mlfoundations/mint-1t-6690216ca4d0df7e518dde1c).  ## Updates ### 9/19/24 We have removed roughly 10% of the PDF samples as there was a mismatch between the frames in the TIFF images and the document metadata. ### 8/8/24 We have become aware that the image hashes in the PDF subset of MINT-1T do not match the images in the documents. We want to emphasize that the images for each document are correct, and only the image hashes in the documents' metadata are mislabeled. ## Dataset Details ### Dataset Sources - **Repository**: https://github.com/mlfoundations/MINT-1T - **Paper:** https://arxiv.org/abs/2406.11271 - **Blog:** https://blog.salesforceairesearch.com/mint-1t/ ## Uses ### Direct Use <!-- This section describes suitable use cases for the dataset. --> 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. The dataset can be used for training multimodal models that can reson about interleaved text and images sequences such as [Idefics2](https://huggingface.co/HuggingFaceM4/idefics2-8b), [XGen-MM](https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1), and [Chameleon](https://huggingface.co/facebook/chameleon-30b). ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> 🍃 MINT-1T was built to make research into large multimodal models more accessible. Using the dataset to train models that ingest or generate personally identifying information (such as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of 🍃 MINT-1T. ## Dataset Creation ### Curation Rationale 🍃 MINT-1T was created to address a significant gap in the open-source domain by providing a large-scale multimodal interleaved dataset for pre-training large multimodal models. This dataset aims to be a valuable resource for the research community, facilitating open science in multimodal pretraining. ### Source Data The dataset is a comprehensive collection of multimodal documents from various sources: - HTML documents: Filtered from CommonCrawl WARC dumps spanning from 2017 to 2024 - PDF documents: Extracted from CommonCrawl WAT dumps covering 2023 to 2024 - ArXiv documents: A subset of papers from the ArXiv repository In total, 🍃 MINT-1T contains 1056.8 million documents, broken down as follows: - 1029.4 million HTML documents - 24.0 million PDF documents - 0.6 million ArXiv documents #### Data Collection and Processing The data collection and processing involved several steps: 1. Document Extraction: - HTML documents were parsed from CommonCrawl WARC files - PDF documents were extracted from CommonCrawl WAT files - ArXiv papers were directly sourced from ArXiv S3 buckets 2. Filtering Process: - Applied text quality filters to ensure content relevance and readability - Removed duplicate content at both paragraph and document levels - Filtered out undesirable content based on predefined criteria - Verified image availability and quality for HTML documents - Limited PDF size to 50MB and 50 pages to manage dataset size and quality 3. Image Processing: - Used NSFW image detection to remove pornographic or otherwise undesirable images - Removed images smaller than 150 pixels or larger than 20,000 pixels - Adjusted aspect ratio thresholds for HTML (2:1) and PDF (3:1) to preserve scientific figures 4. Text Processing: - Used fasttext for language identification, focusing on English content - Masked personally identifiable information such as email addresses and IP addresses - Applied paragraph and document-level deduplication using Bloom filters 5. PDF Specific Processing: - Used PyMuPDF for parsing PDFs and extracting reading order - Clustered text blocks based on columns and ordered from top left to bottom right 6. ArXiv Specific Processing: - Used TexSoup to parse LaTeX source code and interleave images with text - Cleaned up LaTeX code by removing imports, bibliography, tables, and citation tags Various open-source tools were utilized in this process, including fasttext, [PyMuPDF](https://github.com/pymupdf/PyMuPDF), and [DCLM](https://www.datacomp.ai/dclm/) and [bff](https://github.com/revbucket/bff) for deduplication and content filtering. #### Personal and Sensitive Information Despite sourcing from public web data, significant efforts were made to minimize the inclusion of personal and sensitive information: - Email addresses and IP addresses were masked to protect privacy - An NSFW image classifierto remove inappropriate visual content - URLs containing substrings associated with undesirable or sensitive content were filtered out However, users should be aware that as the data originates from the public web, it may still contain some sensitive or personal information. The dataset creators acknowledge this limitation and advise users to exercise caution and potentially apply additional filtering based on their specific use cases. ## Bias, Risks, and Limitations Several potential biases, risks, and limitations have been identified: 1. Data Bias: As the dataset is sourced from web crawls, it may inherit biases present in online content. 2. Content Risks: Despite extensive filtering, there's a possibility that some offensive, insensitive, or inappropriate content may remain in the dataset. 3. Image Availability: The dataset relies on external image URLs, which may become unavailable over time due to link rot, potentially affecting the dataset's long-term usability. 4. PDF Parsing Limitations: The current method for extracting reading order from PDFs may not always accurately capture the intended flow, especially for documents with complex layouts. 5. Potential Legal and Ethical Concerns: While efforts were made to respect robots.txt files and remove sensitive information, there may still be content that individuals did not explicitly consent to include. ### Recommendations Given these considerations, the following recommendations are provided: 1. Additional Filtering: Users are strongly encouraged to apply additional filtering based on their specific use case and ethical considerations. 2. Inappropriate Use Cases: The dataset is not recommended for applications involving the processing or generation of personally identifying information, nor for military applications. 3. Legal Compliance: Users should independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. 4. Bias Awareness: Researchers and developers should be cognizant of potential biases in the dataset and consider their impact on model training and outputs. ## License We release 🍃 MINT-1T under a CC-BY-4.0 license, designating it primarily as a research artifact. While the dataset is freely available, users are responsible for ensuring its legal use in commercial settings. Users must independently verify compliance with applicable laws before employing MINT-1T for commercial purposes. ## Citation ``` @article{awadalla2024mint1t, title={MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens}, author={Anas Awadalla and Le Xue and Oscar Lo and Manli Shu and Hannah Lee and Etash Kumar Guha and Matt Jordan and Sheng Shen and Mohamed Awadalla and Silvio Savarese and Caiming Xiong and Ran Xu and Yejin Choi and Ludwig Schmidt}, year={2024} } ```

jacobbieker/eumetsat-cloudmask-rss

jacobbieker

--- license: mit ---

Skywork/SkyPile-150B

Skywork

--- task_categories: - text-generation language: - zh tags: - 'llm ' - casual-lm - language-modeling pretty_name: SkyPile-150B size_categories: - 100B<n<1T --- # SkyPile-150B ## Dataset Summary SkyPile-150B is a comprehensive, large-scale Chinese dataset specifically designed for the pre-training of large language models. It is derived from a broad array of publicly accessible Chinese Internet web pages. Rigorous filtering, extensive deduplication, and thorough sensitive data filtering have been employed to ensure its quality. Furthermore, we have utilized advanced tools such as fastText and BERT to filter out low-quality data. The publicly accessible portion of the SkyPile-150B dataset encompasses approximately 233 million unique web pages, each containing an average of over 1,000 Chinese characters. In total, the dataset includes approximately 150 billion tokens and 620 gigabytes of plain text data. ## Language The SkyPile-150B dataset is exclusively composed of Chinese data. ## Data Field Explanation - text: the processed and cleaned text extracted from each page. ## Dataset Safety We utilized more than 200w rules and the BERT-base model to determine the sensitive data present in the dataset, and subsequently removed any harmful entries we detect. ## Sensitive Information and Bias Despite our best efforts, SkyPile-150B, given its construction from publicly available web pages, might contain sensitive information such as email addresses, phone numbers, or IP addresses. We have endeavored to minimize this through deduplication and low-quality filtering, but users of SkyPile-150B should remain vigilant. The Internet is rife with potentially toxic or biased data. We have attempted to mitigate this with specific URL filtering methods, but we encourage users to remain conscious of this potential issue. ## Social Impact of the Dataset The open-source release of the SkyPile-150B dataset represents our commitment to enhancing access to high-quality web data, which has traditionally been a closely guarded resource among model developers. We believe that this release will foster greater accessibility and the proliferation of high-performance large language models, thereby contributing significantly to the advancement of the field. ## License Agreement The community usage of SkyPile dataset requires Skywork Community License. The SkyPile dataset supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within Skywork Community License as well as Apache2.0. ## Contact Us and Citation If you find our work helpful, please feel free to cite our paper~ ``` @misc{wei2023skywork, title={Skywork: A More Open Bilingual Foundation Model}, author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou}, year={2023}, eprint={2310.19341}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

sal4ahm/RealCQA

sal4ahm

--- license: mit --- # RealCQA: Real-World Complex Question Answering Dataset This repository contains the dataset used in the paper "[RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic](https://arxiv.org/pdf/2308.01979)" (ICDAR 2023). The dataset is designed to facilitate research in complex question answering, involving a diverse set of real-world images and associated textual question-answer pairs. ## Dataset Overview The RealCQA dataset consists of 28,266 images, and corresponding 2 million question-answer pairs organized into three complementary subsets. Each image is accompanied by a JSON file containing one or more question blocks. The dataset is structured to address a range of question-answering tasks that require an understanding of the visual content. ### Dataset Structure The dataset is organized into the following folders: - **Images** - `images`: Contains the first 10,000 images. - `images2`: Contains the next 10,000 images. - `images3`: Contains the remaining 8,266 images. - **JSON Files** - `jsons`: Contains the JSON files corresponding to the images in the `images` folder. - `jsons2`: Contains the JSON files corresponding to the images in the `images2` folder. - `jsons3`: Contains the JSON files corresponding to the images in the `images3` folder. - **QA Files** These are the QA created in our proposed dataset. - `qa`: Contains the QA files corresponding to the images in the `images` folder. - `qa2`: Contains the QA files corresponding to the images in the `images2` folder. - `qa3`: Contains the QA files corresponding to the images in the `images3` folder. ### File Details - **Images**: JPEG files named in the format `PMCxxxxxx_abc.jpg`, where `xxxxxx` represents the PubMed Central ID and `abc` represents an identifier specific to the image. - **JSON Files**: JSON files named in the same format as the images. These are groundtruth annotations from the https://chartinfo.github.io challenge, they provide annotations for chart type, text(OCR), text location, text type (axis/tick/legend), data used to plot the chart. - **QA Files**: QA files named in the same format as the images. Each QA file is a list of question blocks associated with the corresponding image we created in our proposed dataset. #### QA Structure Each QA file contains a list of question blocks in the following format: ```json [ { "taxonomy id": "2j", "QID": "16", "question": "Are all the bars in the chart visually horizontal?", "answer": "no", "answer_type": "Binary", "qa_id": "XbUzFtjqsEOF", "PMC_ID": "PMC8439477___g003" }, { "taxonomy id": "1a", "QID": "7a", "question": "What is the type of chart?", "answer": "Vertical Bar chart", "answer_type": "String", "qa_id": "wzcdDijkrHtt", "PMC_ID": "PMC8439477___g003" } ] ``` ### Dataset Loader To facilitate loading and using the dataset, we provide a custom dataset loader script, `dataset.py`. This script defines a PyTorch `Dataset` class to handle loading, preprocessing, and batching of the images and question-answer pairs. #### How to Use the Dataset Loader 1. **Setup and Requirements** Ensure you have the following Python packages installed: ```bash pip install torch torchvision Pillow ``` 2. **Dataset Loader Script** Use the provided `dataset.py` to load the dataset. The script is designed to load the dataset efficiently and handle both training and testing cases. ```python from dataset import RQADataset from torch.utils.data import DataLoader dataset = RQADataset(data_dir='.', split='train') # split='test' for RQA9357 split used in the paper # Test loading a single item print(f"Number of samples in dataset: {len(dataset)}") sample = dataset[0] print("Sample data:", sample) # Initialize DataLoader dataloader = DataLoader(dataset, batch_size=4, collate_fn=RQADataset.custom_collate) # Test DataLoader for batch in dataloader: print("Batch data:", batch) break # Load only one batch for testing ``` ### Citation If you use this dataset in your research, please cite the following paper: ```bibtex @InProceedings{10.1007/978-3-031-41682-8_5, author="Ahmed, Saleem and Jawade, Bhavin and Pandey, Shubham and Setlur, Srirangaraj and Govindaraju, Venu", editor="Fink, Gernot A. and Jain, Rajiv and Kise, Koichi and Zanibbi, Richard", title="RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic", booktitle="Document Analysis and Recognition - ICDAR 2023", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="66--83", abstract="We present a comprehensive study of chart visual question-answering(QA) task, to address the challenges faced in comprehending and extracting data from chart visualizations within documents. Despite efforts to tackle this problem using synthetic charts, solutions are limited by the shortage of annotated real-world data. To fill this gap, we introduce a benchmark and dataset for chart visual QA on real-world charts, offering a systematic analysis of the task and a novel taxonomy for template-based chart question creation. Our contribution includes the introduction of a new answer type, `list', with both ranked and unranked variations. Our study is conducted on a real-world chart dataset from scientific literature, showcasing higher visual complexity compared to other works. Our focus is on template-based QA and how it can serve as a standard for evaluating the first-order logic capabilities of models. The results of our experiments, conducted on a real-world out-of-distribution dataset, provide a robust evaluation of large-scale pre-trained models and advance the field of chart visual QA and formal logic verification for neural networks in general. Our code and dataset is publicly available (https://github.com/cse-ai-lab/RealCQA).", isbn="978-3-031-41682-8" } } ``` ### License This dataset is licensed under the [MIT License](LICENSE). By using this dataset, you agree to abide by its terms and conditions. ### Contact For any questions or issues, please contact the authors of the paper or open an issue in this repository.

HAERAE-HUB/KMMLU

HAERAE-HUB

--- configs: - config_name: Accounting data_files: - split: train path: data/Accounting-train.csv - split: dev path: data/Accounting-dev.csv - split: test path: data/Accounting-test.csv - config_name: Agricultural-Sciences data_files: - split: train path: data/Agricultural-Sciences-train.csv - split: dev path: data/Agricultural-Sciences-dev.csv - split: test path: data/Agricultural-Sciences-test.csv - config_name: Aviation-Engineering-and-Maintenance data_files: - split: train path: data/Aviation-Engineering-and-Maintenance-train.csv - split: dev path: data/Aviation-Engineering-and-Maintenance-dev.csv - split: test path: data/Aviation-Engineering-and-Maintenance-test.csv - config_name: Biology data_files: - split: train path: data/Biology-train.csv - split: dev path: data/Biology-dev.csv - split: test path: data/Biology-test.csv - config_name: Chemical-Engineering data_files: - split: train path: data/Chemical-Engineering-train.csv - split: dev path: data/Chemical-Engineering-dev.csv - split: test path: data/Chemical-Engineering-test.csv - config_name: Chemistry data_files: - split: train path: data/Chemistry-train.csv - split: dev path: data/Chemistry-dev.csv - split: test path: data/Chemistry-test.csv - config_name: Civil-Engineering data_files: - split: train path: data/Civil-Engineering-train.csv - split: dev path: data/Civil-Engineering-dev.csv - split: test path: data/Civil-Engineering-test.csv - config_name: Computer-Science data_files: - split: train path: data/Computer-Science-train.csv - split: dev path: data/Computer-Science-dev.csv - split: test path: data/Computer-Science-test.csv - config_name: Construction data_files: - split: train path: data/Construction-train.csv - split: dev path: data/Construction-dev.csv - split: test path: data/Construction-test.csv - config_name: Criminal-Law data_files: - split: train path: data/Criminal-Law-train.csv - split: dev path: data/Criminal-Law-dev.csv - split: test path: data/Criminal-Law-test.csv - config_name: Ecology data_files: - split: train path: data/Ecology-train.csv - split: dev path: data/Ecology-dev.csv - split: test path: data/Ecology-test.csv - config_name: Economics data_files: - split: train path: data/Economics-train.csv - split: dev path: data/Economics-dev.csv - split: test path: data/Economics-test.csv - config_name: Education data_files: - split: train path: data/Education-train.csv - split: dev path: data/Education-dev.csv - split: test path: data/Education-test.csv - config_name: Electrical-Engineering data_files: - split: train path: data/Electrical-Engineering-train.csv - split: dev path: data/Electrical-Engineering-dev.csv - split: test path: data/Electrical-Engineering-test.csv - config_name: Electronics-Engineering data_files: - split: train path: data/Electronics-Engineering-train.csv - split: dev path: data/Electronics-Engineering-dev.csv - split: test path: data/Electronics-Engineering-test.csv - config_name: Energy-Management data_files: - split: train path: data/Energy-Management-train.csv - split: dev path: data/Energy-Management-dev.csv - split: test path: data/Energy-Management-test.csv - config_name: Environmental-Science data_files: - split: train path: data/Environmental-Science-train.csv - split: dev path: data/Environmental-Science-dev.csv - split: test path: data/Environmental-Science-test.csv - config_name: Fashion data_files: - split: train path: data/Fashion-train.csv - split: dev path: data/Fashion-dev.csv - split: test path: data/Fashion-test.csv - config_name: Food-Processing data_files: - split: train path: data/Food-Processing-train.csv - split: dev path: data/Food-Processing-dev.csv - split: test path: data/Food-Processing-test.csv - config_name: Gas-Technology-and-Engineering data_files: - split: train path: data/Gas-Technology-and-Engineering-train.csv - split: dev path: data/Gas-Technology-and-Engineering-dev.csv - split: test path: data/Gas-Technology-and-Engineering-test.csv - config_name: Geomatics data_files: - split: train path: data/Geomatics-train.csv - split: dev path: data/Geomatics-dev.csv - split: test path: data/Geomatics-test.csv - config_name: Health data_files: - split: train path: data/Health-train.csv - split: dev path: data/Health-dev.csv - split: test path: data/Health-test.csv - config_name: Industrial-Engineer data_files: - split: train path: data/Industrial-Engineer-train.csv - split: dev path: data/Industrial-Engineer-dev.csv - split: test path: data/Industrial-Engineer-test.csv - config_name: Information-Technology data_files: - split: train path: data/Information-Technology-train.csv - split: dev path: data/Information-Technology-dev.csv - split: test path: data/Information-Technology-test.csv - config_name: Interior-Architecture-and-Design data_files: - split: train path: data/Interior-Architecture-and-Design-train.csv - split: dev path: data/Interior-Architecture-and-Design-dev.csv - split: test path: data/Interior-Architecture-and-Design-test.csv - config_name: Law data_files: - split: train path: data/Law-train.csv - split: dev path: data/Law-dev.csv - split: test path: data/Law-test.csv - config_name: Machine-Design-and-Manufacturing data_files: - split: train path: data/Machine-Design-and-Manufacturing-train.csv - split: dev path: data/Machine-Design-and-Manufacturing-dev.csv - split: test path: data/Machine-Design-and-Manufacturing-test.csv - config_name: Management data_files: - split: train path: data/Management-train.csv - split: dev path: data/Management-dev.csv - split: test path: data/Management-test.csv - config_name: Maritime-Engineering data_files: - split: train path: data/Maritime-Engineering-train.csv - split: dev path: data/Maritime-Engineering-dev.csv - split: test path: data/Maritime-Engineering-test.csv - config_name: Marketing data_files: - split: train path: data/Marketing-train.csv - split: dev path: data/Marketing-dev.csv - split: test path: data/Marketing-test.csv - config_name: Materials-Engineering data_files: - split: train path: data/Materials-Engineering-train.csv - split: dev path: data/Materials-Engineering-dev.csv - split: test path: data/Materials-Engineering-test.csv - config_name: Mechanical-Engineering data_files: - split: train path: data/Mechanical-Engineering-train.csv - split: dev path: data/Mechanical-Engineering-dev.csv - split: test path: data/Mechanical-Engineering-test.csv - config_name: Nondestructive-Testing data_files: - split: train path: data/Nondestructive-Testing-train.csv - split: dev path: data/Nondestructive-Testing-dev.csv - split: test path: data/Nondestructive-Testing-test.csv - config_name: Patent data_files: - split: train path: data/Patent-train.csv - split: dev path: data/Patent-dev.csv - split: test path: data/Patent-test.csv - config_name: Political-Science-and-Sociology data_files: - split: train path: data/Political-Science-and-Sociology-train.csv - split: dev path: data/Political-Science-and-Sociology-dev.csv - split: test path: data/Political-Science-and-Sociology-test.csv - config_name: Psychology data_files: - split: train path: data/Psychology-train.csv - split: dev path: data/Psychology-dev.csv - split: test path: data/Psychology-test.csv - config_name: Public-Safety data_files: - split: train path: data/Public-Safety-train.csv - split: dev path: data/Public-Safety-dev.csv - split: test path: data/Public-Safety-test.csv - config_name: Railway-and-Automotive-Engineering data_files: - split: train path: data/Railway-and-Automotive-Engineering-train.csv - split: dev path: data/Railway-and-Automotive-Engineering-dev.csv - split: test path: data/Railway-and-Automotive-Engineering-test.csv - config_name: Real-Estate data_files: - split: train path: data/Real-Estate-train.csv - split: dev path: data/Real-Estate-dev.csv - split: test path: data/Real-Estate-test.csv - config_name: Refrigerating-Machinery data_files: - split: train path: data/Refrigerating-Machinery-train.csv - split: dev path: data/Refrigerating-Machinery-dev.csv - split: test path: data/Refrigerating-Machinery-test.csv - config_name: Social-Welfare data_files: - split: train path: data/Social-Welfare-train.csv - split: dev path: data/Social-Welfare-dev.csv - split: test path: data/Social-Welfare-test.csv - config_name: Taxation data_files: - split: train path: data/Taxation-train.csv - split: dev path: data/Taxation-dev.csv - split: test path: data/Taxation-test.csv - config_name: Telecommunications-and-Wireless-Technology data_files: - split: train path: data/Telecommunications-and-Wireless-Technology-train.csv - split: dev path: data/Telecommunications-and-Wireless-Technology-dev.csv - split: test path: data/Telecommunications-and-Wireless-Technology-test.csv - config_name: Korean-History data_files: - split: train path: data/korean-history-train.csv - split: dev path: data/korean-history-dev.csv - split: test path: data/korean-history-test.csv - config_name: Math data_files: - split: train path: data/math-train.csv - split: dev path: data/math-dev.csv - split: test path: data/math-test.csv task_categories: - multiple-choice language: - ko tags: - mmlu - haerae size_categories: - 10K<n<100K license: cc-by-nd-4.0 --- # KMMLU (Korean-MMLU) We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks that are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 26 publically available and proprietary LLMs, identifying significant room for improvement. The best publicly available model achieves 50.54% on KMMLU, far below the average human performance of 62.6%. This model was primarily trained for English and Chinese, not Korean. Current LLMs tailored to Korean, such as Polyglot-Ko, perform far worse. Surprisingly, even the most capable proprietary LLMs, e.g., GPT-4 and HyperCLOVA X, achieve 59.95% and 53.40%, respectively. This suggests that further work is needed to improve Korean LLMs, and KMMLU offers the right tool to track this progress. We make our dataset publicly available on the Hugging Face Hub and integrate the benchmark into EleutherAI's Language Model Evaluation Harness. Link to Paper: [KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://arxiv.org/abs/2402.11548) ### KMMLU Statistics | Category | # Questions | |------------------------------|-------------| | **Prerequisites** | | | None | 59,909 | | 1 Prerequisite Test | 12,316 | | 2 Prerequisite Tests | 776 | | 2+ Years of Experience | 65,135 | | 4+ Years of Experience | 98,678 | | 9+ Years of Experience | 6,963 | | **Question Type** | | | Positive | 207,030 | | Negation | 36,777 | | **Split** | | | Train | 208,522 | | Validation | 225 | | Test | 35,030 | | **Total** | 243,777 | ### Categories To reimplement the categories in the paper, refer to the following: ``` supercategories = { "accounting": "HUMSS", "agricultural_sciences": "Other", "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_engineering": "STEM", "electronics_engineering": "Applied Science", "energy_management": "Applied Science", "environmental_science": "Applied Science", "fashion": "Other", "food_processing": "Other", "gas_technology_and_engineering": "Applied Science", "geomatics": "Applied Science", "health": "Other", "industrial_engineer": "Applied Science", "information_technology": "STEM", "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "patent": "Other", "political_science_and_sociology": "HUMSS", "psychology": "HUMSS", "public_safety": "Other", "railway_and_automotive_engineering": "Applied Science", "real_estate": "Other", "refrigerating_machinery": "Other", "social_welfare": "HUMSS", "taxation": "HUMSS", "telecommunications_and_wireless_technology": "Applied Science", "korean_history": "HUMSS", "math": "STEM" } ``` ### Point of Contact For any questions contact us via the following email:) ``` spthsrbwls123@yonsei.ac.kr ```

ArmelR/the-pile-splitted

ArmelR

--- configs: - config_name: all data_files: - split: train path: - "data/ArXiv/train/*.arrow" - "data/BookCorpus2/train/*.arrow" - "data/Books3/train/*.arrow" - "data/DM Mathematics/train/*.arrow" - "data/Enron Emails/train/*.arrow" - "data/EuroParl/train/*.arrow" - "data/FreeLaw/train/*.arrow" - "data/Github/train/*.arrow" - "data/Gutenberg (PG-19)/train/*.arrow" - "data/HackerNews/train/*.arrow" - "data/NIH ExPorter/train/*.arrow" - "data/OpenSubtitles/train/*.arrow" - "data/OpenWebText2/train/*.arrow" - "data/PhilPapers/train/*.arrow" - "data/Pile-CC/train/*.arrow" - "data/PubMed Abstracts/train/*.arrow" - "data/PubMed Central/train/*.arrow" - "data/StackExchange/train/*.arrow" - "data/UPSTO Backgrounds/train/*.arrow" - "data/Ubuntu IRC/train/*.arrow" - "data/Wikipedia (en)/train/*.arrow" - "data/YoutubeSubtitles/train/*.arrow" - split: test path: - "data/ArXiv/test/*.arrow" - "data/BookCorpus2/test/*.arrow" - "data/Books3/test/*.arrow" - "data/DM Mathematics/test/*.arrow" - "data/Enron Emails/test/*.arrow" - "data/EuroParl/test/*.arrow" - "data/FreeLaw/test/*.arrow" - "data/Github/test/*.arrow" - "data/Gutenberg (PG-19)/test/*.arrow" - "data/HackerNews/test/*.arrow" - "data/NIH ExPorter/test/*.arrow" - "data/OpenSubtitles/test/*.arrow" - "data/OpenWebText2/test/*.arrow" - "data/PhilPapers/test/*.arrow" - "data/Pile-CC/test/*.arrow" - "data/PubMed Abstracts/test/*.arrow" - "data/PubMed Central/test/*.arrow" - "data/StackExchange/test/*.arrow" - "data/UPSTO Backgrounds/test/*.arrow" - "data/Ubuntu IRC/test/*.arrow" - "data/Wikipedia (en)/test/*.arrow" - "data/YoutubeSubtitles/test/*.arrow" default: true - config_name: ArXiv data_files: - split: train path: "data/ArXiv/train/*.arrow" - split: test path: "data/ArXiv/test/*.arrow" - config_name: BookCorpus2 data_files: - split: train path: "data/BookCorpus2/train/*.arrow" - split: test path: "data/BookCorpus2/test/*.arrow" - config_name: Books3 data_files: - split: train path: "data/Books3/train/*.arrow" - split: test path: "data/Books3/test/*.arrow" - config_name: DM Mathematics data_files: - split: train path: "data/DM Mathematics/train/*.arrow" - split: test path: "data/DM Mathematics/test/*.arrow" - config_name: Enron Emails data_files: - split: train path: "data/Enron Emails/train/*.arrow" - split: test path: "data/Enron Emails/test/*.arrow" - config_name: EuroParl data_files: - split: train path: "data/EuroParl/train/*.arrow" - split: test path: "data/EuroParl/test/*.arrow" - config_name: FreeLaw data_files: - split: train path: "data/FreeLaw/train/*.arrow" - split: test path: "data/FreeLaw/test/*.arrow" - config_name: Github data_files: - split: train path: "data/Github/train/*.arrow" - split: test path: "data/Github/test/*.arrow" - config_name: Gutenberg (PG-19) data_files: - split: train path: "data/Gutenberg (PG-19)/train/*.arrow" - split: test path: "data/Gutenberg (PG-19)/test/*.arrow" - config_name: HackerNews data_files: - split: train path: "data/HackerNews/train/*.arrow" - split: test path: "data/HackerNews/test/*.arrow" - config_name: NIH ExPorter data_files: - split: train path: "data/NIH ExPorter/train/*.arrow" - split: test path: "data/NIH ExPorter/test/*.arrow" - config_name: OpenSubtitles data_files: - split: train path: "data/OpenSubtitles/train/*.arrow" - split: test path: "data/OpenSubtitles/test/*.arrow" - config_name: OpenWebText2 data_files: - split: train path: "data/OpenWebText2/train/*.arrow" - split: test path: "data/OpenWebText2/test/*.arrow" - config_name: PhilPapers data_files: - split: train path: "data/PhilPapers/train/*.arrow" - split: test path: "data/PhilPapers/test/*.arrow" - config_name: Pile-CC data_files: - split: train path: "data/Pile-CC/train/*.arrow" - split: test path: "data/Pile-CC/test/*.arrow" - config_name: PubMed Abstracts data_files: - split: train path: "data/PubMed Abstracts/train/*.arrow" - split: test path: "data/PubMed Abstracts/test/*.arrow" - config_name: PubMed Central data_files: - split: train path: "data/PubMed Central/train/*.arrow" - split: test path: "data/PubMed Central/test/*.arrow" - config_name: StackExchange data_files: - split: train path: "data/StackExchange/train/*.arrow" - split: test path: "data/StackExchange/test/*.arrow" - config_name: UPSTO Backgrounds data_files: - split: train path: "data/UPSTO Backgrounds/train/*.arrow" - split: test path: "data/UPSTO Backgrounds/test/*.arrow" - config_name: Ubuntu IRC data_files: - split: train path: "data/Ubuntu IRC/train/*.arrow" - split: test path: "data/Ubuntu IRC/test/*.arrow" - config_name: Wikipedia (en) data_files: - split: train path: "data/Wikipedia (en)/train/*.arrow" - split: test path: "data/Wikipedia (en)/test/*.arrow" - config_name: YoutubeSubtitles data_files: - split: train path: "data/YoutubeSubtitles/train/*.arrow" - split: test path: "data/YoutubeSubtitles/test/*.arrow" --- # Dataset description [The pile](https://arxiv.org/abs/2101.00027) is an 800GB dataset of english text designed by EleutherAI to train large-scale language models. The original version of the dataset can be found [here](https://huggingface.co/datasets/EleutherAI/pile). The dataset is divided into 22 smaller high-quality datasets. For more information each of them, please refer to [the datasheet for the pile](https://arxiv.org/abs/2201.07311). However, the current version of the dataset, available on the Hub, is not splitted accordingly. We had to solve this problem in order to improve the user experience when it comes to deal with the pile via the hub. Here is an instance of the pile ``` { 'meta': {'pile_set_name': 'Pile-CC'}, 'text': 'It is done, and submitted. You can play “Survival of the Tastiest” on Android, and on the web. Playing on...' } ``` We used the `meta` column to properly divide the dataset in subsets. Each instance `example` belongs to the subset `domain` and `domain = example['meta']['pile_set_name']`. By doing this, we were able to create a [new version of the pile](https://huggingface.co/datasets/ArmelR/sharded-pile) that is properly divided, each instance having a new column `domain`. We further splitted each subset in train/test (97%/3%) to build the current dataset which the following structure ``` data ArXiv train test BookCorpus2 train test Books3 train test ``` # Usage ```python from datasets import load_dataset dataset = load_dataset( "ArmelR/the-pile-splitted", subset_of_interest, num_proc=8 ) ``` Using `subset_of_interest = "default"` will load the whole dataset.

EdinburghNLP/xsum

EdinburghNLP

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual pretty_name: Extreme Summarization (XSum) paperswithcode_id: xsum size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: document: text summary: target metrics: - type: rouge name: Rouge dataset_info: features: - name: document dtype: string - name: summary dtype: string - name: id dtype: string splits: - name: train num_bytes: 479206608 num_examples: 204045 - name: validation num_bytes: 26292901 num_examples: 11332 - name: test num_bytes: 26756165 num_examples: 11334 download_size: 257302866 dataset_size: 532255674 --- # Dataset Card for "xsum" ## 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/EdinburghNLP/XSum - **Paper:** [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://arxiv.org/abs/1808.08745) - **Point of Contact:** [Shashi Narayan](mailto:shashi.narayan@ed.ac.uk) - **Size of downloaded dataset files:** 257.30 MB - **Size of the generated dataset:** 532.26 MB - **Total amount of disk used:** 789.56 MB ### Dataset Summary Extreme Summarization (XSum) Dataset. There are three features: - document: Input news article. - summary: One sentence summary of the article. - id: BBC ID of the article. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 257.30 MB - **Size of the generated dataset:** 532.26 MB - **Total amount of disk used:** 789.56 MB An example of 'validation' looks as follows. ``` { "document": "some-body", "id": "29750031", "summary": "some-sentence" } ``` ### Data Fields The data fields are the same among all splits. #### default - `document`: a `string` feature. - `summary`: a `string` feature. - `id`: a `string` feature. ### Data Splits | name |train |validation|test | |-------|-----:|---------:|----:| |default|204045| 11332|11334| ## 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Narayan2018DontGM, title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization}, author={Shashi Narayan and Shay B. Cohen and Mirella Lapata}, journal={ArXiv}, year={2018}, volume={abs/1808.08745} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@jbragg](https://github.com/jbragg), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

zh-plus/tiny-imagenet

zh-plus

--- annotations_creators: - crowdsourced extra_gated_prompt: "By clicking on \u201CAccess repository\u201D below, you also\ \ agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\"\ ) has requested permission to use the ImageNet database (the \"Database\") at Princeton\ \ University and Stanford University. In exchange for such permission, Researcher\ \ hereby agrees to the following terms and conditions:\n1. Researcher shall use\ \ the Database only for non-commercial research and educational purposes.\n2. Princeton\ \ University, Stanford University and Hugging Face make no representations or warranties\ \ regarding the Database, including but not limited to warranties of non-infringement\ \ or fitness for a particular purpose.\n3. Researcher accepts full responsibility\ \ for his or her use of the Database and shall defend and indemnify the ImageNet\ \ team, Princeton University, Stanford University and Hugging Face, including their\ \ employees, Trustees, officers and agents, against any and all claims arising from\ \ Researcher's use of the Database, including but not limited to Researcher's use\ \ of any copies of copyrighted images that he or she may create from the Database.\n\ 4. Researcher may provide research associates and colleagues with access to the\ \ Database provided that they first agree to be bound by these terms and conditions.\n\ 5. Princeton University, Stanford University and Hugging Face reserve the right\ \ to terminate Researcher's access to the Database at any time.\n6. If Researcher\ \ is employed by a for-profit, commercial entity, Researcher's employer shall also\ \ be bound by these terms and conditions, and Researcher hereby represents that\ \ he or she is fully authorized to enter into this agreement on behalf of such employer.\n\ 7. The law of the State of New Jersey shall apply to all disputes under this agreement." language: - en language_creators: - crowdsourced license: [] multilinguality: - monolingual paperswithcode_id: imagenet pretty_name: Tiny-ImageNet size_categories: - 100K<n<1M source_datasets: - extended|imagenet-1k task_categories: - image-classification task_ids: - multi-class-image-classification --- # Dataset Card for tiny-imagenet ## Dataset Description - **Homepage:** https://www.kaggle.com/c/tiny-imagenet - **Repository:** [Needs More Information] - **Paper:** http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf - **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-tiny-imagenet-1 ### Dataset Summary Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images. ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances ```json { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190, 'label': 15 } ``` ### Data Fields - image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]. - label: an int classification label. -1 for test set as the labels are missing. Check `classes.py` for the map of numbers & labels. ### Data Splits | | Train | Valid | | ------------ | ------ | ----- | | # of samples | 100000 | 10000 | ## Usage ### Example #### Load Dataset ```python def example_usage(): tiny_imagenet = load_dataset('Maysee/tiny-imagenet', split='train') print(tiny_imagenet[0]) if __name__ == '__main__': example_usage() ```

facebook/mlqa

facebook

--- pretty_name: MLQA (MultiLingual Question Answering) language: - en - de - es - ar - zh - vi - hi license: - cc-by-sa-3.0 source_datasets: - original size_categories: - 10K<n<100K language_creators: - crowdsourced annotations_creators: - crowdsourced multilinguality: - multilingual task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: mlqa dataset_info: - config_name: mlqa-translate-train.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 101227245 num_examples: 78058 - name: validation num_bytes: 13144332 num_examples: 9512 download_size: 63364123 dataset_size: 114371577 - config_name: mlqa-translate-train.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 77996825 num_examples: 80069 - name: validation num_bytes: 10322113 num_examples: 9927 download_size: 63364123 dataset_size: 88318938 - config_name: mlqa-translate-train.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 97387431 num_examples: 84816 - name: validation num_bytes: 12731112 num_examples: 10356 download_size: 63364123 dataset_size: 110118543 - config_name: mlqa-translate-train.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 55143547 num_examples: 76285 - name: validation num_bytes: 7418070 num_examples: 9568 download_size: 63364123 dataset_size: 62561617 - config_name: mlqa-translate-train.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 80789653 num_examples: 81810 - name: validation num_bytes: 10718376 num_examples: 10123 download_size: 63364123 dataset_size: 91508029 - config_name: mlqa-translate-train.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 168117671 num_examples: 82451 - name: validation num_bytes: 22422152 num_examples: 10253 download_size: 63364123 dataset_size: 190539823 - config_name: mlqa-translate-test.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 5484467 num_examples: 5335 download_size: 10075488 dataset_size: 5484467 - config_name: mlqa-translate-test.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3884332 num_examples: 4517 download_size: 10075488 dataset_size: 3884332 - config_name: mlqa-translate-test.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 5998327 num_examples: 5495 download_size: 10075488 dataset_size: 5998327 - config_name: mlqa-translate-test.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4831704 num_examples: 5137 download_size: 10075488 dataset_size: 4831704 - config_name: mlqa-translate-test.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3916758 num_examples: 5253 download_size: 10075488 dataset_size: 3916758 - config_name: mlqa-translate-test.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4608811 num_examples: 4918 download_size: 10075488 dataset_size: 4608811 - config_name: mlqa.ar.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 8216837 num_examples: 5335 - name: validation num_bytes: 808830 num_examples: 517 download_size: 75719050 dataset_size: 9025667 - config_name: mlqa.ar.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2132247 num_examples: 1649 - name: validation num_bytes: 358554 num_examples: 207 download_size: 75719050 dataset_size: 2490801 - config_name: mlqa.ar.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3235363 num_examples: 2047 - name: validation num_bytes: 283834 num_examples: 163 download_size: 75719050 dataset_size: 3519197 - config_name: mlqa.ar.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3175660 num_examples: 1912 - name: validation num_bytes: 334016 num_examples: 188 download_size: 75719050 dataset_size: 3509676 - config_name: mlqa.ar.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 8074057 num_examples: 5335 - name: validation num_bytes: 794775 num_examples: 517 download_size: 75719050 dataset_size: 8868832 - config_name: mlqa.ar.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2981237 num_examples: 1978 - name: validation num_bytes: 223188 num_examples: 161 download_size: 75719050 dataset_size: 3204425 - config_name: mlqa.ar.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2993225 num_examples: 1831 - name: validation num_bytes: 276727 num_examples: 186 download_size: 75719050 dataset_size: 3269952 - config_name: mlqa.de.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1587005 num_examples: 1649 - name: validation num_bytes: 195822 num_examples: 207 download_size: 75719050 dataset_size: 1782827 - config_name: mlqa.de.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4274496 num_examples: 4517 - name: validation num_bytes: 477366 num_examples: 512 download_size: 75719050 dataset_size: 4751862 - config_name: mlqa.de.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1654540 num_examples: 1675 - name: validation num_bytes: 211985 num_examples: 182 download_size: 75719050 dataset_size: 1866525 - config_name: mlqa.de.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1645937 num_examples: 1621 - name: validation num_bytes: 180114 num_examples: 190 download_size: 75719050 dataset_size: 1826051 - config_name: mlqa.de.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4251153 num_examples: 4517 - name: validation num_bytes: 474863 num_examples: 512 download_size: 75719050 dataset_size: 4726016 - config_name: mlqa.de.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1678176 num_examples: 1776 - name: validation num_bytes: 166193 num_examples: 196 download_size: 75719050 dataset_size: 1844369 - config_name: mlqa.de.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1343983 num_examples: 1430 - name: validation num_bytes: 150679 num_examples: 163 download_size: 75719050 dataset_size: 1494662 - config_name: mlqa.vi.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3164094 num_examples: 2047 - name: validation num_bytes: 226724 num_examples: 163 download_size: 75719050 dataset_size: 3390818 - config_name: mlqa.vi.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2189315 num_examples: 1675 - name: validation num_bytes: 272794 num_examples: 182 download_size: 75719050 dataset_size: 2462109 - config_name: mlqa.vi.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 7807045 num_examples: 5495 - name: validation num_bytes: 715291 num_examples: 511 download_size: 75719050 dataset_size: 8522336 - config_name: mlqa.vi.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2947458 num_examples: 1943 - name: validation num_bytes: 265154 num_examples: 184 download_size: 75719050 dataset_size: 3212612 - config_name: mlqa.vi.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 7727204 num_examples: 5495 - name: validation num_bytes: 707925 num_examples: 511 download_size: 75719050 dataset_size: 8435129 - config_name: mlqa.vi.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2822481 num_examples: 2018 - name: validation num_bytes: 279235 num_examples: 189 download_size: 75719050 dataset_size: 3101716 - config_name: mlqa.vi.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2738045 num_examples: 1947 - name: validation num_bytes: 251470 num_examples: 177 download_size: 75719050 dataset_size: 2989515 - config_name: mlqa.zh.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1697005 num_examples: 1912 - name: validation num_bytes: 171743 num_examples: 188 download_size: 75719050 dataset_size: 1868748 - config_name: mlqa.zh.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1356268 num_examples: 1621 - name: validation num_bytes: 170686 num_examples: 190 download_size: 75719050 dataset_size: 1526954 - config_name: mlqa.zh.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1770535 num_examples: 1943 - name: validation num_bytes: 169651 num_examples: 184 download_size: 75719050 dataset_size: 1940186 - config_name: mlqa.zh.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4324740 num_examples: 5137 - name: validation num_bytes: 433960 num_examples: 504 download_size: 75719050 dataset_size: 4758700 - config_name: mlqa.zh.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4353361 num_examples: 5137 - name: validation num_bytes: 437016 num_examples: 504 download_size: 75719050 dataset_size: 4790377 - config_name: mlqa.zh.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1697983 num_examples: 1947 - name: validation num_bytes: 134693 num_examples: 161 download_size: 75719050 dataset_size: 1832676 - config_name: mlqa.zh.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1547159 num_examples: 1767 - name: validation num_bytes: 180928 num_examples: 189 download_size: 75719050 dataset_size: 1728087 - config_name: mlqa.en.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6641971 num_examples: 5335 - name: validation num_bytes: 621075 num_examples: 517 download_size: 75719050 dataset_size: 7263046 - config_name: mlqa.en.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4966262 num_examples: 4517 - name: validation num_bytes: 584725 num_examples: 512 download_size: 75719050 dataset_size: 5550987 - config_name: mlqa.en.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6958087 num_examples: 5495 - name: validation num_bytes: 631268 num_examples: 511 download_size: 75719050 dataset_size: 7589355 - config_name: mlqa.en.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6441614 num_examples: 5137 - name: validation num_bytes: 598772 num_examples: 504 download_size: 75719050 dataset_size: 7040386 - config_name: mlqa.en.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 13787522 num_examples: 11590 - name: validation num_bytes: 1307399 num_examples: 1148 download_size: 75719050 dataset_size: 15094921 - config_name: mlqa.en.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6074990 num_examples: 5253 - name: validation num_bytes: 545657 num_examples: 500 download_size: 75719050 dataset_size: 6620647 - config_name: mlqa.en.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 6293785 num_examples: 4918 - name: validation num_bytes: 614223 num_examples: 507 download_size: 75719050 dataset_size: 6908008 - config_name: mlqa.es.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1696778 num_examples: 1978 - name: validation num_bytes: 145105 num_examples: 161 download_size: 75719050 dataset_size: 1841883 - config_name: mlqa.es.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1361983 num_examples: 1776 - name: validation num_bytes: 139968 num_examples: 196 download_size: 75719050 dataset_size: 1501951 - config_name: mlqa.es.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1707141 num_examples: 2018 - name: validation num_bytes: 172801 num_examples: 189 download_size: 75719050 dataset_size: 1879942 - config_name: mlqa.es.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1635294 num_examples: 1947 - name: validation num_bytes: 122829 num_examples: 161 download_size: 75719050 dataset_size: 1758123 - config_name: mlqa.es.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4249431 num_examples: 5253 - name: validation num_bytes: 408169 num_examples: 500 download_size: 75719050 dataset_size: 4657600 - config_name: mlqa.es.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4281273 num_examples: 5253 - name: validation num_bytes: 411196 num_examples: 500 download_size: 75719050 dataset_size: 4692469 - config_name: mlqa.es.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 1489611 num_examples: 1723 - name: validation num_bytes: 178003 num_examples: 187 download_size: 75719050 dataset_size: 1667614 - config_name: mlqa.hi.ar features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4374373 num_examples: 1831 - name: validation num_bytes: 402817 num_examples: 186 download_size: 75719050 dataset_size: 4777190 - config_name: mlqa.hi.de features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 2961556 num_examples: 1430 - name: validation num_bytes: 294325 num_examples: 163 download_size: 75719050 dataset_size: 3255881 - config_name: mlqa.hi.vi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4664436 num_examples: 1947 - name: validation num_bytes: 411654 num_examples: 177 download_size: 75719050 dataset_size: 5076090 - config_name: mlqa.hi.zh features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 4281309 num_examples: 1767 - name: validation num_bytes: 416192 num_examples: 189 download_size: 75719050 dataset_size: 4697501 - config_name: mlqa.hi.en features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 11245629 num_examples: 4918 - name: validation num_bytes: 1076115 num_examples: 507 download_size: 75719050 dataset_size: 12321744 - config_name: mlqa.hi.es features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 3789337 num_examples: 1723 - name: validation num_bytes: 412469 num_examples: 187 download_size: 75719050 dataset_size: 4201806 - config_name: mlqa.hi.hi features: - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string - name: id dtype: string splits: - name: test num_bytes: 11606982 num_examples: 4918 - name: validation num_bytes: 1115055 num_examples: 507 download_size: 75719050 dataset_size: 12722037 --- # Dataset Card for "mlqa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/facebookresearch/MLQA](https://github.com/facebookresearch/MLQA) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **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:** 4.15 GB - **Size of the generated dataset:** 910.01 MB - **Total amount of disk used:** 5.06 GB ### Dataset Summary MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. ## Dataset Structure ### Data Instances #### mlqa-translate-test.ar - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 5.48 MB - **Total amount of disk used:** 15.56 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.de - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 3.88 MB - **Total amount of disk used:** 13.96 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.es - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 3.92 MB - **Total amount of disk used:** 13.99 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.hi - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 4.61 MB - **Total amount of disk used:** 14.68 MB An example of 'test' looks as follows. ``` ``` #### mlqa-translate-test.vi - **Size of downloaded dataset files:** 10.08 MB - **Size of the generated dataset:** 6.00 MB - **Total amount of disk used:** 16.07 MB An example of 'test' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### mlqa-translate-test.ar - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.de - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.es - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.hi - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. #### mlqa-translate-test.vi - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `answer_start`: a `int32` feature. - `text`: a `string` feature. - `id`: a `string` feature. ### Data Splits | name |test| |----------------------|---:| |mlqa-translate-test.ar|5335| |mlqa-translate-test.de|4517| |mlqa-translate-test.es|5253| |mlqa-translate-test.hi|4918| |mlqa-translate-test.vi|5495| ## 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{lewis2019mlqa, title = {MLQA: Evaluating Cross-lingual Extractive Question Answering}, author = {Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, journal = {arXiv preprint arXiv:1910.07475}, year = 2019, eid = {arXiv: 1910.07475} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@M-Salti](https://github.com/M-Salti), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

lukaemon/bbh

lukaemon

--- dataset_info: - config_name: boolean_expressions features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 11790 num_examples: 250 download_size: 17172 dataset_size: 11790 - config_name: causal_judgement features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 198021 num_examples: 187 download_size: 202943 dataset_size: 198021 - config_name: date_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 54666 num_examples: 250 download_size: 61760 dataset_size: 54666 - config_name: disambiguation_qa features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 78620 num_examples: 250 download_size: 85255 dataset_size: 78620 - config_name: dyck_languages features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38432 num_examples: 250 download_size: 43814 dataset_size: 38432 - config_name: formal_fallacies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 138224 num_examples: 250 download_size: 145562 dataset_size: 138224 - config_name: geometric_shapes features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 68560 num_examples: 250 download_size: 77242 dataset_size: 68560 - config_name: hyperbaton features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38574 num_examples: 250 download_size: 44706 dataset_size: 38574 - config_name: logical_deduction_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 148595 num_examples: 250 download_size: 155477 dataset_size: 148595 - config_name: logical_deduction_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 191022 num_examples: 250 download_size: 198404 dataset_size: 191022 - config_name: logical_deduction_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 105831 num_examples: 250 download_size: 112213 dataset_size: 105831 - config_name: movie_recommendation features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 50985 num_examples: 250 download_size: 57684 dataset_size: 50985 - config_name: multistep_arithmetic_two features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 12943 num_examples: 250 download_size: 18325 dataset_size: 12943 - config_name: navigate features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 49031 num_examples: 250 download_size: 55163 dataset_size: 49031 - config_name: object_counting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 30508 num_examples: 250 download_size: 35890 dataset_size: 30508 - config_name: penguins_in_a_table features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 70062 num_examples: 146 download_size: 74516 dataset_size: 70062 - config_name: reasoning_about_colored_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 89579 num_examples: 250 download_size: 98694 dataset_size: 89579 - config_name: ruin_names features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 46537 num_examples: 250 download_size: 53178 dataset_size: 46537 - config_name: salient_translation_error_detection features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 277110 num_examples: 250 download_size: 286443 dataset_size: 277110 - config_name: snarks features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 38223 num_examples: 178 download_size: 42646 dataset_size: 38223 - config_name: sports_understanding features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 22723 num_examples: 250 download_size: 28617 dataset_size: 22723 - config_name: temporal_sequences features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 139546 num_examples: 250 download_size: 148176 dataset_size: 139546 - config_name: tracking_shuffled_objects_five_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 162590 num_examples: 250 download_size: 169722 dataset_size: 162590 - config_name: tracking_shuffled_objects_seven_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 207274 num_examples: 250 download_size: 214906 dataset_size: 207274 - config_name: tracking_shuffled_objects_three_objects features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 122104 num_examples: 250 download_size: 128736 dataset_size: 122104 - config_name: web_of_lies features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 47582 num_examples: 250 download_size: 52964 dataset_size: 47582 - config_name: word_sorting features: - name: input dtype: string - name: target dtype: string splits: - name: test num_bytes: 60918 num_examples: 250 download_size: 66300 dataset_size: 60918 --- # BIG-bench Hard dataset homepage: https://github.com/suzgunmirac/BIG-Bench-Hard ``` @article{suzgun2022challenging, title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them}, author={Suzgun, Mirac and Scales, Nathan and Sch{\"a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V and Chi, Ed H and Zhou, Denny and and Wei, Jason}, journal={arXiv preprint arXiv:2210.09261}, year={2022} } ```

tau/commonsense_qa

tau

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: commonsenseqa pretty_name: CommonsenseQA dataset_info: features: - name: id dtype: string - name: question dtype: string - name: question_concept dtype: string - name: choices sequence: - name: label dtype: string - name: text dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 2207794 num_examples: 9741 - name: validation num_bytes: 273848 num_examples: 1221 - name: test num_bytes: 257842 num_examples: 1140 download_size: 1558570 dataset_size: 2739484 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "commonsense_qa" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.tau-nlp.org/commonsenseqa - **Repository:** https://github.com/jonathanherzig/commonsenseqa - **Paper:** https://arxiv.org/abs/1811.00937 - **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:** 4.68 MB - **Size of the generated dataset:** 2.18 MB - **Total amount of disk used:** 6.86 MB ### Dataset Summary CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers. The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation split, and "Question token split", see paper for details. ### 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 dataset is in English (`en`). ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 4.68 MB - **Size of the generated dataset:** 2.18 MB - **Total amount of disk used:** 6.86 MB An example of 'train' looks as follows: ``` {'id': '075e483d21c29a511267ef62bedc0461', 'question': 'The sanctions against the school were a punishing blow, and they seemed to what the efforts the school had made to change?', 'question_concept': 'punishing', 'choices': {'label': ['A', 'B', 'C', 'D', 'E'], 'text': ['ignore', 'enforce', 'authoritarian', 'yell at', 'avoid']}, 'answerKey': 'A'} ``` ### Data Fields The data fields are the same among all splits. #### default - `id` (`str`): Unique ID. - `question`: a `string` feature. - `question_concept` (`str`): ConceptNet concept associated to the question. - `choices`: a dictionary feature containing: - `label`: a `string` feature. - `text`: a `string` feature. - `answerKey`: a `string` feature. ### Data Splits | name | train | validation | test | |---------|------:|-----------:|-----:| | default | 9741 | 1221 | 1140 | ## 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 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### 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 the MIT License. See: https://github.com/jonathanherzig/commonsenseqa/issues/5 ### Citation Information ``` @inproceedings{talmor-etal-2019-commonsenseqa, title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge", author = "Talmor, Alon and Herzig, Jonathan and Lourie, Nicholas and Berant, Jonathan", booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N19-1421", doi = "10.18653/v1/N19-1421", pages = "4149--4158", archivePrefix = "arXiv", eprint = "1811.00937", primaryClass = "cs", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

ricdomolm/lawma-tasks

ricdomolm

--- license: mit configs: - config_name: sc_adminaction data_files: - split: train path: sc_adminaction/train-* - split: val path: sc_adminaction/val-* - split: test path: sc_adminaction/test-* - config_name: sc_adminaction_is data_files: - split: train path: sc_adminaction_is/train-* - split: val path: sc_adminaction_is/val-* - split: test path: sc_adminaction_is/test-* - config_name: sc_adminactionstate data_files: - split: train path: sc_adminactionstate/train-* - split: val path: sc_adminactionstate/val-* - split: test path: sc_adminactionstate/test-* - config_name: sc_authoritydecision data_files: - split: train path: sc_authoritydecision/train-* - split: val path: sc_authoritydecision/val-* - split: test path: sc_authoritydecision/test-* - config_name: sc_casedisposition data_files: - split: train path: sc_casedisposition/train-* - split: val path: sc_casedisposition/val-* - split: test path: sc_casedisposition/test-* - config_name: sc_caseorigin data_files: - split: train path: sc_caseorigin/train-* - split: val path: sc_caseorigin/val-* - split: test path: sc_caseorigin/test-* - config_name: sc_caseoriginstate data_files: - split: train path: sc_caseoriginstate/train-* - split: val path: sc_caseoriginstate/val-* - split: test path: sc_caseoriginstate/test-* - config_name: sc_casesource data_files: - split: train path: sc_casesource/train-* - split: val path: sc_casesource/val-* - split: test path: sc_casesource/test-* - config_name: sc_casesourcestate data_files: - split: train path: sc_casesourcestate/train-* - split: val path: sc_casesourcestate/val-* - split: test path: sc_casesourcestate/test-* - config_name: sc_certreason data_files: - split: train path: sc_certreason/train-* - split: val path: sc_certreason/val-* - split: test path: sc_certreason/test-* - config_name: sc_decisiondirection data_files: - split: train path: sc_decisiondirection/train-* - split: val path: sc_decisiondirection/val-* - split: test path: sc_decisiondirection/test-* - config_name: sc_decisiontype data_files: - split: train path: sc_decisiontype/train-* - split: val path: sc_decisiontype/val-* - split: test path: sc_decisiontype/test-* - config_name: sc_declarationuncon data_files: - split: train path: sc_declarationuncon/train-* - split: val path: sc_declarationuncon/val-* - split: test path: sc_declarationuncon/test-* - config_name: sc_issue_1 data_files: - split: train path: sc_issue_1/train-* - split: val path: sc_issue_1/val-* - split: test path: sc_issue_1/test-* - config_name: sc_issue_10 data_files: - split: train path: sc_issue_10/train-* - split: val path: sc_issue_10/val-* - split: test path: sc_issue_10/test-* - config_name: sc_issue_11 data_files: - split: train path: sc_issue_11/train-* - split: val path: sc_issue_11/val-* - split: test path: sc_issue_11/test-* - config_name: sc_issue_12 data_files: - split: train path: sc_issue_12/train-* - split: val path: sc_issue_12/val-* - split: test path: sc_issue_12/test-* - config_name: sc_issue_2 data_files: - split: train path: sc_issue_2/train-* - split: val path: sc_issue_2/val-* - split: test path: sc_issue_2/test-* - config_name: sc_issue_3 data_files: - split: train path: sc_issue_3/train-* - split: val path: sc_issue_3/val-* - split: test path: sc_issue_3/test-* - config_name: sc_issue_4 data_files: - split: train path: sc_issue_4/train-* - split: val path: sc_issue_4/val-* - split: test path: sc_issue_4/test-* - config_name: sc_issue_5 data_files: - split: train path: sc_issue_5/train-* - split: val path: sc_issue_5/val-* - split: test path: sc_issue_5/test-* - config_name: sc_issue_6 data_files: - split: train path: sc_issue_6/train-* - split: val path: sc_issue_6/val-* - split: test path: sc_issue_6/test-* - config_name: sc_issue_7 data_files: - split: train path: sc_issue_7/train-* - split: val path: sc_issue_7/val-* - split: test path: sc_issue_7/test-* - config_name: sc_issue_8 data_files: - split: train path: sc_issue_8/train-* - split: val path: sc_issue_8/val-* - split: test path: sc_issue_8/test-* - config_name: sc_issue_9 data_files: - split: train path: sc_issue_9/train-* - split: val path: sc_issue_9/val-* - split: test path: sc_issue_9/test-* - config_name: sc_issuearea data_files: - split: train path: sc_issuearea/train-* - split: val path: sc_issuearea/val-* - split: test path: sc_issuearea/test-* - config_name: sc_jurisdiction data_files: - split: train path: sc_jurisdiction/train-* - split: val path: sc_jurisdiction/val-* - split: test path: sc_jurisdiction/test-* - config_name: sc_lcdisagreement data_files: - split: train path: sc_lcdisagreement/train-* - split: val path: sc_lcdisagreement/val-* - split: test path: sc_lcdisagreement/test-* - config_name: sc_lcdisposition data_files: - split: train path: sc_lcdisposition/train-* - split: val path: sc_lcdisposition/val-* - split: test path: sc_lcdisposition/test-* - config_name: sc_lcdispositiondirection data_files: - split: train path: sc_lcdispositiondirection/train-* - split: val path: sc_lcdispositiondirection/val-* - split: test path: sc_lcdispositiondirection/test-* - config_name: sc_partywinning data_files: - split: train path: sc_partywinning/train-* - split: val path: sc_partywinning/val-* - split: test path: sc_partywinning/test-* - config_name: sc_petitioner data_files: - split: train path: sc_petitioner/train-* - split: val path: sc_petitioner/val-* - split: test path: sc_petitioner/test-* - config_name: sc_petitionerstate data_files: - split: train path: sc_petitionerstate/train-* - split: val path: sc_petitionerstate/val-* - split: test path: sc_petitionerstate/test-* - config_name: sc_precedentalteration data_files: - split: train path: sc_precedentalteration/train-* - split: val path: sc_precedentalteration/val-* - split: test path: sc_precedentalteration/test-* - config_name: sc_respondent data_files: - split: train path: sc_respondent/train-* - split: val path: sc_respondent/val-* - split: test path: sc_respondent/test-* - config_name: sc_respondentstate data_files: - split: train path: sc_respondentstate/train-* - split: val path: sc_respondentstate/val-* - split: test path: sc_respondentstate/test-* - config_name: sc_threejudgefdc data_files: - split: train path: sc_threejudgefdc/train-* - split: val path: sc_threejudgefdc/val-* - split: test path: sc_threejudgefdc/test-* - config_name: songer_abusedis data_files: - split: train path: songer_abusedis/train-* - split: val path: songer_abusedis/val-* - split: test path: songer_abusedis/test-* - config_name: songer_adminrev data_files: - split: train path: songer_adminrev/train-* - split: val path: songer_adminrev/val-* - split: test path: songer_adminrev/test-* - config_name: songer_agen_acq data_files: - split: train path: songer_agen_acq/train-* - split: val path: songer_agen_acq/val-* - split: test path: songer_agen_acq/test-* - config_name: songer_alj data_files: - split: train path: songer_alj/train-* - split: val path: songer_alj/val-* - split: test path: songer_alj/test-* - config_name: songer_altdisp data_files: - split: train path: songer_altdisp/train-* - split: val path: songer_altdisp/val-* - split: test path: songer_altdisp/test-* - config_name: songer_amicus data_files: - split: train path: songer_amicus/train-* - split: val path: songer_amicus/val-* - split: test path: songer_amicus/test-* - config_name: songer_app_stid data_files: - split: train path: songer_app_stid/train-* - split: val path: songer_app_stid/val-* - split: test path: songer_app_stid/test-* - config_name: songer_appbus data_files: - split: train path: songer_appbus/train-* - split: val path: songer_appbus/val-* - split: test path: songer_appbus/test-* - config_name: songer_appel1_1_2 data_files: - split: train path: songer_appel1_1_2/train-* - split: val path: songer_appel1_1_2/val-* - split: test path: songer_appel1_1_2/test-* - config_name: songer_appel1_1_3 data_files: - split: train path: songer_appel1_1_3/train-* - split: val path: songer_appel1_1_3/val-* - split: test path: songer_appel1_1_3/test-* - config_name: songer_appel1_1_4 data_files: - split: train path: songer_appel1_1_4/train-* - split: val path: songer_appel1_1_4/val-* - split: test path: songer_appel1_1_4/test-* - config_name: songer_appel1_2_2 data_files: - split: train path: songer_appel1_2_2/train-* - split: val path: songer_appel1_2_2/val-* - split: test path: songer_appel1_2_2/test-* - config_name: songer_appel1_2_3 data_files: - split: train path: songer_appel1_2_3/train-* - split: val path: songer_appel1_2_3/val-* - split: test path: songer_appel1_2_3/test-* - config_name: songer_appel1_3_2 data_files: - split: train path: songer_appel1_3_2/train-* - split: val path: songer_appel1_3_2/val-* - split: test path: songer_appel1_3_2/test-* - config_name: songer_appel1_3_3 data_files: - split: train path: songer_appel1_3_3/train-* - split: val path: songer_appel1_3_3/val-* - split: test path: songer_appel1_3_3/test-* - config_name: songer_appel1_4_2 data_files: - split: train path: songer_appel1_4_2/train-* - split: val path: songer_appel1_4_2/val-* - split: test path: songer_appel1_4_2/test-* - config_name: songer_appel1_4_3 data_files: - split: train path: songer_appel1_4_3/train-* - split: val path: songer_appel1_4_3/val-* - split: test path: songer_appel1_4_3/test-* - config_name: songer_appel1_5_2 data_files: - split: train path: songer_appel1_5_2/train-* - split: val path: songer_appel1_5_2/val-* - split: test path: songer_appel1_5_2/test-* - config_name: songer_appel1_5_3 data_files: - split: train path: songer_appel1_5_3/train-* - split: val path: songer_appel1_5_3/val-* - split: test path: songer_appel1_5_3/test-* - config_name: songer_appel1_7_2 data_files: - split: train path: songer_appel1_7_2/train-* - split: val path: songer_appel1_7_2/val-* - split: test path: songer_appel1_7_2/test-* - config_name: songer_appel1_7_3 data_files: - split: train path: songer_appel1_7_3/train-* - split: val path: songer_appel1_7_3/val-* - split: test path: songer_appel1_7_3/test-* - config_name: songer_appel1_7_4 data_files: - split: train path: songer_appel1_7_4/train-* - split: val path: songer_appel1_7_4/val-* - split: test path: songer_appel1_7_4/test-* - config_name: songer_appel1_7_5 data_files: - split: train path: songer_appel1_7_5/train-* - split: val path: songer_appel1_7_5/val-* - split: test path: songer_appel1_7_5/test-* - config_name: songer_appel1_8_2 data_files: - split: train path: songer_appel1_8_2/train-* - split: val path: songer_appel1_8_2/val-* - split: test path: songer_appel1_8_2/test-* - config_name: songer_appel1_8_3 data_files: - split: train path: songer_appel1_8_3/train-* - split: val path: songer_appel1_8_3/val-* - split: test path: songer_appel1_8_3/test-* - config_name: songer_appel2_1_2 data_files: - split: train path: songer_appel2_1_2/train-* - split: val path: songer_appel2_1_2/val-* - split: test path: songer_appel2_1_2/test-* - config_name: songer_appel2_1_3 data_files: - split: train path: songer_appel2_1_3/train-* - split: val path: songer_appel2_1_3/val-* - split: test path: songer_appel2_1_3/test-* - config_name: songer_appel2_1_4 data_files: - split: train path: songer_appel2_1_4/train-* - split: val path: songer_appel2_1_4/val-* - split: test path: songer_appel2_1_4/test-* - config_name: songer_appel2_2_2 data_files: - split: train path: songer_appel2_2_2/train-* - split: val path: songer_appel2_2_2/val-* - split: test path: songer_appel2_2_2/test-* - config_name: songer_appel2_2_3 data_files: - split: train path: songer_appel2_2_3/train-* - split: val path: songer_appel2_2_3/val-* - split: test path: songer_appel2_2_3/test-* - config_name: songer_appel2_3_2 data_files: - split: train path: songer_appel2_3_2/train-* - split: val path: songer_appel2_3_2/val-* - split: test path: songer_appel2_3_2/test-* - config_name: songer_appel2_3_3 data_files: - split: train path: songer_appel2_3_3/train-* - split: val path: songer_appel2_3_3/val-* - split: test path: songer_appel2_3_3/test-* - config_name: songer_appel2_4_2 data_files: - split: train path: songer_appel2_4_2/train-* - split: val path: songer_appel2_4_2/val-* - split: test path: songer_appel2_4_2/test-* - config_name: songer_appel2_4_3 data_files: - split: train path: songer_appel2_4_3/train-* - split: val path: songer_appel2_4_3/val-* - split: test path: songer_appel2_4_3/test-* - config_name: songer_appel2_5_2 data_files: - split: train path: songer_appel2_5_2/train-* - split: val path: songer_appel2_5_2/val-* - split: test path: songer_appel2_5_2/test-* - config_name: songer_appel2_5_3 data_files: - split: train path: songer_appel2_5_3/train-* - split: val path: songer_appel2_5_3/val-* - split: test path: songer_appel2_5_3/test-* - config_name: songer_appel2_7_2 data_files: - split: train path: songer_appel2_7_2/train-* - split: val path: songer_appel2_7_2/val-* - split: test path: songer_appel2_7_2/test-* - config_name: songer_appel2_7_3 data_files: - split: train path: songer_appel2_7_3/train-* - split: val path: songer_appel2_7_3/val-* - split: test path: songer_appel2_7_3/test-* - config_name: songer_appel2_7_4 data_files: - split: train path: songer_appel2_7_4/train-* - split: val path: songer_appel2_7_4/val-* - split: test path: songer_appel2_7_4/test-* - config_name: songer_appel2_7_5 data_files: - split: train path: songer_appel2_7_5/train-* - split: val path: songer_appel2_7_5/val-* - split: test path: songer_appel2_7_5/test-* - config_name: songer_appel2_8_2 data_files: - split: train path: songer_appel2_8_2/train-* - split: val path: songer_appel2_8_2/val-* - split: test path: songer_appel2_8_2/test-* - config_name: songer_appel2_8_3 data_files: - split: train path: songer_appel2_8_3/train-* - split: val path: songer_appel2_8_3/val-* - split: test path: songer_appel2_8_3/test-* - config_name: songer_appfed data_files: - split: train path: songer_appfed/train-* - split: val path: songer_appfed/val-* - split: test path: songer_appfed/test-* - config_name: songer_appfiduc data_files: - split: train path: songer_appfiduc/train-* - split: val path: songer_appfiduc/val-* - split: test path: songer_appfiduc/test-* - config_name: songer_applfrom data_files: - split: train path: songer_applfrom/train-* - split: val path: songer_applfrom/val-* - split: test path: songer_applfrom/test-* - config_name: songer_appnatpr data_files: - split: train path: songer_appnatpr/train-* - split: val path: songer_appnatpr/val-* - split: test path: songer_appnatpr/test-* - config_name: songer_appnonp data_files: - split: train path: songer_appnonp/train-* - split: val path: songer_appnonp/val-* - split: test path: songer_appnonp/test-* - config_name: songer_appstate data_files: - split: train path: songer_appstate/train-* - split: val path: songer_appstate/val-* - split: test path: songer_appstate/test-* - config_name: songer_appsubst data_files: - split: train path: songer_appsubst/train-* - split: val path: songer_appsubst/val-* - split: test path: songer_appsubst/test-* - config_name: songer_attyfee data_files: - split: train path: songer_attyfee/train-* - split: val path: songer_attyfee/val-* - split: test path: songer_attyfee/test-* - config_name: songer_bank_app1 data_files: - split: train path: songer_bank_app1/train-* - split: val path: songer_bank_app1/val-* - split: test path: songer_bank_app1/test-* - config_name: songer_bank_app2 data_files: - split: train path: songer_bank_app2/train-* - split: val path: songer_bank_app2/val-* - split: test path: songer_bank_app2/test-* - config_name: songer_bank_r1 data_files: - split: train path: songer_bank_r1/train-* - split: val path: songer_bank_r1/val-* - split: test path: songer_bank_r1/test-* - config_name: songer_bank_r2 data_files: - split: train path: songer_bank_r2/train-* - split: val path: songer_bank_r2/val-* - split: test path: songer_bank_r2/test-* - config_name: songer_capric data_files: - split: train path: songer_capric/train-* - split: val path: songer_capric/val-* - split: test path: songer_capric/test-* - config_name: songer_casetyp1_1-2 data_files: - split: train path: songer_casetyp1_1-2/train-* - split: val path: songer_casetyp1_1-2/val-* - split: test path: songer_casetyp1_1-2/test-* - config_name: songer_casetyp1_1-3-1 data_files: - split: train path: songer_casetyp1_1-3-1/train-* - split: val path: songer_casetyp1_1-3-1/val-* - split: test path: songer_casetyp1_1-3-1/test-* - config_name: songer_casetyp1_1-3-2 data_files: - split: train path: songer_casetyp1_1-3-2/train-* - split: val path: songer_casetyp1_1-3-2/val-* - split: test path: songer_casetyp1_1-3-2/test-* - config_name: songer_casetyp1_1-3-3 data_files: - split: train path: songer_casetyp1_1-3-3/train-* - split: val path: songer_casetyp1_1-3-3/val-* - split: test path: songer_casetyp1_1-3-3/test-* - config_name: songer_casetyp1_2-2 data_files: - split: train path: songer_casetyp1_2-2/train-* - split: val path: songer_casetyp1_2-2/val-* - split: test path: songer_casetyp1_2-2/test-* - config_name: songer_casetyp1_2-3-1 data_files: - split: train path: songer_casetyp1_2-3-1/train-* - split: val path: songer_casetyp1_2-3-1/val-* - split: test path: songer_casetyp1_2-3-1/test-* - config_name: songer_casetyp1_2-3-2 data_files: - split: train path: songer_casetyp1_2-3-2/train-* - split: val path: songer_casetyp1_2-3-2/val-* - split: test path: songer_casetyp1_2-3-2/test-* - config_name: songer_casetyp1_2-3-3 data_files: - split: train path: songer_casetyp1_2-3-3/train-* - split: val path: songer_casetyp1_2-3-3/val-* - split: test path: songer_casetyp1_2-3-3/test-* - config_name: songer_casetyp1_3-2 data_files: - split: train path: songer_casetyp1_3-2/train-* - split: val path: songer_casetyp1_3-2/val-* - split: test path: songer_casetyp1_3-2/test-* - config_name: songer_casetyp1_3-3-1 data_files: - split: train path: songer_casetyp1_3-3-1/train-* - split: val path: songer_casetyp1_3-3-1/val-* - split: test path: songer_casetyp1_3-3-1/test-* - config_name: songer_casetyp1_3-3-2 data_files: - split: train path: songer_casetyp1_3-3-2/train-* - split: val path: songer_casetyp1_3-3-2/val-* - split: test path: songer_casetyp1_3-3-2/test-* - config_name: songer_casetyp1_4-3 data_files: - split: train path: songer_casetyp1_4-3/train-* - split: val path: songer_casetyp1_4-3/val-* - split: test path: songer_casetyp1_4-3/test-* - config_name: songer_casetyp1_5-3 data_files: - split: train path: songer_casetyp1_5-3/train-* - split: val path: songer_casetyp1_5-3/val-* - split: test path: songer_casetyp1_5-3/test-* - config_name: songer_casetyp1_6-3 data_files: - split: train path: songer_casetyp1_6-3/train-* - split: val path: songer_casetyp1_6-3/val-* - split: test path: songer_casetyp1_6-3/test-* - config_name: songer_casetyp1_7-2 data_files: - split: train path: songer_casetyp1_7-2/train-* - split: val path: songer_casetyp1_7-2/val-* - split: test path: songer_casetyp1_7-2/test-* - config_name: songer_casetyp1_7-3-1 data_files: - split: train path: songer_casetyp1_7-3-1/train-* - split: val path: songer_casetyp1_7-3-1/val-* - split: test path: songer_casetyp1_7-3-1/test-* - config_name: songer_casetyp1_7-3-2 data_files: - split: train path: songer_casetyp1_7-3-2/train-* - split: val path: songer_casetyp1_7-3-2/val-* - split: test path: songer_casetyp1_7-3-2/test-* - config_name: songer_casetyp1_7-3-3 data_files: - split: train path: songer_casetyp1_7-3-3/train-* - split: val path: songer_casetyp1_7-3-3/val-* - split: test path: songer_casetyp1_7-3-3/test-* - config_name: songer_casetyp1_7-3-4 data_files: - split: train path: songer_casetyp1_7-3-4/train-* - split: val path: songer_casetyp1_7-3-4/val-* - split: test path: songer_casetyp1_7-3-4/test-* - config_name: songer_casetyp1_7-3-5 data_files: - split: train path: songer_casetyp1_7-3-5/train-* - split: val path: songer_casetyp1_7-3-5/val-* - split: test path: songer_casetyp1_7-3-5/test-* - config_name: songer_casetyp1_7-3-6 data_files: - split: train path: songer_casetyp1_7-3-6/train-* - split: val path: songer_casetyp1_7-3-6/val-* - split: test path: songer_casetyp1_7-3-6/test-* - config_name: songer_casetyp1_9-3 data_files: - split: train path: songer_casetyp1_9-3/train-* - split: val path: songer_casetyp1_9-3/val-* - split: test path: songer_casetyp1_9-3/test-* - config_name: songer_casetyp2_geniss data_files: - split: train path: songer_casetyp2_geniss/train-* - split: val path: songer_casetyp2_geniss/val-* - split: test path: songer_casetyp2_geniss/test-* - config_name: songer_circuit data_files: - split: train path: songer_circuit/train-* - split: val path: songer_circuit/val-* - split: test path: songer_circuit/test-* - config_name: songer_civproc1 data_files: - split: train path: songer_civproc1/train-* - split: val path: songer_civproc1/val-* - split: test path: songer_civproc1/test-* - config_name: songer_civproc2 data_files: - split: train path: songer_civproc2/train-* - split: val path: songer_civproc2/val-* - split: test path: songer_civproc2/test-* - config_name: songer_classact data_files: - split: train path: songer_classact/train-* - split: val path: songer_classact/val-* - split: test path: songer_classact/test-* - config_name: songer_comment data_files: - split: train path: songer_comment/train-* - split: val path: songer_comment/val-* - split: test path: songer_comment/test-* - config_name: songer_concur data_files: - split: train path: songer_concur/train-* - split: val path: songer_concur/val-* - split: test path: songer_concur/test-* - config_name: songer_confess data_files: - split: train path: songer_confess/train-* - split: val path: songer_confess/val-* - split: test path: songer_confess/test-* - config_name: songer_const1 data_files: - split: train path: songer_const1/train-* - split: val path: songer_const1/val-* - split: test path: songer_const1/test-* - config_name: songer_const2 data_files: - split: train path: songer_const2/train-* - split: val path: songer_const2/val-* - split: test path: songer_const2/test-* - config_name: songer_constit data_files: - split: train path: songer_constit/train-* - split: val path: songer_constit/val-* - split: test path: songer_constit/test-* - config_name: songer_counsel data_files: - split: train path: songer_counsel/train-* - split: val path: songer_counsel/val-* - split: test path: songer_counsel/test-* - config_name: songer_counsel1 data_files: - split: train path: songer_counsel1/train-* - split: val path: songer_counsel1/val-* - split: test path: songer_counsel1/test-* - config_name: songer_counsel2 data_files: - split: train path: songer_counsel2/train-* - split: val path: songer_counsel2/val-* - split: test path: songer_counsel2/test-* - config_name: songer_crmproc1 data_files: - split: train path: songer_crmproc1/train-* - split: val path: songer_crmproc1/val-* - split: test path: songer_crmproc1/test-* - config_name: songer_crmproc2 data_files: - split: train path: songer_crmproc2/train-* - split: val path: songer_crmproc2/val-* - split: test path: songer_crmproc2/test-* - config_name: songer_crossapp data_files: - split: train path: songer_crossapp/train-* - split: val path: songer_crossapp/val-* - split: test path: songer_crossapp/test-* - config_name: songer_deathpen data_files: - split: train path: songer_deathpen/train-* - split: val path: songer_deathpen/val-* - split: test path: songer_deathpen/test-* - config_name: songer_decuncon data_files: - split: train path: songer_decuncon/train-* - split: val path: songer_decuncon/val-* - split: test path: songer_decuncon/test-* - config_name: songer_denovo data_files: - split: train path: songer_denovo/train-* - split: val path: songer_denovo/val-* - split: test path: songer_denovo/test-* - config_name: songer_direct1 data_files: - split: train path: songer_direct1/train-* - split: val path: songer_direct1/val-* - split: test path: songer_direct1/test-* - config_name: songer_direct2 data_files: - split: train path: songer_direct2/train-* - split: val path: songer_direct2/val-* - split: test path: songer_direct2/test-* - config_name: songer_discover data_files: - split: train path: songer_discover/train-* - split: val path: songer_discover/val-* - split: test path: songer_discover/test-* - config_name: songer_dissent data_files: - split: train path: songer_dissent/train-* - split: val path: songer_dissent/val-* - split: test path: songer_dissent/test-* - config_name: songer_district data_files: - split: train path: songer_district/train-* - split: val path: songer_district/val-* - split: test path: songer_district/test-* - config_name: songer_diverse data_files: - split: train path: songer_diverse/train-* - split: val path: songer_diverse/val-* - split: test path: songer_diverse/test-* - config_name: songer_dueproc data_files: - split: train path: songer_dueproc/train-* - split: val path: songer_dueproc/val-* - split: test path: songer_dueproc/test-* - config_name: songer_entrap data_files: - split: train path: songer_entrap/train-* - split: val path: songer_entrap/val-* - split: test path: songer_entrap/test-* - config_name: songer_erron data_files: - split: train path: songer_erron/train-* - split: val path: songer_erron/val-* - split: test path: songer_erron/test-* - config_name: songer_execord data_files: - split: train path: songer_execord/train-* - split: val path: songer_execord/val-* - split: test path: songer_execord/test-* - config_name: songer_exhaust data_files: - split: train path: songer_exhaust/train-* - split: val path: songer_exhaust/val-* - split: test path: songer_exhaust/test-* - config_name: songer_fedlaw data_files: - split: train path: songer_fedlaw/train-* - split: val path: songer_fedlaw/val-* - split: test path: songer_fedlaw/test-* - config_name: songer_fedvst data_files: - split: train path: songer_fedvst/train-* - split: val path: songer_fedvst/val-* - split: test path: songer_fedvst/test-* - config_name: songer_foreign data_files: - split: train path: songer_foreign/train-* - split: val path: songer_foreign/val-* - split: test path: songer_foreign/test-* - config_name: songer_freeinfo data_files: - split: train path: songer_freeinfo/train-* - split: val path: songer_freeinfo/val-* - split: test path: songer_freeinfo/test-* - config_name: songer_frivapp data_files: - split: train path: songer_frivapp/train-* - split: val path: songer_frivapp/val-* - split: test path: songer_frivapp/test-* - config_name: songer_frivol data_files: - split: train path: songer_frivol/train-* - split: val path: songer_frivol/val-* - split: test path: songer_frivol/test-* - config_name: songer_genapel1 data_files: - split: train path: songer_genapel1/train-* - split: val path: songer_genapel1/val-* - split: test path: songer_genapel1/test-* - config_name: songer_genapel2 data_files: - split: train path: songer_genapel2/train-* - split: val path: songer_genapel2/val-* - split: test path: songer_genapel2/test-* - config_name: songer_geniss data_files: - split: train path: songer_geniss/train-* - split: val path: songer_geniss/val-* - split: test path: songer_geniss/test-* - config_name: songer_genresp1 data_files: - split: train path: songer_genresp1/train-* - split: val path: songer_genresp1/val-* - split: test path: songer_genresp1/test-* - config_name: songer_genresp2 data_files: - split: train path: songer_genresp2/train-* - split: val path: songer_genresp2/val-* - split: test path: songer_genresp2/test-* - config_name: songer_genstand data_files: - split: train path: songer_genstand/train-* - split: val path: songer_genstand/val-* - split: test path: songer_genstand/test-* - config_name: songer_habeas data_files: - split: train path: songer_habeas/train-* - split: val path: songer_habeas/val-* - split: test path: songer_habeas/test-* - config_name: songer_immunity data_files: - split: train path: songer_immunity/train-* - split: val path: songer_immunity/val-* - split: test path: songer_immunity/test-* - config_name: songer_improper data_files: - split: train path: songer_improper/train-* - split: val path: songer_improper/val-* - split: test path: songer_improper/test-* - config_name: songer_indict data_files: - split: train path: songer_indict/train-* - split: val path: songer_indict/val-* - split: test path: songer_indict/test-* - config_name: songer_indigent data_files: - split: train path: songer_indigent/train-* - split: val path: songer_indigent/val-* - split: test path: songer_indigent/test-* - config_name: songer_initiate data_files: - split: train path: songer_initiate/train-* - split: val path: songer_initiate/val-* - split: test path: songer_initiate/test-* - config_name: songer_injunct data_files: - split: train path: songer_injunct/train-* - split: val path: songer_injunct/val-* - split: test path: songer_injunct/test-* - config_name: songer_insane data_files: - split: train path: songer_insane/train-* - split: val path: songer_insane/val-* - split: test path: songer_insane/test-* - config_name: songer_int_law data_files: - split: train path: songer_int_law/train-* - split: val path: songer_int_law/val-* - split: test path: songer_int_law/test-* - config_name: songer_interven data_files: - split: train path: songer_interven/train-* - split: val path: songer_interven/val-* - split: test path: songer_interven/test-* - config_name: songer_judgdisc data_files: - split: train path: songer_judgdisc/train-* - split: val path: songer_judgdisc/val-* - split: test path: songer_judgdisc/test-* - config_name: songer_judrev data_files: - split: train path: songer_judrev/train-* - split: val path: songer_judrev/val-* - split: test path: songer_judrev/test-* - config_name: songer_jurisdiction data_files: - split: train path: songer_jurisdiction/train-* - split: val path: songer_jurisdiction/val-* - split: test path: songer_jurisdiction/test-* - config_name: songer_juryinst data_files: - split: train path: songer_juryinst/train-* - split: val path: songer_juryinst/val-* - split: test path: songer_juryinst/test-* - config_name: songer_late data_files: - split: train path: songer_late/train-* - split: val path: songer_late/val-* - split: test path: songer_late/test-* - config_name: songer_majvotes data_files: - split: train path: songer_majvotes/train-* - split: val path: songer_majvotes/val-* - split: test path: songer_majvotes/test-* - config_name: songer_method data_files: - split: train path: songer_method/train-* - split: val path: songer_method/val-* - split: test path: songer_method/test-* - config_name: songer_mootness data_files: - split: train path: songer_mootness/train-* - split: val path: songer_mootness/val-* - split: test path: songer_mootness/test-* - config_name: songer_notice data_files: - split: train path: songer_notice/train-* - split: val path: songer_notice/val-* - split: test path: songer_notice/test-* - config_name: songer_numappel data_files: - split: train path: songer_numappel/train-* - split: val path: songer_numappel/val-* - split: test path: songer_numappel/test-* - config_name: songer_numresp data_files: - split: train path: songer_numresp/train-* - split: val path: songer_numresp/val-* - split: test path: songer_numresp/test-* - config_name: songer_opinstat data_files: - split: train path: songer_opinstat/train-* - split: val path: songer_opinstat/val-* - split: test path: songer_opinstat/test-* - config_name: songer_origin data_files: - split: train path: songer_origin/train-* - split: val path: songer_origin/val-* - split: test path: songer_origin/test-* - config_name: songer_othadmis data_files: - split: train path: songer_othadmis/train-* - split: val path: songer_othadmis/val-* - split: test path: songer_othadmis/test-* - config_name: songer_othappth data_files: - split: train path: songer_othappth/train-* - split: val path: songer_othappth/val-* - split: test path: songer_othappth/test-* - config_name: songer_othcrim data_files: - split: train path: songer_othcrim/train-* - split: val path: songer_othcrim/val-* - split: test path: songer_othcrim/test-* - config_name: songer_othjury data_files: - split: train path: songer_othjury/train-* - split: val path: songer_othjury/val-* - split: test path: songer_othjury/test-* - config_name: songer_oththres data_files: - split: train path: songer_oththres/train-* - split: val path: songer_oththres/val-* - split: test path: songer_oththres/test-* - config_name: songer_plea data_files: - split: train path: songer_plea/train-* - split: val path: songer_plea/val-* - split: test path: songer_plea/test-* - config_name: songer_polquest data_files: - split: train path: songer_polquest/train-* - split: val path: songer_polquest/val-* - split: test path: songer_polquest/test-* - config_name: songer_post_trl data_files: - split: train path: songer_post_trl/train-* - split: val path: songer_post_trl/val-* - split: test path: songer_post_trl/test-* - config_name: songer_prejud data_files: - split: train path: songer_prejud/train-* - split: val path: songer_prejud/val-* - split: test path: songer_prejud/test-* - config_name: songer_pretrial data_files: - split: train path: songer_pretrial/train-* - split: val path: songer_pretrial/val-* - split: test path: songer_pretrial/test-* - config_name: songer_procdis data_files: - split: train path: songer_procdis/train-* - split: val path: songer_procdis/val-* - split: test path: songer_procdis/test-* - config_name: songer_procedur data_files: - split: train path: songer_procedur/train-* - split: val path: songer_procedur/val-* - split: test path: songer_procedur/test-* - config_name: songer_r_bus data_files: - split: train path: songer_r_bus/train-* - split: val path: songer_r_bus/val-* - split: test path: songer_r_bus/test-* - config_name: songer_r_fed data_files: - split: train path: songer_r_fed/train-* - split: val path: songer_r_fed/val-* - split: test path: songer_r_fed/test-* - config_name: songer_r_fiduc data_files: - split: train path: songer_r_fiduc/train-* - split: val path: songer_r_fiduc/val-* - split: test path: songer_r_fiduc/test-* - config_name: songer_r_natpr data_files: - split: train path: songer_r_natpr/train-* - split: val path: songer_r_natpr/val-* - split: test path: songer_r_natpr/test-* - config_name: songer_r_nonp data_files: - split: train path: songer_r_nonp/train-* - split: val path: songer_r_nonp/val-* - split: test path: songer_r_nonp/test-* - config_name: songer_r_state data_files: - split: train path: songer_r_state/train-* - split: val path: songer_r_state/val-* - split: test path: songer_r_state/test-* - config_name: songer_r_stid data_files: - split: train path: songer_r_stid/train-* - split: val path: songer_r_stid/val-* - split: test path: songer_r_stid/test-* - config_name: songer_r_subst data_files: - split: train path: songer_r_subst/train-* - split: val path: songer_r_subst/val-* - split: test path: songer_r_subst/test-* - config_name: songer_realapp data_files: - split: train path: songer_realapp/train-* - split: val path: songer_realapp/val-* - split: test path: songer_realapp/test-* - config_name: songer_realresp data_files: - split: train path: songer_realresp/train-* - split: val path: songer_realresp/val-* - split: test path: songer_realresp/test-* - config_name: songer_record data_files: - split: train path: songer_record/train-* - split: val path: songer_record/val-* - split: test path: songer_record/test-* - config_name: songer_respond1_1_2 data_files: - split: train path: songer_respond1_1_2/train-* - split: val path: songer_respond1_1_2/val-* - split: test path: songer_respond1_1_2/test-* - config_name: songer_respond1_1_3 data_files: - split: train path: songer_respond1_1_3/train-* - split: val path: songer_respond1_1_3/val-* - split: test path: songer_respond1_1_3/test-* - config_name: songer_respond1_1_4 data_files: - split: train path: songer_respond1_1_4/train-* - split: val path: songer_respond1_1_4/val-* - split: test path: songer_respond1_1_4/test-* - config_name: songer_respond1_2_2 data_files: - split: train path: songer_respond1_2_2/train-* - split: val path: songer_respond1_2_2/val-* - split: test path: songer_respond1_2_2/test-* - config_name: songer_respond1_2_3 data_files: - split: train path: songer_respond1_2_3/train-* - split: val path: songer_respond1_2_3/val-* - split: test path: songer_respond1_2_3/test-* - config_name: songer_respond1_3_2 data_files: - split: train path: songer_respond1_3_2/train-* - split: val path: songer_respond1_3_2/val-* - split: test path: songer_respond1_3_2/test-* - config_name: songer_respond1_3_3 data_files: - split: train path: songer_respond1_3_3/train-* - split: val path: songer_respond1_3_3/val-* - split: test path: songer_respond1_3_3/test-* - config_name: songer_respond1_4_2 data_files: - split: train path: songer_respond1_4_2/train-* - split: val path: songer_respond1_4_2/val-* - split: test path: songer_respond1_4_2/test-* - config_name: songer_respond1_4_3 data_files: - split: train path: songer_respond1_4_3/train-* - split: val path: songer_respond1_4_3/val-* - split: test path: songer_respond1_4_3/test-* - config_name: songer_respond1_5_2 data_files: - split: train path: songer_respond1_5_2/train-* - split: val path: songer_respond1_5_2/val-* - split: test path: songer_respond1_5_2/test-* - config_name: songer_respond1_5_3 data_files: - split: train path: songer_respond1_5_3/train-* - split: val path: songer_respond1_5_3/val-* - split: test path: songer_respond1_5_3/test-* - config_name: songer_respond1_7_2 data_files: - split: train path: songer_respond1_7_2/train-* - split: val path: songer_respond1_7_2/val-* - split: test path: songer_respond1_7_2/test-* - config_name: songer_respond1_7_3 data_files: - split: train path: songer_respond1_7_3/train-* - split: val path: songer_respond1_7_3/val-* - split: test path: songer_respond1_7_3/test-* - config_name: songer_respond1_7_4 data_files: - split: train path: songer_respond1_7_4/train-* - split: val path: songer_respond1_7_4/val-* - split: test path: songer_respond1_7_4/test-* - config_name: songer_respond1_7_5 data_files: - split: train path: songer_respond1_7_5/train-* - split: val path: songer_respond1_7_5/val-* - split: test path: songer_respond1_7_5/test-* - config_name: songer_respond1_8_2 data_files: - split: train path: songer_respond1_8_2/train-* - split: val path: songer_respond1_8_2/val-* - split: test path: songer_respond1_8_2/test-* - config_name: songer_respond1_8_3 data_files: - split: train path: songer_respond1_8_3/train-* - split: val path: songer_respond1_8_3/val-* - split: test path: songer_respond1_8_3/test-* - config_name: songer_respond2_1_2 data_files: - split: train path: songer_respond2_1_2/train-* - split: val path: songer_respond2_1_2/val-* - split: test path: songer_respond2_1_2/test-* - config_name: songer_respond2_1_3 data_files: - split: train path: songer_respond2_1_3/train-* - split: val path: songer_respond2_1_3/val-* - split: test path: songer_respond2_1_3/test-* - config_name: songer_respond2_1_4 data_files: - split: train path: songer_respond2_1_4/train-* - split: val path: songer_respond2_1_4/val-* - split: test path: songer_respond2_1_4/test-* - config_name: songer_respond2_2_2 data_files: - split: train path: songer_respond2_2_2/train-* - split: val path: songer_respond2_2_2/val-* - split: test path: songer_respond2_2_2/test-* - config_name: songer_respond2_2_3 data_files: - split: train path: songer_respond2_2_3/train-* - split: val path: songer_respond2_2_3/val-* - split: test path: songer_respond2_2_3/test-* - config_name: songer_respond2_3_2 data_files: - split: train path: songer_respond2_3_2/train-* - split: val path: songer_respond2_3_2/val-* - split: test path: songer_respond2_3_2/test-* - config_name: songer_respond2_3_3 data_files: - split: train path: songer_respond2_3_3/train-* - split: val path: songer_respond2_3_3/val-* - split: test path: songer_respond2_3_3/test-* - config_name: songer_respond2_4_2 data_files: - split: train path: songer_respond2_4_2/train-* - split: val path: songer_respond2_4_2/val-* - split: test path: songer_respond2_4_2/test-* - config_name: songer_respond2_4_3 data_files: - split: train path: songer_respond2_4_3/train-* - split: val path: songer_respond2_4_3/val-* - split: test path: songer_respond2_4_3/test-* - config_name: songer_respond2_5_2 data_files: - split: train path: songer_respond2_5_2/train-* - split: val path: songer_respond2_5_2/val-* - split: test path: songer_respond2_5_2/test-* - config_name: songer_respond2_5_3 data_files: - split: train path: songer_respond2_5_3/train-* - split: val path: songer_respond2_5_3/val-* - split: test path: songer_respond2_5_3/test-* - config_name: songer_respond2_7_2 data_files: - split: train path: songer_respond2_7_2/train-* - split: val path: songer_respond2_7_2/val-* - split: test path: songer_respond2_7_2/test-* - config_name: songer_respond2_7_3 data_files: - split: train path: songer_respond2_7_3/train-* - split: val path: songer_respond2_7_3/val-* - split: test path: songer_respond2_7_3/test-* - config_name: songer_respond2_7_4 data_files: - split: train path: songer_respond2_7_4/train-* - split: val path: songer_respond2_7_4/val-* - split: test path: songer_respond2_7_4/test-* - config_name: songer_respond2_7_5 data_files: - split: train path: songer_respond2_7_5/train-* - split: val path: songer_respond2_7_5/val-* - split: test path: songer_respond2_7_5/test-* - config_name: songer_respond2_8_2 data_files: - split: train path: songer_respond2_8_2/train-* - split: val path: songer_respond2_8_2/val-* - split: test path: songer_respond2_8_2/test-* - config_name: songer_respond2_8_3 data_files: - split: train path: songer_respond2_8_3/train-* - split: val path: songer_respond2_8_3/val-* - split: test path: songer_respond2_8_3/test-* - config_name: songer_rtcouns data_files: - split: train path: songer_rtcouns/train-* - split: val path: songer_rtcouns/val-* - split: test path: songer_rtcouns/test-* - config_name: songer_search data_files: - split: train path: songer_search/train-* - split: val path: songer_search/val-* - split: test path: songer_search/test-* - config_name: songer_sentence data_files: - split: train path: songer_sentence/train-* - split: val path: songer_sentence/val-* - split: test path: songer_sentence/test-* - config_name: songer_source data_files: - split: train path: songer_source/train-* - split: val path: songer_source/val-* - split: test path: songer_source/test-* - config_name: songer_st_v_st data_files: - split: train path: songer_st_v_st/train-* - split: val path: songer_st_v_st/val-* - split: test path: songer_st_v_st/test-* - config_name: songer_standing data_files: - split: train path: songer_standing/train-* - split: val path: songer_standing/val-* - split: test path: songer_standing/test-* - config_name: songer_state data_files: - split: train path: songer_state/train-* - split: val path: songer_state/val-* - split: test path: songer_state/test-* - config_name: songer_stateclaim data_files: - split: train path: songer_stateclaim/train-* - split: val path: songer_stateclaim/val-* - split: test path: songer_stateclaim/test-* - config_name: songer_stpolicy data_files: - split: train path: songer_stpolicy/train-* - split: val path: songer_stpolicy/val-* - split: test path: songer_stpolicy/test-* - config_name: songer_subevid data_files: - split: train path: songer_subevid/train-* - split: val path: songer_subevid/val-* - split: test path: songer_subevid/test-* - config_name: songer_suffic data_files: - split: train path: songer_suffic/train-* - split: val path: songer_suffic/val-* - split: test path: songer_suffic/test-* - config_name: songer_summary data_files: - split: train path: songer_summary/train-* - split: val path: songer_summary/val-* - split: test path: songer_summary/test-* - config_name: songer_timely data_files: - split: train path: songer_timely/train-* - split: val path: songer_timely/val-* - split: test path: songer_timely/test-* - config_name: songer_treat data_files: - split: train path: songer_treat/train-* - split: val path: songer_treat/val-* - split: test path: songer_treat/test-* - config_name: songer_trialpro data_files: - split: train path: songer_trialpro/train-* - split: val path: songer_trialpro/val-* - split: test path: songer_trialpro/test-* - config_name: songer_two_issues data_files: - split: train path: songer_two_issues/train-* - split: val path: songer_two_issues/val-* - split: test path: songer_two_issues/test-* - config_name: songer_typeiss data_files: - split: train path: songer_typeiss/train-* - split: val path: songer_typeiss/val-* - split: test path: songer_typeiss/test-* - config_name: songer_usc1 data_files: - split: train path: songer_usc1/train-* - split: val path: songer_usc1/val-* - split: test path: songer_usc1/test-* - config_name: songer_usc1sect data_files: - split: train path: songer_usc1sect/train-* - split: val path: songer_usc1sect/val-* - split: test path: songer_usc1sect/test-* - config_name: songer_usc2 data_files: - split: train path: songer_usc2/train-* - split: val path: songer_usc2/val-* - split: test path: songer_usc2/test-* - config_name: songer_usc2sect data_files: - split: train path: songer_usc2sect/train-* - split: val path: songer_usc2sect/val-* - split: test path: songer_usc2sect/test-* - config_name: songer_weightev data_files: - split: train path: songer_weightev/train-* - split: val path: songer_weightev/val-* - split: test path: songer_weightev/test-* - config_name: songer_whlaws data_files: - split: train path: songer_whlaws/train-* - split: val path: songer_whlaws/val-* - split: test path: songer_whlaws/test-* task_categories: - text-classification - question-answering - feature-extraction - zero-shot-classification language: - en pretty_name: Lawma legal classification tasks size_categories: - 100K<n<1M --- # Lawma legal classification tasks This repository contains the legal classification tasks from [Lawma](https://arxiv.org/abs/2407.16615). These tasks were derived from the [Supreme Court](http://scdb.wustl.edu/data.php) and [Songer Court of Appeals](www.songerproject.org/us-courts-of-appeals-databases.html) databases. See the project's [GitHub repository](https://github.com/socialfoundations/lawma) for more details. Please cite as: ``` @misc{dominguezolmedo2024lawmapowerspecializationlegal, title={Lawma: The Power of Specialization for Legal Tasks}, author={Ricardo Dominguez-Olmedo and Vedant Nanda and Rediet Abebe and Stefan Bechtold and Christoph Engel and Jens Frankenreiter and Krishna Gummadi and Moritz Hardt and Michael Livermore}, year={2024}, eprint={2407.16615}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.16615}, } ```

Voxel51/emnist-letters-tiny

Voxel51

--- annotations_creators: [] language: en size_categories: - 10K<n<100K task_categories: - image-classification task_ids: [] pretty_name: EMNIST-Letters-10k tags: - fiftyone - image - image-classification dataset_summary: '  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10000 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = load_from_hub("Voxel51/emnist-letters-tiny") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for EMNIST-Letters-10k <!-- Provide a quick summary of the dataset. --> A random subset of the train and test splits from the letters portion of [EMNIST](https://pytorch.org/vision/0.18/generated/torchvision.datasets.EMNIST.html)  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10000 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo from fiftyone.utils.huggingface import load_from_hub # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = load_from_hub("Voxel51/emnist-letters-tiny") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** en - **License:** [More Information Needed] ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Homepage:** https://www.nist.gov/itl/products-and-services/emnist-dataset - **Paper :** https://arxiv.org/abs/1702.05373v1 ## Citation **BibTeX:** ```bibtex @misc{cohen2017emnistextensionmnisthandwritten, title={EMNIST: an extension of MNIST to handwritten letters}, author={Gregory Cohen and Saeed Afshar and Jonathan Tapson and André van Schaik}, year={2017}, eprint={1702.05373}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/1702.05373}, } ``` ## Dataset Card Author [Jacob Marks](https://huggingface.co/jamarks)

TempoFunk/webvid-10M

TempoFunk

--- license: agpl-3.0 task_categories: - text-to-video - text-to-image - video-classification - image-classification language: - en size_categories: - 1M<n<10M ---

HuggingFaceH4/ultrachat_200k

HuggingFaceH4

--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - 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: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: | train_sft | test_sft | train_gen | test_gen | |:-------:|:-----------:|:-----:| :-----:| | 207865 | 23110 | 256032 | 28304 | The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

pixparse/cc3m-wds

pixparse

--- license: other license_name: conceptual-captions license_link: >- https://github.com/google-research-datasets/conceptual-captions/blob/master/LICENSE task_categories: - image-to-text size_categories: - 1M<n<10M --- # Dataset Card for Conceptual Captions (CC3M) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Conceptual Captions homepage](https://ai.google.com/research/ConceptualCaptions/) - **Repository:** [Conceptual Captions repository](https://github.com/google-research-datasets/conceptual-captions) - **Paper:** [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://www.aclweb.org/anthology/P18-1238/) - **Leaderboard:** [Conceptual Captions leaderboard](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard)https://ai.google.com/research/ConceptualCaptions/leaderboard?active_tab=leaderboard - **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com) ### Dataset Summary Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. ### Usage This instance of Conceptual Captions is in [webdataset](https://github.com/webdataset/webdataset/commits/main) .tar format. It can be used with webdataset library or upcoming releases of Hugging Face `datasets`. ...More Detail TBD ### Data Splits This dataset was downloaded using img2dataset. Images resized on download if shortest edge > 512 to shortest edge = 512. #### Train * `cc3m-train-*.tar` * Downloaded on 2021/12/22 * 576 shards, 2905954 (of 3318333) samples #### Validation * `cc3m-validation-*.tar` * Downloaded on 2023/12/13 (original validation set download in 2021 was corrupted) * 16 shards, 13443 (of 15840) samples ## Additional Information ### Dataset Curators Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut. ### Licensing Information The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. ### Citation Information ```bibtex @inproceedings{sharma2018conceptual, title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning}, author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu}, booktitle = {Proceedings of ACL}, year = {2018}, } ```