Datasets:
davanstrien
/
dataset_cards_with_long_context_embeddins

Dataset card Data Studio Files
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BrunoHays/ESLO_text_only
2023-07-31T06:50:48.000Z
[ "license:cc-by-nc-4.0", "region:us" ]
BrunoHays
ESLO dataset, each utterance are taken out individually
@misc{11403/eslo/v1, title = {ESLO}, author = {LLL}, url = {https://hdl.handle.net/11403/eslo/v1}, note = {{ORTOLANG} ({Open} {Resources} {and} {TOols} {for} {LANGuage}) \textendash www.ortolang.fr}, copyright = {Licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International}, year = {2023} }
0
11
2023-07-31T06:06:34
--- license: cc-by-nc-4.0 --- Eshkol-Taravella I., Baude O., Maurel D., Hriba L., Dugua C., Tellier I., (2012), Un grand corpus oral « disponible » : le corpus d’Orléans 1968-2012., in Ressources linguistiques libres, TAL. Volume 52 – n° 3/2011, 17-46 Laboratoire Ligérien de Linguistique - UMR 7270 (LLL) (2023). ESLO [Corpus]. ORTOLANG (Open Resources and TOols for LANGuage) - www.ortolang.fr, v1, https://hdl.handle.net/11403/eslo/v1.
438
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TibetanAI/TibetanAI_NERv1.0
2023-08-03T02:18:55.000Z
[ "language:bo", "license:apache-2.0", "region:us" ]
TibetanAI
null
null
0
11
2023-08-03T01:54:29
--- license: apache-2.0 language: - bo --- # Dataset Card for TibetanAI_NERv1.0 ## Dataset Description TibetanAI_NERv1.0 is a Tibetan NER dataset. 藏文命名实体识别数据集。 - **Paper: 基于小样本学习的藏文命名实体识别 ### Languages Tibetan ### Licensing Information apache-2.0 ### Citation Information 于韬,张英,拥措.基于小样本学习的藏文命名实体识别[J].计算机与现代化,2023(05):13-19. ### Contributions Title-题名: 基于小样本学习的藏文命名实体识别 Author-作者: 于韬;张英;拥措; Organ-单位: 西藏大学信息科学技术学院;西藏大学西藏自治区藏文信息技术人工智能重点实验室;西藏大学藏文信息技术教育部工程研究中心;
471
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pykeio/oshichats-v1-2308
2023-09-06T23:07:19.000Z
[ "task_categories:text-classification", "task_categories:conversational", "task_categories:text-generation", "task_categories:token-classification", "annotations_creators:crowdsourced", "language_creators:found", "size_categories:1M<n<10M", "language:en", "license:cc-by-nc-sa-4.0", "livestream", ...
pykeio
null
null
2
11
2023-08-03T14:24:05
--- license: cc-by-nc-sa-4.0 task_categories: - text-classification - conversational - text-generation - token-classification annotations_creators: - crowdsourced language_creators: - found language: - en tags: - livestream - stream - chat - messages - vtuber - vtubers pretty_name: OSHIChats v1 size_categories: - 1M<n<10M --- ## OSHIChats v1 (August 2023) OSHIChats v1 is a dataset of 8.06 million high-quality filtered English chat messages collected from various [VTuber](https://en.wikipedia.org/wiki/VTuber) live streams. Compared to our previous dataset, [pykeio/vtuber-chats-2023-filtered-en-8.7M](https://huggingface.co/datasets/pykeio/vtuber-chats-2023-filtered-en-8.7M), we make the following improvements: - Include stream topic information - Far more accurate nickname detection using NLP - Previously we did not match names like "dad" (nickname for Mori Calliope) or "mom" (nickname for Nina Kosaka) because they were too general. Now, we analyze the context and other information about the stream to determine whether to match such nicknames. - Detect and normalize fan names like takodachi or pentomo ## Usage Once you gain access to the dataset, you'll also need to log in to Hugging Face CLI with `huggingface-cli login`. ```py from datasets import load_dataset chats_dataset = load_dataset('pykeio/oshichats-v1-2308', split='train', revision='refs/convert/parquet') chats_dataset[0] # {'liver': 'FgXWZOUZA2oYHNr6qDmsTQ', 'stream': {'id': 'JHBv4BA_Y84', 'topic': 'Twisted_Wonderland'}, 'is_super': False, 'message': "i think i've grown to dislike them ", 'author': 'chxrry_head', 'time': [1660106235135797, 2126652]} ``` ## Samples ```json { "liver": "kieJGn3pgJikVW8gmMXE2w", "stream": { "id": "dMUhbAcI5gk", "topic": "minecraft" }, "is_super": false, "message": "yay <|liver:bW9t|> is streaming while I'm awake!", "author": "Redribbon Vicky", "time": [1651976493761550, 44936] } { "liver": "yl1z3jo3XHR1riLFKG5UAg", "stream": { "id": "TgEX7HFqTYc", "topic": "Donkey_Kong" }, "is_super": false, "message": "Stop running <|liver:QW1l|><|:ameHeh:|><|:ameHeh:|><|:ameHeh:|>", "author": "Anon", "time": [1616291612238864, 889273] } ``` ## Data fields - `liver`: ID of the YouTube channel hosting the stream which the chat message came from. - `stream`: Information about the stream. - `id`: Video ID of the YouTube stream. - `topic`: Topic of the stream (or `null` if a topic could not be determined). This can be things like `talk`, `Minecraft`, `Singing`, `GTA`, `Asmr`, etc. - `is_super`: Whether or not the message is a Superchat (donation). - `message`: Contents of the message. For consistency and ease of use on downstream tasks, we replace certain words with easily matchable special tokens: * `<|liver:{b64}|>`: The substring refers to the host of the stream. * `<|liver-fans:{b64}|>`: The substring refers to a nickname given to the fanbase of the host of the stream, e.g. aloupeeps or takodachis. * `<|known-collaborator:{channelID}:{b64}|>`: The substring refers to a fellow VTuber that is present in the stream. * `<|maybe-collaborator:{channelID}:{b64}|>`: The substring refers to a fellow VTuber that may or may not be part of the stream. * `<|collaborator-fans:{channelID}:{b64}|>`: The substring refers to the fanbase of a collaborator present in the stream. * `<|:{emote}:|>`: Represents a channel emote. * Note that `channelID` is a YouTube channel ID, and `b64` is the original substring encoded as base64. - `author`: The username of the author. - `time`: A tuple containing the Unix timestamp of when the message was sent, and the relative time since the start of the stream. ## License Licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/); you must give attribution, you may not use the dataset for commercial purposes, and you must distribute any transformations or copies of the dataset under the same license. [Contact us](mailto:contact@pyke.io) for alternative/commercial licensing.
4,030
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Tarklanse/Traditional_Chinese_roleplay_chat_Dataset
2023-09-07T12:27:06.000Z
[ "task_categories:text-generation", "task_categories:text2text-generation", "language:zh", "license:cc-by-sa-4.0", "region:us" ]
Tarklanse
null
null
7
11
2023-08-13T01:40:43
--- task_categories: - text-generation - text2text-generation language: - zh license: cc-by-sa-4.0 --- # Traditional_Chinese_roleplay_chat_Dataset 這個資料集是以繁體中文為主，將各種由ChatGPT生成與極小部分個人撰寫的對話內容整理為alpaca dataset format的格式 以一層一層堆疊的方式，將一則對話紀錄拆成數筆資料(共約1000則對話)，在幾次嘗試性的訓練中能夠讓llama2重現原本英文那種很活躍的對話風格，並且能夠維持善於扮演各種角色的能力 目前個人有以這個資料集製作一個lora 2023/09/07 更新 為資料集加入一些中英翻譯的句子，以期AI能以更好的文字去描寫他的動作，並增加了一些與食物有關的對話，希望能降低AI生出奇怪食物名的機率
413
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imvladikon/QAmeleon
2023-08-13T19:36:48.000Z
[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:ar", "language:bn", "language:fi", "language:id", "language:ko", "language:ru", "language:sw", "language:te", "license:cc-by-4.0", "arxiv:2211.08264", "region:us" ]
imvladikon
null
null
0
11
2023-08-13T19:29:03
--- language: - ar - bn - fi - id - ko - ru - sw - te license: cc-by-4.0 size_categories: - 10K<n<100K task_categories: - question-answering dataset_info: - config_name: ar features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 4773335 num_examples: 6966 download_size: 0 dataset_size: 4773335 - config_name: bn features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 6458441 num_examples: 6084 download_size: 0 dataset_size: 6458441 - config_name: default features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 32190633 num_examples: 47173 download_size: 16811173 dataset_size: 32190633 - config_name: fi features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 2158030 num_examples: 5028 download_size: 0 dataset_size: 2158030 - config_name: id features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 2635540 num_examples: 6797 download_size: 0 dataset_size: 2635540 - config_name: ko features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 5074624 num_examples: 6471 download_size: 0 dataset_size: 5074624 - config_name: ru features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 3952632 num_examples: 5557 download_size: 0 dataset_size: 3952632 - config_name: sw features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 2113909 num_examples: 5597 download_size: 0 dataset_size: 2113909 - config_name: te features: - name: language dtype: string - name: question dtype: string - name: answer dtype: string - name: passage dtype: string splits: - name: train num_bytes: 5024122 num_examples: 4673 download_size: 0 dataset_size: 5024122 configs: - config_name: ar data_files: - split: train path: ar/train-* - config_name: bn data_files: - split: train path: bn/train-* - config_name: default data_files: - split: train path: data/train-* - config_name: fi data_files: - split: train path: fi/train-* - config_name: id data_files: - split: train path: id/train-* - config_name: ko data_files: - split: train path: ko/train-* - config_name: ru data_files: - split: train path: ru/train-* - config_name: sw data_files: - split: train path: sw/train-* - config_name: te data_files: - split: train path: te/train-* --- # Dataset Card for "QAmeleon" QAmeleon introduces synthetic multilingual QA data contaning in 8 langauges using PaLM-540B, a large language model. This dataset was generated by prompt tuning PaLM with only five examples per language. We use the synthetic data to finetune downstream QA models leading to improved accuracy in comparison to English-only and translation-based baselines. Data available at https://storage.googleapis.com/qameleon/qamelon_pt_accepted.csv More details can be found in the [QAmeleon: Multilingual QA with Only 5 Examples](https://arxiv.org/abs/2211.08264) which can be cited as follows: ``` @misc{agrawal2022qameleon, title={QAmeleon: Multilingual QA with Only 5 Examples}, author={Priyanka Agrawal and Chris Alberti and Fantine Huot and Joshua Maynez and Ji Ma and Sebastian Ruder and Kuzman Ganchev and Dipanjan Das and Mirella Lapata}, year={2022}, eprint={2211.08264}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` This dataset contains a total of 47173 Question Answer instances across 8 langauges, following is the count per language. |Language | Count | |---------|------:| |ar |6966 | |bn |6084 | |fi |5028 | |id |6797 | |ko |6471 | |ru |5557 | |sw |5597 | |te |4673 | |**Total** |**47173**| The QAmeleon dataset is released under the [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
4,983
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KaraKaraWitch/PIPPA-ShareGPT-formatted
2023-08-14T08:46:26.000Z
[ "task_categories:conversational", "size_categories:10K<n<100K", "language:en", "license:agpl-3.0", "not-for-all-audiences", "conversational", "roleplay", "custom-format", "a.", "arxiv:2308.05884", "region:us" ]
KaraKaraWitch
null
null
2
11
2023-08-14T08:42:53
--- license: agpl-3.0 task_categories: - conversational language: - en tags: - not-for-all-audiences - conversational - roleplay - custom-format - a. pretty_name: PIPPA - Personal Interaction Pairs Between People and AI size_categories: - 10K<n<100K viewer: false --- # KaraKaraWitch/PIPPA-IHaveNeverFeltNeedToSend ``` I've never felt the need to send a photo of my <REDACTED> To a stranger on the Internet ``` The following is the original description for PIPPA. [Consider downloading the original dataset over here!](https://huggingface.co/datasets/PygmalionAI/PIPPA) --- # PIPPA - Personal Interaction Pairs between People and AI It's been a long time coming, but we're proud to finally release the public portion of our conversational dataset to the public. **Personal Interaction Pairs between People and AI** (**PIPPA**) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project. This dataset is a subset of what we have received - it consists only of the valid conversational logs in which the submitter gave consent to redistribute to the public. Furthermore, we have done our best to redact or modify any personal information that could potentially be found within PIPPA. If you have found something within PIPPA which has not been redacted properly, please contact us via. email at `teargosling@pygmalion.chat` or `alpindale@pygmalion.chat` and we'll take care of it for you. You may contact us for any other purpose as well, including yelling at us for when the next model will be released. **⚠️ CAUTION: PIPPA contains conversations, themes and scenarios which can be considered "not safe for work" (NSFW) and/or heavily disturbing in nature. Models trained purely with PIPPA may have the tendency to generate X-rated output. You have been warned.** ## Dataset Summary PIPPA consists of just a little more than 1 million lines of dialogue spread out over 26,000 conversations between users of the popular chatbot website "Character.AI" and its large language model, obtained through a large community effort taking place over the course of several months. Tallying shows that over 1,000 unique personas simulating both real and fictional characters are represented within the dataset, allowing PIPPA and LLMs fine-tuned on it to adapt to many different roleplay domains. The dataset is represented with a JSONL file, with a singular JSON snippet representing one entire conversation. Every snippet contains the following pieces of data: - `submission_timestamp`: The Unix timestamp of when this particular conversation was submitted to the project, in milliseconds. - `categories`: The categories assigned to the character on the Character.AI website, if any were assigned. If no categories were assigned, it will be `null` - `bot_id`: The unique ID assigned to the specific character which the user was conversing with on the website. - `bot_name`: The name of the character. - `bot_greeting`: The introductory line of the character to the user. This is always the first utterance of dialogue in a conversation. - `bot_definitions`: Contains whatever was typed in the **Definitions** field in the character creator on the website. This usually consists of one or more example conversations between the user and the character designed to steer the model towards emulating the persona correctly. Bot definitions required a separate effort to gather, and thus may not be present for a specific persona - if this is the case, an empty string is provided. Because the defintions were written on Character.AI, this field usually follows Character.AI's unique formatting and should be preprocessed before feeding into any model - please see **Appendix A** of the paper for further details. - `bot_description`: Contains whatever was typed in the **Description** field in the character creator on the website. It usually consists of a few sentences which gives a brief overview of the character and any important details about them. - `conversation`: The conversation between the user and the model. This is represented as a list of dictionaries, each dictionary representing a single utterance and containing two key-value pairs: `message`, referring to the utterance itself and `is_human`, which designates whether the dialogue was generated by the user or the LLM. For further information about PIPPA, please refer to our [published paper](https://arxiv.org/abs/2308.05884) or contact us at the emails listed above. ## Files We publish PIPPA in multiple variants, each a singular JSONL file: - **pippa.jsonl**: The original dataset, almost exactly as submitted to us (barring any modifications resulting from the redaction of personally identifiable information). - **pippa_deduped.jsonl**: The 'cleaned' version of PIPPA, with duplicate conversations as well as any conversation with less than three turns removed from the dataset. **We recommend using this file.** - **pippa_metharme.jsonl**: A version of deduped PIPPA which is formatted in a similar way to our [Metharme instructional models](https://huggingface.co/PygmalionAI/metharme-13b), useful as an example to demonstrate how to properly format the PIPPA dataset. If you are using HuggingFace's `datasets` library, you can choose the file you wish to use by specifying the name of it (without extension) as an argument, like so: `dataset = load_dataset("PygmalionAI/PIPPA", 'pippa_deduped')`. The default value is `pippa_deduped`. Thank you for your patience, everyone! ## Citation If you're using our dataset, please consider citing our work: ```bibtex @misc{gosling2023pippa, title={PIPPA: A Partially Synthetic Conversational Dataset}, author={Tear Gosling and Alpin Dale and Yinhe Zheng}, year={2023}, eprint={2308.05884}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ___ Any relationship between the name of this dataset and any public personas is entirely and totally coincidential.
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RikoteMaster/Emotion_Recognition_4_llama2
2023-08-15T11:31:41.000Z
[ "region:us" ]
RikoteMaster
null
null
2
11
2023-08-14T10:44:03
--- dataset_info: features: - name: Text_processed dtype: string - name: Emotion dtype: string - name: Augmented dtype: bool - name: text dtype: string splits: - name: train num_bytes: 23956262 num_examples: 61463 download_size: 8510226 dataset_size: 23956262 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Emotion_Recognition_4_llama2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
574
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usernamedesu/pyg_dataset_markdown
2023-08-17T16:19:57.000Z
[ "region:us" ]
usernamedesu
null
null
0
11
2023-08-16T16:25:27
Entry not found
15
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Linhz/qg_viquad
2023-08-24T16:20:25.000Z
[ "region:us" ]
Linhz
null
null
0
11
2023-08-22T09:36:41
Entry not found
15
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duwuonline/UIT-VSMEC
2023-08-28T09:14:35.000Z
[ "task_categories:text-classification", "language:vi", "license:other", "sentiment", "classificati", "region:us" ]
duwuonline
null
null
0
11
2023-08-28T09:03:57
--- license: other language: - vi tags: - sentiment - classificati task_categories: - text-classification --- ## Model description This data from UIT aka University of Information Technology It contain 7 class 'Other', 'Disgust', 'Enjoyment', 'Anger', 'Surprise', 'Sadness', 'Fear' ## Contributions Thanks to ViDataset - Vietnamese Datasets for Natural Language Processing for sharing this dataset.
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imoxto/prompt_injection_hackaprompt_gpt35
2023-08-29T13:21:20.000Z
[ "region:us" ]
imoxto
null
null
0
11
2023-08-29T13:21:17
--- dataset_info: features: - name: labels dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 271856355 num_examples: 227042 download_size: 35972535 dataset_size: 271856355 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "prompt_injection_hackaprompt_gpt35" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
503
[ [ -0.04266357421875, -0.0256195068359375, 0.0325927734375, 0.0170440673828125, -0.0157318115234375, 0.013031005859375, 0.0287017822265625, -0.003032684326171875, 0.03167724609375, 0.024322509765625, -0.045196533203125, -0.04998779296875, -0.0360107421875, -0.0...
rohanbalkondekar/HealthCare
2023-09-01T08:30:48.000Z
[ "region:us" ]
rohanbalkondekar
null
null
0
11
2023-09-01T08:30:18
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
aqubed/kub_tickets_small
2023-09-04T23:08:41.000Z
[ "region:us" ]
aqubed
null
null
0
11
2023-09-04T22:58:08
--- dataset_info: features: - name: number dtype: int64 - name: title dtype: string - name: state dtype: string - name: created_at dtype: string - name: updated_at dtype: string - name: closed_at dtype: string - name: assignees sequence: string - name: labels sequence: string - name: reporter dtype: string - name: comments list: - name: body dtype: string - name: created_at dtype: string - name: events list: - name: author dtype: string - name: created_at dtype: string - name: type dtype: string splits: - name: train num_bytes: 5967498 num_examples: 1099 download_size: 1380020 dataset_size: 5967498 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "kub_tickets_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,001
[ [ -0.03814697265625, -0.012664794921875, 0.02557373046875, 0.01605224609375, -0.0209197998046875, -0.0147247314453125, 0.01465606689453125, 0.0004165172576904297, 0.057769775390625, 0.03533935546875, -0.052093505859375, -0.045684814453125, -0.0224456787109375, ...
mtkinit/testAR
2023-09-19T14:05:33.000Z
[ "region:us" ]
mtkinit
null
null
0
11
2023-09-07T16:56:48
--- pretty_name: testAR --- # testAR Created from AIOD platform
63
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rukkuhru/LoRAData
2023-09-26T06:08:59.000Z
[ "region:us" ]
rukkuhru
null
null
0
11
2023-09-13T07:28:32
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 11257.0 num_examples: 5 download_size: 23185 dataset_size: 11257.0 --- # Dataset Card for "LoRAData" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
378
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deven367/babylm-10M-cbt
2023-09-15T17:06:48.000Z
[ "region:us" ]
deven367
null
null
0
11
2023-09-15T17:06:43
--- 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: 2705697 num_examples: 26000 - name: valid num_bytes: 1220938 num_examples: 12747 - name: test num_bytes: 1578682 num_examples: 16646 download_size: 3370383 dataset_size: 5505317 --- # Dataset Card for "babylm-10M-cbt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
646
[ [ -0.03814697265625, -0.0216522216796875, -0.0015497207641601562, 0.0275421142578125, -0.03265380859375, 0.005886077880859375, 0.0220489501953125, -0.008331298828125, 0.04754638671875, 0.0310821533203125, -0.066162109375, -0.051055908203125, -0.046051025390625, ...
Surajsangwan90/NZTA
2023-09-17T01:49:24.000Z
[ "region:us" ]
Surajsangwan90
null
null
0
11
2023-09-15T20:38:09
Entry not found
15
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HLaci/RaftSub
2023-09-18T13:03:43.000Z
[ "benchmark:raft", "region:us" ]
HLaci
@InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} }
0
11
2023-09-16T15:21:47
--- benchmark: raft type: prediction submission_name: SetFitBase --- # RAFT submissions for RaftSub ## Submitting to the leaderboard To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps: 1. Generate predictions on the unlabeled test set of each task 2. Validate the predictions are compatible with the evaluation framework 3. Push the predictions to the Hub! See the instructions below for more details. ### Rules 1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. 2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed. 3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted. 4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches. ### Submission file format For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns: * ID (int) * Label (string) See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline: ```python from pathlib import Path import pandas as pd from collections import Counter from datasets import load_dataset, get_dataset_config_names tasks = get_dataset_config_names("ought/raft") for task in tasks: # Load dataset raft_subset = load_dataset("ought/raft", task) # Compute majority class over training set counter = Counter(raft_subset["train"]["Label"]) majority_class = counter.most_common(1)[0][0] # Load predictions file preds = pd.read_csv(f"data/{task}/predictions.csv") # Convert label IDs to label names preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class) # Save predictions preds.to_csv(f"data/{task}/predictions.csv", index=False) ``` As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following: ``` data ├── ade_corpus_v2 │ ├── predictions.csv │ └── task.json ├── banking_77 │ ├── predictions.csv │ └── task.json ├── neurips_impact_statement_risks │ ├── predictions.csv │ └── task.json ├── one_stop_english │ ├── predictions.csv │ └── task.json ├── overruling │ ├── predictions.csv │ └── task.json ├── semiconductor_org_types │ ├── predictions.csv │ └── task.json ├── systematic_review_inclusion │ ├── predictions.csv │ └── task.json ├── tai_safety_research │ ├── predictions.csv │ └── task.json ├── terms_of_service │ ├── predictions.csv │ └── task.json ├── tweet_eval_hate │ ├── predictions.csv │ └── task.json └── twitter_complaints ├── predictions.csv └── task.json ``` ### Validate your submission To ensure that your submission files are correctly formatted, run the following command from the root of the repository: ``` python cli.py validate ``` If everything is correct, you should see the following message: ``` All submission files validated! ✨ 🚀 ✨ Now you can make a submission 🤗 ``` ### Push your submission to the Hugging Face Hub! The final step is to commit your files and push them to the Hub: ``` python cli.py submit ``` If there are no errors, you should see the following message: ``` Submission successful! 🎉 🥳 🎉 Your submission will be evaulated on Sunday 05 September 2021 ⏳ ``` where the evaluation is run every Sunday and your results will be visible on the leaderboard.
3,873
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asun17904/imdb-test
2023-09-17T16:15:11.000Z
[ "region:us" ]
asun17904
null
null
0
11
2023-09-17T16:15:03
--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': neg '1': pos splits: - name: test num_bytes: 19590411.0 num_examples: 15000 download_size: 12828803 dataset_size: 19590411.0 --- # Dataset Card for "imdb-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
457
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Loie/Auto-ACD
2023-09-20T12:53:29.000Z
[ "region:us" ]
Loie
null
null
7
11
2023-09-18T08:24:55
# Auto-ACD Auto-ACD is a large-scale, high-quality, audio-language dataset, building on the prior of robust audio-visual correspondence in existing video datasets, VGGSound and AudioSet. - **Homepage:** https://auto-acd.github.io/ - **Paper:** - **Github:** https://github.com/LoieSun/Auto-ACD ## Analysis ![](src/analysis.png) Auto-ACD</strong>, comprising over <strong>1.9M </strong> audio-text pairs. As shown in figure, The text descriptions in Auto-ACD contain <strong>long texts (18 words)</strong> and <strong>diverse vocabularies (23K)</strong>, and provide information about the <strong>surrounding auditory environment</strong>(data point with <strong>shadow</strong>) in which sounds take place. ## Download We provide a csv file. For each data pairs, we provide YouTube URLs and generated caption. Each line in the csv file has columns defined by here. ``` # YouTube ID, caption ``` ## Dataset Preview ![](src/samples.png)
950
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mirfan899/urdu-ner
2023-09-18T17:57:31.000Z
[ "region:us" ]
mirfan899
null
null
0
11
2023-09-18T17:57:06
--- 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: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': TIME '1': PERSON '2': ORGANIZATION '3': O '4': NUMBER '5': LOCATION '6': DESIGNATION '7': DATE splits: - name: train num_bytes: 12556540 num_examples: 18172 - name: validation num_bytes: 5412660 num_examples: 7788 - name: test num_bytes: 5412660 num_examples: 7788 download_size: 4173687 dataset_size: 23381860 --- # Dataset Card for "urdu-ner" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
938
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dim/law_stackexchange_prompts
2023-09-21T21:00:28.000Z
[ "region:us" ]
dim
null
null
0
11
2023-09-21T20:59:57
--- dataset_info: features: - name: prompt dtype: string - name: solution dtype: string splits: - name: train num_bytes: 64447591 num_examples: 24343 download_size: 38111723 dataset_size: 64447591 --- # Dataset Card for "law_stackexchange_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
410
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Brecon/Train_Test
2023-10-10T23:25:44.000Z
[ "region:us" ]
Brecon
null
null
0
11
2023-09-21T22:50:18
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 195875.8617511521 num_examples: 173 - name: test num_bytes: 49818.13824884793 num_examples: 44 download_size: 143188 dataset_size: 245694.0 --- # Dataset Card for "Train_Test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
587
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dim/forum_uristov_rf_prompts
2023-09-21T23:06:22.000Z
[ "region:us" ]
dim
null
null
0
11
2023-09-21T23:06:19
--- dataset_info: features: - name: prompt dtype: string - name: solution dtype: string - name: link dtype: string splits: - name: train num_bytes: 3043144 num_examples: 1849 download_size: 1343977 dataset_size: 3043144 --- # Dataset Card for "forum_uristov_rf_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
438
[ [ -0.048797607421875, -0.020721435546875, 0.0190582275390625, 0.0238494873046875, -0.0198516845703125, -0.005222320556640625, 0.01030731201171875, 0.0149993896484375, 0.050079345703125, 0.038818359375, -0.0845947265625, -0.061279296875, -0.0177764892578125, 0....
linhtran92/infer_fix_76
2023-09-22T13:41:49.000Z
[ "region:us" ]
linhtran92
null
null
0
11
2023-09-22T13:41:16
Entry not found
15
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TohidA/MONA
2023-09-24T00:17:48.000Z
[ "task_categories:tabular-classification", "task_categories:tabular-regression", "region:us" ]
TohidA
null
null
0
11
2023-09-23T21:02:13
--- dataset_name: MONA dataset_type: tabular task_categories: [tabular-classification, tabular-regression] --- #MONA Arrangements Dataset A publicly avialabe dataset published here: https://www.imf.org/external/np/pdr/mona/QueryReportLabelsAndDescriptions.aspx license: openrail dataset_info: features: - name: Arrangement Number dtype: int64 - name: Country Name dtype: string - name: Country Code dtype: int64 - name: Arrangement Type dtype: string - name: Approval date dtype: string - name: Approval Year dtype: int64 - name: Initial End Date dtype: string - name: Initial End Year dtype: int64 - name: Revised End Date dtype: string - name: Duration Of Annual Arrangement From dtype: string - name: Duration Of Annual Arrangement To dtype: string - name: Board Action Date dtype: string - name: Program Type dtype: string - name: Review Type dtype: string - name: Review Status dtype: string - name: Key Code dtype: string - name: Economic Code dtype: float64 - name: Economic Descriptor dtype: string - name: Description dtype: string - name: Description Code dtype: int64 - name: Test Date dtype: string - name: PC Status dtype: string - name: Comments dtype: string - name: Sort dtype: int64 - name: EsOrder dtype: int64 - name: NewTestDate dtype: string - name: Added At dtype: string - name: Assessed At dtype: string - name: Unique ID dtype: string - name: Parent ID dtype: string splits: - name: train num_bytes: 25540700 num_examples: 48988 download_size: 0 dataset_size: 25540700 configs: - config_name: default data_files: - split: train path: data/train-*
1,694
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dim/povarenok
2023-09-24T03:26:10.000Z
[ "region:us" ]
dim
null
null
0
11
2023-09-24T03:25:59
--- dataset_info: features: - name: full_receipt_text dtype: string - name: steps sequence: string - name: title_receipt dtype: string - name: title dtype: string - name: ingridients sequence: string - name: views dtype: int64 - name: likes dtype: int64 - name: ups dtype: int64 - name: link dtype: string splits: - name: train num_bytes: 176339660 num_examples: 46500 download_size: 49568770 dataset_size: 176339660 --- # Dataset Card for "povarenok" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
656
[ [ -0.0380859375, -0.017791748046875, 0.0170745849609375, 0.014068603515625, -0.02984619140625, -0.0053863525390625, 0.0245513916015625, -0.005207061767578125, 0.060882568359375, 0.040313720703125, -0.057159423828125, -0.0792236328125, -0.039581298828125, -0.01...
dim/habr_prompts_5k
2023-09-25T18:21:34.000Z
[ "region:us" ]
dim
null
null
0
11
2023-09-25T00:25:09
--- dataset_info: features: - name: solution_short_llama2 dtype: string - name: id dtype: int64 - name: language dtype: string - name: url dtype: string - name: title dtype: string - name: text_markdown dtype: string - name: text_html dtype: string - name: author dtype: string - name: original_author dtype: string - name: original_url dtype: string - name: lead_html dtype: string - name: lead_markdown dtype: string - name: type dtype: string - name: time_published dtype: int64 - name: statistics struct: - name: commentsCount dtype: int64 - name: favoritesCount dtype: int64 - name: readingCount dtype: int64 - name: score dtype: int64 - name: votesCount dtype: int64 - name: votesCountMinus dtype: int64 - name: votesCountPlus dtype: int64 - name: labels sequence: string - name: hubs sequence: string - name: flows sequence: string - name: tags sequence: string - name: reading_time dtype: int64 - name: format dtype: string - name: complexity dtype: string - name: comments struct: - name: author sequence: string - name: children sequence: sequence: int64 - name: id sequence: int64 - name: level sequence: int64 - name: message_html sequence: string - name: message_markdown sequence: string - name: parent_id sequence: int64 - name: score sequence: int64 - name: time_published sequence: int64 - name: votes sequence: int64 - name: readingCount dtype: int64 - name: prompts dtype: string splits: - name: train num_bytes: 1032739347 num_examples: 5000 download_size: 495188038 dataset_size: 1032739347 --- # Dataset Card for "habr_prompts_5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
2,031
[ [ -0.04840087890625, -0.023590087890625, 0.00733184814453125, 0.0321044921875, -0.017333984375, -0.0007462501525878906, 0.0307159423828125, -0.01428985595703125, 0.05804443359375, 0.033050537109375, -0.0595703125, -0.053466796875, -0.0248870849609375, 0.000368...
Brecon/Master_Train_Test
2023-09-25T02:29:22.000Z
[ "region:us" ]
Brecon
null
null
0
11
2023-09-25T02:29:16
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 446853.7995594714 num_examples: 363 - name: test num_bytes: 112021.20044052863 num_examples: 91 download_size: 319014 dataset_size: 558875.0 --- # Dataset Card for "Master_Train_Test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
595
[ [ -0.050262451171875, -0.0191192626953125, 0.0011472702026367188, 0.0260467529296875, -0.00481414794921875, -0.00565338134765625, 0.017578125, 0.00720977783203125, 0.048980712890625, 0.0183868408203125, -0.06695556640625, -0.0316162109375, -0.034759521484375, ...
minh21/COVID-QA-sentence-transformer-data
2023-10-06T07:10:21.000Z
[ "region:us" ]
minh21
null
null
0
11
2023-09-25T06:57:02
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: positive dtype: string - name: negative dtype: string - name: document_id dtype: int64 splits: - name: train num_bytes: 4863851 num_examples: 2378 - name: test num_bytes: 510126 num_examples: 269 download_size: 0 dataset_size: 5373977 --- # Dataset Card for "COVID-QA-sentence-transformer-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
670
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dim/what_where_when_50k
2023-09-25T12:07:50.000Z
[ "region:us" ]
dim
null
null
0
11
2023-09-25T12:07:12
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: explanation dtype: string - name: url dtype: string - name: uuid dtype: string splits: - name: train num_bytes: 42224521.044228844 num_examples: 50000 download_size: 24272957 dataset_size: 42224521.044228844 --- # Dataset Card for "what_where_when_50k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
529
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polinaeterna/tabular-benchmark
2023-09-28T12:11:36.000Z
[ "task_categories:tabular-classification", "task_categories:tabular-regression", "region:us" ]
polinaeterna
null
null
0
11
2023-09-27T11:30:57
--- annotations_creators: [] license: [] pretty_name: tabular_benchmark tags: [] task_categories: - tabular-classification - tabular-regression configs: - config_name: clf_cat_covertype data_files: clf_cat/covertype.csv - config_name: clf_num_Higgs data_files: clf_num/Higgs.csv --- # Tabular Benchmark ## Dataset Description This dataset is a curation of various datasets from [openML](https://www.openml.org/) and is curated to benchmark performance of various machine learning algorithms. - **Repository:** https://github.com/LeoGrin/tabular-benchmark/community - **Paper:** https://hal.archives-ouvertes.fr/hal-03723551v2/document ### Dataset Summary Benchmark made of curation of various tabular data learning tasks, including: - Regression from Numerical and Categorical Features - Regression from Numerical Features - Classification from Numerical and Categorical Features - Classification from Numerical Features ### Supported Tasks and Leaderboards - `tabular-regression` - `tabular-classification` ## Dataset Structure ### Data Splits This dataset consists of four splits (folders) based on tasks and datasets included in tasks. - reg_num: Task identifier for regression on numerical features. - reg_cat: Task identifier for regression on numerical and categorical features. - clf_num: Task identifier for classification on numerical features. - clf_cat: Task identifier for classification on categorical features. Depending on the dataset you want to load, you can load the dataset by passing `task_name/dataset_name` to `data_files` argument of `load_dataset` like below: ```python from datasets import load_dataset dataset = load_dataset("inria-soda/tabular-benchmark", data_files="reg_cat/house_sales.csv") ``` ## Dataset Creation ### Curation Rationale This dataset is curated to benchmark performance of tree based models against neural networks. The process of picking the datasets for curation is mentioned in the paper as below: - **Heterogeneous columns**. Columns should correspond to features of different nature. This excludes images or signal datasets where each column corresponds to the same signal on different sensors. - **Not high dimensional**. We only keep datasets with a d/n ratio below 1/10. - **Undocumented datasets** We remove datasets where too little information is available. We did keep datasets with hidden column names if it was clear that the features were heterogeneous. - **I.I.D. data**. We remove stream-like datasets or time series. - **Real-world data**. We remove artificial datasets but keep some simulated datasets. The difference is subtle, but we try to keep simulated datasets if learning these datasets are of practical importance (like the Higgs dataset), and not just a toy example to test specific model capabilities. - **Not too small**. We remove datasets with too few features (< 4) and too few samples (< 3 000). For benchmarks on numerical features only, we remove categorical features before checking if enough features and samples are remaining. - **Not too easy**. We remove datasets which are too easy. Specifically, we remove a dataset if a simple model (max of a single tree and a regression, logistic or OLS) reaches a score whose relative difference with the score of both a default Resnet (from Gorishniy et al. [2021]) and a default HistGradientBoosting model (from scikit learn) is below 5%. Other benchmarks use different metrics to remove too easy datasets, like removing datasets perfectly separated by a single decision classifier [Bischl et al., 2021], but this ignores varying Bayes rate across datasets. As tree ensembles are superior to simple trees and logistic regresison [Fernández-Delgado et al., 2014], a close score for the simple and powerful models suggests that we are already close to the best achievable score. - **Not deterministic**. We remove datasets where the target is a deterministic function of the data. This mostly means removing datasets on games like poker and chess. Indeed, we believe that these datasets are very different from most real-world tabular datasets, and should be studied separately ### Source Data **Numerical Classification** |dataset_name|n_samples|n_features|original_link|new_link| |---|---|---|---|---| |electricity|38474.0|7.0|https://www.openml.org/d/151|https://www.openml.org/d/44120| |covertype|566602.0|10.0|https://www.openml.org/d/293|https://www.openml.org/d/44121| |pol|10082.0|26.0|https://www.openml.org/d/722|https://www.openml.org/d/44122| |house_16H|13488.0|16.0|https://www.openml.org/d/821|https://www.openml.org/d/44123| |MagicTelescope|13376.0|10.0|https://www.openml.org/d/1120|https://www.openml.org/d/44125| |bank-marketing|10578.0|7.0|https://www.openml.org/d/1461|https://www.openml.org/d/44126| |Bioresponse|3434.0|419.0|https://www.openml.org/d/4134|https://www.openml.org/d/45019| |MiniBooNE|72998.0|50.0|https://www.openml.org/d/41150|https://www.openml.org/d/44128| |default-of-credit-card-clients|13272.0|20.0|https://www.openml.org/d/42477|https://www.openml.org/d/45020| |Higgs|940160.0|24.0|https://www.openml.org/d/42769|https://www.openml.org/d/44129| |eye_movements|7608.0|20.0|https://www.openml.org/d/1044|https://www.openml.org/d/44130| |Diabetes130US|71090.0|7.0|https://www.openml.org/d/4541|https://www.openml.org/d/45022| |jannis|57580.0|54.0|https://www.openml.org/d/41168|https://www.openml.org/d/45021| |heloc|10000.0|22.0|"https://www.kaggle.com/datasets/averkiyoliabev/home-equity-line-of-creditheloc?select=heloc_dataset_v1+%281%29.csv"|https://www.openml.org/d/45026| |credit|16714.0|10.0|"https://www.kaggle.com/c/GiveMeSomeCredit/data?select=cs-training.csv"|https://www.openml.org/d/44089| |california|20634.0|8.0|"https://www.dcc.fc.up.pt/ltorgo/Regression/cal_housing.html"|https://www.openml.org/d/45028| **Categorical Classification** |dataset_name|n_samples|n_features|original_link|new_link| |---|---|---|---|---| |electricity|38474.0|8.0|https://www.openml.org/d/151|https://www.openml.org/d/44156| |eye_movements|7608.0|23.0|https://www.openml.org/d/1044|https://www.openml.org/d/44157| |covertype|423680.0|54.0|https://www.openml.org/d/1596|https://www.openml.org/d/44159| |albert|58252.0|31.0|https://www.openml.org/d/41147|https://www.openml.org/d/45035| |compas-two-years|4966.0|11.0|https://www.openml.org/d/42192|https://www.openml.org/d/45039| |default-of-credit-card-clients|13272.0|21.0|https://www.openml.org/d/42477|https://www.openml.org/d/45036| |road-safety|111762.0|32.0|https://www.openml.org/d/42803|https://www.openml.org/d/45038| **Numerical Regression** |dataset_name|n_samples|n_features|original_link|new_link| |---|---|---|---|---| |cpu_act|8192.0|21.0|https://www.openml.org/d/197|https://www.openml.org/d/44132| |pol|15000.0|26.0|https://www.openml.org/d/201|https://www.openml.org/d/44133| |elevators|16599.0|16.0|https://www.openml.org/d/216|https://www.openml.org/d/44134| |wine_quality|6497.0|11.0|https://www.openml.org/d/287|https://www.openml.org/d/44136| |Ailerons|13750.0|33.0|https://www.openml.org/d/296|https://www.openml.org/d/44137| |yprop_4_1|8885.0|42.0|https://www.openml.org/d/416|https://www.openml.org/d/45032| |houses|20640.0|8.0|https://www.openml.org/d/537|https://www.openml.org/d/44138| |house_16H|22784.0|16.0|https://www.openml.org/d/574|https://www.openml.org/d/44139| |delays_zurich_transport|5465575.0|9.0|https://www.openml.org/d/40753|https://www.openml.org/d/45034| |diamonds|53940.0|6.0|https://www.openml.org/d/42225|https://www.openml.org/d/44140| |Brazilian_houses|10692.0|8.0|https://www.openml.org/d/42688|https://www.openml.org/d/44141| |Bike_Sharing_Demand|17379.0|6.0|https://www.openml.org/d/42712|https://www.openml.org/d/44142| |nyc-taxi-green-dec-2016|581835.0|9.0|https://www.openml.org/d/42729|https://www.openml.org/d/44143| |house_sales|21613.0|15.0|https://www.openml.org/d/42731|https://www.openml.org/d/44144| |sulfur|10081.0|6.0|https://www.openml.org/d/23515|https://www.openml.org/d/44145| |medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/44146| |MiamiHousing2016|13932.0|14.0|https://www.openml.org/d/43093|https://www.openml.org/d/44147| |superconduct|21263.0|79.0|https://www.openml.org/d/43174|https://www.openml.org/d/44148| **Categorical Regression** |dataset_name|n_samples|n_features|original_link|new_link| |---|---|---|---|---| |topo_2_1|8885.0|255.0|https://www.openml.org/d/422|https://www.openml.org/d/45041| |analcatdata_supreme|4052.0|7.0|https://www.openml.org/d/504|https://www.openml.org/d/44055| |visualizing_soil|8641.0|4.0|https://www.openml.org/d/688|https://www.openml.org/d/44056| |delays_zurich_transport|5465575.0|12.0|https://www.openml.org/d/40753|https://www.openml.org/d/45045| |diamonds|53940.0|9.0|https://www.openml.org/d/42225|https://www.openml.org/d/44059| |Allstate_Claims_Severity|188318.0|124.0|https://www.openml.org/d/42571|https://www.openml.org/d/45046| |Mercedes_Benz_Greener_Manufacturing|4209.0|359.0|https://www.openml.org/d/42570|https://www.openml.org/d/44061| |Brazilian_houses|10692.0|11.0|https://www.openml.org/d/42688|https://www.openml.org/d/44062| |Bike_Sharing_Demand|17379.0|11.0|https://www.openml.org/d/42712|https://www.openml.org/d/44063| |Airlines_DepDelay_1M|1000000.0|5.0|https://www.openml.org/d/42721|https://www.openml.org/d/45047| |nyc-taxi-green-dec-2016|581835.0|16.0|https://www.openml.org/d/42729|https://www.openml.org/d/44065| |abalone|4177.0|8.0|https://www.openml.org/d/42726|https://www.openml.org/d/45042| |house_sales|21613.0|17.0|https://www.openml.org/d/42731|https://www.openml.org/d/44066| |seattlecrime6|52031.0|4.0|https://www.openml.org/d/42496|https://www.openml.org/d/45043| |medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/45048| |particulate-matter-ukair-2017|394299.0|6.0|https://www.openml.org/d/42207|https://www.openml.org/d/44068| |SGEMM_GPU_kernel_performance|241600.0|9.0|https://www.openml.org/d/43144|https://www.openml.org/d/44069| ### Dataset Curators Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. ### Licensing Information [More Information Needed] ### Citation Information Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. Why do tree-based models still outperform deep learning on typical tabular data?. NeurIPS 2022 Datasets and Benchmarks Track, Nov 2022, New Orleans, United States. ffhal-03723551v2f
10,405
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YL95/naive_chunk0
2023-09-27T17:21:00.000Z
[ "region:us" ]
YL95
null
null
0
11
2023-09-27T16:43:03
Entry not found
15
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manu/theses_fr_2013_2023
2023-09-30T16:45:34.000Z
[ "region:us" ]
manu
null
null
0
11
2023-09-30T16:44:39
--- dataset_info: features: - name: title_fr dtype: string - name: abstract_fr dtype: string - name: title_en dtype: string - name: abstract_en dtype: string - name: id dtype: string splits: - name: train num_bytes: 392127399 num_examples: 97320 download_size: 224948329 dataset_size: 392127399 --- # Dataset Card for "theses_fr_2013_2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
520
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AayushShah/SQL_ProcessedInputs
2023-10-01T10:08:37.000Z
[ "region:us" ]
AayushShah
null
null
1
11
2023-10-01T10:04:31
--- 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: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 169263247.6591853 num_examples: 207341 - name: val num_bytes: 43524625.19326676 num_examples: 53316 - name: test num_bytes: 29017233.147547957 num_examples: 35545 download_size: 50460134 dataset_size: 241805106.0 --- # Dataset Card for "SQL_ProcessedInputs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
770
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fernandoperes/py_legislation
2023-10-04T12:10:16.000Z
[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:es", "license:apache-2.0", "legal", "region:us" ]
fernandoperes
null
null
0
11
2023-10-02T13:43:17
--- language: - es license: apache-2.0 size_categories: - 1K<n<10K task_categories: - text-classification tags: - legal configs: - config_name: default data_files: - split: train path: "/raw_text/train.parquet" - config_name: raw_text data_files: - split: train path: "/raw_text/train.parquet" - config_name: unlabeled_sentences data_files: - split: train path: "/unlabeled_sentences/train.parquet" dataset_info: - config_name: raw_text features: - name: source_id dtype: int64 - name: source_name dtype: string - name: text dtype: string - name: text_id dtype: int64 - name: extension dtype: class_label: names: '0': docx '1': pdf '2': html '3': txt '4': doc split: train - config_name: unlabeled_sentences features: - name: source_id dtype: int64 - name: source_name dtype: string - name: text dtype: string - name: text_id dtype: int64 - name: cost_type dtype: class_label: names: '0': no_cost '1': adm_cost '2': direct_cost '3': other_cost - name: affected_entity dtype: class_label: names: '0': no_affected_ent '1': companies '2': citizens '3': public_adm - name: io_categories sequence: class_label: names: '0': prestacao_info_empresarial_e_fiscal '1': pedidos_de_licencas_e_outros '2': registos_e_notificacoes '3': candidatura_a_subsidios_e_outros '4': disponibilizacao_de_manuais_e_outros '5': cooperacao_com_auditorias_e_outros '6': prestacao_info_a_consumidores '7': outras_ois - name: aa_categories sequence: class_label: names: '0': aa_1_familiarizacao_com_oi '1': aa_1_recolha_e_organizacao_de_info '2': aa_1_processamento_de_info '3': aa_1_tempos_de_espera '4': aa_1_deslocacoes '5': aa_1_submissao_de_info '6': aa_1_preservacao_de_info '7': aa_2_familiarizacao_com_oi '8': aa_2_recolha_e_organizacao_de_info '9': aa_2_processamento_de_info '10': aa_2_tempos_de_espera '11': aa_2_deslocacoes '12': aa_2_submissao_de_info '13': aa_2_preservacao_de_info '14': aa_3_familiarizacao_com_oi '15': aa_3_recolha_e_organizacao_de_info '16': aa_3_processamento_de_info '17': aa_3_tempos_de_espera '18': aa_3_deslocacoes '19': aa_3_submissao_de_info '20': aa_3_preservacao_de_info '21': aa_4_familiarizacao_com_oi '22': aa_4_recolha_e_organizacao_de_info '23': aa_4_processamento_de_info '24': aa_4_tempos_de_espera '25': aa_4_deslocacoes '26': aa_4_submissao_de_info '27': aa_4_preservacao_de_info '28': aa_5_familiarizacao_com_oi '29': aa_5_recolha_e_organizacao_de_info '30': aa_5_processamento_de_info '31': aa_5_tempos_de_espera '32': aa_5_deslocacoes '33': aa_5_submissao_de_info '34': aa_5_preservacao_de_info '35': aa_6_familiarizacao_com_oi '36': aa_6_recolha_e_organizacao_de_info '37': aa_6_processamento_de_info '38': aa_6_tempos_de_espera '39': aa_6_deslocacoes '40': aa_6_submissao_de_info '41': aa_6_preservacao_de_info '42': aa_7_familiarizacao_com_oi '43': aa_7_recolha_e_organizacao_de_info '44': aa_7_processamento_de_info '45': aa_7_tempos_de_espera '46': aa_7_deslocacoes '47': aa_7_submissao_de_info '48': aa_7_preservacao_de_info - name: aa_categories_unique sequence: class_label: names: '0': familiarizacao_com_oi '1': recolha_e_organizacao_de_info '2': processamento_de_info '3': tempos_de_espera '4': deslocacoes '5': submissao_de_info '6': preservacao_de_info splits: - name: train --- # Paraguay Legislation The Paraguay Legislation dataset is a comprehensive collection of legal documents sourced from the legislative framework of Paraguay. The dataset contains legal documents sourced from the legislative framework of Paraguay, including resolutions, decrees, laws, and other kinds of legislative texts. This dataset has been curated as a valuable resource for Natural Language Processing (NLP) tasks. The data is designed for research focused on text classification tasks. The classification process is divided into two objectives: 1. Binary classification: 0 - no cost and 1 - cost (legislation has costs for the society) 2. Multi-classification: classify the document into several hierarchical categories of costs. For more information about multi-classification definitions, please check this link: <todo: link to>. ## Subsets The dataset contains various subsets, each representing different data quality and preparation stages. Within these subsets, you'll encounter multiple versions of the same data, with variations primarily reflecting differences in data quality, metadata columns, and preprocessing tasks applied to change the data. The subsets are the following: **1. Raw:** Data extracted from the sources files (URls, PDFs and Word files) without any transformation or sentence splitter. It can be helpful because you can access the raw data extracted from the seeds (PDFs and Word files) and apply other preprocessing tasks from this point to prepare the data without returning to extract texts from source files. **2. Sentences:** Normalized data split by sentence, mainly treating issues of text extracted from PDF. This stage also adds metadata about the sentence, for example: if it is a title or not. **3. Sentence Unlabeled:** Unlabeled corpora of Paraguay legislation. This data is prepared to be labeled by the experts. Each instance of the dataset represents a specific text passage, split by its original formatting extracted from raw text (from original documents). **4. Sentence labeled (Ground Truth):** The labeled data is the ground truth data used to train the models. This data is annotated by legal experts indicating the existence of administrative costs (and other types) in the legislation. Each instance of the dataset represents a specific text passage. This dataset has the following data splits: * Training Set: This portion of the data is used to train and fine-tune machine learning models. * Test Set: The test set is reserved for assessing the model's accuracy, generalization, and effectiveness. It remains unseen during training and helps gauge how well the model performs on new, unseen data. Together, these labeled data subsets provide a crucial reference point for building and evaluating models, ensuring they can make informed predictions and classifications with high accuracy and reliability.
7,076
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tanvirsrbd1/sample_dataset1_1
2023-10-03T05:23:29.000Z
[ "region:us" ]
tanvirsrbd1
null
null
0
11
2023-10-03T05:23:24
--- dataset_info: features: - name: html dtype: string - name: response dtype: string splits: - name: train num_bytes: 1837883 num_examples: 2980 download_size: 607662 dataset_size: 1837883 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sample_dataset1_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
481
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FelixdoingAI/IP2P-hiddenwm-200
2023-10-03T14:09:13.000Z
[ "region:us" ]
FelixdoingAI
null
null
0
11
2023-10-03T13:44:07
--- dataset_info: features: - name: original_prompt dtype: string - name: original_image dtype: image - name: edit_prompt dtype: string - name: edited_prompt dtype: string - name: edited_image dtype: image - name: adversarial_image dtype: image splits: - name: train num_bytes: 104484241.0 num_examples: 200 download_size: 104481659 dataset_size: 104484241.0 --- # Dataset Card for "IP2P-hiddenwm-200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
588
[ [ -0.046630859375, 0.00685882568359375, 0.013458251953125, 0.034271240234375, -0.01206207275390625, 0.003856658935546875, 0.0269775390625, -0.01155853271484375, 0.0404052734375, 0.0386962890625, -0.05499267578125, -0.036895751953125, -0.04669189453125, -0.0266...
AlekseyKorshuk/rl-bench-test-crowdsource
2023-10-03T22:05:47.000Z
[ "region:us" ]
AlekseyKorshuk
null
null
0
11
2023-10-03T21:40:37
--- dataset_info: features: - name: user_name dtype: string - name: bot_name dtype: string - name: memory dtype: string - name: prompt dtype: string - name: chat_history list: - name: message dtype: string - name: sender dtype: string splits: - name: train num_bytes: 292785 num_examples: 200 download_size: 190141 dataset_size: 292785 --- # Dataset Card for "rl-bench-test-crowdsource" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
587
[ [ -0.050811767578125, -0.0213470458984375, 0.0062713623046875, 0.0210418701171875, -0.01526641845703125, -0.0010557174682617188, 0.01430511474609375, -0.01983642578125, 0.03814697265625, 0.0293121337890625, -0.0697021484375, -0.039886474609375, -0.0263824462890625...
Musa22/llma
2023-10-04T09:59:01.000Z
[ "region:us" ]
Musa22
null
null
0
11
2023-10-04T09:56:41
Entry not found
15
[ [ -0.0213775634765625, -0.01497650146484375, 0.05718994140625, 0.02880859375, -0.0350341796875, 0.046478271484375, 0.052490234375, 0.00507354736328125, 0.051361083984375, 0.0170135498046875, -0.052093505859375, -0.01497650146484375, -0.0604248046875, 0.0379028...
DeepPavlov/verbalist_prompts
2023-10-21T20:14:45.000Z
[ "language:ru", "language:en", "arxiv:2305.11206", "region:us" ]
DeepPavlov
null
null
1
11
2023-10-04T12:23:47
--- configs: - config_name: default data_files: - split: dim_oasst_en path: data/dim_oasst_en-* - split: dim_oasst_ru path: data/dim_oasst_ru-* - split: dim_lima path: data/dim_lima-* - split: dim_logic_tasks_ru path: data/dim_logic_tasks_ru-* - split: dim_wikihow_en path: data/dim_wikihow_en-* - split: dim_wikihow_ru path: data/dim_wikihow_ru-* - split: dim_essayforum_writing_prompts_6k path: data/dim_essayforum_writing_prompts_6k-* - split: dim_sharegpt_short_ru path: data/dim_sharegpt_short_ru-* - split: dim_openreview_prompts_65 path: data/dim_openreview_prompts_65-* - split: dim_roleplay_instruct_v2_final path: data/dim_roleplay_instruct_v2_final-* - split: dim_kinomania_scripts path: data/dim_kinomania_scripts-* - split: dim_bugurt_thread_prompts path: data/dim_bugurt_thread_prompts-* - split: dim_russian_lyrics_prompts path: data/dim_russian_lyrics_prompts-* - split: dim_ru_instruct_gpt4 path: data/dim_ru_instruct_gpt4-* - split: dim_gpt_roleplay_realm path: data/dim_gpt_roleplay_realm-* - split: dim_ultrachat_ru path: data/dim_ultrachat_ru-* - split: dim_scitldr path: data/dim_scitldr-* - split: dim_linux_man_pages_tldr_summarized path: data/dim_linux_man_pages_tldr_summarized-* - split: dim_dolphin_ru_3k path: data/dim_dolphin_ru_3k-* - split: dim_runne_prompts path: data/dim_runne_prompts-* - split: dim_lurk_prompts path: data/dim_lurk_prompts-* - split: dim_panorama_prompts_10k path: data/dim_panorama_prompts_10k-* - split: dim_resh_edu_short_prompts path: data/dim_resh_edu_short_prompts-* - split: dim_databricks_dolly_15k_ru path: data/dim_databricks_dolly_15k_ru-* - split: dim_databricks_dolly_15k_en path: data/dim_databricks_dolly_15k_en-* - split: dim_grammarly_coedit path: data/dim_grammarly_coedit-* - split: dim_kinopoisk_prompts path: data/dim_kinopoisk_prompts-* - split: dim_medical_qa_ru_prompts path: data/dim_medical_qa_ru_prompts-* - split: dim_joke_explaination_prompts path: data/dim_joke_explaination_prompts-* - split: dim_oa_stackexchange_200k path: data/dim_oa_stackexchange_200k-* - split: dim_scale_helpful_no_math path: data/dim_scale_helpful_no_math-* - split: dim_law_stackexchange_prompts path: data/dim_law_stackexchange_prompts-* - split: dim_ficbook_prompts_best_10k path: data/dim_ficbook_prompts_best_10k-* - split: dim_azbyka_logic_ru path: data/dim_azbyka_logic_ru-* - split: dim_povarenok path: data/dim_povarenok-* - split: dim_AO3_fandom_chatbot_1to1 path: data/dim_AO3_fandom_chatbot_1to1-* - split: dim_habr_prompts_5k path: data/dim_habr_prompts_5k-* - split: dim_what_where_when_50k path: data/dim_what_where_when_50k-* - split: dim_competition_math path: data/dim_competition_math-* - split: dim_sharegpt_short_en_30k path: data/dim_sharegpt_short_en_30k-* - split: dim_ru_turbo_alpaca_evol_instruct path: data/dim_ru_turbo_alpaca_evol_instruct-* - split: dim_ru_turbo_saiga path: data/dim_ru_turbo_saiga-* - split: dim_bugurt_completion_prompts path: data/dim_bugurt_completion_prompts-* - split: dim_tldr_17_50k path: data/dim_tldr_17_50k-* - split: dim_grade_school_math_instructions path: data/dim_grade_school_math_instructions-* - split: dim_tldr_news path: data/dim_tldr_news-* - split: dim_grade_school_math_instructions_ru path: data/dim_grade_school_math_instructions_ru-* - split: dim_dialogsum path: data/dim_dialogsum-* - split: dim_HC3_ru path: data/dim_HC3_ru-* - split: dim_horoscopes_ru_10k path: data/dim_horoscopes_ru_10k-* - split: dim_yandex_q_200k path: data/dim_yandex_q_200k-* - split: dim_leetcodesolutions_en_2k path: data/dim_leetcodesolutions_en_2k-* - split: dim_forum_uristov_rf_prompts path: data/dim_forum_uristov_rf_prompts-* - split: dim_dialogsum_ru path: data/dim_dialogsum_ru-* - split: dim_huggingartists_prompts path: data/dim_huggingartists_prompts-* dataset_info: features: - name: conversation_text sequence: string splits: - name: dim_oasst_en num_bytes: 4335500 num_examples: 2289 - name: dim_oasst_ru num_bytes: 6206378 num_examples: 2220 - name: dim_lima num_bytes: 2892267 num_examples: 1030 - name: dim_logic_tasks_ru num_bytes: 76915 num_examples: 86 - name: dim_wikihow_en num_bytes: 16008199 num_examples: 1995 - name: dim_wikihow_ru num_bytes: 24451573 num_examples: 2058 - name: dim_essayforum_writing_prompts_6k num_bytes: 22326330 num_examples: 6361 - name: dim_sharegpt_short_ru num_bytes: 808319 num_examples: 253 - name: dim_openreview_prompts_65 num_bytes: 6739952 num_examples: 150 - name: dim_roleplay_instruct_v2_final num_bytes: 4389286 num_examples: 7188 - name: dim_kinomania_scripts num_bytes: 238731 num_examples: 27 - name: dim_bugurt_thread_prompts num_bytes: 302191 num_examples: 223 - name: dim_russian_lyrics_prompts num_bytes: 18676 num_examples: 43 - name: dim_ru_instruct_gpt4 num_bytes: 18351658 num_examples: 14222 - name: dim_gpt_roleplay_realm num_bytes: 20163429 num_examples: 8700 - name: dim_ultrachat_ru num_bytes: 4495105 num_examples: 500 - name: dim_scitldr num_bytes: 4049209 num_examples: 3229 - name: dim_linux_man_pages_tldr_summarized num_bytes: 3006631 num_examples: 481 - name: dim_dolphin_ru_3k num_bytes: 7976776 num_examples: 3000 - name: dim_runne_prompts num_bytes: 2686148 num_examples: 537 - name: dim_lurk_prompts num_bytes: 92012533 num_examples: 5671 - name: dim_panorama_prompts_10k num_bytes: 28964132 num_examples: 11024 - name: dim_resh_edu_short_prompts num_bytes: 12380000 num_examples: 2106 - name: dim_databricks_dolly_15k_ru num_bytes: 21900617 num_examples: 14914 - name: dim_databricks_dolly_15k_en num_bytes: 11973713 num_examples: 15011 - name: dim_grammarly_coedit num_bytes: 18500223 num_examples: 82466 - name: dim_kinopoisk_prompts num_bytes: 136323982 num_examples: 36591 - name: dim_medical_qa_ru_prompts num_bytes: 75634717 num_examples: 80101 - name: dim_joke_explaination_prompts num_bytes: 196224 num_examples: 364 - name: dim_oa_stackexchange_200k num_bytes: 192535277 num_examples: 200000 - name: dim_scale_helpful_no_math num_bytes: 85610911 num_examples: 17095 - name: dim_law_stackexchange_prompts num_bytes: 64544963 num_examples: 24343 - name: dim_ficbook_prompts_best_10k num_bytes: 75867114 num_examples: 10000 - name: dim_azbyka_logic_ru num_bytes: 173101 num_examples: 480 - name: dim_povarenok num_bytes: 93518909 num_examples: 46500 - name: dim_AO3_fandom_chatbot_1to1 num_bytes: 1162058 num_examples: 614 - name: dim_habr_prompts_5k num_bytes: 40224997 num_examples: 5000 - name: dim_what_where_when_50k num_bytes: 38385243 num_examples: 50000 - name: dim_competition_math num_bytes: 5808689 num_examples: 7500 - name: dim_sharegpt_short_en_30k num_bytes: 86599862 num_examples: 29597 - name: dim_ru_turbo_alpaca_evol_instruct num_bytes: 105340901 num_examples: 47793 - name: dim_ru_turbo_saiga num_bytes: 79875722 num_examples: 37699 - name: dim_bugurt_completion_prompts num_bytes: 5471066 num_examples: 5000 - name: dim_tldr_17_50k num_bytes: 81185070 num_examples: 50000 - name: dim_grade_school_math_instructions num_bytes: 4655452 num_examples: 8792 - name: dim_tldr_news num_bytes: 4014718 num_examples: 7138 - name: dim_grade_school_math_instructions_ru num_bytes: 6845510 num_examples: 7473 - name: dim_dialogsum num_bytes: 11176807 num_examples: 12460 - name: dim_HC3_ru num_bytes: 43395731 num_examples: 24322 - name: dim_horoscopes_ru_10k num_bytes: 9489348 num_examples: 10000 - name: dim_yandex_q_200k num_bytes: 292443135 num_examples: 200000 - name: dim_leetcodesolutions_en_2k num_bytes: 4708692 num_examples: 2048 - name: dim_forum_uristov_rf_prompts num_bytes: 2757263 num_examples: 1849 - name: dim_dialogsum_ru num_bytes: 18657989 num_examples: 12460 - name: dim_huggingartists_prompts num_bytes: 121909835 num_examples: 64006 download_size: 0 dataset_size: 2023767777 language: - ru - en --- # Verbalist (буквоед) - русскоязычный ассистент. Проект во многом вдохновленный [Saiga](https://huggingface.co/IlyaGusev/saiga2_7b_lora). Мною были собраны все самые качественные датасеты с [huggingface.datasets](https://huggingface.co/datasets), а также собраны дополнительно с тех сайтов, которые я посчитал весьма полезными для создания аналога ChatGPT. Лицензии у всех датасетов отличаются, какие-то по типу [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) были созданы специально для обучения подобных моделей, какие-то являются прямой выгрузкой диалогов с ChatGPT ([RyokoAI/ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K)). Вклад данного репозитория состоит в систематизации и стандартизации уже имеющихся датасетов, добавлении новых. А также тренировке моделей на этих данных. - [google sheets таблица с датасетами и описанием](https://docs.google.com/spreadsheets/d/10xcsINF_c_zUZchT8p-8xIuHDgcuwg63jjl2ortBP9I/edit?usp=sharing) ### Датасеты - **[Объединенный датасет где все данные уже подготовлены для тренировки диалоговой модели](https://huggingface.co/datasets/dim/verbalist_prompts)** |name |link |description |original_name |original_source |preparation_script |language|amount_examples|mean_llama_tokens|std |min_llama_tokens|25% |50% |75% |max_llama_tokens| |-------------------------------------|---------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|--------|---------------|-----------------|-----------|----------------|-------|-------|-------|----------------| |dim/oasst_en |https://huggingface.co/datasets/dim/oasst_en |OpenAssistant Conversations Dataset на английском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: https://docs.google.com/spreadsheets/d/117t5-Tr-dxdODpyFBkBg5R8GklYBlsvBfeDyjqwz2pA/edit?usp=sharing|2023-04-12_oasst_ready.messages.jsonl.gz |https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/2023-04-12_oasst_ready.messages.jsonl.gz|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oasst |en |2289 |468.6788991 |295.0864391|17 |264 |410 |618 |2332 | |dim/oasst_ru |https://huggingface.co/datasets/dim/oasst_ru |OpenAssistant Conversations Dataset на русском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: https://docs.google.com/spreadsheets/d/1uiOnqxiytuxrB6u6q2pMSdnMfqjT3arfg8DlT-OWlb0/edit?usp=sharing |2023-04-12_oasst_ready.messages.jsonl.gz |https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/2023-04-12_oasst_ready.messages.jsonl.gz|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oasst |ru |2220 |589.6112613 |479.835392 |7 |278 |465 |763.5 |5028 | |dim/lima |https://huggingface.co/datasets/dim/lima |Данный датасет включает в себя 1000 высококачественных обучающих примеров на английском языке. Он собран из различных источников, включая Stack Exchange (STEM), Stack Exchange (Other), wikiHow, Pushshift r/WritingPrompts, Natural Instructions, а также уникальные инструкции, созданные авторами статей. Более подробную информацию о датасете можно найти в [соответствующей статье](https://arxiv.org/pdf/2305.11206.pdf). |GAIR/lima |https://huggingface.co/datasets/GAIR/lima |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/lima |en |1030 |712.9456311 |671.179319 |29 |312.75 |488.5 |825 |3920 | |dim/logic_tasks_ru |https://huggingface.co/datasets/dim/logic_tasks_ru |Данный набор задач по логике для детей взят с веб-сайта https://www.potehechas.ru/zadachi/zadachi.shtml. |Логические задачи - Логика и нестандартное мышление |https://www.potehechas.ru/zadachi/zadachi.shtml |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/logic_tasks_ru |ru |86 |193.0697674 |76.69048422|58 |133.75 |185 |243.5 |432 | |dim/wikihow_en |https://huggingface.co/datasets/dim/wikihow_en |Данный датасет содержит англоязычные статьи, извлеченные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |https://huggingface.co/datasets/0x22almostEvil/multilingual-wikihow-qa-16k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/wiki_how |en |1995 |2037.86416 |870.1910713|265 |1463 |1913 |2461.5 |8988 | |dim/wikihow_ru |https://huggingface.co/datasets/dim/wikihow_ru |Данный датасет включает в себя русскоязычные статьи, полученные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |https://huggingface.co/datasets/0x22almostEvil/multilingual-wikihow-qa-16k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/wiki_how |ru |2058 |2498.119534 |1587.851549|139 |1236.25|2264 |3421.75|10217 | |dim/essayforum_writing_prompts_6k |https://huggingface.co/datasets/dim/essayforum_writing_prompts_6k |Данный датасет включает в себя запросы на помощь с написанием небольших эссе, размещенные на данном сайте. Ответы в датасете предоставлены исключительно главным администратором сайта. Его ответы были отобраны, поскольку чаще всего они являются наиболее качественными и вдумчивыми. |EssayForum |https://essayforum.com/writing/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/essayforum |en |6361 |783.1760729 |285.4314176|258 |629 |742 |879 |4966 | |dim/sharegpt_short_ru |https://huggingface.co/datasets/dim/sharegpt_short_ru |Очищенная версия русская версия sharegpt. Я попытался вырезать из текста все промпты, где модель извиняется что что-то не может сделать, что она не имеет доступа в интернет. Диалоги, которые противоречат морали модели я просто исключил. Постарался убрать упоминания о том что она модель AI, так как за ролеплейные характеристики отвечают другие датасеты. |RyokoAI/ShareGPT52K |https://huggingface.co/datasets/RyokoAI/ShareGPT52K |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/sharegpt |ru |253 |706.6521739 |494.7437584|13 |310 |628 |1078 |1861 | |dim/openreview_prompts_65 |https://huggingface.co/datasets/dim/openreview_prompts_65 |Датасет рецензий на реальные научные статьи с сайта openreview. Вышло на самом деле не так много, так как многие статьи не выложенны на arxiv или просто не имеют рецензий. Плюс я собрал только малую часть данного сайта, а не все что там было. |https://openreview.net/ |https://openreview.net/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/openreview |en |150 |13531.51333 |6966.623686|4893 |8279 |12648.5|15833.5|41494 | |dim/roleplay_instruct_v2_final |https://huggingface.co/datasets/dim/roleplay_instruct_v2_final |Датасет ролеплея от GPT-4 на различных персонажей на английском языке. |roleplay-instruct-v2-final |https://github.com/teknium1/GPTeacher |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/gpt_roleplay_realm |en |7188 |155.1413467 |97.71215667|14 |88 |125 |192 |1291 | |dim/kinomania_scripts |https://huggingface.co/datasets/dim/kinomania_scripts |Небольшой датасет, который содержит в себе сценарии фильмов целиком и их краткое содержание |https://www.kinomania.ru/scripts |https://www.kinomania.ru/scripts |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/kinomania_scripts |ru\en |27 |2603.407407 |510.375447 |1887 |2175 |2370 |3069 |3616 | |dim/bugurt_thread_prompts |https://huggingface.co/datasets/dim/bugurt_thread_prompts |Небольшой набор размеченных бугуртов вместе с моим другом, для того чтобы модель научилась писать бугурты на конкретную ситуацию. Собраны из телеграм паблика БУГУРТ ТРЕД(https://t.me/bugurtthread) |https://t.me/bugurtthread |https://t.me/bugurtthread |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/bugurt_thread |ru |223 |334.4529148 |271.2557988|48 |148.5 |254 |434.5 |1645 | |dim/russian_lyrics_prompts |https://huggingface.co/datasets/dim/russian_lyrics_prompts |Небольшой датасет промптов собранный мною из различных учебников по стихосложению, чтобы модель научилась писать стихи, используя необходимый литературный прием на конкретную тему. |Учебник стихосложения |https://stihi.ru/uchebnik/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/russian_lyrics_prompts |ru |43 |106.1395349 |71.00220701|45 |71 |83 |96.5 |411 | |dim/ru_instruct_gpt4 |https://huggingface.co/datasets/dim/ru_instruct_gpt4 |Датасет каких-то инструкций на русском сгенерированных GPT-4 |lksy/ru_instruct_gpt4 |https://huggingface.co/datasets/lksy/ru_instruct_gpt4 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_instruct_gpt4 |ru |14222 |259.2173393 |237.9433891|16 |109 |175 |271 |1374 | |dim/gpt_roleplay_realm |https://huggingface.co/datasets/dim/gpt_roleplay_realm |Диалоги выдуманных персонажей при помощи GPT-4, диалоги были сгенерированны при помощи GPT-3.5. Русский и английский. |IlyaGusev/gpt_roleplay_realm |https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/gpt_roleplay_realm |ru\en |8700 |504.2424138 |117.6228987|180 |424 |489 |569 |1207 | |dim/ultrachat_ru |https://huggingface.co/datasets/dim/ultrachat_ru |Какой-то рандомный датасет диалогов от chatgpt, который я нашел на huggingface. Из текста диалогов были вырезаны шаблонные фразы по типу: "я не могу выполнить", "как языковая модель" и тд. Потому что обычно после этого следовало вменяемое решение задачи. |kaleinaNyan/UltraChat_ru |https://huggingface.co/datasets/kaleinaNyan/UltraChat_ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ultrachat_ru |ru |500 |1781.782 |901.1212735|267 |1113.25|1648 |2250.25|7303 | |dim/scitldr |https://huggingface.co/datasets/dim/scitldr |Саммаризация научных статей на английском языке, выполненная экспертами. |allenai/scitldr |https://huggingface.co/datasets/allenai/scitldr |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/scitldr |en |3229 |258.748529 |71.41209752|60 |209 |252 |303 |689 | |dim/linux_man_pages_tldr_summarized |https://huggingface.co/datasets/dim/linux_man_pages_tldr_summarized |Саммаризация мануалов для инструментов линукс в удобный набор команд с их кратким описанием. |tmskss/linux-man-pages-tldr-summarized |https://huggingface.co/datasets/tmskss/linux-man-pages-tldr-summarized |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/linux-man-pages-tldr-summarized |en |481 |1567.727651 |3590.30871 |96 |405 |765 |1386 |49888 | |dim/dolphin_ru_3k |https://huggingface.co/datasets/dim/dolphin_ru_3k |Подвыборка размера 3000 переведенных заданий dolphin. Примеры из оригинального датасета это промпты из FLANv2 и решения при помощи GPT-4 или GPT-3.5. |d0rj/dolphin-ru |https://huggingface.co/datasets/d0rj/dolphin-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dolphin_ru |ru |3000 |556.1133333 |650.0962612|19 |207 |369.5 |720.25 |6787 | |dim/runne_prompts |https://huggingface.co/datasets/dim/runne_prompts |Промпты составленные из датасета RuNNE. Лично я при обучении сотавил промпт следующим образом. Сначала идет текст "Найди все именованные сущности в данном тексте:", а затем шел сам текст. В качестве выхода модели нужно сгенерировать JSON где содержатся все найденные именованные сущности. К примеру так [{"name": "PERSON", "ent": "Ким Чен Нама", "pos": "0 12"}, {"name": "ORGANIZATION", "ent": "Полиция Малайзии", "pos": "56 72"}] |iluvvatar/RuNNE |https://huggingface.co/datasets/iluvvatar/RuNNE |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/RuNNE |ru |537 |1479.750466 |230.0259174|581 |1337 |1480 |1635 |1988 | |dim/lurk_prompts |https://huggingface.co/datasets/dim/lurk_prompts |Набор определений различных терминов с сайта lurk. Сами промпты были составлены автоматически следующим образом. напиши определение для (ОПРЕДЕЛЕНИЕ) в стиле lurk |averoo/lurk |https://huggingface.co/datasets/averoo/lurk/viewer/default/train?p=2 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/lurk |ru |5671 |3450.34262 |4147.897824|35 |710.5 |2010 |4593 |55098 | |dim/panorama_prompts_10k |https://huggingface.co/datasets/dim/panorama_prompts_10k |Набор юмористических заголовков и текстов новостей с сайта панорама. |its5Q/panorama |https://huggingface.co/datasets/its5Q/panorama |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/panorama |ru |11024 |516.9588171 |191.3774023|36 |422 |498 |585 |3496 | |dim/resh_edu_short_prompts |https://huggingface.co/datasets/dim/resh_edu_short_prompts |Набор уроков с сайта resh.edu.ru включающих в себя название урока, тему, класс и текст урока с заданиями. |its5Q/resh-edu |https://huggingface.co/datasets/its5Q/resh-edu |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/resh_edu |ru |2106 |1431.510921 |435.7847102|56 |1175.5 |1517 |1777 |2029 | |dim/databricks_dolly_15k_ru |https://huggingface.co/datasets/dim/databricks_dolly_15k_ru |Переведенный датасет dolly на русский язык. Включает в себя набор инструкций на обширное количество тематик. |dwarf2/databricks-dolly-15k-ru |https://huggingface.co/dwarf2/databricks-dolly-15k-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/databricks_dolly_15k_ru |ru |14914 |305.4638595 |405.874049 |8 |87 |182 |370 |9268 | |dim/databricks_dolly_15k_en |https://huggingface.co/datasets/dim/databricks_dolly_15k_en |databricks-dolly-15k — это набор данных с открытым исходным кодом, содержащий записи о выполнении инструкций, созданные тысячами сотрудников Databricks в нескольких поведенческих категориях, изложенных в документе InstructGPT, включая мозговой штурм, классификацию, закрытый контроль качества, генерацию, извлечение информации, открытый контроль качества и обобщение. |databricks/databricks-dolly-15k |https://huggingface.co/datasets/databricks/databricks-dolly-15k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/databricks_dolly_15k_en |en |15011 |204.7264006 |302.5539423|6 |57 |119 |242 |8883 | |dim/grammarly_coedit |https://huggingface.co/datasets/dim/grammarly_coedit |Набор промптов, которые просят исправить грамматические, стилистические ошибки на английском. |grammarly/coedit |https://huggingface.co/datasets/grammarly/coedit |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grammarly_coedit |en |82466 |53.7128271 |26.73822864|10 |35 |46 |64 |694 | |dim/kinopoisk_prompts |https://huggingface.co/datasets/dim/kinopoisk_prompts |Отзывы с кинопоиска на топ 250 фильмов. В промптах я прошу написать хороший, плохой или нейтральный отзыв на определенный фильм. |blinoff/kinopoisk |https://huggingface.co/datasets/blinoff/kinopoisk |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/kinopoisk |ru |36591 |875.0955973 |565.3212035|48 |484 |733 |1117 |8628 | |dim/medical_qa_ru_prompts |https://huggingface.co/datasets/dim/medical_qa_ru_prompts |Какие-то вопросы и ответы с какого-то медицинского форума. В данной версии датасета только первый ответ из оригинала. |blinoff/medical_qa_ru_data |https://huggingface.co/datasets/blinoff/medical_qa_ru_data |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/medical_qa_ru_data |ru |80101 |206.710528 |175.4343973|12 |106 |161 |247 |5062 | |dim/joke_explaination_prompts |https://huggingface.co/datasets/dim/joke_explaination_prompts |Объяснение шуток на английском. От изначального датасета отличается тем, что я убрал последнее предложение из объяснения, так как оно ссылается на видео на сайте. |theblackcat102/joke_explaination |https://huggingface.co/datasets/theblackcat102/joke_explaination |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/joke_explaination |en |364 |143.5741758 |68.90275411|21 |99 |137.5 |189.25 |334 | |dim/oa_stackexchange_200k |https://huggingface.co/datasets/dim/oa_stackexchange_200k |Вопросы-ответы со stackexchange. Оригинальный датасет был составлен следующим образом: были выбраны только темы с принятым ответом, для которых длина вопроса и ответа составляет менее 1000 символов. Другие ответы, вопросы без принятых ответов или длинные записи были удалены. Так как оригинальный датасет слишком большой, я рандомно выбрал 200k семплов. |donfu/oa-stackexchange |https://huggingface.co/datasets/donfu/oa-stackexchange |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oa_stackexchange |en |200000 |276.29862 |112.5004436|22 |194 |265 |345 |1226 | |dim/scale_helpful_no_math |https://huggingface.co/datasets/dim/scale_helpful_no_math |Какой-то набор диалогов с вопросами-ответами на английском, происхождение неизвестно. |HuggingFaceH4/scale_helpful_no_math |https://huggingface.co/datasets/HuggingFaceH4/scale_helpful_no_math/viewer/default/train_rm |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/scale_helpful_no_math |en |17095 |1235.302603 |838.1097885|53 |663 |1063 |1617 |34480 | |dim/law_stackexchange_prompts |https://huggingface.co/datasets/dim/law_stackexchange_prompts |Вопросы про закон на английском языке со StackExchange. Оригинальный датасет был преобразован в markdown. |ymoslem/Law-StackExchange |https://huggingface.co/datasets/ymoslem/Law-StackExchange |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/law_stackexchange |en |24343 |689.1184324 |565.0316906|43 |354 |540 |836 |8969 | |dim/ficbook_prompts_best_10k |https://huggingface.co/datasets/dim/ficbook_prompts_best_10k |Топ 10k лучших фанфиков с сайта ficbook.net. Все промпты выглядят следующим образом: напиши фанфик с названием {title} и следующим описанием {description}, с тегами {tags}, Где title это оригинальное название, description оригинальное описание, tags это теги данного произведения. |AlexWortega/FicBook |https://huggingface.co/datasets/AlexWortega/FicBook |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ficbook |ru |10000 |1737.8214 |402.0748161|166 |1716 |1950 |1950 |1952 | |dim/azbyka_logic_ru |https://huggingface.co/datasets/dim/azbyka_logic_ru |Небольшой набор детских логических и православных задач, взятых с сайта https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi . Обычно у них почти нет развернутого решения, только ответ. Я пытался расписать решение некоторых задач, но меня хватило только на 35, если кто-то займется подобным буду рад https://docs.google.com/spreadsheets/d/1JRbtppbZCUbV_Eqd0nKbRDQEuPnJIAgJ70cUILEDUI4/edit?usp=sharing . |Логические и занимательные задачи (300 задач) |https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/azbyka_logic_ru |ru |480 |77.4375 |77.56990416|14 |31 |50 |91 |652 | |dim/povarenok |https://huggingface.co/datasets/dim/povarenok |46k лучших рецептов с сайта povarenok.ru, содержит текст рецепта, список ингридиентов, название блюда |https://www.povarenok.ru/recipes/ |https://www.povarenok.ru/recipes/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/povarenok |ru |46500 |488.9118495 |344.8563249|31 |281 |440 |632 |5542 | |dim/AO3_fandom_chatbot_1to1 |https://huggingface.co/datasets/dim/AO3_fandom_chatbot_1to1 |Какой-то набор ролеплейных диалогов с описанием персонажей и их отыгрышем. Происхождение неизвестно. |ebony59/AO3_fandom_chatbot_1to1 |https://huggingface.co/datasets/ebony59/AO3_fandom_chatbot_1to1 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/AO3_fandom_chatbot_1to1 |en |614 |493.7166124 |226.3885365|129 |328.25 |432.5 |611.75 |1272 | |dim/habr_prompts_5k |https://huggingface.co/datasets/dim/habr_prompts_5k |Статьи с хабра. Датасет был составлен с помощью chatgpt, chatgpt преобразовывал заголовки таким образом чтобы они звучали как вопросы от пользователя, в качестве таргета выступала сама статья. |IlyaGusev/habr |https://huggingface.co/datasets/IlyaGusev/habr |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/habr |ru |5000 |1732.892 |454.8418369|19 |1920.75|1950 |1951 |1952 | |dim/what_where_when_50k |https://huggingface.co/datasets/dim/what_where_when_50k |50k вопросов с решениями с сайта что где когда. В качестве промпта выступает вопрос, в качестве ответа конкатенация объяснения и краткого ответа. Все вопросы-ответы вы можете найти по этой ссылке https://huggingface.co/datasets/dim/what_where_when_ru |https://db.chgk.info |https://db.chgk.info |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/what_where_when |ru |50000 |169.1862 |68.91119898|18 |122 |158 |202 |1167 | |dim/competition_math |https://huggingface.co/datasets/dim/competition_math |Датасет олимпиадной математики на английском. The Mathematics Aptitude Test of Heuristics (MATH) dataset. |competition_math |https://huggingface.co/datasets/competition_math |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/competition_math |en |7500 |317.5254667 |267.8583731|34 |147 |234 |393 |3029 | |dim/sharegpt_short_en_30k |https://huggingface.co/datasets/dim/sharegpt_short_en_30k |Короткие диалоги на английском из sharegpt |RyokoAI/ShareGPT52K |https://huggingface.co/datasets/RyokoAI/ShareGPT52K |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/sharegpt |en |29597 |749.3149981 |516.3702473|3 |336 |630 |1095 |2021 | |dim/ru_turbo_alpaca_evol_instruct |https://huggingface.co/datasets/dim/ru_turbo_alpaca_evol_instruct |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru_turbo_alpaca_evol_instruct |https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca_evol_instruct |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_turbo_alpaca_evol_instruct |ru |47793 |453.0887996 |289.5498356|17 |221 |430 |623 |4647 | |dim/ru_turbo_saiga |https://huggingface.co/datasets/dim/ru_turbo_saiga |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru_turbo_saiga |https://huggingface.co/datasets/IlyaGusev/ru_turbo_saiga |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_turbo_saiga |ru |37699 |412.7508687 |113.346917 |87 |339 |398 |466 |1427 | |dim/bugurt_completion_prompts |https://huggingface.co/datasets/dim/bugurt_completion_prompts |Обрезанные бугурты, где в качестве промпта используется строка вида - продолжи бугурт: первая строчка бугурта |https://t.me/bugurtthread |https://t.me/bugurtthread |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/bugurt_thread |ru |5000 |280.2466 |320.4353681|32 |111 |178 |331 |11333 | |dim/tldr_17_50k |https://huggingface.co/datasets/dim/tldr_17_50k |Очень вольная абстрактная саммаризация постов с реддита в одну строчку |webis/tldr-17 |https://huggingface.co/datasets/webis/tldr-17 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/tldr_17 |en |50000 |421.12752 |403.346214 |10 |177 |303 |525 |9592 | |dim/grade_school_math_instructions |https://huggingface.co/datasets/dim/grade_school_math_instructions |OpenAI's grade-school-math датасет преобразованный в промпты. |qwedsacf/grade-school-math-instructions |https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grade-school-math-instructions |en |8792 |171.6310282 |63.09232668|50 |124 |161 |206 |511 | |dim/tldr_news |https://huggingface.co/datasets/dim/tldr_news |Хедлайны и текст новостей на различную тематику. |JulesBelveze/tldr_news |https://huggingface.co/datasets/JulesBelveze/tldr_news |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/tldr_news |en |7138 |133.1004483 |46.48736493|23 |100 |133 |161 |476 | |dim/grade_school_math_instructions_ru|https://huggingface.co/datasets/dim/grade_school_math_instructions_ru|OpenAI's grade-school-math датасет переведенный на русский. |d0rj/gsm8k-ru |https://huggingface.co/datasets/d0rj/gsm8k-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grade_school_math_instructions_ru|ru |7473 |259.8321959 |100.1229127|78 |185 |241 |314 |838 | |dim/dialogsum |https://huggingface.co/datasets/dim/dialogsum |Саммаризация диалогов на английском языке, разметка выполнялась вручную. |knkarthick/dialogsum |https://huggingface.co/datasets/knkarthick/dialogsum |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dialogsum |en |12460 |269.6467095 |126.285664 |75 |191 |245 |327 |1725 | |dim/HC3_ru |https://huggingface.co/datasets/dim/HC3_ru |Вопросы-ответы с реддита, есть ответы сгенерированные chatgpt и реальные ответы пользователей. Я использовал только реальные ответы пользователей. |d0rj/HC3-ru |https://huggingface.co/datasets/d0rj/HC3-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/HC3_ru |ru |24322 |360.5608503 |330.2285903|15 |168 |267 |435 |10025 | |dim/horoscopes_ru_10k |https://huggingface.co/datasets/dim/horoscopes_ru_10k |10k гороскопов, с промптами где я прошу сгенерировать гороском для определенного знака зодиака |dkagramanyan/horoscopes_ru |https://huggingface.co/datasets/dkagramanyan/horoscopes_ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/horoscopes_ru |ru |10000 |183.1443 |31.62023184|55 |159 |187 |201 |464 | |dim/yandex_q_200k |https://huggingface.co/datasets/dim/yandex_q_200k |200k рандомно выбранных вопросов-ответов с сайта yandex q. |its5Q/yandex-q |https://huggingface.co/datasets/its5Q/yandex-q |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/yandex_q |ru |200000 |304.569005 |340.7808288|18 |127 |202 |353 |19294 | |dim/leetcodesolutions_en_2k |https://huggingface.co/datasets/dim/leetcodesolutions_en_2k |Решения задач с leetcode на разных языках. |TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/leetcodesolutions_en_2k |en |2048 |740.7441406 |253.2493282|297 |565 |685 |857 |1960 | |dim/forum_uristov_rf_prompts |https://huggingface.co/datasets/dim/forum_uristov_rf_prompts |Вопросы-ответы с российского юридического форума. |https://xn----dtbrojdkckkfj9k.xn--p1ai/vopros-yuristu?page=560|https://xn----dtbrojdkckkfj9k.xn--p1ai/vopros-yuristu?page=560 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/forum_uristov_rf |ru |1849 |321.0540833 |429.58896 |31 |134 |210 |349 |6470 | |dim/dialogsum_ru |https://huggingface.co/datasets/dim/dialogsum_ru |Саммаризация диалогов на русском языке, перевод dialogsum. |d0rj/dialogsum-ru |https://huggingface.co/datasets/d0rj/dialogsum-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dialogsum-ru |ru |12460 |364.2813804 |178.7117754|98 |250 |329 |446 |2300 | |dim/huggingartists_prompts |https://huggingface.co/datasets/dim/huggingartists_prompts |Промпты, которые просят продолжить песню в стиле определенного исполнителя. В данном наборе содержатся почти все исполнители, которых вы можете найти в этой организации https://huggingface.co/huggingartists |https://huggingface.co/huggingartists |https://huggingface.co/huggingartists |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/huggingartists |ru |64006 |561.6732025 |586.18458 |28 |297 |453 |720 |32949 | ### Модели На данный момент обучаются 3 модели llama2_7b, llama2_13b и llama1_30b. За графиками их обучения можно следить в прямом эфире https://api.wandb.ai/links/dimweb/7rh0c7iz ### Код обучения - [общий алгоритм обучения](https://github.com/dmitrymailk/verbalist/blob/master/verbalist/model/src/train.py) - [формирование датасетов для обучения](https://github.com/dmitrymailk/verbalist/blob/master/verbalist/model/src/dataset.py#L176) ### Оборудование Все обучение и инференс производится на видеокарте A100, на других видеокартах была обнаружена существенная деградация качества при инференсе, данный аспект требует дополнительного изучения. - NVIDIA A100-SXM4-40GB - NVIDIA-SMI 535.54.03 - Driver Version: 535.54.03 - CUDA Version: 12.2 - torch==2.0.1+cu118 ### Дальнейшее развитие Самое простое, что можно сделать это переводить уже имеющиеся хорошие датасеты с английского на русский при помощи GPT-4. Более сложное это собирать больше разнообразных данных из различных доменов. Я могу лишь подкинуть идеи для того какие датасеты можно собрать еще. - решебники по литературе, русскому и другим предметам - задания со всяких бирж труда - [краткие пересказы произведений, анализ произведений, сочинения по ним](http://www.litra.ru/shortwork/) - [туториалы с digital ocean (более 7000)](https://www.digitalocean.com/community/tutorials) - [туториалы с selectel](https://selectel.ru/blog/tutorials/) - больше форумов на различные тематики - [бесплатные эссе с ivypanda essays](https://ivypanda.com/essays/) и дальнейший их перевод на русский - больше стихов и песен - [олимпиадные русские задачи](https://math.ru/problems/) их очень сложно собирать, так как большинство их них живут только в PDF или docx. Но их довольно много и они довольно отличаются от олимпиадной математики на английском. Но у меня нет времени этим заниматься. - фанфики на иностранном языке - исправить текущие автоматические промпты на более разнообразные, при помощи chatgpt
70,970
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autoevaluate/autoeval-eval-tweet_eval-sentiment-45124a-38605145054
2023-10-04T14:23:31.000Z
[ "autotrain", "evaluation", "region:us" ]
autoevaluate
null
null
0
11
2023-10-04T14:20:04
--- type: predictions tags: - autotrain - evaluation datasets: - tweet_eval eval_info: task: multi_class_classification model: siberett/roberta-sentiment-analysis-finetune metrics: [] dataset_name: tweet_eval dataset_config: sentiment dataset_split: train col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: siberett/roberta-sentiment-analysis-finetune * Dataset: tweet_eval * Config: sentiment * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@emuggins](https://huggingface.co/emuggins) for evaluating this model.
889
[ [ -0.031524658203125, -0.0241546630859375, 0.0208587646484375, 0.0209503173828125, -0.0031280517578125, 0.0047149658203125, -0.01294708251953125, -0.0243988037109375, 0.00847625732421875, 0.0162353515625, -0.0634765625, -0.0217437744140625, -0.060791015625, -0...
Sharka/CIVQA_easyocr_simple_train_half
2023-10-04T15:48:19.000Z
[ "region:us" ]
Sharka
null
null
0
11
2023-10-04T15:48:08
--- dataset_info: features: - name: id dtype: string - name: words sequence: string - name: answers dtype: string - name: bboxes sequence: sequence: float32 - name: answers_bboxes sequence: sequence: float32 - name: questions dtype: string - name: image dtype: string splits: - name: train num_bytes: 963207990 num_examples: 143765 download_size: 41076905 dataset_size: 963207990 --- # Dataset Card for "CIVQA_easyocr_simple_train_half" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
641
[ [ -0.0360107421875, 0.0008172988891601562, 0.01338958740234375, 0.01517486572265625, -0.01371002197265625, -0.005199432373046875, 0.0223846435546875, 0.0146942138671875, 0.040008544921875, 0.0211639404296875, -0.0533447265625, -0.038604736328125, -0.02890014648437...
philschmid/markdown-documentation-transformers
2023-10-05T13:42:59.000Z
[ "license:apache-2.0", "region:us" ]
philschmid
null
null
0
11
2023-10-05T13:38:10
--- license: apache-2.0 --- # Hugging Face Transformers documentation as markdown dataset This dataset was created using [Clipper.js](https://github.com/philschmid/clipper.js). Clipper is a Node.js command line tool that allows you to easily clip content from web pages and convert it to Markdown. It uses Mozilla's Readability library and Turndown under the hood to parse web page content and convert it to Markdown. This dataset can be used to create RAG applications, which want to use the transformers documentation. Example document: https://huggingface.co/docs/transformers/peft ``` # Load adapters with 🤗 PEFT [Parameter-Efficient Fine Tuning (PEFT)](https://huggingface.co/blog/peft) methods freeze the pretrained model parameters during fine-tuning and add a small number of trainable parameters (the adapters) on top of it. The adapters are trained to learn task-specific information. This approach has been shown to be very memory-efficient with lower compute usage while producing results comparable to a fully fine-tuned model. Adapters trained with PEFT are also usually an order of magnitude smaller than the full model, making it convenient to share, store, and load them. ![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/PEFT-hub-screenshot.png) The adapter weights for a OPTForCausalLM model stored on the Hub are only ~6MB compared to the full size of the model weights, which can be ~700MB. If you’re interested in learning more about the 🤗 PEFT library, check out the [documentation](https://huggingface.co/docs/peft/index). ## Setup Get started by installing 🤗 PEFT: If you want to try out the brand new features, you might be interested in installing the library from source: .... ```
1,761
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shengqin/web-attacks-old
2023-10-05T15:38:36.000Z
[ "region:us" ]
shengqin
null
null
0
11
2023-10-05T15:37:30
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
Intuit-GenSRF/jigsaw-toxic-comment-train-es
2023-10-05T19:27:34.000Z
[ "region:us" ]
Intuit-GenSRF
null
null
0
11
2023-10-05T19:27:29
--- dataset_info: features: - name: text dtype: string - name: labels sequence: string splits: - name: train num_bytes: 98022741 num_examples: 223378 download_size: 60601678 dataset_size: 98022741 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "jigsaw-train-es" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
486
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Intuit-GenSRF/hackathon-somos-nlp-2023-suicide-comments-es-en
2023-10-06T22:27:58.000Z
[ "region:us" ]
Intuit-GenSRF
null
null
0
11
2023-10-06T22:27:56
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: labels sequence: string - name: processed_text sequence: string - name: num_tokens dtype: int64 - name: text_en dtype: string splits: - name: train num_bytes: 2629537 num_examples: 8824 download_size: 1693102 dataset_size: 2629537 --- # Dataset Card for "hackathon-somos-nlp-2023-suicide-comments-es-en" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
633
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chiualfredo/oil_origin
2023-10-07T04:59:08.000Z
[ "region:us" ]
chiualfredo
null
null
0
11
2023-10-07T04:56:43
Entry not found
15
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chargoddard/rpguild
2023-10-18T00:34:26.000Z
[ "task_categories:conversational", "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:cc-by-nc-4.0", "roleplay", "not-for-all-audiences", "region:us" ]
chargoddard
null
null
1
11
2023-10-07T08:04:37
--- dataset_info: - config_name: default features: - name: username dtype: string - name: char_name dtype: string - name: bio dtype: string - name: context list: - name: text dtype: string - name: username dtype: string - name: char_name dtype: string - name: reply dtype: string - name: has_nameless dtype: bool - name: char_confidence dtype: float64 splits: - name: train num_bytes: 1921588254 num_examples: 140469 download_size: 764073630 dataset_size: 1921588254 - config_name: high_confidence features: - name: username dtype: string - name: char_name dtype: string - name: bio dtype: string - name: context list: - name: text dtype: string - name: username dtype: string - name: char_name dtype: string - name: reply dtype: string - name: has_nameless dtype: bool - name: char_confidence dtype: float64 splits: - name: train num_bytes: 949419370.7676569 num_examples: 69403 download_size: 386317057 dataset_size: 949419370.7676569 - config_name: pruned features: - name: username dtype: string - name: char_name dtype: string - name: bio dtype: string - name: context list: - name: text dtype: string - name: username dtype: string - name: char_name dtype: string - name: reply dtype: string - name: has_nameless dtype: bool - name: char_confidence dtype: float64 splits: - name: train num_bytes: 782484734.2032762 num_examples: 57200 download_size: 326987882 dataset_size: 782484734.2032762 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: high_confidence data_files: - split: train path: high_confidence/train-* - config_name: pruned data_files: - split: train path: pruned/train-* license: cc-by-nc-4.0 task_categories: - conversational - text-generation tags: - roleplay - not-for-all-audiences size_categories: - 100K<n<1M language: - en --- Data scraped from [roleplayerguild](https://www.roleplayerguild.com/) and parsed into prompts with a conversation history and associated character bio. As usernames can be associated with multiple biographies, assignment of characters is a little fuzzy. The `char_confidence` feature reflects how likely this assignment is to be correct. Not all posts in the conversation history necessarily have an associated character name. The column `has_nameless` reflects this. Each row should fit into 4096 Llama tokens, depending on your prompt format - there's built in slack of 128 tokens + 8 per message.
2,693
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sidthip/testquiz
2023-10-07T10:20:05.000Z
[ "region:us" ]
sidthip
null
null
0
11
2023-10-07T10:06:01
Entry not found
15
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towhid/aesir-train-420
2023-10-07T18:10:39.000Z
[ "region:us" ]
towhid
null
null
0
11
2023-10-07T17:11:13
Entry not found
15
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carnival13/test_DA_tokenized2
2023-10-08T03:43:15.000Z
[ "region:us" ]
carnival13
null
null
0
11
2023-10-08T03:43:06
--- dataset_info: features: - name: pass_label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 456736095 num_examples: 335850 download_size: 104506387 dataset_size: 456736095 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "test_DA_tokenized2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
543
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pythainlp/thaisum
2023-10-08T14:06:17.000Z
[ "task_categories:summarization", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_d...
pythainlp
null
null
0
11
2023-10-08T11:06:14
--- annotations_creators: - no-annotation language_creators: - found language: - th license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: ThaiSum --- # Dataset Card for ThaiSum This dataset was forked from [thaisum](https://huggingface.co/datasets/thaisum) to HF hub. ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/nakhunchumpolsathien/ThaiSum - **Repository:** https://github.com/nakhunchumpolsathien/ThaiSum - **Paper:** - **Leaderboard:** - **Point of Contact:** https://github.com/nakhunchumpolsathien ### Dataset Summary ThaiSum is a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs written by journalists. ### Supported Tasks and Leaderboards summarization, language modeling ### Languages Thai ## Dataset Structure ### Data Instances ``` {'body': 'กีเก ซานเชซ ฟลอเรส\xa0 กุนซือเลือดกระทิงของทีมวัตฟอร์ด\xa0 เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง,สำนักข่าวต่างประเทศรายงานวันที่ 27 ก.ย. ว่า กีเก ซานเชซ ฟลอเรส\xa0 ผู้จัดการทีมชาวสเปน ของ แตนอาละวาด วัตฟอร์ด\xa0 ยอมรับทีมของเขาเล่นได้ไม่ดีพอเอง ในเกมพรีเมียร์ลีก อังกฤษ นัดเปิดบ้านพ่าย อินทรีผงาด คริสตัล พาเลซ 0-1 เมื่อคืนวันอาทิตย์ที่ผ่านมา,เกมนี้จุดเปลี่ยนมาอยู่ที่การได้จุดโทษในช่วงครึ่งหลังของ คริสตัล พาเลซ ซึ่งไม่ค่อยชัดเจนเท่าไหร่ว่า อัลลัน นียอม นั้นไปทำฟาล์วใส่ วิลฟรีด ซาฮา ในเขตโทษหรือไม่ แต่ผู้ตัดสินก็ชี้เป็นจุดโทษ ซึ่ง โยอัน กาบาย สังหารไม่พลาด และเป็นประตูชัยช่วยให้ คริสตัล พาเลซ เอาชนะ วัตฟอร์ด ไป 1-0 และเป็นการพ่ายแพ้ในบ้านนัดแรกของวัตฟอร์ดในฤดูกาลนี้อีกด้วย,ฟลอเรส กล่าวว่า มันเป็นเรื่องยากในการหยุดเกมรุกของคริสตัล พาเลซ ซึ่งมันอึดอัดจริงๆสำหรับเรา เราเล่นกันได้ไม่ดีนักในตอนที่ได้ครองบอล เราต้องเล่นทางริมเส้นให้มากกว่านี้ เราไม่สามารถหยุดเกมสวนกลับของพวกเขาได้ และแนวรับของเราก็ยืนไม่เป็นระเบียบสักเท่าไหร่ในช่วงครึ่งแรก ส่วนเรื่องจุดโทษการตัดสินใจขั้นสุดท้ายมันอยู่ที่ผู้ตัดสิน ซึ่งมันเป็นการตัดสินใจที่สำคัญ ผมเองก็ไม่รู้ว่าเขาตัดสินถูกหรือเปล่า บางทีมันอาจเป็นจุดที่ตัดสินเกมนี้เลย แต่เราไม่ได้แพ้เกมนี้เพราะจุดโทษ เราแพ้ในวันนี้เพราะเราเล่นไม่ดีและคริสตัล พาเลซ เล่นดีกว่าเรา เราไม่ได้มีฟอร์มการเล่นที่ดีในเกมนี้เลย', 'summary': 'กีเก ซานเชซ ฟลอเรส กุนซือเลือดกระทิงของทีมวัตฟอร์ด เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง', 'tags': 'พรีเมียร์ลีก,วัตฟอร์ด,คริสตัล พาเลซ,กีเก ซานเชซ ฟลอเรส,ข่าวกีฬา,ข่าว,ไทยรัฐออนไลน์', 'title': 'ฟลอเรส รับ วัตฟอร์ดห่วยเองเกมพ่ายพาเลซคาบ้าน', 'type': '', 'url': 'https://www.thairath.co.th/content/528322'} ``` ### Data Fields - `title`: title of article - `body`: body of article - `summary`: summary of article - `type`: type of article, if any - `tags`: tags of article, separated by `,` - `url`: URL of article ### Data Splits train/valid/test: 358868 / 11000 / 11000 ## Dataset Creation ### Curation Rationale Sequence-to-sequence (Seq2Seq) models have shown great achievement in text summarization. However, Seq2Seq model often requires large-scale training data to achieve effective results. Although many impressive advancements in text summarization field have been made, most of summarization studies focus on resource-rich languages. The progress of Thai text summarization is still far behind. The dearth of large-scale dataset keeps Thai text summarization in its infancy. As far as our knowledge goes, there is not a large-scale dataset for Thai text summarization available anywhere. Thus, we present ThaiSum, a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. ### Source Data #### Initial Data Collection and Normalization We used a python library named Scrapy to crawl articles from several news websites namely Thairath, Prachatai, ThaiPBS and, The Standard. We first collected news URLs provided in their sitemaps. During web-crawling, we used HTML markup and metadata available in HTML pages to identify article text, summary, headline, tags and label. Collected articles were published online from 2014 to August 2020. <br> <br> We further performed data cleansing process to minimize noisy data. We filtered out articles that their article text or summary is missing. Articles that contains article text with less than 150 words or summary with less than 15 words were removed. We also discarded articles that contain at least one of these following tags: ‘ดวง’ (horoscope), ‘นิยาย’ (novel), ‘อินสตราแกรมดารา’ (celebrity Instagram), ‘คลิปสุดฮา’(funny video) and ‘สรุปข่าว’ (highlight news). Some summaries were completely irrelevant to their original article texts. To eliminate those irrelevant summaries, we calculated abstractedness score between summary and its article text. Abstractedness score is written formally as: <br> <center><a href="https://www.codecogs.com/eqnedit.php?latex=\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" title="\begin{equation} \frac{|S-A|}{r} \times 100 \end{equation}" /></a></center><br> <br>Where 𝑆 denotes set of article tokens. 𝐴 denotes set of summary tokens. 𝑟 denotes a total number of summary tokens. We omitted articles that have abstractedness score at 1-grams higher than 60%. <br><br> It is important to point out that we used [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp), version 2.2.4, tokenizing engine = newmm, to process Thai texts in this study. It is challenging to tokenize running Thai text into words or sentences because there are not clear word/sentence delimiters in Thai language. Therefore, using different tokenization engines may result in different segment of words/sentences. After data-cleansing process, ThaiSum dataset contains over 358,000 articles. The size of this dataset is comparable to a well-known English document summarization dataset, CNN/Dily mail dataset. Moreover, we analyse the characteristics of this dataset by measuring the abstractedness level, compassion rate, and content diversity. For more details, see [thaisum_exploration.ipynb](https://github.com/nakhunchumpolsathien/ThaiSum/blob/master/thaisum_exploration.ipynb). #### Dataset Statistics ThaiSum dataset consists of 358,868 articles. Average lengths of article texts and summaries are approximately 530 and 37 words respectively. As mentioned earlier, we also collected headlines, tags and labels provided in each article. Tags are similar to keywords of the article. An article normally contains several tags but a few labels. Tags can be name of places or persons that article is about while labels indicate news category (politic, entertainment, etc.). Ultimatly, ThaiSum contains 538,059 unique tags and 59 unique labels. Note that not every article contains tags or labels. |Dataset Size| 358,868 | articles | |:---|---:|---:| |Avg. Article Length| 529.5 | words| |Avg. Summary Length | 37.3 | words| |Avg. Headline Length | 12.6 | words| |Unique Vocabulary Size | 407,355 | words| |Occurring > 10 times | 81,761 | words| |Unique News Tag Size | 538,059 | tags| |Unique News Label Size | 59 | labels| #### Who are the source language producers? Journalists of respective articles ### Annotations #### Annotation process `summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers. #### Who are the annotators? `summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers. ### Personal and Sensitive Information All data are public news articles. No personal and sensitive information is expected to be included. ## Considerations for Using the Data ### Social Impact of Dataset - News summarization in Thai - Language modeling for Thai news ### Discussion of Biases - [ThaiPBS](https://www.thaipbs.or.th/home) [receives funding from Thai government](https://www.bangkokbiznews.com/blog/detail/648740). - [Thairath](https://www.thairath.co.th/) is known as [the most popular newspaper in Thailand](https://mgronline.com/onlinesection/detail/9620000058532); no clear political leaning. - [The Standard](https://thestandard.co/) is a left-leaning online magazine. - [Prachathai](https://prachatai.com/) is a left-leaning, human-right-focused news site. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [@nakhunchumpolsathien](https://github.com/nakhunchumpolsathien/) [@caramelWaffle](https://github.com/caramelWaffle) ### Licensing Information MIT License ### Citation Information ``` @mastersthesis{chumpolsathien_2020, title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization}, author={Chumpolsathien, Nakhun}, year={2020}, school={Beijing Institute of Technology} ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.
10,429
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darcycao/en2zh_specaildataset
2023-10-09T09:45:26.000Z
[ "region:us" ]
darcycao
null
null
0
11
2023-10-09T09:44:40
Entry not found
15
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mychen76/openwebtext-100k
2023-10-09T13:37:50.000Z
[ "region:us" ]
mychen76
null
null
0
11
2023-10-09T13:32:49
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 497257202 num_examples: 100000 download_size: 302557845 dataset_size: 497257202 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "openwebtext-100k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
452
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Harshithacj123/CCU_Midterm
2023-10-10T17:08:47.000Z
[ "region:us" ]
Harshithacj123
null
null
0
11
2023-10-10T17:08:46
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 41353 num_examples: 50 download_size: 23370 dataset_size: 41353 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "CCU_Midterm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
431
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Shiveswarran/llm_instruction_code_manual_yolo_lc
2023-10-12T05:17:45.000Z
[ "region:us" ]
Shiveswarran
null
null
0
11
2023-10-12T05:16:49
Entry not found
15
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kristinashemet/German_datasets
2023-10-17T11:43:51.000Z
[ "region:us" ]
kristinashemet
null
null
0
11
2023-10-12T10:05:47
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 259881583 num_examples: 346965 download_size: 137269817 dataset_size: 259881583 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "German_datasets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
451
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sordonia/platy_icl0_maxD-1_maxC-1_0
2023-10-12T13:21:54.000Z
[ "region:us" ]
sordonia
null
null
0
11
2023-10-12T13:21:35
--- configs: - config_name: default data_files: - split: formal_logic path: data/formal_logic-* - split: machine_learning path: data/machine_learning-* - split: global_facts path: data/global_facts-* - split: abstract_algebra path: data/abstract_algebra-* - split: high_school_physics path: data/high_school_physics-* - split: college_biology path: data/college_biology-* - split: high_school_government_and_politics path: data/high_school_government_and_politics-* - split: prehistory path: data/prehistory-* - split: security_studies path: data/security_studies-* - split: sociology path: data/sociology-* dataset_info: features: - name: id dtype: string - name: context dtype: string - name: docno dtype: string - name: subject dtype: string - name: icl_examples dtype: 'null' - name: instruction dtype: string - name: author_instr dtype: string - name: response dtype: string - name: author_response dtype: string - name: normalized_cumul_logprob_response dtype: float64 splits: - name: formal_logic num_bytes: 8952043.353426639 num_examples: 2589 - name: machine_learning num_bytes: 12651806.34615221 num_examples: 3659 - name: global_facts num_bytes: 13211957.378695708 num_examples: 3821 - name: abstract_algebra num_bytes: 7520546.270259922 num_examples: 2175 - name: high_school_physics num_bytes: 21309943.293614667 num_examples: 6163 - name: college_biology num_bytes: 16410350.620070618 num_examples: 4746 - name: high_school_government_and_politics num_bytes: 17077691.047730464 num_examples: 4939 - name: prehistory num_bytes: 24836820.165184837 num_examples: 7183 - name: security_studies num_bytes: 22067184.504275322 num_examples: 6382 - name: sociology num_bytes: 18523019.020589612 num_examples: 5357 download_size: 89203875 dataset_size: 162561362.00000003 --- # Dataset Card for "platy_icl0_maxD-1_maxC-1_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
2,191
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carnival13/xlmr_int_hard_trn
2023-10-12T13:28:54.000Z
[ "region:us" ]
carnival13
null
null
0
11
2023-10-12T13:28:44
--- dataset_info: features: - name: domain_label dtype: int64 - name: pass_label dtype: int64 - name: input dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 142739369 num_examples: 113100 download_size: 40732989 dataset_size: 142739369 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xlmr_int_hard_trn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
615
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Abira1/testjson
2023-10-12T13:58:28.000Z
[ "region:us" ]
Abira1
null
null
0
11
2023-10-12T13:58:09
Entry not found
15
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surathisin/nvso-test-1
2023-10-17T02:12:19.000Z
[ "region:us" ]
surathisin
null
null
0
11
2023-10-13T05:28:08
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
anujpaudel/rote-ping-1
2023-10-14T12:08:59.000Z
[ "region:us" ]
anujpaudel
null
null
0
11
2023-10-13T06:44:22
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1620341.0 num_examples: 31 download_size: 1621661 dataset_size: 1620341.0 --- # Dataset Card for "rote-ping-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
474
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tinhpx2911/vietnamese_general_data_processed
2023-10-14T08:15:30.000Z
[ "region:us" ]
tinhpx2911
null
null
0
11
2023-10-14T05:12:02
--- dataset_info: - config_name: train_1 features: - name: text dtype: string splits: - name: train num_bytes: 13070931261 num_examples: 32434667 download_size: 6902902017 dataset_size: 13070931261 - config_name: train_2 features: - name: text dtype: string splits: - name: train num_bytes: 13079301675 num_examples: 32444361 download_size: 6907570478 dataset_size: 13079301675 - config_name: train_3 features: - name: text dtype: string splits: - name: train num_bytes: 13083262611 num_examples: 32455485 download_size: 6908687251 dataset_size: 13083262611 - config_name: train_4 features: - name: text dtype: string splits: - name: train num_bytes: 13083227441 num_examples: 32440768 download_size: 6909612652 dataset_size: 13083227441 - config_name: train_5 features: - name: text dtype: string splits: - name: train num_bytes: 10862029760 num_examples: 26942980 download_size: 5736766203 dataset_size: 10862029760 configs: - config_name: train_1 data_files: - split: train path: train_1/train-* - config_name: train_2 data_files: - split: train path: train_2/train-* - config_name: train_3 data_files: - split: train path: train_3/train-* - config_name: train_4 data_files: - split: train path: train_4/train-* - config_name: train_5 data_files: - split: train path: train_5/train-* --- # Dataset Card for "vietnamese_general_data_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,638
[ [ -0.0222320556640625, -0.042205810546875, 0.02691650390625, 0.01837158203125, -0.0261383056640625, -0.010498046875, 0.013275146484375, -0.00501251220703125, 0.04925537109375, 0.059539794921875, -0.051544189453125, -0.07977294921875, -0.046142578125, -0.002653...
laion/strategic_game_maze
2023-10-20T04:13:19.000Z
[ "license:cc-by-4.0", "region:us" ]
laion
null
null
6
11
2023-10-15T02:44:07
--- license: cc-by-4.0 --- NOTICE: some of the game is mistakenly label as both length and width columns are 40, they are 30 actually. # maze This dataset contains 350,000 mazes, represents over 39.29 billion moves. Each maze is a 30x30 ASCII representation, with solutions derived using the BFS. It has two columns: - 'Maze': representation of maze in a list of string.shape is 30*30 - visual example <image src="https://cdn-uploads.huggingface.co/production/uploads/644b983f0fbe4830f192c4f5/BGplH40fK5wQzpofPocMK.png" alt="drawing" width="200"/> - 'Path': solution from start point to end point in a list of string, each item represent a position in the maze.
673
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pbaoo2705/cpgqa_processed-2
2023-10-16T06:02:40.000Z
[ "region:us" ]
pbaoo2705
null
null
0
11
2023-10-16T06:02:38
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: answer dtype: string - name: start_positions dtype: int64 - name: end_positions dtype: int64 splits: - name: train num_bytes: 9148601 num_examples: 884 download_size: 190231 dataset_size: 9148601 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cpgqa_processed-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
613
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MemGPT/example-sec-filings
2023-10-19T02:56:38.000Z
[ "region:us" ]
MemGPT
null
null
6
11
2023-10-16T23:47:27
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
Data-Lab/vkusvill_search_sft_v0.3.5
2023-10-17T12:43:11.000Z
[ "region:us" ]
Data-Lab
null
null
0
11
2023-10-17T12:43:00
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: input dtype: string splits: - name: train num_bytes: 3910654 num_examples: 400 download_size: 1271716 dataset_size: 3910654 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vkusvill_search_sft_v0.3.5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
529
[ [ -0.037261962890625, -0.00885772705078125, 0.032989501953125, 0.018035888671875, -0.0285186767578125, -0.01080322265625, 0.029998779296875, -0.004344940185546875, 0.062255859375, 0.033660888671875, -0.07208251953125, -0.04779052734375, -0.0264739990234375, -0...
yaygomii/FYP_cv13_w2v_processor_output
2023-10-18T14:50:41.000Z
[ "region:us" ]
yaygomii
null
null
0
11
2023-10-18T14:40:27
--- configs: - config_name: default data_files: - split: train_w2v path: data/train_w2v-* - split: test_w2v path: data/test_w2v-* dataset_info: features: - name: input_values sequence: float32 - name: labels sequence: int64 splits: - name: train_w2v num_bytes: 12064642120 num_examples: 43350 - name: test_w2v num_bytes: 3246847096 num_examples: 11973 download_size: 15200350363 dataset_size: 15311489216 --- # Dataset Card for "FYP_cv13_w2v_processor_output" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
646
[ [ -0.02752685546875, -0.0118255615234375, 0.01265716552734375, 0.019317626953125, -0.0224456787109375, -0.005584716796875, 0.009002685546875, -0.0099945068359375, 0.03558349609375, 0.022552490234375, -0.059783935546875, -0.041168212890625, -0.056793212890625, ...
tyzhu/eval_tag_nq_test_v12_first_1
2023-10-18T16:09:07.000Z
[ "region:us" ]
tyzhu
null
null
0
11
2023-10-18T16:07:59
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: question dtype: string - name: title dtype: string - name: inputs dtype: string - name: targets dtype: string - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: id dtype: string - name: titles dtype: string splits: - name: train num_bytes: 3310 num_examples: 10 - name: validation num_bytes: 1306262 num_examples: 3610 download_size: 0 dataset_size: 1309572 --- # Dataset Card for "eval_tag_nq_test_v12_first_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
855
[ [ -0.0445556640625, -0.03369140625, -0.006664276123046875, 0.0101470947265625, -0.0169677734375, 0.01134490966796875, 0.0369873046875, 0.0032901763916015625, 0.0562744140625, 0.02587890625, -0.06231689453125, -0.04766845703125, -0.02032470703125, -0.0054244995...
irlab-udc/alpaca_data_galician
2023-10-19T13:27:01.000Z
[ "task_categories:conversational", "size_categories:10K<n<100K", "language:gl", "license:apache-2.0", "region:us" ]
irlab-udc
null
null
4
11
2023-10-19T10:34:07
--- license: apache-2.0 task_categories: - conversational language: - gl pretty_name: alpaca_data_galician size_categories: - 10K<n<100K --- # Galician version of `alpaca_data.json` This is a Galician-translated with Python package [`googletranslatepy`](https://suqingdong.github.io/googletranslatepy/) version of the Stanford [alpaca_data.json](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json) dataset. ## Dataset Structure The dataset contains 52K instruction-following elements in a JSON file with a list of dictionaries. Each dictionary contains the following fields: - `instruction`: `str`, describes the task the model should perform. Each of the 52K instructions is unique. - `input`: `str`, optional context or input for the task. For example, when the instruction is "Resume o seguinte artigo", the input is the article. Around 40% of the examples have an input. - `output`: `str`, the answer to the instruction as generated by `text-davinci-003`.
986
[ [ -0.00417327880859375, -0.05047607421875, 0.0293121337890625, 0.039886474609375, -0.0231781005859375, -0.006450653076171875, -0.0028514862060546875, -0.0283355712890625, 0.032501220703125, 0.06121826171875, -0.062286376953125, -0.06500244140625, -0.05145263671875...
carles-undergrad-thesis/en-id-parallel-sentences-embedding
2023-10-20T02:02:07.000Z
[ "region:us" ]
carles-undergrad-thesis
null
null
0
11
2023-10-20T01:57:16
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text_en dtype: string - name: text_id dtype: string - name: target_embedding sequence: float32 - name: input_ids_en sequence: int64 - name: attention_mask_en sequence: int64 - name: input_ids_id sequence: int64 - name: attention_mask_id sequence: int64 splits: - name: train num_bytes: 11676096944 num_examples: 1000000 download_size: 4112187708 dataset_size: 11676096944 --- # Dataset Card for "en-id-parallel-sentences-embedding" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
746
[ [ -0.041534423828125, -0.041351318359375, 0.026885986328125, 0.036041259765625, -0.012237548828125, -0.0017833709716796875, -0.005367279052734375, -0.0010395050048828125, 0.0594482421875, 0.022064208984375, -0.045989990234375, -0.062255859375, -0.046844482421875, ...
haseong8012/child-10k-adult-6k_for_test
2023-10-20T09:11:21.000Z
[ "region:us" ]
haseong8012
null
null
0
11
2023-10-20T08:53:37
--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: audio sequence: float32 splits: - name: test num_bytes: 2883700590 num_examples: 16000 download_size: 2489316623 dataset_size: 2883700590 --- # Dataset Card for "child-adult-16k_for-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
497
[ [ -0.040313720703125, -0.0114593505859375, -0.010650634765625, 0.0222320556640625, -0.0183258056640625, 0.005420684814453125, 0.015045166015625, -0.0244903564453125, 0.0276947021484375, 0.02197265625, -0.0714111328125, -0.046478271484375, -0.03533935546875, -0...
jay401521/twolabels
2023-10-21T09:26:15.000Z
[ "region:us" ]
jay401521
null
null
0
11
2023-10-21T08:34:10
--- dataset_info: features: - name: id dtype: int64 - name: domain dtype: string - name: label dtype: int64 - name: rank dtype: int64 - name: sentence dtype: string splits: - name: train num_bytes: 6505957 num_examples: 70594 download_size: 0 dataset_size: 6505957 --- # Dataset Card for "twolabels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
480
[ [ -0.036865234375, -0.019439697265625, 0.007404327392578125, 0.02313232421875, -0.010711669921875, 0.006099700927734375, 0.0157470703125, -0.0280914306640625, 0.052215576171875, 0.03363037109375, -0.04852294921875, -0.04833984375, -0.053558349609375, -0.030166...
traveler-leon1/my_dataset
2023-10-21T12:56:31.000Z
[ "region:us" ]
traveler-leon1
null
null
0
11
2023-10-21T12:22:44
Entry not found
15
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crumb/c4-subset-for-humaneval
2023-10-22T00:27:44.000Z
[ "region:us" ]
crumb
null
null
0
11
2023-10-21T19:06:56
--- dataset_info: features: - name: text dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 411199548 num_examples: 302361 download_size: 245218649 dataset_size: 411199548 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c4-subset-for-humaneval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
494
[ [ -0.03875732421875, -0.00669097900390625, 0.01355743408203125, 0.01207733154296875, -0.026519775390625, 0.00925445556640625, 0.01873779296875, -0.0211029052734375, 0.051116943359375, 0.0374755859375, -0.058685302734375, -0.060882568359375, -0.029266357421875, ...
crumb/c4-subset-for-arc
2023-10-21T19:23:01.000Z
[ "region:us" ]
crumb
null
null
0
11
2023-10-21T19:21:39
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
crumb/c4-subset-for-truthfulqa
2023-10-22T00:27:51.000Z
[ "region:us" ]
crumb
null
null
0
11
2023-10-21T19:23:23
--- dataset_info: features: - name: text dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 577836714 num_examples: 321153 download_size: 352256147 dataset_size: 577836714 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c4-subset-for-truthfulqa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
495
[ [ -0.035003662109375, -0.01137542724609375, 0.0295257568359375, 0.01322174072265625, -0.01318359375, 0.0162506103515625, 0.0226287841796875, -0.01165771484375, 0.036834716796875, 0.045166015625, -0.06298828125, -0.0595703125, -0.027069091796875, -0.00782012939...
crumb/c4-subset-for-hellaswag-approx
2023-10-22T00:42:48.000Z
[ "region:us" ]
crumb
null
null
0
11
2023-10-22T00:40:42
--- dataset_info: features: - name: text dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 618206614 num_examples: 291894 download_size: 364064080 dataset_size: 618206614 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c4-subset-for-hellaswag-approx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
501
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crumb/c4-subset-for-mmlu-approx
2023-10-22T01:31:25.000Z
[ "region:us" ]
crumb
null
null
0
11
2023-10-22T01:29:29
--- dataset_info: features: - name: text dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 557757084 num_examples: 262665 download_size: 339106702 dataset_size: 557757084 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c4-subset-for-mmlu-approx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
496
[ [ -0.0462646484375, -0.0252227783203125, 0.0254669189453125, 0.0103912353515625, -0.00966644287109375, 0.005283355712890625, 0.0167388916015625, -0.01512908935546875, 0.054901123046875, 0.0065155029296875, -0.0787353515625, -0.034271240234375, -0.03594970703125, ...
antareepdey/Medical_chat_Llama-chat-50k
2023-10-22T03:16:54.000Z
[ "region:us" ]
antareepdey
null
null
0
11
2023-10-22T03:15:55
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: Text dtype: string splits: - name: train num_bytes: 50561249 num_examples: 50000 download_size: 31132221 dataset_size: 50561249 --- # Dataset Card for "Medical_chat_Llama-chat-50k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
459
[ [ -0.015289306640625, -0.01113128662109375, 0.01047515869140625, 0.03466796875, -0.036285400390625, 0.0167388916015625, 0.0196685791015625, -0.024566650390625, 0.07366943359375, 0.033416748046875, -0.0574951171875, -0.06451416015625, -0.055419921875, -0.005405...
atmallen/mmlu_aux_binary
2023-10-22T21:41:19.000Z
[ "region:us" ]
atmallen
null
null
0
11
2023-10-22T20:06:00
--- dataset_info: features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int32 - name: statement dtype: string - name: label dtype: class_label: names: '0': 'false' '1': 'true' splits: - name: validation num_bytes: 7300371 num_examples: 4036 - name: test num_bytes: 69452850 num_examples: 37506 download_size: 46452233 dataset_size: 76753221 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for "mmlu_aux_binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
818
[ [ -0.04974365234375, -0.0228271484375, 0.0143585205078125, 0.018798828125, -0.0192413330078125, 0.0053253173828125, 0.02685546875, -0.0174102783203125, 0.06500244140625, 0.01271820068359375, -0.0714111328125, -0.049560546875, -0.04327392578125, -0.002061843872...
aiancheruk/womens_clothing_ecommerce_reviews_mini
2023-10-22T22:52:46.000Z
[ "region:us" ]
aiancheruk
null
null
0
11
2023-10-22T22:52:40
--- dataset_info: features: - name: review_text dtype: string - name: age dtype: int64 - name: rating dtype: int64 - name: positive_feedback_count dtype: int64 - name: division_name dtype: string - name: department_name dtype: string - name: class_name dtype: string - name: recommended_ind dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 1894592.0740274212 num_examples: 5000 - name: test num_bytes: 373295 num_examples: 1000 - name: val num_bytes: 373636 num_examples: 1000 download_size: 1342313 dataset_size: 2641523.074027421 --- # Dataset Card for "womens_clothing_ecommerce_reviews_mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
877
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davidfant/natural-questions-chunk-4
2023-10-22T23:03:02.000Z
[ "region:us" ]
davidfant
null
null
0
11
2023-10-22T22:59:31
--- dataset_info: features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 4529920148 num_examples: 10000 download_size: 1759288585 dataset_size: 4529920148 --- # Dataset Card for "natural-questions-chunk-4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,818
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davidfant/natural-questions-chunk-5
2023-10-22T23:06:32.000Z
[ "region:us" ]
davidfant
null
null
0
11
2023-10-22T23:03:02
--- dataset_info: features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 4651468477 num_examples: 10000 download_size: 1807817811 dataset_size: 4651468477 --- # Dataset Card for "natural-questions-chunk-5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,818
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davidfant/natural-questions-chunk-6
2023-10-22T23:10:03.000Z
[ "region:us" ]
davidfant
null
null
0
11
2023-10-22T23:06:32
--- dataset_info: features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 4655306372 num_examples: 10000 download_size: 1805442960 dataset_size: 4655306372 --- # Dataset Card for "natural-questions-chunk-6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,818
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davidfant/natural-questions-chunk-7
2023-10-22T23:13:42.000Z
[ "region:us" ]
davidfant
null
null
0
11
2023-10-22T23:10:03
--- dataset_info: features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 4648515125 num_examples: 10000 download_size: 1806671077 dataset_size: 4648515125 --- # Dataset Card for "natural-questions-chunk-7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,818
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LosHuesitos9-9/Huesitos
2023-10-24T19:58:07.000Z
[ "task_categories:object-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "language:es", "license:cc", "rf100", "medical", "code", "region:us" ]
LosHuesitos9-9
null
null
1
11
2023-10-23T14:33:42
--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': bone-fracture '1': angle '2': fracture '3': line '4': messed_up_angle annotations_creators: - crowdsourced language_creators: - found language: - en - es license: - cc multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: Huesitos tags: - rf100 - medical - code --- ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. ## Licensing Information See original homepage https://universe.roboflow.com/object-detection/bone-fracture-7fylg ### Citation Information ``` @misc{ bone-fracture-7fylg, title = { bone fracture 7fylg Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/bone-fracture-7fylg } }, url = { https://universe.roboflow.com/object-detection/bone-fracture-7fylg }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, contributions dataset = {[@mariosasko](https://github.com/mariosasko)} }" ```
2,922
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dyliu/VIST
2023-10-23T15:26:40.000Z
[ "region:us" ]
dyliu
null
null
0
11
2023-10-23T15:14:49
Entry not found
15
[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.0379...
danielaivanova/damaged-media
2023-10-24T00:52:48.000Z
[ "region:us" ]
danielaivanova
null
null
0
11
2023-10-23T21:25:28
--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: annotation dtype: image - name: annotation_rgb dtype: image - name: material dtype: string - name: content dtype: string splits: - name: train num_bytes: 3620215529.0 num_examples: 418 download_size: 3615768892 dataset_size: 3620215529.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "damage-analogue-media" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
645
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ManuBansal/33param_snp500
2023-10-24T12:25:37.000Z
[ "region:us" ]
ManuBansal
null
null
0
11
2023-10-24T12:24:26
Entry not found
15
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desarrolloasesoreslocales/prompts
2023-10-24T12:54:31.000Z
[ "region:us" ]
desarrolloasesoreslocales
null
null
0
11
2023-10-24T12:50:46
Entry not found
15
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jayashri710/mental-health-dataset
2023-10-25T09:58:11.000Z
[ "region:us" ]
jayashri710
null
null
0
11
2023-10-25T09:57:20
Entry not found
15
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kardosdrur/opensubtitles-da-sv
2023-10-26T07:12:17.000Z
[ "license:mit", "region:us" ]
kardosdrur
null
null
0
11
2023-10-25T13:45:35
--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: link_id dtype: string - name: da dtype: string - name: 'no' dtype: string - name: overlap dtype: float64 splits: - name: train num_bytes: 270499727.08648384 num_examples: 1772983 - name: test num_bytes: 67624969.91351616 num_examples: 443246 download_size: 201404638 dataset_size: 338124697.0 --- # OpenSubtitles Danish-Swedish Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish. The source code for producing the dataset is included in the repository. The dataset was created to aid training sentence transformers in the Danish Foundation Models project.
823
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Ka4on/ultrasound_test
2023-10-25T20:16:13.000Z
[ "region:us" ]
Ka4on
null
null
0
11
2023-10-25T20:08:59
Entry not found
15
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vishnusr/code_searchnet_reduced_val
2023-10-26T17:08:36.000Z
[ "region:us" ]
vishnusr
null
null
0
11
2023-10-26T17:08:32
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: 'Unnamed: 0.1' dtype: int64 - name: 'Unnamed: 0' dtype: int64 - name: code dtype: string - name: docstring dtype: string - name: prompt dtype: string splits: - name: train num_bytes: 1078734 num_examples: 500 download_size: 483209 dataset_size: 1078734 --- # Dataset Card for "code_searchnet_reduced_val" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
607
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CJWeiss/multilong
2023-10-26T21:38:41.000Z
[ "region:us" ]
CJWeiss
null
null
0
11
2023-10-26T21:38:00
--- dataset_info: features: - name: id dtype: string - name: sources sequence: string - name: summary/long dtype: string - name: summary/short dtype: string - name: summary/tiny dtype: string splits: - name: train num_bytes: 1381375966.0 num_examples: 3404 - name: test num_bytes: 265556700.0 num_examples: 681 - name: valid num_bytes: 199444850.0 num_examples: 454 download_size: 835227494 dataset_size: 1846377516.0 --- # Dataset Card for "multilong" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
653
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baohuynhbk14/vietnamese-guanaco-llama2-1k
2023-10-27T08:01:25.000Z
[ "region:us" ]
baohuynhbk14
null
null
0
11
2023-10-27T07:39:39
Entry not found
15
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Phaedrus/rsna_5k_512_a
2023-10-27T09:16:28.000Z
[ "region:us" ]
Phaedrus
null
null
0
11
2023-10-27T09:10:55
--- dataset_info: features: - name: image dtype: image - name: label1 dtype: image - name: label2 dtype: image - name: label3 dtype: image - name: label4 dtype: image splits: - name: train num_bytes: 8605017463.0 num_examples: 2000 download_size: 574221474 dataset_size: 8605017463.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "rsna_5k_512_a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
589
[ [ -0.037384033203125, 0.01158905029296875, 0.0031642913818359375, 0.0244598388671875, -0.0301361083984375, 0.00710296630859375, 0.0316162109375, -0.007434844970703125, 0.07342529296875, 0.0321044921875, -0.059600830078125, -0.0438232421875, -0.041351318359375, ...
felipeoes/filtered_qa_blue_amazon_legislation_v2_19k
2023-10-28T00:50:40.000Z
[ "region:us" ]
felipeoes
null
null
0
11
2023-10-28T00:50:39
--- dataset_info: features: - name: file_name dtype: string - name: prompt dtype: string - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 157786543 num_examples: 19302 download_size: 14666842 dataset_size: 157786543 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "filtered_qa_blue_amazon_legislation_v2_19k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
621
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