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davanstrien
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SinKove/synthetic_brain_mri
2023-09-03T17:10:57.000Z
[ "task_categories:image-classification", "size_categories:n<1K", "language:en", "license:openrail", "medical", "brain-data", "mri", "arxiv:2209.07162", "region:us" ]
SinKove
This dataset was obtained as part of the Generative Modelling project from the Artificial Medical Intelligence Group - AMIGO (https://amigos.ai/). It consists on of 1,000 synthetic T1w images sampled from generative models trained on data originally from the UK Biobank dataset (https://www.ukbiobank.ac.uk/).
@misc{pinaya2022brain, title={Brain Imaging Generation with Latent Diffusion Models}, author={Walter H. L. Pinaya and Petru-Daniel Tudosiu and Jessica Dafflon and Pedro F da Costa and Virginia Fernandez and Parashkev Nachev and Sebastien Ourselin and M. Jorge Cardoso}, year={2022}, eprint={2209.07162}, archivePrefix={arXiv}, primaryClass={eess.IV} }
null
3
3
--- license: openrail task_categories: - image-classification language: - en tags: - medical - brain-data - mri pretty_name: Brain imaging generation with Latent Diffusion Models size_categories: - n<1K --- # Dataset Card for Brain imaging generation with Latent Diffusion Models ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [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) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Amigo homepage](https://amigos.ai/) - **Paper:** [Brain imaging generation with Latent Diffusion Models](https://arxiv.org/abs/2209.07162) - **Point of Contact:** [Walter H. L. Pinaya](mailto:walter.diaz_sanz@kcl.ac.uk) ### Dataset Summary This dataset was obtained as part of the Generative Modelling project from the Artificial Medical Intelligence Group - AMIGO (https://amigos.ai/). It consists on of 1,000 synthetic T1w images sampled from generative models trained on data originally from the UK Biobank dataset (https://www.ukbiobank.ac.uk/). ### Languages The language in the dataset is English. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `prompt_age`: a float value used during the sampling to specify the age of the generated brain image (defined in years) - `prompt_sex`: a string used during the sampling to specify the sex ("M" for male and "F" for female) - `prompt_ventricular_volume`: a float whose value used during the sampling to specify the volume of ventricular cerebrospinal fluid (in mm^3; based on UKB Data-Field 25004) - `prompt_brain_volume`: a float whose value used during the sampling to specify the brain volume normalised for head size (in mm^3; based on UKB Data-Field 25009) ## 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] ### 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 ### Licensing Information The "Brain imaging generation with Latent Diffusion Models" dataset is released under the [OpenRAIL License](https://huggingface.co/blog/open_rail). ### Citation Information ``` @inproceedings{pinaya2022brain, title={Brain imaging generation with latent diffusion models}, author={Pinaya, Walter HL and Tudosiu, Petru-Daniel and Dafflon, Jessica and Da Costa, Pedro F and Fernandez, Virginia and Nachev, Parashkev and Ourselin, Sebastien and Cardoso, M Jorge}, booktitle={MICCAI Workshop on Deep Generative Models}, pages={117--126}, year={2022}, organization={Springer} } ``` ### Contributions Thanks to [@Warvito](https://github.com/Warvito) for adding this dataset.
Siddish/change-my-view-subreddit-cleaned
2023-09-02T16:00:46.000Z
[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "region:us" ]
Siddish
null
null
null
0
3
--- task_categories: - text-generation language: - en pretty_name: Opinionated LLM with r/CMV size_categories: - 1K<n<10K --- # Opinionated LLM
Twenty1/aws-lambda-developer-guide-docs
2023-09-03T15:08:57.000Z
[ "license:openrail", "region:us" ]
Twenty1
null
null
null
0
3
--- license: openrail ---
qqlu1992/Adobe_EntitySeg
2023-09-07T01:03:14.000Z
[ "region:us" ]
qqlu1992
null
null
null
2
3
--- viewer: false --- The images and pretrained-models used in the ICCV oral paper 'High-Quality Entity Segmentation'. The offical link is https://github.com/adobe-research/EntitySeg-Dataset. The code link is https://github.com/qqlu/Entity/tree/main/Entityv2. We noted that we do not own the copyright of the images. It is solely your responsibility to check the original licenses of the images before using them. Any use of the images are at your own discretion and risk.
frankier/multiscale_rt_critics_subsets
2023-10-04T06:16:28.000Z
[ "region:us" ]
frankier
null
null
null
0
3
--- dataset_info: - config_name: multiscale_rt_critics features: - name: movie_title dtype: string - name: publisher_name dtype: string - name: critic_name dtype: string - name: text dtype: string - name: review_score dtype: string - name: grade_type dtype: string - name: orig_num dtype: float32 - name: orig_denom dtype: float32 - name: includes_zero dtype: bool - name: label dtype: uint8 - name: scale_points dtype: uint8 - name: multiplier dtype: uint8 - name: task_ids dtype: uint32 splits: - name: train num_bytes: 4951005 num_examples: 23182 - name: test num_bytes: 1644530 num_examples: 7745 - name: validation num_bytes: 1646302 num_examples: 7731 download_size: 0 dataset_size: 8241837 - config_name: rt_critics_big_irregular_5 features: - name: movie_title dtype: string - name: publisher_name dtype: string - name: critic_name dtype: string - name: text dtype: string - name: review_score dtype: string - name: grade_type dtype: string - name: orig_num dtype: float32 - name: orig_denom dtype: float32 - name: includes_zero dtype: bool - name: label dtype: uint8 - name: scale_points dtype: uint8 - name: multiplier dtype: uint8 - name: task_ids dtype: uint32 - name: orig_group_id dtype: uint32 splits: - name: train num_bytes: 2336759 num_examples: 10312 - name: test num_bytes: 781228 num_examples: 3441 - name: validation num_bytes: 779150 num_examples: 3438 download_size: 1927630 dataset_size: 3897137 - config_name: rt_critics_by_critic_1000pl features: - name: movie_title dtype: string - name: publisher_name dtype: string - name: critic_name dtype: string - name: text dtype: string - name: review_score dtype: string - name: grade_type dtype: string - name: orig_num dtype: float32 - name: orig_denom dtype: float32 - name: includes_zero dtype: bool - name: label dtype: uint8 - name: scale_points dtype: uint8 - name: multiplier dtype: uint8 - name: task_ids dtype: uint32 - name: orig_group_id dtype: uint32 splits: - name: train num_bytes: 27083039 num_examples: 124055 - name: test num_bytes: 9049344 num_examples: 41406 - name: validation num_bytes: 9026209 num_examples: 41368 download_size: 22594175 dataset_size: 45158592 - config_name: rt_critics_by_critic_500pl features: - name: movie_title dtype: string - name: publisher_name dtype: string - name: critic_name dtype: string - name: text dtype: string - name: review_score dtype: string - name: grade_type dtype: string - name: orig_num dtype: float32 - name: orig_denom dtype: float32 - name: includes_zero dtype: bool - name: label dtype: uint8 - name: scale_points dtype: uint8 - name: multiplier dtype: uint8 - name: task_ids dtype: uint32 - name: orig_group_id dtype: uint32 splits: - name: train num_bytes: 41656780 num_examples: 189382 - name: test num_bytes: 13929707 num_examples: 63263 - name: validation num_bytes: 13917936 num_examples: 63157 download_size: 35087274 dataset_size: 69504423 - config_name: rt_critics_one features: - name: movie_title dtype: string - name: publisher_name dtype: string - name: critic_name dtype: string - name: text dtype: string - name: review_score dtype: string - name: grade_type dtype: string - name: orig_num dtype: float32 - name: orig_denom dtype: float32 - name: includes_zero dtype: bool - name: label dtype: uint8 - name: scale_points dtype: uint8 - name: multiplier dtype: uint8 splits: - name: train num_bytes: 988767 num_examples: 4606 - name: test num_bytes: 327725 num_examples: 1536 - name: validation num_bytes: 327038 num_examples: 1536 download_size: 951057 dataset_size: 1643530 configs: - config_name: multiscale_rt_critics data_files: - split: train path: multiscale_rt_critics/train-* - split: test path: multiscale_rt_critics/test-* - split: validation path: multiscale_rt_critics/validation-* - config_name: rt_critics_big_irregular_5 data_files: - split: train path: rt_critics_big_irregular_5/train-* - split: test path: rt_critics_big_irregular_5/test-* - split: validation path: rt_critics_big_irregular_5/validation-* - config_name: rt_critics_by_critic_1000pl data_files: - split: train path: rt_critics_by_critic_1000pl/train-* - split: test path: rt_critics_by_critic_1000pl/test-* - split: validation path: rt_critics_by_critic_1000pl/validation-* - config_name: rt_critics_by_critic_500pl data_files: - split: train path: rt_critics_by_critic_500pl/train-* - split: test path: rt_critics_by_critic_500pl/test-* - split: validation path: rt_critics_by_critic_500pl/validation-* - config_name: rt_critics_one data_files: - split: train path: rt_critics_one/train-* - split: test path: rt_critics_one/test-* - split: validation path: rt_critics_one/validation-* --- # Dataset Card for "multiscale_rt_critics_subsets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NITHUB-AI/Ehn-bible-bbc-gpt3.5
2023-09-04T23:20:38.000Z
[ "task_categories:text-classification", "task_categories:translation", "size_categories:10K<n<100K", "license:cc-by-4.0", "region:us" ]
NITHUB-AI
null
null
null
0
3
--- license: cc-by-4.0 task_categories: - text-classification - translation size_categories: - 10K<n<100K --- # Dataset Card for Ehn-Bible-BBC-GPT3.5 ## Dataset Description - **Repository:** https://huggingface.co/datasets/NITHUB-AI/Ehn-bible-bbc-gpt3.5/ - **Paper:** To be added - **Point of Contact:** fortuneadekogbe@gmail.com ### Dataset Summary This dataset card contains parallel Nigerian Pidgin and English sentences split into three files, namely: `train.csv`, `valid.csv` and `test.csv`. The original data was split in the ratio of 8:1:1 to obtain these files. ### Supported Tasks and Leaderboards - Language Translation - Language Identification ### Languages - English - Nigerian Pidgin ## Dataset Structure ### Data Instances ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6212bf377b3af3ccd458002a/dnL7SM_Lvom534sqJBGGE.png) ### Data Fields - English: contains sentences in the English language - Pidgin: contains corresponding sentences in Nigerian Pidgin language ### Data Splits - train (80%) - validation (10%) - test (10%) ## Dataset Creation This section details the process involved in creating this Data. ### Curation Rationale The data was curated first from the context of the Bible, which proved to be the largest available source of English-Nigerian Pidgin parallel sentences. For the English sentences, The Message translation of the Bible was used because it presented the most modern form of English. This data was, however, not versatile enough, so we scraped Pidgin data from the BBC Pidgin website. This platform provided data in wider contexts, from politics to entertainment. Naturally, this makes the model more versatile. ### Source Data #### Initial Data Collection and Normalization - The data was scraped using BeautifulSoup in Python and stored in a MongoDB database - The Bible-sourced data was split into samples by verses because that was the easiest way to retain context between parallel sentences. Primarily because sentences in English and Nigerian Pidgin were not perfect matches. - The BBC Pidgin data was translated using Open AI's GPT3.5-turbo via the API and the [LangChain](https://python.langchain.com/) package. #### Who are the source language producers? - [Domot - BBC News Pidgin](https://www.bbc.com/pidgin/) - [YouVersion PCM Bible](https://www.bible.com/bible/2516/GEN.1.PCM) - [YouVersion Message Translation Bible](https://www.bible.com/bible/97/GEN.1.MSG) ### Personal and Sensitive Information No additional effort was taken to remove sensitive information aside from what was done by the writers at BBC News Pidgin and the Bible. ## Considerations for Using the Data ### Social Impact of Dataset This data makes it easier for Engineers to build language tools that work for a less literate but digitally connected Nigerian audience. ### Discussion of Biases The data is primarily focused on News and Biblical texts. While this has a reasonably wide scope, it is quite limited, and the model will perform considerably poorly in completely alien contexts. ### Other Known Limitations - The data does not contain other versions of Pidgin, like Warri Pidgin or Pidgin from other African nations. - The data does not have sentences that contain a lot of domain-specific Jargon. ## Additional Information ### Dataset Curators - [Fortune Adekogbe](https://www.linkedin.com/in/fortune-adekogbe) - [Joseph Olaide](https://ng.linkedin.com/in/josepholaide) ### Citation Information - [Domot - BBC News Pidgin](https://www.bbc.com/pidgin/) - (Open AI GPT3.5-Turbo)[https://platform.openai.com] ### Contributions We welcome contributions from individuals who understand Nigerian Pidgin to help scale up our manual data translation efforts. Motivated developers interested in building interfaces for this are also welcome.
khalidalt/arc
2023-09-05T04:28:01.000Z
[ "region:us" ]
khalidalt
null
null
null
0
3
Entry not found
BlahBlah1/Datavisualisation
2023-09-05T07:15:18.000Z
[ "license:apache-2.0", "region:us" ]
BlahBlah1
null
null
null
0
3
--- license: apache-2.0 --- Data prepared for training llama2 model Data such that to differentiate different types of charts based on X axis and Y axis
Falah/framed_wall_art_prompts_SDXL
2023-09-05T06:41:04.000Z
[ "region:us" ]
Falah
null
null
null
0
3
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 390982557 num_examples: 1000000 download_size: 39212995 dataset_size: 390982557 --- # Dataset Card for "framed_wall_art_prompts_SDXL" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cherry1556/testsft
2023-09-05T08:35:14.000Z
[ "region:us" ]
cherry1556
null
null
null
0
3
Entry not found
Elliot4AI/testpatent
2023-09-05T09:51:49.000Z
[ "task_categories:text-classification", "size_categories:n<1K", "language:zh", "license:apache-2.0", "chemistry", "region:us" ]
Elliot4AI
null
null
null
0
3
--- license: apache-2.0 task_categories: - text-classification language: - zh tags: - chemistry size_categories: - n<1K --- test
aboix/GB_EXAMPLE_V1_GROUPED1_DOWNSAMPLED_SIMPLE
2023-09-05T11:22:56.000Z
[ "region:us" ]
aboix
null
null
null
0
3
--- dataset_info: features: - name: text dtype: string - name: inputs struct: - name: text dtype: string - name: prediction dtype: 'null' - name: prediction_agent dtype: 'null' - name: annotation sequence: string - name: annotation_agent dtype: string - name: vectors dtype: 'null' - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata struct: - name: split dtype: string - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 6151886.959430213 num_examples: 20330 - name: test num_bytes: 1538123.0405697871 num_examples: 5083 download_size: 4293230 dataset_size: 7690010.0 --- # Dataset Card for "GB_EXAMPLE_V1_GROUPED1_DOWNSAMPLED_SIMPLE" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saurastha/nepali-speech-dataset
2023-09-05T16:43:44.000Z
[ "task_categories:automatic-speech-recognition", "size_categories:1K<n<10K", "language:ne", "asr", "nepali speech recognition", "nepali asr", "arxiv:2205.12446", "region:us" ]
saurastha
null
null
null
0
3
--- dataset_info: features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 4317304223.418399 num_examples: 7091 download_size: 5789483340 dataset_size: 4317304223.418399 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - automatic-speech-recognition language: - ne tags: - asr - nepali speech recognition - nepali asr pretty_name: Nepali Speech Dataset size_categories: - 1K<n<10K --- # Dataset Card for "nepali-speech-dataset" ### Dataset Summary The Nepali Speech Dataset is a collection of audio recordings and corresponding transcriptions in the Nepali language. It is designed to facilitate research and development in the field of speech recognition, natural language processing, and other related areas. ### Use Case Speech recognition ### Languages Nepali ## Dataset Creation ### Data Instances {'path': '/home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/<br>7f8541f130925e9b2af7d37256f2f61f9d6ff21bf4a94f7c1a3803ec648d7d79/xs_chunks_0000/YOU0000000315_S0000660.wav', 'audio':<br> {'path': '/home/sanchit_huggingface_co/.cache/huggingface/datasets/downloads/extracted/<br>7f8541f130925e9b2af7d37256f2f61f9d6ff21bf4a94f7c1a3803ec648d7d79/xs_chunks_0000/YOU0000000315_S0000660.wav',<br> 'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294, 0.00036621], dtype=float32),<br> 'sampling_rate': 16000<br> },<br> 'transcription': 'जुलियन बार्न्सद्वारा लिखित अङ्ग्रेजी उपन्यास' } ### Data Splits Training Set: Around 6.7K of audio data with corresponding transcriptions ## Source The dataset was created by extracting Nepali data points from *Common Voice*, *OpenSLR*, and *FLEURS* datasets. **Common Voice**: Common Voice is a Mozilla project that collects and shares multilingual, open-source, and crowdsourced voice data. @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.},<br> title = {Common Voice: A Massively-Multilingual Speech Corpus},<br> booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},<br> pages = {4211--4215},<br> year = 2020<br> } **OpenSLR**: OpenSLR is a repository of open-source speech and language resources. @inproceedings{kjartansson-etal-tts-sltu2018, title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},<br> author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin},<br> booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},<br> year = {2018},<br> address = {Gurugram, India},<br> month = aug,<br> pages = {66--70},<br> URL = {https://dx.doi.org/10.21437/SLTU.2018-14}<br> } **FLEURS**: FLEURS (Foreign Language Endangered Resources and Unicode Solutions) is a project that focuses on preserving and sharing linguistic resources for under-resourced languages. @article{fleurs2022arxiv, title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},<br> author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},<br> journal={arXiv preprint arXiv:2205.12446},<br> url = {https://arxiv.org/abs/2205.12446},<br> year = {2022}<br> **Nepali Speech to Text Dataset**: @misc{ishwor subedi_2023, title={Nepali Speech to Text Dataset},<br> url={https://www.kaggle.com/dsv/5806065},<br> DOI={10.34740/KAGGLE/DSV/5806065},<br> publisher={Kaggle},<br> author={Ishwor Subedi},<br> year={2023}<br> }
stefan-it/flair-base-model-detection
2023-09-05T22:19:30.000Z
[ "license:mit", "region:us" ]
stefan-it
null
null
null
1
3
--- license: mit --- # Flair Base Model Detection For detailed instructions of dataset generation process, please refer to this [GIST](https://gist.github.com/stefan-it/c746ed3562a9b5162f8229724d136975).
jtatman/civil_comments_hatebert
2023-09-06T08:15:58.000Z
[ "task_categories:text-classification", "task_categories:text2text-generation", "task_categories:fill-mask", "size_categories:100K<n<1M", "language:en", "license:mit", "masked", "mask-scored", "comment scoring", "masked-model", "region:us" ]
jtatman
null
null
null
0
3
--- dataset_info: features: - name: text dtype: string - name: text_masked dtype: string - name: text_replaced list: - name: score dtype: float64 - name: sequence dtype: string - name: token dtype: int64 - name: token_str dtype: string splits: - name: train num_bytes: 872262083 num_examples: 451219 download_size: 333147199 dataset_size: 872262083 configs: - config_name: default data_files: - split: train path: data/train-* license: mit task_categories: - text-classification - text2text-generation - fill-mask language: - en tags: - masked - mask-scored - comment scoring - masked-model pretty_name: civil comments w/hatebert scoring size_categories: - 100K<n<1M --- # Dataset Card for "civil_comments_hatebert" This is an experiment to see how "civil-comments" can be changed by models without much manipulation to offensive speech in certain cases. This data is a reformat of the civil comments dataset, discarding all scoring attributes of abusive speech, masking random tokens, and processing with hatebert to fill-masked tokens with possible abusive language. This merely sets up some good data for three things: fill-mask activities, text training, and scored responses based on random tokens being manipulatible according to this model. Showing the progress of incarnation, three columns illustrate the original text data extracted, the randomly masked text, and the filled text with scores in a list for the hatebert output. So far in practice, the hatebert model mostly fills with innocuous placeholders, from *very* limited testing. Hatebert is as it sounds, a BERT based model trained on fill-mask activites. [civil_comments dataset](https://huggingface.co/datasets/civil_comments) [hatebert model](https://huggingface.co/datasets/civil_comments) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
922-CA/ly2_09062023_test1_raw_YuChA_1a
2023-09-22T08:08:43.000Z
[ "license:openrail", "region:us" ]
922-CA
null
null
null
0
3
--- license: openrail --- # Yuri Chat 09062023 raw * Dataset of Yuri dialogue from DDLC (dataset of ~1300 items augmented by [MythoMax-l2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) to turn into multi-turn chat dialogue) * Curated version planned
malteee/SynTruckObjDet
2023-09-06T13:06:47.000Z
[ "region:us" ]
malteee
null
null
null
0
3
--- dataset_info: features: - name: image dtype: image - name: mask dtype: image - name: bbox list: - name: category dtype: int64 - name: position sequence: float64 splits: - name: train num_bytes: 100362995.0 num_examples: 100 download_size: 99562410 dataset_size: 100362995.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "SynTruckUnity" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vhtran/uniq-de-en
2023-09-06T13:44:59.000Z
[ "license:cc-by-4.0", "region:us" ]
vhtran
null
null
null
1
3
--- license: cc-by-4.0 --- German to English
boomb0om/MS-COCO-validation
2023-09-07T21:34:58.000Z
[ "region:us" ]
boomb0om
null
null
null
0
3
Entry not found
rombodawg/LimitlessCodeTraining
2023-09-08T04:19:23.000Z
[ "license:mit", "region:us" ]
rombodawg
null
null
null
11
3
--- license: mit --- _________________ ----- BREAK THROUGH YOUR LIMITS ----- _________________ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/FPna59yMG52VSq_5xbaHI.png) LimitlessCodeTraining is the direct sequal to Megacodetraining that is now called Legacy_MegaCodeTraining200k. This dataset is just over 646k lines of pure refined coding data. It is the pinacle of open source code training. It is the combination of the filtered Megacode training dataset filtered by shahules786 (shoutout to him) and the bigcode commitpackft dataset I converted to alpaca format. The dataset that were used to create this dataset are linked bellow: - https://huggingface.co/datasets/rombodawg/Rombodawgs_commitpackft_Evolinstruct_Converted - https://huggingface.co/datasets/shahules786/megacode-best
zxbsmk/instruct_short_novel
2023-09-07T09:24:10.000Z
[ "task_categories:text2text-generation", "size_categories:10K<n<100K", "language:zh", "license:apache-2.0", "region:us" ]
zxbsmk
null
null
null
0
3
--- license: apache-2.0 task_categories: - text2text-generation language: - zh size_categories: - 10K<n<100K dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: history dtype: string --- # Introduction This dataset is a mixup subset of several Chinese instruct datasets (about 21k). Join group via https://t.me/+JbovpBG6-gBiNDI1
ammarinjtkrbh/llm-menu-2-category
2023-09-10T14:26:34.000Z
[ "task_categories:text2text-generation", "region:us" ]
ammarinjtkrbh
null
null
null
0
3
--- task_categories: - text2text-generation ---
indonlp/nusatranslation_senti
2023-09-07T12:58:31.000Z
[ "license:apache-2.0", "region:us" ]
indonlp
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. While these methods have proven effective and cost-efficient, we have identified limitations in the resulting corpora, including a lack of lexical diversity and cultural relevance to local communities. To address this gap, we conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content. In addition, we present the NusaWrites benchmark, encompassing 12 underrepresented and extremely low-resource languages spoken by millions of individuals in Indonesia. Our empirical experiment results using existing multilingual large language models conclude the need to extend these models to more underrepresented languages. We introduce a novel high quality human curated corpora, i.e., NusaMenulis, which covers 12 languages spoken in Indonesia. The resource extend the coverage of languages to 5 new languages, i.e., Ambon (abs), Bima (bhp), Makassarese (mak), Palembang / Musi (mui), and Rejang (rej). For the rhetoric mode classification task, we cover 5 rhetoric modes, i.e., narrative, persuasive, argumentative, descriptive, and expository.
@unpublished{anonymous2023nusawrites:, title={NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages}, author={Anonymous}, journal={OpenReview Preprint}, year={2023}, note={anonymous preprint under review} }
null
0
3
--- license: apache-2.0 ---
HusainMehdi/alpaca-shortened
2023-09-08T14:26:26.000Z
[ "region:us" ]
HusainMehdi
null
null
null
0
3
harshal-07/speech_to_text
2023-09-09T07:04:07.000Z
[ "region:us" ]
harshal-07
null
null
null
0
3
Entry not found
chuyin0321/extended-trading-stocks
2023-09-07T22:24:04.000Z
[ "region:us" ]
chuyin0321
null
null
null
0
3
--- dataset_info: features: - name: symbol dtype: string - name: date dtype: string - name: time dtype: string - name: price dtype: float64 - name: share_volume dtype: string splits: - name: train num_bytes: 4680296 num_examples: 98899 download_size: 824886 dataset_size: 4680296 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "extended-trading-stocks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saurabh1896/OMR-forms
2023-09-08T07:24:42.000Z
[ "region:us" ]
saurabh1896
null
null
null
0
3
--- dataset_info: features: - name: image dtype: image - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 8632972.0 num_examples: 14 - name: test num_bytes: 1629831.0 num_examples: 4 download_size: 7181972 dataset_size: 10262803.0 --- # Dataset Card for "OMR-forms" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nampdn-ai/mini-stack
2023-09-08T09:28:07.000Z
[ "region:us" ]
nampdn-ai
null
null
null
1
3
Entry not found
FischlVonLuftschlossNarfidort/sample-genshin-character
2023-09-08T10:41:42.000Z
[ "license:unknown", "region:us" ]
FischlVonLuftschlossNarfidort
null
null
null
0
3
--- license: unknown ---
ctu-aic/csfever_v2_pvi
2023-09-08T11:33:32.000Z
[ "task_categories:text-classification", "task_ids:natural-language-inference", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:fever", "language:cs", "license:cc-by-sa-3.0", "Fact-checking", "arxiv:2201.11115", "arxiv:2110.08420", "region:us" ]
ctu-aic
null
null
null
0
3
--- license: cc-by-sa-3.0 task_categories: - text-classification task_ids: - natural-language-inference language: - cs tags: - Fact-checking pretty_name: CsFEVERv2-PVI multilinguality: monolingual source_datasets: fever size_categories: - 100K<n<1M --- # Dataset Card for "CsFEVERv2" ## Dataset Description CsFEVERv2_pvi is a dataset for Czech fact-checking (NLI) developed as part of a bachelor thesis at the Artificial Intelligence Center of the Faculty of Electrical Engineering of the Czech technical university in Prague. ### Languages Czech ## Dataset Usage Example ```python from datasets import load_dataset dataset = load_dataset("/home/mlynatom/csfever_v2_pvi") ``` ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ```json {'id': 155439, 'label': 2, 'claim': 'Newcastle United FC vyhrál pět ligových titulů.', 'evidence': "Ronnie Simpson. Ronnie Simpson (21. října 1930, Glasgow – 19. dubna 2004, Edinburgh) byl skotský fotbalový brankář..."} ``` ### Data Fields - `id`: a `int32` feature. - `label`: a `int32` feature. - `claim`: a `string` feature. - `evidence`: a `string` feature. ### Data Splits | | train | dev | test | |----------|-------:|-----:|------:| | num_rows | 106209 | 6319 | 6261 | # Citation ```bibtex @article{Ullrich_2023, doi = {10.1007/s10579-023-09654-3}, url = {https://doi.org/10.1007%2Fs10579-023-09654-3}, year = 2023, month = {may}, publisher = {Springer Science and Business Media {LLC}}, author = {Herbert Ullrich and Jan Drchal and Martin Rýpar and Hana Vincourová and Václav Moravec}, title = {{CsFEVER} and {CTKFacts}: acquiring Czech data for fact verification}, journal = {Language Resources and Evaluation}, archivePrefix={arXiv}, eprint={2201.11115}, } ``` ```bibtex @misc{ethayarajh2022understanding, title={Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information}, author={Kawin Ethayarajh and Yejin Choi and Swabha Swayamdipta}, year={2022}, eprint={2110.08420}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @thesis{Mlynar_2023, author = {Mlynář, Tomáš}, type = {Bachelor's Thesis} title = {Automated Fact Checking Based on Czech Wikipedia}, institution = {Czech Technical University in Prague, Faculty of Electrical Engineering}, date = {2023}, url = {http://hdl.handle.net/10467/109219} } ```
Admin08077/__features_vectors_store
2023-09-08T15:03:42.000Z
[ "task_categories:feature-extraction", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:translation", "task_categories:summarizati...
Admin08077
null
null
null
0
3
--- license: other task_categories: - feature-extraction - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - translation - summarization - conversational - text2text-generation - fill-mask - text-generation - text-to-speech language: - en --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
bibidentuhanoi/gideon_self_cognition_text
2023-09-10T17:10:52.000Z
[ "region:us" ]
bibidentuhanoi
null
null
null
0
3
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 98623 num_examples: 362 download_size: 39518 dataset_size: 98623 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gideon_self_cognition_text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Minglii/a
2023-09-09T03:03:18.000Z
[ "region:us" ]
Minglii
null
null
null
0
3
--- dataset_info: features: - name: data struct: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: id dtype: string splits: - name: train num_bytes: 98287163 num_examples: 52002 download_size: 50705625 dataset_size: 98287163 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mwz/UrduQuotes
2023-09-10T12:00:49.000Z
[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:ur", "license:mit", "region:us" ]
mwz
null
null
null
0
3
--- license: mit language: - ur task_categories: - text-generation size_categories: - 1K<n<10K --- The Urdu Quotes Dataset contains a collection of quotes in Urdu.
rombodawg/LosslessMegaCodeTrainingV3_1.6m_Evol_Guanaco_Format
2023-09-10T02:13:12.000Z
[ "license:other", "region:us" ]
rombodawg
null
null
null
0
3
--- license: other --- This is the LosslessMegaCodeTrainingV3 dataset converted to guanaco format. Enjoy Original model card: This is the ultimate code training data, created to be lossless so the AI model does not lose any other abilities it had previously, such as logical skills, after training on this dataset. The reason why this dataset is so large is to ensure that as the model learns to code, it continues to remember to follow regular instructions so as not to lose previously learned abilities. This is the result of all my work gathering data, testing AI models, and discovering what, why, and how coding models perform well or don't perform well. The content of this dataset is roughly 50% coding instruction data and 50% non-coding instruction data. Amounting to 1.5 million evol instruction-formatted lines of data. The outcome of having 50% non coding instruction data in the dataset is to preserve logic and reasoning skills within the model while training on coding. The lack of such skills has been observed to be a major issue with coding models such as Wizardcoder-15b and NewHope, but training models on this dataset alleviates that issue while also giving similar levels of coding knowledge. This dataset is a combination of the following datasets, along with additional deduping and uncensoring techniques: Coding: - https://huggingface.co/datasets/rombodawg/2XUNCENSORED_MegaCodeTraining188k - https://huggingface.co/datasets/rombodawg/Rombodawgs_commitpackft_Evolinstruct_Converted Instruction following: - https://huggingface.co/datasets/rombodawg/2XUNCENSORED_alpaca_840k_Evol_USER_ASSIST - https://huggingface.co/datasets/garage-bAInd/Open-Platypus
aadajinkya/python_code
2023-09-13T19:08:43.000Z
[ "region:us" ]
aadajinkya
null
null
null
0
3
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
aadajinkya/python_codes_sample
2023-09-13T00:24:34.000Z
[ "region:us" ]
aadajinkya
null
null
null
0
3
mangostin2010/Korean-Wise-Saying
2023-09-10T08:19:06.000Z
[ "license:unknown", "region:us" ]
mangostin2010
null
null
null
0
3
--- license: unknown ---
Falah/presidents_prompts
2023-09-10T08:22:24.000Z
[ "region:us" ]
Falah
null
null
null
0
3
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 33180376 num_examples: 100000 download_size: 4643870 dataset_size: 33180376 --- # Dataset Card for "presidents_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kph68/cs_zerospeech
2023-09-10T15:21:27.000Z
[ "region:us" ]
kph68
null
null
null
0
3
Entry not found
Taegyuu/KoAlpaca-v1.1a
2023-09-10T16:04:27.000Z
[ "task_categories:text-generation", "language:ko", "KoAlpaca", "region:us" ]
Taegyuu
null
null
null
0
3
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: url dtype: string splits: - name: train num_bytes: 23371027 num_examples: 21155 download_size: 12856014 dataset_size: 23371027 task_categories: - text-generation language: - ko tags: - KoAlpaca pretty_name: KoAlpaca-v1.1a --- # Dataset Card for "KoAlpaca-v1.1a" ## Project Repo - Github Repo: [Beomi/KoAlpaca](https://github.com/Beomi/KoAlpaca) ## How to use ```python >>> from datasets import load_dataset >>> ds = load_dataset("beomi/KoAlpaca-v1.1a", split="train") >>> ds Dataset({ features: ['instruction', 'input', 'output'], num_rows: 21155 }) ``` ```python >>> ds[0] {'instruction': '양파는 어떤 식물 부위인가요? 그리고 고구마는 뿌리인가요?', 'output': '양파는 잎이 아닌 식물의 줄기 부분입니다. 고구마는 식물의 뿌리 부분입니다. \n\n식물의 부위의 구분에 대해 궁금해하는 분이라면 분명 이 질문에 대한 답을 찾고 있을 것입니다. 양파는 잎이 아닌 줄기 부분입니다. 고구마는 다른 질문과 답변에서 언급된 것과 같이 뿌리 부분입니다. 따라서, 양파는 식물의 줄기 부분이 되고, 고구마는 식물의 뿌리 부분입니다.\n\n 덧붙이는 답변: 고구마 줄기도 볶아먹을 수 있나요? \n\n고구마 줄기도 식용으로 볶아먹을 수 있습니다. 하지만 줄기 뿐만 아니라, 잎, 씨, 뿌리까지 모든 부위가 식용으로 활용되기도 합니다. 다만, 한국에서는 일반적으로 뿌리 부분인 고구마를 주로 먹습니다.', 'url': 'https://kin.naver.com/qna/detail.naver?d1id=11&dirId=1116&docId=55320268'}
sajidhameed63/prepaid_packages
2023-09-10T18:06:24.000Z
[ "license:apache-2.0", "region:us" ]
sajidhameed63
null
null
null
0
3
--- license: apache-2.0 ---
Sangrish/sprites
2023-09-10T21:43:24.000Z
[ "region:us" ]
Sangrish
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 498863.0 num_examples: 10 download_size: 500416 dataset_size: 498863.0 --- # Dataset Card for "sprites" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kavinilavan/pythia_dataset_json
2023-09-11T07:00:02.000Z
[ "region:us" ]
kavinilavan
null
null
null
0
3
Entry not found
missvector/asd-qa-train
2023-09-13T12:30:54.000Z
[ "license:mit", "region:us" ]
missvector
null
null
null
0
3
--- license: mit dataset_info: features: - name: question dtype: string - name: answers struct: - name: answer_end dtype: int64 - name: answer_start dtype: int64 - name: text dtype: string - name: paragraph dtype: string splits: - name: train num_bytes: 3060746 num_examples: 2593 download_size: 450478 dataset_size: 3060746 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for The ASD QA Dataset (train set) ## Dataset Description - **Repository:** https://github.com/vifirsanova/empi ### Dataset Summary A dataset for question-answering used for building an informational Russian language chatbot for the inclusion of people with autism spectrum disorder and Asperger syndrome in particular, based on data from the following website: https://aspergers.ru. ### Languages Russian ## Dataset Structure The dataset inherits SQuAD 2.0 structure. ### Source Data https://aspergers.ru ### Dataset Curators Victoria Firsanova
missvector/asd-qa-val
2023-09-13T12:31:20.000Z
[ "license:mit", "region:us" ]
missvector
null
null
null
0
3
--- license: mit dataset_info: features: - name: question dtype: string - name: answers struct: - name: answer_end dtype: int64 - name: answer_start dtype: int64 - name: text dtype: string - name: paragraph dtype: string splits: - name: train num_bytes: 316067 num_examples: 261 download_size: 54962 dataset_size: 316067 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for The ASD QA Dataset (validation set) ## Dataset Description - **Repository:** https://github.com/vifirsanova/empi ### Dataset Summary A dataset for question-answering used for building an informational Russian language chatbot for the inclusion of people with autism spectrum disorder and Asperger syndrome in particular, based on data from the following website: https://aspergers.ru. ### Languages Russian ## Dataset Structure The dataset inherits SQuAD 2.0 structure. ### Source Data https://aspergers.ru ### Dataset Curators Victoria Firsanova
missvector/asd-qa-test
2023-09-13T12:31:42.000Z
[ "license:mit", "region:us" ]
missvector
null
null
null
0
3
--- license: mit dataset_info: features: - name: question dtype: string - name: answers struct: - name: answer_end dtype: int64 - name: answer_start dtype: int64 - name: text dtype: string - name: paragraph dtype: string splits: - name: train num_bytes: 1573377 num_examples: 1284 download_size: 218618 dataset_size: 1573377 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for The ASD QA Dataset (test set) ## Dataset Description - **Repository:** https://github.com/vifirsanova/empi ### Dataset Summary A dataset for question-answering used for building an informational Russian language chatbot for the inclusion of people with autism spectrum disorder and Asperger syndrome in particular, based on data from the following website: https://aspergers.ru. ### Languages Russian ## Dataset Structure The dataset inherits SQuAD 2.0 structure. ### Source Data https://aspergers.ru ### Dataset Curators Victoria Firsanova
DmitryBaltin/first_test_dataset
2023-09-11T14:47:20.000Z
[ "region:us" ]
DmitryBaltin
null
null
null
0
3
Entry not found
open-llm-leaderboard/details_CobraMamba__mamba-gpt-3b-v4
2023-09-11T14:18:43.000Z
[ "region:us" ]
open-llm-leaderboard
null
null
null
0
3
--- pretty_name: Evaluation run of CobraMamba/mamba-gpt-3b-v4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CobraMamba/mamba-gpt-3b-v4](https://huggingface.co/CobraMamba/mamba-gpt-3b-v4)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CobraMamba__mamba-gpt-3b-v4\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-11T14:17:28.228620](https://huggingface.co/datasets/open-llm-leaderboard/details_CobraMamba__mamba-gpt-3b-v4/blob/main/results_2023-09-11T14-17-28.228620.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.3057836214261249,\n\ \ \"acc_stderr\": 0.033396300983373435,\n \"acc_norm\": 0.30943896084991157,\n\ \ \"acc_norm_stderr\": 0.03339247033423146,\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.014679255032111075,\n \"mc2\": 0.37259736037797425,\n\ \ \"mc2_stderr\": 0.013997831938424934\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3856655290102389,\n \"acc_stderr\": 0.01422425097325717,\n\ \ \"acc_norm\": 0.4257679180887372,\n \"acc_norm_stderr\": 0.014449464278868803\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5348536148177654,\n\ \ \"acc_stderr\": 0.0049776437308485895,\n \"acc_norm\": 0.7104162517426807,\n\ \ \"acc_norm_stderr\": 0.004526422125860677\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.34074074074074073,\n\ \ \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.34074074074074073,\n\ \ \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.28289473684210525,\n \"acc_stderr\": 0.03665349695640767,\n\ \ \"acc_norm\": 0.28289473684210525,\n \"acc_norm_stderr\": 0.03665349695640767\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.28,\n\ \ \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n \ \ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.33962264150943394,\n \"acc_stderr\": 0.029146904747798328,\n\ \ \"acc_norm\": 0.33962264150943394,\n \"acc_norm_stderr\": 0.029146904747798328\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2152777777777778,\n\ \ \"acc_stderr\": 0.03437079344106136,\n \"acc_norm\": 0.2152777777777778,\n\ \ \"acc_norm_stderr\": 0.03437079344106136\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n\ \ \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2832369942196532,\n\ \ \"acc_stderr\": 0.034355680560478746,\n \"acc_norm\": 0.2832369942196532,\n\ \ \"acc_norm_stderr\": 0.034355680560478746\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.045126085985421296,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.045126085985421296\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102977,\n\ \ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102977\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\ \ \"acc_stderr\": 0.041424397194893624,\n \"acc_norm\": 0.2631578947368421,\n\ \ \"acc_norm_stderr\": 0.041424397194893624\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2206896551724138,\n \"acc_stderr\": 0.03455930201924812,\n\ \ \"acc_norm\": 0.2206896551724138,\n \"acc_norm_stderr\": 0.03455930201924812\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.29894179894179895,\n \"acc_stderr\": 0.02357760479165582,\n \"\ acc_norm\": 0.29894179894179895,\n \"acc_norm_stderr\": 0.02357760479165582\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.037184890068181146,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.037184890068181146\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.3064516129032258,\n \"acc_stderr\": 0.026226485652553873,\n \"\ acc_norm\": 0.3064516129032258,\n \"acc_norm_stderr\": 0.026226485652553873\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.27586206896551724,\n \"acc_stderr\": 0.03144712581678242,\n \"\ acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.03144712581678242\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\"\ : 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.3393939393939394,\n \"acc_stderr\": 0.03697442205031596,\n\ \ \"acc_norm\": 0.3393939393939394,\n \"acc_norm_stderr\": 0.03697442205031596\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.31313131313131315,\n \"acc_stderr\": 0.033042050878136525,\n \"\ acc_norm\": 0.31313131313131315,\n \"acc_norm_stderr\": 0.033042050878136525\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.27979274611398963,\n \"acc_stderr\": 0.032396370467357036,\n\ \ \"acc_norm\": 0.27979274611398963,\n \"acc_norm_stderr\": 0.032396370467357036\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2923076923076923,\n \"acc_stderr\": 0.02306043838085775,\n \ \ \"acc_norm\": 0.2923076923076923,\n \"acc_norm_stderr\": 0.02306043838085775\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \ \ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.31092436974789917,\n \"acc_stderr\": 0.030066761582977934,\n\ \ \"acc_norm\": 0.31092436974789917,\n \"acc_norm_stderr\": 0.030066761582977934\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.28623853211009176,\n \"acc_stderr\": 0.019379436628919968,\n \"\ acc_norm\": 0.28623853211009176,\n \"acc_norm_stderr\": 0.019379436628919968\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.3333333333333333,\n \"acc_stderr\": 0.0321495214780275,\n \"acc_norm\"\ : 0.3333333333333333,\n \"acc_norm_stderr\": 0.0321495214780275\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25980392156862747,\n\ \ \"acc_stderr\": 0.030778554678693257,\n \"acc_norm\": 0.25980392156862747,\n\ \ \"acc_norm_stderr\": 0.030778554678693257\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.3037974683544304,\n \"acc_stderr\": 0.029936696387138594,\n\ \ \"acc_norm\": 0.3037974683544304,\n \"acc_norm_stderr\": 0.029936696387138594\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3901345291479821,\n\ \ \"acc_stderr\": 0.03273766725459157,\n \"acc_norm\": 0.3901345291479821,\n\ \ \"acc_norm_stderr\": 0.03273766725459157\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.31297709923664124,\n \"acc_stderr\": 0.04066962905677697,\n\ \ \"acc_norm\": 0.31297709923664124,\n \"acc_norm_stderr\": 0.04066962905677697\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.34710743801652894,\n \"acc_stderr\": 0.04345724570292534,\n \"\ acc_norm\": 0.34710743801652894,\n \"acc_norm_stderr\": 0.04345724570292534\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.35185185185185186,\n\ \ \"acc_stderr\": 0.04616631111801713,\n \"acc_norm\": 0.35185185185185186,\n\ \ \"acc_norm_stderr\": 0.04616631111801713\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.31901840490797545,\n \"acc_stderr\": 0.03661997551073836,\n\ \ \"acc_norm\": 0.31901840490797545,\n \"acc_norm_stderr\": 0.03661997551073836\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n\ \ \"acc_stderr\": 0.0432704093257873,\n \"acc_norm\": 0.29464285714285715,\n\ \ \"acc_norm_stderr\": 0.0432704093257873\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.23300970873786409,\n \"acc_stderr\": 0.041858325989283164,\n\ \ \"acc_norm\": 0.23300970873786409,\n \"acc_norm_stderr\": 0.041858325989283164\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3717948717948718,\n\ \ \"acc_stderr\": 0.031660988918880785,\n \"acc_norm\": 0.3717948717948718,\n\ \ \"acc_norm_stderr\": 0.031660988918880785\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.3448275862068966,\n\ \ \"acc_stderr\": 0.016997123346113443,\n \"acc_norm\": 0.3448275862068966,\n\ \ \"acc_norm_stderr\": 0.016997123346113443\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.2832369942196532,\n \"acc_stderr\": 0.024257901705323385,\n\ \ \"acc_norm\": 0.2832369942196532,\n \"acc_norm_stderr\": 0.024257901705323385\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24804469273743016,\n\ \ \"acc_stderr\": 0.014444157808261445,\n \"acc_norm\": 0.24804469273743016,\n\ \ \"acc_norm_stderr\": 0.014444157808261445\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.025553169991826514,\n\ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.025553169991826514\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.36012861736334406,\n\ \ \"acc_stderr\": 0.02726429759980401,\n \"acc_norm\": 0.36012861736334406,\n\ \ \"acc_norm_stderr\": 0.02726429759980401\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.32407407407407407,\n \"acc_stderr\": 0.026041766202717163,\n\ \ \"acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.026041766202717163\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.28368794326241137,\n \"acc_stderr\": 0.02689170942834396,\n \ \ \"acc_norm\": 0.28368794326241137,\n \"acc_norm_stderr\": 0.02689170942834396\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.28226857887874834,\n\ \ \"acc_stderr\": 0.011495852176241963,\n \"acc_norm\": 0.28226857887874834,\n\ \ \"acc_norm_stderr\": 0.011495852176241963\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.2977941176470588,\n \"acc_stderr\": 0.02777829870154544,\n\ \ \"acc_norm\": 0.2977941176470588,\n \"acc_norm_stderr\": 0.02777829870154544\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.3022875816993464,\n \"acc_stderr\": 0.018579232711113877,\n \ \ \"acc_norm\": 0.3022875816993464,\n \"acc_norm_stderr\": 0.018579232711113877\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.39090909090909093,\n\ \ \"acc_stderr\": 0.046737523336702363,\n \"acc_norm\": 0.39090909090909093,\n\ \ \"acc_norm_stderr\": 0.046737523336702363\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.2530612244897959,\n \"acc_stderr\": 0.027833023871399673,\n\ \ \"acc_norm\": 0.2530612244897959,\n \"acc_norm_stderr\": 0.027833023871399673\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n\ \ \"acc_stderr\": 0.030360490154014645,\n \"acc_norm\": 0.24378109452736318,\n\ \ \"acc_norm_stderr\": 0.030360490154014645\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.29518072289156627,\n\ \ \"acc_stderr\": 0.035509201856896294,\n \"acc_norm\": 0.29518072289156627,\n\ \ \"acc_norm_stderr\": 0.035509201856896294\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.3391812865497076,\n \"acc_stderr\": 0.03631053496488905,\n\ \ \"acc_norm\": 0.3391812865497076,\n \"acc_norm_stderr\": 0.03631053496488905\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.014679255032111075,\n \"mc2\": 0.37259736037797425,\n\ \ \"mc2_stderr\": 0.013997831938424934\n }\n}\n```" repo_url: https://huggingface.co/CobraMamba/mamba-gpt-3b-v4 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|arc:challenge|25_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hellaswag|10_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-11T14-17-28.228620.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-management|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T14-17-28.228620.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_11T14_17_28.228620 path: - '**/details_harness|truthfulqa:mc|0_2023-09-11T14-17-28.228620.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-11T14-17-28.228620.parquet' - config_name: results data_files: - split: 2023_09_11T14_17_28.228620 path: - results_2023-09-11T14-17-28.228620.parquet - split: latest path: - results_2023-09-11T14-17-28.228620.parquet --- # Dataset Card for Evaluation run of CobraMamba/mamba-gpt-3b-v4 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CobraMamba/mamba-gpt-3b-v4 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [CobraMamba/mamba-gpt-3b-v4](https://huggingface.co/CobraMamba/mamba-gpt-3b-v4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CobraMamba__mamba-gpt-3b-v4", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-11T14:17:28.228620](https://huggingface.co/datasets/open-llm-leaderboard/details_CobraMamba__mamba-gpt-3b-v4/blob/main/results_2023-09-11T14-17-28.228620.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.3057836214261249, "acc_stderr": 0.033396300983373435, "acc_norm": 0.30943896084991157, "acc_norm_stderr": 0.03339247033423146, "mc1": 0.22766217870257038, "mc1_stderr": 0.014679255032111075, "mc2": 0.37259736037797425, "mc2_stderr": 0.013997831938424934 }, "harness|arc:challenge|25": { "acc": 0.3856655290102389, "acc_stderr": 0.01422425097325717, "acc_norm": 0.4257679180887372, "acc_norm_stderr": 0.014449464278868803 }, "harness|hellaswag|10": { "acc": 0.5348536148177654, "acc_stderr": 0.0049776437308485895, "acc_norm": 0.7104162517426807, "acc_norm_stderr": 0.004526422125860677 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.34074074074074073, "acc_stderr": 0.04094376269996793, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.04094376269996793 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.28289473684210525, "acc_stderr": 0.03665349695640767, "acc_norm": 0.28289473684210525, "acc_norm_stderr": 0.03665349695640767 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.33962264150943394, "acc_stderr": 0.029146904747798328, "acc_norm": 0.33962264150943394, "acc_norm_stderr": 0.029146904747798328 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2152777777777778, "acc_stderr": 0.03437079344106136, "acc_norm": 0.2152777777777778, "acc_norm_stderr": 0.03437079344106136 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.24, "acc_stderr": 0.04292346959909284, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909284 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2832369942196532, "acc_stderr": 0.034355680560478746, "acc_norm": 0.2832369942196532, "acc_norm_stderr": 0.034355680560478746 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201942, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201942 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.045126085985421296, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421296 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102977, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102977 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2631578947368421, "acc_stderr": 0.041424397194893624, "acc_norm": 0.2631578947368421, "acc_norm_stderr": 0.041424397194893624 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2206896551724138, "acc_stderr": 0.03455930201924812, "acc_norm": 0.2206896551724138, "acc_norm_stderr": 0.03455930201924812 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.29894179894179895, "acc_stderr": 0.02357760479165582, "acc_norm": 0.29894179894179895, "acc_norm_stderr": 0.02357760479165582 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2222222222222222, "acc_stderr": 0.037184890068181146, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.037184890068181146 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3064516129032258, "acc_stderr": 0.026226485652553873, "acc_norm": 0.3064516129032258, "acc_norm_stderr": 0.026226485652553873 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.27586206896551724, "acc_stderr": 0.03144712581678242, "acc_norm": 0.27586206896551724, "acc_norm_stderr": 0.03144712581678242 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.28, "acc_stderr": 0.04512608598542129, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542129 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.3393939393939394, "acc_stderr": 0.03697442205031596, "acc_norm": 0.3393939393939394, "acc_norm_stderr": 0.03697442205031596 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.31313131313131315, "acc_stderr": 0.033042050878136525, "acc_norm": 0.31313131313131315, "acc_norm_stderr": 0.033042050878136525 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.27979274611398963, "acc_stderr": 0.032396370467357036, "acc_norm": 0.27979274611398963, "acc_norm_stderr": 0.032396370467357036 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2923076923076923, "acc_stderr": 0.02306043838085775, "acc_norm": 0.2923076923076923, "acc_norm_stderr": 0.02306043838085775 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2740740740740741, "acc_stderr": 0.027195934804085626, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.027195934804085626 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.31092436974789917, "acc_stderr": 0.030066761582977934, "acc_norm": 0.31092436974789917, "acc_norm_stderr": 0.030066761582977934 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763743, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763743 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.28623853211009176, "acc_stderr": 0.019379436628919968, "acc_norm": 0.28623853211009176, "acc_norm_stderr": 0.019379436628919968 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3333333333333333, "acc_stderr": 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"harness|hendrycksTest-prehistory|5": { "acc": 0.32407407407407407, "acc_stderr": 0.026041766202717163, "acc_norm": 0.32407407407407407, "acc_norm_stderr": 0.026041766202717163 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.28368794326241137, "acc_stderr": 0.02689170942834396, "acc_norm": 0.28368794326241137, "acc_norm_stderr": 0.02689170942834396 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.28226857887874834, "acc_stderr": 0.011495852176241963, "acc_norm": 0.28226857887874834, "acc_norm_stderr": 0.011495852176241963 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.2977941176470588, "acc_stderr": 0.02777829870154544, "acc_norm": 0.2977941176470588, "acc_norm_stderr": 0.02777829870154544 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.3022875816993464, "acc_stderr": 0.018579232711113877, "acc_norm": 0.3022875816993464, "acc_norm_stderr": 0.018579232711113877 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.39090909090909093, "acc_stderr": 0.046737523336702363, "acc_norm": 0.39090909090909093, "acc_norm_stderr": 0.046737523336702363 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.2530612244897959, "acc_stderr": 0.027833023871399673, "acc_norm": 0.2530612244897959, "acc_norm_stderr": 0.027833023871399673 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24378109452736318, "acc_stderr": 0.030360490154014645, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.030360490154014645 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-virology|5": { "acc": 0.29518072289156627, "acc_stderr": 0.035509201856896294, "acc_norm": 0.29518072289156627, "acc_norm_stderr": 0.035509201856896294 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.3391812865497076, "acc_stderr": 0.03631053496488905, "acc_norm": 0.3391812865497076, "acc_norm_stderr": 0.03631053496488905 }, "harness|truthfulqa:mc|0": { "mc1": 0.22766217870257038, "mc1_stderr": 0.014679255032111075, "mc2": 0.37259736037797425, "mc2_stderr": 0.013997831938424934 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
diazangga/readme-falcon
2023-09-11T16:55:13.000Z
[ "region:us" ]
diazangga
null
null
null
0
3
Entry not found
dhanush23/aaa
2023-09-11T18:13:55.000Z
[ "region:us" ]
dhanush23
null
null
null
0
3
Entry not found
asoria/draft-list-column
2023-09-11T20:04:38.000Z
[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:multi-label-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ru", "license:apache-2...
asoria
This new dataset is designed to solve emotion recognition task for text data in Russian. The Corpus for Emotions Detecting in Russian-language text sentences of different social sources (CEDR) contains 9410 sentences in Russian labeled for 5 emotion categories. The data collected from different sources: posts of the LiveJournal social network, texts of the online news agency Lenta.ru, and Twitter microblog posts. There are two variants of the corpus: main and enriched. The enriched variant is include tokenization and lemmatization. Dataset with predefined train/test splits.
@article{sboev2021data, title={Data-Driven Model for Emotion Detection in Russian Texts}, author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman}, journal={Procedia Computer Science}, volume={190}, pages={637--642}, year={2021}, publisher={Elsevier} }
null
0
3
--- annotations_creators: - crowdsourced language_creators: - found language: - ru license: - apache-2.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - multi-label-classification pretty_name: The Corpus for Emotions Detecting in Russian-language text sentences (CEDR) tags: - emotion-classification dataset_info: - config_name: main features: - name: text dtype: string - name: labels sequence: class_label: names: '0': joy '1': sadness '2': surprise '3': fear '4': anger - name: source dtype: string splits: - name: train num_bytes: 1418355 num_examples: 7528 - name: test num_bytes: 350275 num_examples: 1882 download_size: 693026 dataset_size: 1768630 - config_name: enriched features: - name: text dtype: string - name: labels sequence: class_label: names: '0': joy '1': sadness '2': surprise '3': fear '4': anger - name: source dtype: string - name: sentences list: list: - name: forma dtype: string - name: lemma dtype: string splits: - name: train num_bytes: 4792366 num_examples: 7528 - name: test num_bytes: 1182343 num_examples: 1882 download_size: 1822522 dataset_size: 5974709 --- # Dataset Card for [cedr] ## 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:** [GitHub](https://github.com/sag111/CEDR) - **Repository:** [GitHub](https://github.com/sag111/CEDR) - **Paper:** [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S1877050921013247) - **Leaderboard:** - **Point of Contact:** [@sag111](mailto:sag111@mail.ru) ### Dataset Summary The Corpus for Emotions Detecting in Russian-language text sentences of different social sources (CEDR) contains 9410 comments labeled for 5 emotion categories (joy, sadness, surprise, fear, and anger). Here are 2 dataset configurations: - "main" - contains "text", "labels", and "source" features; - "enriched" - includes all "main" features and "sentences". Dataset with predefined train/test splits. ### Supported Tasks and Leaderboards This dataset is intended for multi-label emotion classification. ### Languages The data is in Russian. ## Dataset Structure ### Data Instances Each instance is a text sentence in Russian from several sources with one or more emotion annotations (or no emotion at all). An example for an instance from the dataset is shown below: ``` { 'text': 'Забавно как люди в возрасте удивляются входящим звонкам на мобильник)', 'labels': [0], 'source': 'twitter', 'sentences': [ [ {'forma': 'Забавно', 'lemma': 'Забавно'}, {'forma': 'как', 'lemma': 'как'}, {'forma': 'люди', 'lemma': 'человек'}, {'forma': 'в', 'lemma': 'в'}, {'forma': 'возрасте', 'lemma': 'возраст'}, {'forma': 'удивляются', 'lemma': 'удивляться'}, {'forma': 'входящим', 'lemma': 'входить'}, {'forma': 'звонкам', 'lemma': 'звонок'}, {'forma': 'на', 'lemma': 'на'}, {'forma': 'мобильник', 'lemma': 'мобильник'}, {'forma': ')', 'lemma': ')'} ] ] } ``` Emotion label codes: {0: "joy", 1: "sadness", 2: "surprise", 3: "fear", 4: "anger"} ### Data Fields The main configuration includes: - text: the text of the sentence; - labels: the emotion annotations; - source: the tag name of the corresponding source In addition to the above, the raw data includes: - sentences: text tokenized and lemmatized with [udpipe](https://ufal.mff.cuni.cz/udpipe) - 'forma': the original word form; - 'lemma': the lemma of this word ### Data Splits The dataset includes a set of train/test splits. with 7528, and 1882 examples respectively. ## Dataset Creation ### Curation Rationale The formed dataset of examples consists of sentences in Russian from several sources (blogs, microblogs, news), which allows creating methods to analyse various types of texts. The created methodology for building the dataset based on applying a crowdsourcing service can be used to expand the number of examples to improve the accuracy of supervised classifiers. ### Source Data #### Initial Data Collection and Normalization Data was collected from several sources: posts of the Live Journal social network, texts of the online news agency Lenta.ru, and Twitter microblog posts. Only those sentences were selected that contained marker words from the dictionary of [the emotive vocabulary of the Russian language](http://lexrus.ru/default.aspx?p=2876). The authors manually formed a list of marker words for each emotion by choosing words from different categories of the dictionary. In total, 3069 sentences were selected from LiveJournal posts, 2851 sentences from Lenta.Ru, and 3490 sentencesfrom Twitter. After selection, sentences were offered to annotators for labeling. #### Who are the source language producers? Russian-speaking LiveJournal and Tweeter users, and authors of news articles on the site lenta.ru. ### Annotations #### Annotation process Annotating sentences with labels of their emotions was performed with the help of [a crowdsourcing platform](https://yandex.ru/support/toloka/index.html?lang=en). The annotators’ task was: “What emotions did the author express in the sentence?”. The annotators were allowed to put an arbitrary number of the following emotion labels: "joy", "sadness", "anger", "fear", and "surprise". If the accuracy of an annotator on the control sentences (including the trial run) became less than 70%, or if the accuracy was less than 66% over the last six control samples, the annotator was dismissed. Sentences were split into tasks and assigned to annotators so that each sentence was annotated at least three times. A label of a specific emotion was assigned to a sentence if put by more than half of the annotators. #### Who are the annotators? Only those of the 30% of the best-performing active users (by the platform’s internal rating) who spoke Russian and were over 18 years old were allowed into the annotation process. Moreover, before a platform user could be employed as an annotator, they underwent a training task, after which they were to mark 25 trial samples with more than 80% agreement compared to the annotation that the authors had performed themselves. ### Personal and Sensitive Information The text of the sentences may contain profanity. ## 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 Researchers at AI technology lab at NRC "Kurchatov Institute". See the author [list](https://www.sciencedirect.com/science/article/pii/S1877050921013247). ### Licensing Information The GitHub repository which houses this dataset has an Apache License 2.0. ### Citation Information If you have found our results helpful in your work, feel free to cite our publication. This is an updated version of the dataset, the collection and preparation of which is described here: ``` @article{sboev2021data, title={Data-Driven Model for Emotion Detection in Russian Texts}, author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman}, journal={Procedia Computer Science}, volume={190}, pages={637--642}, year={2021}, publisher={Elsevier} } ``` ### Contributions Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset.
JeisonJA/CSV_TRAIN
2023-09-11T20:48:28.000Z
[ "license:apache-2.0", "region:us" ]
JeisonJA
null
null
null
0
3
--- license: apache-2.0 ---
Jalbers42/WhatAmI
2023-09-11T23:33:07.000Z
[ "region:us" ]
Jalbers42
null
null
null
0
3
Entry not found
a686d380/sis-novel
2023-09-12T03:59:29.000Z
[ "license:openrail", "region:us" ]
a686d380
null
null
null
7
3
--- license: openrail viewer: false --- 这是一个中文H小说数据集，收集自sis001 sis-novel1为中短篇小说，112182项，解压缩后大小5.7GB，数据截止2022年7月 sis-novel2为长篇小说，4555项，解压缩后大小3.6GB，数据截止2023年3月 数据均为未清洗的txt版本，并且可能包含有评论
macarious/sv_corpora_parliament_processed
2023-09-15T18:12:02.000Z
[ "region:us" ]
macarious
null
null
null
0
3
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 292351437 num_examples: 1892723 download_size: 0 dataset_size: 292351437 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sv_corpora_parliament_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shijli/iwslt14-deen
2023-09-27T07:26:53.000Z
[ "region:us" ]
shijli
null
null
null
1
3
# IWSLT 2014 German-English Translation Dataset w/ further processing This dataset was built with the fairseq's processing script, which can be originally found [here](https://github.com/facebookresearch/fairseq/blob/main/examples/translation/prepare-iwslt14.sh) `iwslt14.tokenized.de-en.zip` and `binarized.zip` can be built by running: ``` git clone https://huggingface.co/datasets/shijli/iwslt14-deen cd iwslt14-deen/data bash prepare-iwslt14.sh ``` `binarized.dist.de-en.zip` is a distilled dataset generated by a transformer base model. It can be built by running: ``` bash prepare-iwslt14-distill.sh /path/to/fairseq/model source-lang target-lang ``` To build this dataset, you need to create `binarized.zip` first. Note that the distilled dataset only uses model-generated target sentences, which means that different translation directions result in different datasets. Therefore, you need to specify `source-lang` and `target-lang` explicitly. Also, you need to replace `/path/to/fairseq/model` with the path of your pretrained model.
kudyadi/utatest
2023-09-12T07:37:27.000Z
[ "region:us" ]
kudyadi
null
null
null
0
3
Entry not found
pavol58/test
2023-09-12T08:18:04.000Z
[ "region:us" ]
pavol58
null
null
null
0
3
This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples. This dataset was used to train Guanaco with QLoRA. For further information, please see the original dataset. License: Apache 2.0
rkf2778/amazon_reviews_us_Mobile_Electronics_v1_00
2023-09-12T13:06:00.000Z
[ "license:mit", "region:us" ]
rkf2778
null
null
null
0
3
--- license: mit ---
rshrott/description
2023-09-12T14:19:49.000Z
[ "region:us" ]
rshrott
null
null
null
0
3
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 91160798 num_examples: 24489 download_size: 19465126 dataset_size: 91160798 --- # Dataset Card for "description" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pssubitha/sales4-formatted
2023-09-13T09:20:26.000Z
[ "region:us" ]
pssubitha
null
null
null
0
3
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 46461 num_examples: 120 download_size: 24850 dataset_size: 46461 --- # Dataset Card for "sales4-formatted" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sarthakk88/embeddings
2023-09-20T07:57:03.000Z
[ "region:us" ]
sarthakk88
null
null
null
0
3
Entry not found
NewstaR/Camildae
2023-09-13T08:24:01.000Z
[ "region:us" ]
NewstaR
null
null
null
0
3
Entry not found
Dippi9845/arxiv-fragments-generated
2023-09-13T08:24:13.000Z
[ "license:cc-by-nc-sa-4.0", "region:us" ]
Dippi9845
null
null
null
0
3
--- license: cc-by-nc-sa-4.0 ---
under-tree/sts_traces
2023-09-13T15:51:47.000Z
[ "region:us" ]
under-tree
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: text1 dtype: string - name: text2 dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 28555408 num_examples: 15000 - name: val num_bytes: 5686916 num_examples: 3000 download_size: 11941770 dataset_size: 34242324 --- # Dataset Card for "sts_traces" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ironchanchellor/Metallography_segmenter_Dataset_B1
2023-09-13T19:11:03.000Z
[ "region:us" ]
ironchanchellor
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 84529692.0 num_examples: 410 - name: validation num_bytes: 21840002.0 num_examples: 103 download_size: 106032508 dataset_size: 106369694.0 --- # Dataset Card for "Metallography_segmenter_Dataset_B1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
seansullivan/biz-data-comm-2
2023-09-13T20:44:35.000Z
[ "license:other", "region:us" ]
seansullivan
null
null
null
0
3
--- license: other ---
hanho/test2
2023-09-14T04:51:35.000Z
[ "license:openrail", "region:us" ]
hanho
null
null
null
0
3
--- license: openrail dataset_info: features: - name: pokemon dtype: string - name: type dtype: string splits: - name: train num_bytes: 43 num_examples: 2 download_size: 1215 dataset_size: 43 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
rajj0/AbstractAI
2023-09-14T09:24:03.000Z
[ "region:us" ]
rajj0
null
null
null
0
3
Entry not found
nixudos/danish150k
2023-09-14T11:22:42.000Z
[ "region:us" ]
nixudos
null
null
null
0
3
Entry not found
tannguyencd/testdataset
2023-09-14T15:32:58.000Z
[ "license:llama2", "region:us" ]
tannguyencd
null
null
null
0
3
--- license: llama2 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 23665 num_examples: 10 download_size: 27131 dataset_size: 23665 configs: - config_name: default data_files: - split: train path: data/train-* ---
eugenepentland/axolotl_docs
2023-09-14T15:46:13.000Z
[ "license:mit", "region:us" ]
eugenepentland
null
null
null
0
3
--- license: mit ---
HydraLM/clustered_2
2023-09-14T17:20:34.000Z
[ "region:us" ]
HydraLM
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string - name: embedding sequence: float32 - name: __index_level_0__ dtype: int64 - name: cluster sequence: int64 splits: - name: train num_bytes: 13588132382 num_examples: 2297193 download_size: 13051782294 dataset_size: 13588132382 --- # Dataset Card for "clustered_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/corpus_1_embedded_deduplicated
2023-09-14T19:47:14.000Z
[ "region:us" ]
HydraLM
null
null
null
0
3
--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string - name: embedding sequence: float64 splits: - name: train num_bytes: 14843809239 num_examples: 1472917 download_size: 11121975605 dataset_size: 14843809239 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "corpus_1_embedded_deduplicated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/expanded_artistic_prompts
2023-09-15T04:18:05.000Z
[ "region:us" ]
Falah
null
null
null
0
3
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 221011 num_examples: 1000 download_size: 33944 dataset_size: 221011 --- # Dataset Card for "expanded_artistic_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/SkunkData-Corpus-001
2023-09-15T04:30:30.000Z
[ "region:us" ]
HydraLM
null
null
null
0
3
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 3109254774 num_examples: 3278633 download_size: 1470922120 dataset_size: 3109254774 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "SkunkData-Corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Jollyraman/nissardataset
2023-09-15T08:05:30.000Z
[ "region:us" ]
Jollyraman
null
null
null
0
3
Entry not found
ncoban/trWiki
2023-09-18T18:14:20.000Z
[ "region:us" ]
ncoban
null
null
null
0
3
Entry not found
InstaDeepAI/instanovo_highconfidence_proteometools
2023-09-19T11:34:01.000Z
[ "license:cc0-1.0", "region:us" ]
InstaDeepAI
null
null
null
0
3
--- license: cc0-1.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: experiment_name dtype: string - name: evidence_index dtype: int64 - name: scan_number dtype: int64 - name: sequence dtype: string - name: modified_sequence dtype: string - name: precursor_mz dtype: float64 - name: precursor_recalibrated_mz dtype: float64 - name: precursor_mass dtype: float64 - name: precursor_charge dtype: int64 - name: retention_time dtype: float64 - name: mz_array sequence: float32 - name: intensity_array sequence: float32 splits: - name: train num_bytes: 3370985593 num_examples: 2132847 - name: validation num_bytes: 413243959 num_examples: 257187 - name: test num_bytes: 421581021 num_examples: 265369 download_size: 3944832530 dataset_size: 4205810573 --- # Dataset Card for High-Confidence ProteomeTools Dataset used to train, validate and test InstaNovo and InstaNovo+. ## Dataset Description - **Repository:** [InstaNovo](https://github.com/instadeepai/InstaNovo) - **Paper:** [De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments](https://www.biorxiv.org/content/10.1101/2023.08.30.555055v1) ### Dataset Summary This dataset consists of the highest-confidence peptide-spectral matches from three parts of the [ProteomeTools](https://www.proteometools.org/) datasets. The original datasets may be found in the PRIDE repository with identifiers: - `PXD004732` (Part I) - `PXD010595` (Part II) - `PXD021013` (Part III) The dataset has been split on unique peptides with the following ratio: - 80% train - 10% validation - 10% test ## Dataset Structure The dataset is tabular, where each row corresponds to a labelled MS2 spectra. - `sequence (string)` \ The target peptide sequence excluding post-translational modifications - `modified_sequence (string)` \ The target peptide sequence including post-translational modifications - `precursor_mz (float64)` \ The mass-to-charge of the precursor (from MS1) - `charge (int64)` \ The charge of the precursor (from MS1) - `mz_array (list[float64])` \ The mass-to-charge values of the MS2 spectrum - `mz_array (list[float32])` \ The intensity values of the MS2 spectrum MaxQuant additional columns: - `experiment_name (string)` - `evidence_index (in64)` - `scan_number (in64)` - `precursor_recalibrated_mz (float64)` ## Citation Information If you use this dataset, please cite the original authors. The original [ProteomeTools](https://www.proteometools.org/) data is available on [PRIDE](https://www.ebi.ac.uk/pride/) with identifiers `PXD004732` (Part I), `PXD010595` (Part II), and `PXD021013` (Part III). Please also cite InstaNovo: ```bibtex @article{eloff_kalogeropoulos_2023_instanovo, title = {De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments}, author = {Kevin Eloff and Konstantinos Kalogeropoulos and Oliver Morell and Amandla Mabona and Jakob Berg Jespersen and Wesley Williams and Sam van Beljouw and Marcin Skwark and Andreas Hougaard Laustsen and Stan J. J. Brouns and Anne Ljungars and Erwin Marten Schoof and Jeroen Van Goey and Ulrich auf dem Keller and Karim Beguir and Nicolas Lopez Carranza and Timothy Patrick Jenkins}, year = {2023}, doi = {10.1101/2023.08.30.555055}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/10.1101/2023.08.30.555055v1}, journal = {bioRxiv} } ```
Falah/fantasy_in_bottle
2023-09-15T15:30:05.000Z
[ "region:us" ]
Falah
null
null
null
0
3
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 2199838 num_examples: 5000 download_size: 276724 dataset_size: 2199838 --- # Dataset Card for "fantasy_in_bottle" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/Military_ships_prompts
2023-09-15T16:14:46.000Z
[ "region:us" ]
Falah
null
null
null
0
3
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 4722667 num_examples: 10000 download_size: 598184 dataset_size: 4722667 --- # Dataset Card for "Military_ships_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deven367/babylm-10M-bnc_spoken
2023-09-16T02:07:41.000Z
[ "region:us" ]
deven367
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 4764585 num_examples: 89932 - name: valid num_bytes: 4721951 num_examples: 89921 - name: test num_bytes: 5165775 num_examples: 99951 download_size: 8864201 dataset_size: 14652311 --- # Dataset Card for "babylm-10M-bnc_spoken" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shariqfarooq/cs323_densepred_seg
2023-09-16T02:20:07.000Z
[ "region:us" ]
shariqfarooq
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: image dtype: image - name: mask dtype: image splits: - name: train num_bytes: 170701125.0 num_examples: 1464 - name: val num_bytes: 170428139.75 num_examples: 1449 download_size: 341307796 dataset_size: 341129264.75 --- # Dataset Card for "cs323_densepred_seg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Abyx/60
2023-09-16T08:51:04.000Z
[ "region:us" ]
Abyx
null
null
null
0
3
Entry not found
indiejoseph/wikipedia-en-filtered
2023-10-02T20:50:06.000Z
[ "language:en", "region:us" ]
indiejoseph
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 49741517 num_examples: 17260 download_size: 27011805 dataset_size: 49741517 language: - en --- # Dataset Card for "wikipedia-en-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Chris126/guanaco-llama2-1k
2023-09-17T20:04:46.000Z
[ "region:us" ]
Chris126
null
null
null
0
3
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 0 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liyucheng/trivia_qa_wiki_val
2023-09-16T23:21:49.000Z
[ "region:us" ]
liyucheng
null
null
null
0
3
--- dataset_info: features: - name: question dtype: string - name: question_id dtype: string - name: question_source dtype: string - name: entity_pages sequence: - name: doc_source dtype: string - name: filename dtype: string - name: title dtype: string - name: wiki_context dtype: string - name: search_results sequence: - name: description dtype: string - name: filename dtype: string - name: rank dtype: int32 - name: title dtype: string - name: url dtype: string - name: search_context dtype: string - name: answer struct: - name: aliases sequence: string - name: normalized_aliases sequence: string - name: matched_wiki_entity_name dtype: string - name: normalized_matched_wiki_entity_name dtype: string - name: normalized_value dtype: string - name: type dtype: string - name: value dtype: string - name: wiki_context_sample dtype: string splits: - name: validation num_bytes: 662010582 num_examples: 7993 download_size: 355772611 dataset_size: 662010582 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "trivia_qa_wiki_val" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HustonMatthew/LenghtPrediction
2023-09-17T12:07:10.000Z
[ "license:cc", "region:us" ]
HustonMatthew
null
null
null
0
3
--- license: cc ---
AllenTAN/image_sentiment
2023-09-17T12:59:34.000Z
[ "region:us" ]
AllenTAN
null
null
null
0
3
Entry not found
vincenttttt/CtoDepartment_all_ForFineTune
2023-09-17T12:36:25.000Z
[ "region:us" ]
vincenttttt
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 1560937 num_examples: 3673 download_size: 304590 dataset_size: 1560937 --- # Dataset Card for "CtoDepartment_all_ForFineTune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
stealthwriter/humanAIsentencesnewsmedium100k
2023-09-17T13:19:43.000Z
[ "region:us" ]
stealthwriter
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 23908976 num_examples: 180000 - name: validation num_bytes: 2654251 num_examples: 20000 download_size: 17496159 dataset_size: 26563227 --- # Dataset Card for "humanAIsentencesnewsmedium100k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vincenttttt/department_college_raw
2023-09-17T15:22:14.000Z
[ "region:us" ]
vincenttttt
null
null
null
0
3
Entry not found
mwitiderrick/squadv2
2023-09-17T15:50:06.000Z
[ "region:us" ]
mwitiderrick
null
null
null
0
3
Entry not found
juanluisrto/marques
2023-09-17T22:02:47.000Z
[ "region:us" ]
juanluisrto
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 607598 num_examples: 289 download_size: 283004 dataset_size: 607598 --- # Dataset Card for "marques" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hwattenberger/test_qa_article
2023-09-17T20:29:18.000Z
[ "region:us" ]
hwattenberger
null
null
null
0
3
Entry not found
amongglue/books3-pretok-phi-1.5-uint16
2023-09-18T03:58:46.000Z
[ "region:us" ]
amongglue
null
null
null
0
3
Entry not found
Cherishh/asr-slu
2023-09-18T04:14:33.000Z
[ "region:us" ]
Cherishh
null
null
null
0
3
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: speech sequence: float64 - name: sampling_rate dtype: int64 - name: target_text dtype: string splits: - name: train num_bytes: 3131199570 num_examples: 6002 - name: val num_bytes: 351773643 num_examples: 667 - name: test num_bytes: 380367632 num_examples: 741 download_size: 916274597 dataset_size: 3863340845 --- # Dataset Card for "asr-slu" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mwitiderrick/lamini_llama
2023-09-18T05:36:13.000Z
[ "region:us" ]
mwitiderrick
null
null
null
0
3
Entry not found
boopysaur/bpd-twitter-plus
2023-09-18T08:38:20.000Z
[ "region:us" ]
boopysaur
null
null
null
0
3
--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 2872991.0 num_examples: 42389 download_size: 2139467 dataset_size: 2872991.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "bpd-twitter-plus" I scraped my twitter timeline some time in late 2022 / v early 2023