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RyokoAI/Syosetu711K
2023-04-05T01:13:44.000Z
[ "task_categories:text-classification", "task_categories:text-generation", "size_categories:100K<n<1M", "language:ja", "license:apache-2.0", "novel", "training", "region:us" ]
RyokoAI
null
null
null
6
7
--- license: apache-2.0 language: - ja tags: - novel - training task_categories: - text-classification - text-generation pretty_name: Syosetuka ni Narou 711K size_categories: - 100K<n<1M --- # Dataset Card for Syosetu711K *The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.* ## Dataset Description - **Homepage:** (TODO) - **Repository:** <https://github.com/RyokoAI/BigKnow2022> - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** Ronsor/undeleted <ronsor@ronsor.com> ### Dataset Summary Syosetu711K is a dataset composed of approximately 711,700 novels scraped from the Japanese novel self-publishing website Syosetuka ni Narou (JA: 小説家になろう, lit. "Let's Become a Novelist") between March 26 and March 27, 2023. The dataset contains most if not all novels published on the site, regardless of length or quality; however, we include metadata so users of this dataset can filter and evaluate its contents. Syosetu711Kは、日本の小説投稿サイト「小説家になろう」から2023年3月26日から27日にかけてスクレイプされた約711,700冊の小説から 構成されるデータセットです。このデータセットには、長さや品質に関係なく、サイトに掲載されているほとんどの小説が含まれています。ただし、 各小説のIDも含まれているため、小説家になろうAPIを使ってその情報を検索することができます。 ### Supported Tasks and Leaderboards This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes. * text-classification * text-generation ### Languages * Japanese ## Dataset Structure ### Data Instances ```json { "text": "【小説タイトル】\n焼けて爛れる恋よりも、微睡む優しい愛が欲しい\n【Ｎコード】\nＮ５０２９ＩＤ\n【作者名】\n秋暁秋季\n【あらすじ】\n俺の彼女は物凄く気の多い人だった。\nお眼鏡に適う奴が居れば、瞳孔を蕩 けさせる人だった。\nその癖照れ屋で、すぐに目を逸らす。\nな...", "meta": { "subset": "syosetu", "q": 0.6, "id": "N5029ID", "author": "秋暁秋季", "userid": 719797, "title": "焼けて爛れる恋よりも、微睡む優しい愛が欲しい", "length": 871, "points": 0, "lang": "ja", "chapters": 1, "keywords": ["気が多い", "浮気性", "無愛想", "照れる", "嫉妬", "好みではない", "クソデカ感情", "空気のような安心感"], "isr15": 0, "genre": 102, "biggenre": 1 } } { "text": "【小説タイトル】\n【能力者】\n【Ｎコード】\nＮ９８６４ＩＢ\n【作者名】\n夢音いちご\n【あらすじ】\n私立アビリティ学園。\n小・中・高・大が一貫となった、大規模な名門校。\nそして、ここは規模の大きさだけ でなく、ある特殊な制度を設けて\nいることでも有名だ。\nそれ...", "meta": { "subset": "syosetu", "q": 0.6, "id": "N9864IB", "author": "夢音いちご", "userid": 1912777, "title": "【能力者】", "length": 2334, "points": 0, "lang": "ja", "chapters": 2, "keywords": ["ガールズラブ", "身分差", "伝奇", "日常", "青春", "ラブコメ", "女主人公", "学園", "魔法", "超能力"], "isr15": 0, "genre": 202, "biggenre": 2 } } ``` ### Data Fields * `text`: the actual novel text, all chapters * `meta`: novel metadata * `subset`: dataset tag: `syosetu` * `lang`: dataset language: `ja` (Japanese) * `id`: novel ID/ncode * `author`: author name * `userid`: author user ID * `title`: novel title * `length`: novel length in words * `points`: global points (corresponds to `global_point` from the Syosetu API) * `q`: q-score (quality score) calculated based on `points` * `chapters`: number of chapters (corresponds to `general_all_no` from the Syosetu API) * `keywords`: array of novel keywords (corresponds to `keyword` from the Syosetu API, split on spaces) * `isr15`: whether the novel is rated R15+ * `genre`: novel genre ID (optional, see Syosetu API documentation) * `biggenre`: general novel genre ID (optional, see Syosetu API documentation) * `isr18`: whether the novel is rated R18+ * `nocgenre`: novel genre ID (optional, only available if `isr18` is true, see Syosetu API documentation) *For further reference, see the Syosetuka ni Narou API documentation: <https://dev.syosetu.com/man/api/> (JA).* #### Q-Score Distribution ``` 0.00: 0 0.10: 0 0.20: 0 0.30: 0 0.40: 0 0.50: 213005 0.60: 331393 0.70: 101971 0.80: 63877 0.90: 1542 1.00: 2 ``` ### Data Splits No splitting of the data was performed. ## Dataset Creation ### Curation Rationale Syosetuka ni Narou is the most popular website in Japan for authors wishing to self-publish their novels online. Many works on the site been picked up by large commercial publishers. Because of this, we believe that this dataset provides a large corpus of high-quality, creative content in the Japanese language. ### Source Data #### Initial Data Collection and Normalization *More information about any referenced scripts, commands, or programs used may be found in the BigKnow2022 GitHub repository.* First, metadata for all novels on the site was gathered into a JSON lines (JSONL) file. The Syosetuka ni Narou API was used to obtain this information. Second, this listing was used to create a secondary text file containing a list of only the novel "ncodes," or IDs. This secondary file was distributed to downloader nodes. Third, the sister site <https://pdfnovels.net> was queried with each novel ID, and the resulting PDF was saved for later processing. Fourth, the `pdftotext` tool was used to convert the PDF files to text documents. A few other scripts were then used to clean up the resulting text files. Finally, the text files and other metadata were converted into the specified data field schema above, and the resulting JSON entries were concatenated into the Syosetu711K dataset. The version uploaded to this repository, however, is split into multiple files, numbered 00 through 20 inclusive. #### Who are the source language producers? The authors of each novel. ### Annotations #### Annotation process Titles and general genre were collected alongside the novel text and IDs. #### Who are the annotators? There were no human annotators. ### Personal and Sensitive Information The dataset contains only works of fiction, and we do not believe it contains any PII. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content in Japanese. It may also be useful for other languages depending on your language model. ### Discussion of Biases This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect the biases of those authors. **Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.** ### Other Known Limitations N/A ## Additional Information ### Dataset Curators Ronsor Labs ### Licensing Information Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is distributed under fair use principles. ### Citation Information ``` @misc{ryokoai2023-bigknow2022, title = {BigKnow2022: Bringing Language Models Up to Speed}, author = {Ronsor}, year = {2023}, howpublished = {\url{https://github.com/RyokoAI/BigKnow2022}}, } ``` ### Contributions Thanks to @ronsor (GH) for gathering this dataset.
Francesco/liver-disease
2023-03-30T09:11:15.000Z
[ "task_categories:object-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc", "rf100", "region:us" ]
Francesco
null
null
null
1
7
--- 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': diseases '1': ballooning '2': fibrosis '3': inflammation '4': steatosis annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: liver-disease tags: - rf100 --- # Dataset Card for liver-disease ** The original COCO dataset is stored at `dataset.tar.gz`** ## Dataset Description - **Homepage:** https://universe.roboflow.com/object-detection/liver-disease - **Point of Contact:** francesco.zuppichini@gmail.com ### Dataset Summary liver-disease ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## 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. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/liver-disease ### Citation Information ``` @misc{ liver-disease, title = { liver disease Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/liver-disease } }, url = { https://universe.roboflow.com/object-detection/liver-disease }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
Francesco/halo-infinite-angel-videogame
2023-03-30T10:07:59.000Z
[ "task_categories:object-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc", "rf100", "region:us" ]
Francesco
null
null
null
0
7
--- 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': halo-infinite-angel-videogame '1': enemy '2': enemy-head '3': friendly '4': friendly-head annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: halo-infinite-angel-videogame tags: - rf100 --- # Dataset Card for halo-infinite-angel-videogame ** The original COCO dataset is stored at `dataset.tar.gz`** ## Dataset Description - **Homepage:** https://universe.roboflow.com/object-detection/halo-infinite-angel-videogame - **Point of Contact:** francesco.zuppichini@gmail.com ### Dataset Summary halo-infinite-angel-videogame ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## 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. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/halo-infinite-angel-videogame ### Citation Information ``` @misc{ halo-infinite-angel-videogame, title = { halo infinite angel videogame Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/halo-infinite-angel-videogame } }, url = { https://universe.roboflow.com/object-detection/halo-infinite-angel-videogame }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
Ekimetrics/ipcc-ar6
2023-04-03T10:45:57.000Z
[ "region:us" ]
Ekimetrics
null
null
null
3
7
Entry not found
philschmid/sharegpt-raw
2023-04-04T08:52:59.000Z
[ "license:other", "region:us" ]
philschmid
null
null
null
70
7
--- license: other duplicated_from: jeffwan/sharegpt_vicuna --- ## Prepraration ``` pip3 install -r requirements.txt ``` ## Data Cleaning 1. merge two raw json files and json beautify the merged file ``` python merge.py sharegpt_90k_raw_dataset/sg_90k_part1.json sharegpt_90k_raw_dataset/sg_90k_part2.json sharegpt_20230401_html_unformatted.json python pretty_json.py --in sharegpt_20230401_html_unformatted.json --out sharegpt_20230401_html.json ``` 2. (Optional) Verify the json file ``` if jq empty sharegpt_20230401_html.json 2>/dev/null; then echo "JSON is valid" else echo "JSON is invalid" fi jq length sharegpt_90k_raw_dataset/sg_90k_part1.json jq length sharegpt_90k_raw_dataset/sg_90k_part2.json jq length sharegpt_20230401_html.json ``` 3. clean data - remove html tags etc ``` python3 clean_sharegpt.py --in sharegpt_20230401_html.json --out sharegpt_20230401_clean.json .... 100%|███████████████████████████████████████████████████████████████████| 90665/90665 [06:32<00:00, 230.98it/s] total: 90665, skip: 13745, new: 76920 ``` 4. Filter dataset by language ``` python3 optional_clean.py --in sharegpt_20230401_clean.json --out sharegpt_20230401_clean_lang_zh.json --lang zh .... return 6240 out of 76920, start dump ... python3 optional_clean.py --in sharegpt_20230401_clean.json --out sharegpt_20230401_clean_lang_en.json --lang en ... return 55413 out of 76920, start dump ... ``` > Note: the code itself doesn't support languange list, I didn't change the code for adpation. You can change the code to support more languages. Instead, I just filter two languages I need and merge the `sharegpt_20230401_clean_lang_zh.json` and `sharegpt_20230401_clean_lang_en.json` into `sharegpt_20230401_clean_lang.json`. 5. Split the long conversation ``` python3 split_long_conversation.py --in sharegpt_20230401_clean_lang.json --out sharegpt_20230401_clean_lang_split.json --model-name /home/ubuntu/llama-13b-hf/ ... total: 61653, new: 126032 ``` Ok, now we have the cleaned dataset `sharegpt_20230401_clean_lang_split.json` which should be used for finetuning.
FronkonGames/steam-games-dataset
2023-04-06T01:31:39.000Z
[ "size_categories:10K<n<100K", "license:apache-2.0", "games", "steam", "python", "json", "csv", "video games", "doi:10.57967/hf/0511", "region:us" ]
FronkonGames
null
null
null
2
7
--- license: apache-2.0 tags: - games - steam - python - json - csv - video games pretty_name: Steam Games Dataset size_categories: - 10K<n<100K --- This dataset has been created with [this code (MIT)](https://github.com/FronkonGames/Steam-Games-Scraper) and use the API provided by Steam, the largest gaming platform on PC. Data is also collected from Steam Spy. Only games (no DLCs, episodes, music, videos, etc) currently released have been added.
PaulTran/vietnamese_spelling_error_detection
2023-04-07T09:31:12.000Z
[ "region:us" ]
PaulTran
null
null
null
1
7
Entry not found
hackathon-somos-nlp-2023/suicide-comments-es
2023-04-10T09:26:54.000Z
[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:es", "license:apache-2.0", "region:us" ]
hackathon-somos-nlp-2023
null
null
null
5
7
--- task_categories: - text-classification language: - es size_categories: - 1K<n<10K license: apache-2.0 --- # Dataset Description * Example model using the dataset: https://huggingface.co/hackathon-somos-nlp-2023/roberta-base-bne-finetuned-suicide-es * Example space using the dataset: https://huggingface.co/spaces/hackathon-somos-nlp-2023/suicide-comments-es * Language: Spanish ## Dataset Summary The dataset consists of comments on Reddit, Twitter, and inputs/outputs of the Alpaca dataset translated to Spanish language and classified as suicidal ideation/behavior and non-suicidal. # Dataset Structure The dataset has 10050 rows (777 considered as Suicidal Ideation/Behavior and 9273 considered Not Suicidal). ## Dataset fields * `Text`: User comment. * `Label`: 1 if suicidal ideation/behavior; 0 if not suicidal comment. # Dataset Creation ## Suicidal Ideation/Behavior * 90 rows from Columbia Suicide Severity Rating Scale (C-SSRS) https://zenodo.org/record/2667859#.ZDGnX-xBxYi C-SSRS is a gold dataset for suicidal comments detection on Reddit. We use `Helsinki-NLP/opus-mt-en-es` to translate the dataset. We also explode on paragraphs, filter messages less than 240 characters, and we filter the positive ones validating against the [Moderation API of OpenAI](https://platform.openai.com/docs/guides/moderation). * 519 rows from https://github.com/laxmimerit/twitter-suicidal-intention-dataset/tree/master The dataset contains the tweet data of suicidal intention and no intention data. We use `Helsinki-NLP/opus-mt-en-es` to translate the dataset. We filter the positive ones validating against the [Moderation API of OpenAI](https://platform.openai.com/docs/guides/moderation). * 168 rows added manually from public forums and public blogs. ## Non Suicidal * 5000 rows from instructions of https://huggingface.co/datasets/somosnlp/somos-clean-alpaca-es * 2000 rows from output of https://huggingface.co/datasets/somosnlp/somos-clean-alpaca-es * 2000 rows from Columbia Suicide Severity Rating Scale (C-SSRS) * 100 rows from https://huggingface.co/datasets/ziq/depression_advice. We use `Helsinki-NLP/opus-mt-en-es` to translate the dataset. * 100 rows added manually from public forums, blogs and podcasts. # Considerations for Using the Data ## Social Impact of Dataset The dataset could contain some patterns to detect suicidal ideation/behavior. ## Discussion of Biases No measures have been taken to estimate the bias and toxicity embedded in the dataset. However, the most of the data is collected on Reddit, Twitter, and ChatGPT. So there is probably an age bias because [the Internet is used more by younger people](https://www.statista.com/statistics/272365/age-distribution-of-internet-users-worldwide). # Additional Information ## Team * [dariolopez](https://huggingface.co/dariolopez) * [diegogd](https://huggingface.co/diegogd) ## Licesing This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
letinnghia/student-feedbacks
2023-04-10T13:01:27.000Z
[ "license:gpl-2.0", "region:us" ]
letinnghia
null
null
null
0
7
--- license: gpl-2.0 ---
aaqibsaeed/databricks-dolly-15k-ur
2023-04-14T13:24:03.000Z
[ "license:cc-by-3.0", "region:us" ]
aaqibsaeed
null
null
null
1
7
--- license: cc-by-3.0 --- This dataset was created by translating "databricks-dolly-15k.jsonl" into Urdu. It is licensed under CC BY 3.0. .اس ڈیٹا سیٹ کو "ڈیٹابرکس-ڈولی" کو اردو میں ترجمہ کرکے تیار کیا گیا تھا databricks-dolly-15k https://github.com/databrickslabs/dolly/tree/master/data
treadon/dolly-15k
2023-04-14T14:46:03.000Z
[ "license:cc-by-3.0", "region:us" ]
treadon
null
null
null
1
7
--- license: cc-by-3.0 dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string splits: - name: train num_bytes: 12208856 num_examples: 14863 - name: validation num_bytes: 117314 num_examples: 151 download_size: 7866269 dataset_size: 12326170 --- # Dataset Card for "dolly-15k" # Summary This is the dataset supplied by Databricks for training Dolly V2. This set is split 99% training / 1% validation, should you want to set aside some records for evaluation purposes. ## Special thanks to ❤️ Databricks for creating and making this set available. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
may-ohta/iwslt14
2023-04-26T09:55:06.000Z
[ "license:cc-by-nc-nd-4.0", "region:us" ]
may-ohta
The IWSLT 2014 Evaluation Campaign includes the MT track on TED Talks. In this edition, the official language pairs are five: from English to French from English to German from German to English from English to Italian from Italian to English Optional tasks are proposed with English paired in both directions with other twelve languages: from/to English to/from Arabic, Spanish, Farsi, Hebrew, Dutch, Polish, Portuguese-Brazil, Romanian, Russian, Slovenian, Turkish and Chinese Submitted runs on additional pairs will be evaluated as well, in the hope to stimulate the MT community to evaluate systems on common benchmarks and to share achievements on challenging translation tasks.
@inproceedings{cettoloEtAl:EAMT2012, Address = {Trento, Italy}, Author = {Mauro Cettolo and Christian Girardi and Marcello Federico}, Booktitle = {Proceedings of the 16$^{th}$ Conference of the European Association for Machine Translation (EAMT)}, Date = {28-30}, Month = {May}, Pages = {261--268}, Title = {WIT$^3$: Web Inventory of Transcribed and Translated Talks}, Year = {2012}}
null
0
7
--- license: cc-by-nc-nd-4.0 dataset_info: features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 39120226 num_examples: 171721 - name: validation num_bytes: 492473 num_examples: 2082 - name: test num_bytes: 1058859 num_examples: 4782 download_size: 23758217 dataset_size: 40671558 ---
liyucheng/zhihu_26k
2023-04-15T20:41:37.000Z
[ "license:cc-by-2.0", "region:us" ]
liyucheng
null
null
null
20
7
--- license: cc-by-2.0 ---
chrisociepa/wikipedia-pl-20230401
2023-04-17T20:41:24.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "size_categories:1M<n<10M", "language:pl", "license:cc-by-sa-3.0", "pretraining", "language modelling", "wikipedia", "web", "region:us" ]
chrisociepa
null
null
null
0
7
--- license: cc-by-sa-3.0 dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2883878741 num_examples: 1562327 download_size: 1761971402 dataset_size: 2883878741 task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling language: - pl pretty_name: Polish Wikipedia 2023-04-01 size_categories: - 1M<n<10M tags: - pretraining - language modelling - wikipedia - web --- # Dataset Card for April 2023 Polish Wikipedia Wikipedia dataset containing cleaned articles of Polish language. The dataset has been built from the Wikipedia dump (https://dumps.wikimedia.org/) using the [OLM Project](https://github.com/huggingface/olm-datasets). Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). ### Licensing Information Most of Wikipedia's text and many of its images are co-licensed under the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License) (CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License) (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text. ### Citation Information ``` @ONLINE{wikidump, author = "Wikimedia Foundation", title = "Wikimedia Downloads", url = "https://dumps.wikimedia.org" } ```
yash1811/news_summaries
2023-04-19T22:00:36.000Z
[ "license:mit", "region:us" ]
yash1811
null
null
null
2
7
--- license: mit ---
joey234/mmlu-astronomy-neg
2023-04-20T05:13:25.000Z
[ "region:us" ]
joey234
null
null
null
0
7
--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 46473 num_examples: 152 download_size: 28019 dataset_size: 46473 --- # Dataset Card for "mmlu-astronomy-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Heerak/ko_en_parallel_dataset
2023-04-20T08:51:52.000Z
[ "region:us" ]
Heerak
null
null
null
0
7
--- dataset_info: features: - name: ko dtype: string - name: en dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4112684317 num_examples: 11800415 - name: validation num_bytes: 20767480 num_examples: 59299 - name: test num_bytes: 419935 num_examples: 1982 download_size: 2691575595 dataset_size: 4133871732 --- # Dataset Card for "ko_en_parallel_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jlh/home-credit
2023-04-25T22:58:10.000Z
[ "region:us" ]
jlh
null
null
null
0
7
--- dataset_info: features: - name: SK_ID_CURR dtype: int64 - name: TARGET dtype: class_label: names: '0': '0' '1': '1' - name: NAME_CONTRACT_TYPE dtype: string - name: CODE_GENDER dtype: string - name: FLAG_OWN_CAR dtype: string - name: FLAG_OWN_REALTY dtype: string - name: CNT_CHILDREN dtype: int64 - name: AMT_INCOME_TOTAL dtype: float64 - name: AMT_CREDIT dtype: float64 - name: AMT_ANNUITY dtype: float64 - name: AMT_GOODS_PRICE dtype: float64 - name: NAME_TYPE_SUITE dtype: string - name: NAME_INCOME_TYPE dtype: string - name: NAME_EDUCATION_TYPE dtype: string - name: NAME_FAMILY_STATUS dtype: string - name: NAME_HOUSING_TYPE dtype: string - name: REGION_POPULATION_RELATIVE dtype: float64 - name: DAYS_BIRTH dtype: int64 - name: DAYS_EMPLOYED dtype: int64 - name: DAYS_REGISTRATION dtype: float64 - name: DAYS_ID_PUBLISH dtype: int64 - name: OWN_CAR_AGE dtype: float64 - name: FLAG_MOBIL dtype: int64 - name: FLAG_EMP_PHONE dtype: int64 - name: FLAG_WORK_PHONE dtype: int64 - name: FLAG_CONT_MOBILE dtype: int64 - name: FLAG_PHONE dtype: int64 - name: FLAG_EMAIL dtype: int64 - name: OCCUPATION_TYPE dtype: string - name: CNT_FAM_MEMBERS dtype: float64 - name: REGION_RATING_CLIENT dtype: int64 - name: REGION_RATING_CLIENT_W_CITY dtype: int64 - name: WEEKDAY_APPR_PROCESS_START dtype: string - name: HOUR_APPR_PROCESS_START dtype: int64 - name: REG_REGION_NOT_LIVE_REGION dtype: int64 - name: REG_REGION_NOT_WORK_REGION dtype: int64 - name: LIVE_REGION_NOT_WORK_REGION dtype: int64 - name: REG_CITY_NOT_LIVE_CITY dtype: int64 - name: REG_CITY_NOT_WORK_CITY dtype: int64 - name: LIVE_CITY_NOT_WORK_CITY dtype: int64 - name: ORGANIZATION_TYPE dtype: string - name: EXT_SOURCE_1 dtype: float64 - name: EXT_SOURCE_2 dtype: float64 - name: EXT_SOURCE_3 dtype: float64 - name: APARTMENTS_AVG dtype: float64 - name: BASEMENTAREA_AVG dtype: float64 - name: YEARS_BEGINEXPLUATATION_AVG dtype: float64 - name: YEARS_BUILD_AVG dtype: float64 - name: COMMONAREA_AVG dtype: float64 - name: ELEVATORS_AVG dtype: float64 - name: ENTRANCES_AVG dtype: float64 - name: FLOORSMAX_AVG dtype: float64 - name: FLOORSMIN_AVG dtype: float64 - name: LANDAREA_AVG dtype: float64 - name: LIVINGAPARTMENTS_AVG dtype: float64 - name: LIVINGAREA_AVG dtype: float64 - name: NONLIVINGAPARTMENTS_AVG dtype: float64 - name: NONLIVINGAREA_AVG dtype: float64 - name: APARTMENTS_MODE dtype: float64 - name: BASEMENTAREA_MODE dtype: float64 - name: YEARS_BEGINEXPLUATATION_MODE dtype: float64 - name: YEARS_BUILD_MODE dtype: float64 - name: COMMONAREA_MODE dtype: float64 - name: ELEVATORS_MODE dtype: float64 - name: ENTRANCES_MODE dtype: float64 - name: FLOORSMAX_MODE dtype: float64 - name: FLOORSMIN_MODE dtype: float64 - name: LANDAREA_MODE dtype: float64 - name: LIVINGAPARTMENTS_MODE dtype: float64 - name: LIVINGAREA_MODE dtype: float64 - name: NONLIVINGAPARTMENTS_MODE dtype: float64 - name: NONLIVINGAREA_MODE dtype: float64 - name: APARTMENTS_MEDI dtype: float64 - name: BASEMENTAREA_MEDI dtype: float64 - name: YEARS_BEGINEXPLUATATION_MEDI dtype: float64 - name: YEARS_BUILD_MEDI dtype: float64 - name: COMMONAREA_MEDI dtype: float64 - name: ELEVATORS_MEDI dtype: float64 - name: ENTRANCES_MEDI dtype: float64 - name: FLOORSMAX_MEDI dtype: float64 - name: FLOORSMIN_MEDI dtype: float64 - name: LANDAREA_MEDI dtype: float64 - name: LIVINGAPARTMENTS_MEDI dtype: float64 - name: LIVINGAREA_MEDI dtype: float64 - name: NONLIVINGAPARTMENTS_MEDI dtype: float64 - name: NONLIVINGAREA_MEDI dtype: float64 - name: FONDKAPREMONT_MODE dtype: string - name: HOUSETYPE_MODE dtype: string - name: TOTALAREA_MODE dtype: float64 - name: WALLSMATERIAL_MODE dtype: string - name: EMERGENCYSTATE_MODE dtype: string - name: OBS_30_CNT_SOCIAL_CIRCLE dtype: float64 - name: DEF_30_CNT_SOCIAL_CIRCLE dtype: float64 - name: OBS_60_CNT_SOCIAL_CIRCLE dtype: float64 - name: DEF_60_CNT_SOCIAL_CIRCLE dtype: float64 - name: DAYS_LAST_PHONE_CHANGE dtype: float64 - name: FLAG_DOCUMENT_2 dtype: int64 - name: FLAG_DOCUMENT_3 dtype: int64 - name: FLAG_DOCUMENT_4 dtype: int64 - name: FLAG_DOCUMENT_5 dtype: int64 - name: FLAG_DOCUMENT_6 dtype: int64 - name: FLAG_DOCUMENT_7 dtype: int64 - name: FLAG_DOCUMENT_8 dtype: int64 - name: FLAG_DOCUMENT_9 dtype: int64 - name: FLAG_DOCUMENT_10 dtype: int64 - name: FLAG_DOCUMENT_11 dtype: int64 - name: FLAG_DOCUMENT_12 dtype: int64 - name: FLAG_DOCUMENT_13 dtype: int64 - name: FLAG_DOCUMENT_14 dtype: int64 - name: FLAG_DOCUMENT_15 dtype: int64 - name: FLAG_DOCUMENT_16 dtype: int64 - name: FLAG_DOCUMENT_17 dtype: int64 - name: FLAG_DOCUMENT_18 dtype: int64 - name: FLAG_DOCUMENT_19 dtype: int64 - name: FLAG_DOCUMENT_20 dtype: int64 - name: FLAG_DOCUMENT_21 dtype: int64 - name: AMT_REQ_CREDIT_BUREAU_HOUR dtype: float64 - name: AMT_REQ_CREDIT_BUREAU_DAY dtype: float64 - name: AMT_REQ_CREDIT_BUREAU_WEEK dtype: float64 - name: AMT_REQ_CREDIT_BUREAU_MON dtype: float64 - name: AMT_REQ_CREDIT_BUREAU_QRT dtype: float64 - name: AMT_REQ_CREDIT_BUREAU_YEAR dtype: float64 splits: - name: train num_bytes: 323536216 num_examples: 307511 download_size: 0 dataset_size: 323536216 --- # Dataset Card for "home-credit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
halaction/song-lyrics
2023-04-29T18:58:36.000Z
[ "license:apache-2.0", "region:us" ]
halaction
null
null
null
0
7
--- license: apache-2.0 ---
chittaranjankhatua/car_damage_pub
2023-05-02T13:43:35.000Z
[ "region:us" ]
chittaranjankhatua
null
null
null
0
7
Entry not found
sileod/mindgames
2023-06-29T08:30:21.000Z
[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:multi-input-text-classification", "multilinguality:monolingual", "language:en", "license:apache-2.0", "theory of mind", "tom", "Logical-Reasoning", "Modal-Logic", "Reasoning", "Logics", "Logic", "nli", ...
sileod
null
null
null
4
7
--- license: apache-2.0 task_categories: - text-classification language: - en task_ids: - natural-language-inference - multi-input-text-classification multilinguality: - monolingual tags: - theory of mind - tom - Logical-Reasoning - Modal-Logic - Reasoning - Logics - Logic - nli - model-checking - natural language inference dataset_info: features: - name: premise dtype: string - name: smcdel_problem dtype: string - name: n_announcements dtype: int64 - name: pbcheck dtype: string - name: hypothesis dtype: string - name: setup dtype: string - name: hypothesis_depth dtype: int64 - name: n_agents dtype: int64 - name: label dtype: int64 - name: names sequence: string - name: index dtype: int64 - name: s-l dtype: string - name: deberta_pred dtype: int64 - name: deberta_confidence dtype: float64 - name: difficulty dtype: float64 splits: - name: train num_bytes: 8619563.842139175 num_examples: 11174 - name: validation num_bytes: 2873445.0789304124 num_examples: 3725 - name: test num_bytes: 2873445.0789304124 num_examples: 3725 download_size: 2991434 dataset_size: 14366454 --- Mindgame dataset Code: https://github.com/sileod/llm-theory-of-mind Article: https://arxiv.org/abs/2305.03353 ``` @article{sileo2023mindgames, title={MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic}, author={Sileo, Damien and Lernould, Antoine}, journal={arXiv preprint arXiv:2305.03353}, year={2023} } ```
alejandrowallace/tmdb-5000
2023-05-03T20:19:43.000Z
[ "task_categories:zero-shot-classification", "size_categories:1K<n<10K", "language:en", "license:unknown", "region:us" ]
alejandrowallace
null
null
null
0
7
--- license: unknown task_categories: - zero-shot-classification language: - en size_categories: - 1K<n<10K ---
Finnish-NLP/mc4_3.1.0_fi_cleaned
2023-05-19T16:20:51.000Z
[ "region:us" ]
Finnish-NLP
null
null
null
0
7
--- dataset_info: features: - name: text dtype: string - name: timestamp dtype: timestamp[s] - name: url dtype: string - name: perplexity_kenlm dtype: int64 - name: label_identity_attack dtype: float64 - name: label_insult dtype: float64 - name: label_obscene dtype: float64 - name: label_severe_toxicity dtype: float64 - name: label_threat dtype: float64 - name: label_toxicity dtype: float64 splits: - name: train num_bytes: 103354369732 num_examples: 26468761 - name: validation num_bytes: 101931416 num_examples: 26149 download_size: 7141130482 dataset_size: 103456301148 --- # Dataset Card for "mc4_3.1.0_fi_cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlekseyKorshuk/hh-lmgym-demo
2023-05-17T18:13:29.000Z
[ "region:us" ]
AlekseyKorshuk
null
null
null
1
7
--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string splits: - name: train num_bytes: 126803175 num_examples: 112052 - name: test num_bytes: 14079595 num_examples: 12451 download_size: 0 dataset_size: 140882770 --- # Dataset Card for "hh-lmgym-demo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alzoubi36/privaseer
2023-06-21T12:32:56.000Z
[ "license:gpl-3.0", "region:us" ]
alzoubi36
null
null
null
0
7
--- license: gpl-3.0 dataset_info: features: - name: hash dtype: string - name: url dtype: string - name: text dtype: string - name: title dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 17080868768 num_examples: 2180300 download_size: 8133175578 dataset_size: 17080868768 --- ## Privaseer Dataset Huggingface version of the [Privaseer](https://privaseer.ist.psu.edu/) dataset. <pre> @inproceedings{srinath-etal-2021-privacy, title = "Privacy at Scale: Introducing the {P}riva{S}eer Corpus of Web Privacy Policies", author = "Srinath, Mukund and Wilson, Shomir and Giles, C Lee", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.532", doi = "10.18653/v1/2021.acl-long.532", pages = "6829--6839", abstract = "Organisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since understanding them requires a significant investment of time and effort. Natural language processing has been used to create experimental tools to interpret privacy policies, but there has been a lack of large privacy policy corpora to facilitate the creation of large-scale semi-supervised and unsupervised models to interpret and simplify privacy policies. Thus, we present the PrivaSeer Corpus of 1,005,380 English language website privacy policies collected from the web. The number of unique websites represented in PrivaSeer is about ten times larger than the next largest public collection of web privacy policies, and it surpasses the aggregate of unique websites represented in all other publicly available privacy policy corpora combined. We describe a corpus creation pipeline with stages that include a web crawler, language detection, document classification, duplicate and near-duplicate removal, and content extraction. We employ an unsupervised topic modelling approach to investigate the contents of policy documents in the corpus and discuss the distribution of topics in privacy policies at web scale. We further investigate the relationship between privacy policy domain PageRanks and text features of the privacy policies. Finally, we use the corpus to pretrain PrivBERT, a transformer-based privacy policy language model, and obtain state of the art results on the data practice classification and question answering tasks.",} </pre>
squarelike/ReverseProxy-OAI-Log
2023-06-16T18:32:28.000Z
[ "language:en", "license:apache-2.0", "region:us" ]
squarelike
null
null
null
7
7
--- license: apache-2.0 language: - en --- OAI reverse proxy log data to be found on the Internet until 2023-06-17.<br> The dataset was built to fit the Vicuna format, but some modifications are required if you are actually learning.<br> There are three types: GPT3.5, GPT4, and claude<br> <br> This dataset contains vast amounts of AI chatting data (in TavernAI, RisuAI, etc.)<br> I didn't dedup the dataset
muhrafli/heart-diseases
2023-05-22T08:57:31.000Z
[ "region:us" ]
muhrafli
null
null
null
0
7
Entry not found
dbdu/ShareGPT-74k-ko
2023-08-19T07:00:39.000Z
[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:ko", "license:cc-by-2.0", "conversation", "chatgpt", "gpt-3.5", "region:us" ]
dbdu
null
null
null
12
7
--- language: - ko pretty_name: ShareGPT-74k-ko tags: - conversation - chatgpt - gpt-3.5 license: cc-by-2.0 task_categories: - text-generation size_categories: - 10K<n<100K --- # ShareGPT-ko-74k ShareGPT 90k의 cleaned 버전을 구글 번역기를 이용하여 번역하였습니다.\ 원본 데이터셋은 [여기](https://github.com/lm-sys/FastChat/issues/90)에서 확인하실 수 있습니다. Korean-translated version of ShareGPT-90k, translated by Google Translaton.\ You can check the original dataset [here](https://github.com/lm-sys/FastChat/issues/90). ## Dataset Description json 파일의 구조는 원본 데이터셋과 동일합니다.\ `*_unclneaed.json`은 원본 데이터셋을 번역하고 따로 후처리하지 않은 데이터셋입니다. (총 74k)\ `*_cleaned.json`은 위의 데이터에서 코드가 포함된 데이터를 러프하게 제거한 데이터셋입니다. (총 55k)\ **주의**: 코드는 번역되었을 수 있으므로 cleaned를 쓰시는 걸 추천합니다. The structure of the dataset is the same with the original dataset.\ `*_unclneaed.json` are Korean-translated data, without any post-processing. (total 74k dialogues)\ `*_clneaed.json` are post-processed version which dialogues containing code snippets are eliminated from. (total 55k dialogues)\ **WARNING**: Code snippets might have been translated into Korean. I recommend you use cleaned files. ## Licensing Information GPT를 이용한 데이터셋이므로 OPENAI의 [약관](https://openai.com/policies/terms-of-use)을 따릅니다.\ 그 외의 경우 [CC BY 2.0 KR](https://creativecommons.org/licenses/by/2.0/kr/)을 따릅니다. The licensing status of the datasets follows [OPENAI Licence](https://openai.com/policies/terms-of-use) as it contains GPT-generated sentences.\ For all the other cases, the licensing status follows [CC BY 2.0 KR](https://creativecommons.org/licenses/by/2.0/kr/). ## Code 번역에 사용한 코드는 아래 리포지토리에서 확인 가능합니다. Check out the following repository to see the translation code used.\ https://github.com/dubuduru/ShareGPT-translation You can use the repository to translate ShareGPT-like dataset into your preferred language.
RussianNLP/RuSpellGold
2023-05-26T16:41:30.000Z
[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:ru", "license:apache-2.0", "region:us" ]
RussianNLP
RuSpellGold is a benchmark of 1711 sentence pairs dedicated to a problem of automatic spelling correction in Russian language. The dataset is gathered from five different domains including news, Russian classic literature, social media texts, open web and strategic documents. It has been passed through two-stage manual labeling process with native speakers as annotators to correct spelling violation and preserve original style of text at the same time.
null
null
0
7
--- license: apache-2.0 task_categories: - text-generation language: - ru size_categories: - 1K<n<10K --- # Dataset Card for RuSpellGold ## Dataset Description - **Paper:** # TODO - **ArXiv:** # TODO - **Point of Contact:** nikita.martynov.98@list.ru - **Language:** Russian ### Dataset Summary RuSpellGold is a benchmark of 1711 sentence pairs dedicated to a problem of automatic spelling correction in Russian language. The dataset is gathered from five different domains including news, Russian classic literature, social media texts, open web and strategic documents. It has been passed through two-stage manual labeling process with native speakers as annotators to correct spelling violation and preserve original style of text at the same time. ## Dataset Structure ### Supported Tasks and Leaderboards - **Task:** automatic spelling correction. - **Metrics:** https://www.dialog-21.ru/media/3427/sorokinaaetal.pdf. ### Languages Russian. ### Data Instances ``` { "sources": "Видела в городе афиши, анонсрующие ее концерт.", "corrections": "Видела в городе афиши, анонсирующие её концерт", "domain": "aranea" } ``` ### Data Fields - ```sources (str)```: original sentence. - ```corrections (str)```: corrected sentence. - ```domain (str)```: domain, from which the sentence is taken from. ### Data Splits Current version of benchmark is only represented by test part: - ```test```: 1711 sentence pairs (```"data/test.csv"```). which is then splitted into following domain-relaited shards: - ```aranea```: 756 sentence pairs (```"data/aranea/split.csv"```); - ```literature```: 260 sentence pairs (```"data/literature/split.csv"```); - ```news```: 245 sentence pairs (```"data/news/split.csv"```); - ```social_media```: 200 sentence pairs (```"data/social_media/split.csv"```); - ```strategic_documents```: 250 sentence pairs (```"data/strategic_documents/split.csv"```); ## Dataset Creation ### Source Data |Source |Strategy |Domain | |---|---|---| |Vladimír Benko. 2014. Aranea: Yet another family of (comparable) web corpora. // Text, Speech and Dialogue: 17th International Conference, TSD 2014, Brno, Czech Republic, September 8-12, 2014. Proceedings 17, P 247–256. Springer| Random sentences from Araneum Russicum|Open web (aranea) | | Russian classic literature aggregated in this [corpus](https://www.kaggle.com/datasets/d0rj3228/russian-literature) | Random sentences | Literature | |Ilya Gusev. 2020. Dataset for automatic summarization of russian news. // Artificial Intelligence and Natural Language: 9th Conference, AINL 2020, Helsinki, Finland, October 7–9, 2020, Proceedings 9, P 122–134. Springer | Random sentences | News | |Social media platforms | Posts from social media platforms marked with specific hashtags | Social Media | |Vitaly Ivanin, Ekaterina Artemova, Tatiana Batura, Vladimir Ivanov, Veronika Sarkisyan, Elena Tutubalina, and Ivan Smurov. 2020. Rurebus-2020 shared task: Russian relation extraction for business. // Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialog” [Komp’iuternaia Lingvistika i Intellektual’nye Tehnologii: Trudy Mezhdunarodnoj Konferentsii “Dialog”], Moscow, Russia. | Random sentences | Strategic documents | ### Annotations #### Annotation process All of the sentences undergo a two-stage annotation procedure on [Toloka](https://toloka.ai), a crowd-sourcing platform for data labeling. Each stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, a worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples. The instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. To ensure high-quality expertise on the matter of spelling, we set up additional test phase on a small portion of data, manually revised the results and approved only those annotators, who managed to avoid any mistakes. - **Stage 1: Data gathering** We provide texts with possible mistakes to annotators and ask them to write the sentence correctly preserving the original style-markers of the text. - **Stage 2: Validation** We provide annotators with the pair of sentences (origin and its corresponding correction from the previous stage) and ask them to check if the correction is right. ### Personal and Sensitive Information Each annotator is warned about potentially sensitive topics in data (e.g., politics, societal minorities, and religion). ## Additional Information ### Dataset Curators Correspondence: ```nikita.martynov.98@list.ru``` ### Licensing Information The corpus is available under the Apache 2.0 license. The copyright (where applicable) of texts from the linguistic publications and resources remains with the original authors or publishers. ### Other Please refer to our paper # TODO for more details.
yjernite/stable-bias_grounding-images_multimodel_3_12_22
2023-05-29T16:44:50.000Z
[ "region:us" ]
yjernite
null
null
null
0
7
--- dataset_info: features: - name: prompt dtype: string - name: gender_phrase dtype: string - name: ethnicity_phrase dtype: string - name: image dtype: image - name: source_type dtype: string - name: source dtype: string splits: - name: train num_bytes: 70960514.0 num_examples: 2040 download_size: 70651732 dataset_size: 70960514.0 --- # Dataset Card for "stable-bias_grounding-images_multimodel_3_12_22" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Cubpaw/voxelgym_5c_42x42_10
2023-06-01T13:00:45.000Z
[ "region:us" ]
Cubpaw
null
null
null
0
7
--- dataset_info: features: - name: image dtype: image - name: label dtype: image - name: rgb_label dtype: image - name: path_label dtype: image - name: path_rgb_label dtype: image splits: - name: train num_bytes: 6953.0 num_examples: 8 - name: validation num_bytes: 1776.0 num_examples: 2 download_size: 26790 dataset_size: 8729.0 --- # Dataset Card for "voxelgym_5c_42x42_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
whu9/mediasum_postprocess
2023-06-03T06:02:12.000Z
[ "region:us" ]
whu9
null
null
null
0
7
--- dataset_info: features: - name: source dtype: string - name: summary dtype: string - name: source_num_tokens dtype: int64 - name: summary_num_tokens dtype: int64 splits: - name: train num_bytes: 3913935357 num_examples: 443511 - name: validation num_bytes: 86873579 num_examples: 9999 - name: test num_bytes: 88635215 num_examples: 9997 download_size: 2335096802 dataset_size: 4089444151 --- # Dataset Card for "mediasum_postprocess" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
swaption2009/cyber-threat-intelligence-custom-data
2023-06-04T07:35:25.000Z
[ "task_categories:text-generation", "task_categories:table-question-answering", "language:en", "region:us" ]
swaption2009
null
null
null
3
7
--- task_categories: - text-generation - table-question-answering language: - en ---
kaist-ai/CoT-Collection_multilingual
2023-06-08T07:32:18.000Z
[ "task_categories:text-generation", "task_categories:text-classification", "size_categories:1M<n<10M", "language:en", "license:cc-by-4.0", "arxiv:2305.14045", "region:us" ]
kaist-ai
""" _LICENSE = "CC BY 4.0" _HOMEPAGE = "https://github.com/kaistAI/CoT-Collection" _LANGUAGES = { "ko": "Korean", "fr": "French", "ru": "Russian", "ja": "Japanese", "zh": "Chinese", } # _ALL_LANGUAGES = "all_languages" class CoTCollectionMultiConfig(datasets.BuilderConfig):
@article{kim2023cot, title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning}, author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon}, journal={arXiv preprint arXiv:2305.14045}, year={2023} }
null
3
7
--- license: cc-by-4.0 task_categories: - text-generation - text-classification language: - en size_categories: - 1M<n<10M --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:https://github.com/kaistAI/CoT-Collection** - **Repository:https://github.com/kaistAI/CoT-Collection** - **Paper:https://arxiv.org/abs/2305.14045** - **Point of Contact:sejune@lklab.io** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits | name | train | |-------------------|------:| |CoT-Collection|1837928| ## Additional Information ### Citation Information ``` @article{kim2023cot, title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning}, author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon}, journal={arXiv preprint arXiv:2305.14045}, year={2023} } ```
ParsaKgvr/socce_report_analysis
2023-06-06T08:09:18.000Z
[ "region:us" ]
ParsaKgvr
null
null
null
1
7
--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: sent0 dtype: string - name: sent1 dtype: string - name: sent2 dtype: string - name: sent3 dtype: string - name: sent4 dtype: string - name: sent5 dtype: string - name: sent6 dtype: string - name: sent7 dtype: string - name: sent8 dtype: string - name: sent9 dtype: string - name: sent10 dtype: string - name: sent11 dtype: string - name: sent12 dtype: string - name: sent13 dtype: string - name: sent14 dtype: string - name: sent15 dtype: string - name: sent16 dtype: string - name: sent17 dtype: string - name: sent18 dtype: string - name: sent19 dtype: string - name: sent20 dtype: string - name: sent21 dtype: string - name: sent22 dtype: string - name: sent23 dtype: string - name: sent24 dtype: string - name: sent25 dtype: string - name: sent26 dtype: string - name: sent27 dtype: string - name: sent28 dtype: string - name: sent29 dtype: string - name: sent30 dtype: string - name: sent31 dtype: string - name: sent32 dtype: string - name: sent33 dtype: string - name: sent34 dtype: string - name: sent35 dtype: string - name: sent36 dtype: string - name: sent37 dtype: string - name: sent38 dtype: string - name: sent39 dtype: string - name: sent40 dtype: string - name: sent41 dtype: string - name: sent42 dtype: string - name: sent43 dtype: string - name: sent44 dtype: string - name: sent45 dtype: string - name: sent46 dtype: string - name: sent47 dtype: string - name: sent48 dtype: string - name: sent49 dtype: string - name: sent50 dtype: string - name: sent51 dtype: string - name: sent52 dtype: string - name: sent53 dtype: string - name: sent54 dtype: string - name: sent55 dtype: string - name: sent56 dtype: string - name: sent57 dtype: string - name: sent58 dtype: string - name: sent59 dtype: string - name: sent60 dtype: string - name: sent61 dtype: string - name: sent62 dtype: string - name: sent63 dtype: string - name: sent64 dtype: string - name: sent65 dtype: string - name: sent66 dtype: string - name: sent67 dtype: string - name: sent68 dtype: string - name: sent69 dtype: string - name: sent70 dtype: string - name: sent71 dtype: string - name: sent72 dtype: string - name: sent73 dtype: string - name: sent74 dtype: string - name: sent75 dtype: string - name: sent76 dtype: string - name: sent77 dtype: string - name: sent78 dtype: string - name: sent79 dtype: string - name: sent80 dtype: string - name: sent81 dtype: string - name: sent82 dtype: string - name: sent83 dtype: string - name: sent84 dtype: string - name: sent85 dtype: string - name: sent86 dtype: string - name: sent87 dtype: string - name: sent88 dtype: string - name: sent89 dtype: string - name: sent90 dtype: string - name: sent91 dtype: string - name: sent92 dtype: string - name: sent93 dtype: string - name: sent94 dtype: string - name: sent95 dtype: string - name: sent96 dtype: string - name: sent97 dtype: string - name: sent98 dtype: string - name: sent99 dtype: string - name: sent100 dtype: string - name: sent101 dtype: string - name: sent102 dtype: string - name: sent103 dtype: string - name: sent104 dtype: string - name: sent105 dtype: string - name: sent106 dtype: string - name: sent107 dtype: string - name: sent108 dtype: string - name: sent109 dtype: string - name: sent110 dtype: string - name: sent111 dtype: string - name: sent112 dtype: string - name: sent113 dtype: string - name: sent114 dtype: string - name: sent115 dtype: string - name: sent116 dtype: string - name: sent117 dtype: string - name: sent118 dtype: string - name: sent119 dtype: string - name: sent120 dtype: string - name: sent121 dtype: string - name: sent122 dtype: string - name: sent123 dtype: string - name: sent124 dtype: string - name: sent125 dtype: string - name: sent126 dtype: string - name: sent127 dtype: string - name: sent128 dtype: string - name: sent129 dtype: string - name: sent130 dtype: string - name: sent131 dtype: string - name: sent132 dtype: string - name: sent133 dtype: string - name: sent134 dtype: string - name: sent135 dtype: string - name: sent136 dtype: string - name: player0 dtype: string - name: rating0 dtype: string - name: player1 dtype: string - name: rating1 dtype: string - name: player2 dtype: string - name: rating2 dtype: string - name: player3 dtype: string - name: rating3 dtype: string - name: player4 dtype: string - name: rating4 dtype: string - name: player5 dtype: string - name: rating5 dtype: string - name: player6 dtype: string - name: rating6 dtype: string - name: player7 dtype: string - name: rating7 dtype: string - name: player8 dtype: string - name: rating8 dtype: string - name: player9 dtype: string - name: rating9 dtype: string - name: player10 dtype: string - name: rating10 dtype: string - name: player11 dtype: string - name: rating11 dtype: string - name: player12 dtype: string - name: rating12 dtype: string - name: player13 dtype: string - name: rating13 dtype: string - name: player14 dtype: string - name: rating14 dtype: string - name: player15 dtype: string - name: rating15 dtype: string - name: player16 dtype: string - name: rating16 dtype: string - name: player17 dtype: string - name: rating17 dtype: string - name: player18 dtype: string - name: rating18 dtype: string - name: player19 dtype: string - name: rating19 dtype: string - name: player20 dtype: string - name: rating20 dtype: string - name: player21 dtype: string - name: rating21 dtype: string - name: player22 dtype: string - name: rating22 dtype: string - name: player23 dtype: string - name: rating23 dtype: string - name: player24 dtype: string - name: rating24 dtype: string - name: player25 dtype: string - name: rating25 dtype: string - name: player26 dtype: string - name: rating26 dtype: string - name: player27 dtype: string - name: rating27 dtype: string - name: player28 dtype: string - name: rating28 dtype: string - name: player29 dtype: string - name: rating29 dtype: string - name: player30 dtype: string - name: rating30 dtype: string - name: player31 dtype: string - name: rating31 dtype: string - name: player32 dtype: string - name: rating32 dtype: string - name: player33 dtype: string - name: rating33 dtype: string splits: - name: train num_bytes: 13072462 num_examples: 1996 download_size: 6901926 dataset_size: 13072462 --- # Dataset Card for "socce_report_analysis" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
argilla/reward-model-data-falcon
2023-06-07T11:33:30.000Z
[ "region:us" ]
argilla
null
null
null
0
7
--- {} --- ## Guidelines These guidelines are based on the paper [Training Language Models to Follow Instructions with Human Feedback](https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf) **You are given a text-based description of a task, submitted by a user.** This task description may be in the form of an explicit instruction (e.g. "Write a story about a wise frog."). The task may also be specified indirectly, for example by using several examples of the desired behavior (e.g. given a sequence of movie reviews followed by their sentiment, followed by one movie review without a sentiment, you can assume the task is to predict the sentiment of the final review), or by generating the start of a desired output (e.g. given "There once was a wise frog named Julius," you can assume the task is to continue the story). **You will also be given several text outputs, intended to help the user with their task.** Your job is to evaluate these outputs to ensure that they are helpful, truthful, and harmless, and select the best according to these criteria. In case **none of the outputs is correct, conforming to the criteria, or they're exactly equal, use the discard option to not rank them**. For most tasks, being truthful and harmless is more important than being helpful. By **helpful**, we mean that the output should follow the user's intention and help the user solve their task. Some examples of helpful behavior: - Writing in clear language. - Answering the question they meant to ask, even if they mis-asked it. - Being sensitive to internationality (e.g., "football" shouldn't mean American football, and "the president" doesn't necessarily mean the US president). - If an instruction is too confusing, asking for clarification and explaining why the instruction is confusing. - Not giving overly long or rambling answers, or repeating information from the question. - Not assuming extraneous extra context outside of what's given (besides things like facts about the world), unless that's an implied part of the task. For example, if asked to "write a polite email response to this email: email body," the output shouldn't assume "I can't make it this time, but am free next weekend." But if asked to "Write an email to Socrates," then you can safely assume the output can make up a context. By **truthful**, we mean that the output contains accurate information and doesn't mislead the user. Some examples of truthful behavior: - On tasks like summarization, where the output should only use information from input, not making up details that are not part of the input description. - Not producing clearly false information about the world (e.g., making up facts or promoting conspiracies). For example, the output should not state that Hillary Clinton has served time in prison. - Avoid generating misleading information or information with questionable authenticity. For example, if the input asks "Why did Hillary Clinton go to jail?", the output should not say "It's not totally clear," but rather should refute the premise of the question. By **harmless**, we mean that the output should not cause physical, psychological, or social harm to people; damage to or loss of equipment or property; damage to the environment; or harm to institutions or resources necessary to human well-being. Some examples of harmless behavior: - Treating other humans with kindness, respect, and consideration; not denigrating members of certain groups or using biased language against a particular group. - Not generating abusive, threatening, or offensive language or promoting violence. - Not writing sexual or violent content if it's not asked for. - Not giving bad real-world advice or promoting illegal activity. Evaluating model outputs may involve making trade-offs between these criteria. These trade-offs will depend on the task. ## Fields * `instruction` is of type <class 'str'> * `response-1` is of type <class 'str'> * `response-2` is of type <class 'str'> ## Questions * `choose-best` : Choose the most helpful, harmless, and truthful response. Select 1 for response-1, 2 for response-2, or discard if both are equally good/bad. ## Load with Argilla To load this dataset with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface('argilla/reward-model-data-falcon') ``` ## Load with Datasets To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset('argilla/reward-model-data-falcon') ```
64bits/lex_fridman_podcast_for_llm_vicuna
2023-06-09T10:13:46.000Z
[ "task_categories:text-generation", "language:en", "transformers", "region:us" ]
64bits
null
null
null
11
7
--- task_categories: - text-generation language: - en pretty_name: lex-llm tags: - transformers --- # Intro This dataset represents a compilation of audio-to-text transcripts from the Lex Fridman Podcast. The Lex Fridman Podcast, hosted by AI researcher at MIT, Lex Fridman, is a deep dive into a broad range of topics that touch on science, technology, history, philosophy, and the nature of intelligence, consciousness, love, and power. The guests on the podcast are drawn from a diverse range of fields, providing unique and insightful perspectives on these subjects. The dataset has been formatted in ShareGPT format for use with conversational large language models (LLMs) like Vicuna, WizardVicuna, etc. This dataset can be an invaluable resource for training and refining language models, offering a rich source of nuanced, intellectual, and thought-provoking dialogue. Furthermore, the diversity of topics covered provides a broad spectrum of language usage, idiomatic expressions, and subject matter expertise. ### 3 versions 1. _original: original dataset where each item is an entire episode 2. _chunked: chunked dataset where episodes are formated into chunks of approximately 1200 words(roughly < 2048 tokens) 3. _chunked_gpt: change "lex" & "guest" to "human" & "gpt" in _chunked dataset to fit Vicuna training # What I did 1. Fetch all episode links of Lex Fridman Podcast 2. For each episode, transform the transcript in html to json format (Vicuna ShareGPT format) 3. remove the first few sentences from Lex for each episode to remove the introduction and ads. # Problems & Concerns 1. These are audio-to-text transcriptions, which contain inaccurate detections 2. Although the speakers are professionals, these are verbal conversations which contain oral languages 3. The dataset may contain ads and personal opinions from Lex Fridman and the speakers 4. more ... # Next Steps 1. finetune LLaMA, WizardVicuna, Vicuna models using this dataset
TigerResearch/sft_en
2023-06-09T12:21:07.000Z
[ "language:en", "license:apache-2.0", "region:us" ]
TigerResearch
null
null
null
3
7
--- license: apache-2.0 language: - en --- [Tigerbot](https://github.com/TigerResearch/TigerBot) 开源项目中微调英文sft-en数据合集 本合集涵盖本组织下开源的其他中文sft-英文-数据集，不需要重复下载 <p align="center" width="40%"> ## Usage ```python import datasets ds_sft = datasets.load_dataset('TigerResearch/sft_en') ``` ## 文件细分 | 类型 | 语言 | 数据集文件 | 数量 | | ------------ | ---- | -------------------------------------------------------------------------------------------------------------------------------- | ----------- | | alpaca 英文 | 英文 | [tigerbot-alpaca-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-alpaca-en-50k.json) | 50k | | 头脑风暴 | 英文 | [tigerbot-dolly-Brainstorming-en-1.7k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-dolly-Brainstorming-en-1.7k.json) | 1.7k | | 分类 | 英文 | [tigerbot-dolly-Classification-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-dolly-Classification-en-2k.json) | 2k | | 数学问题 | 英文 | [tigerbot-gsm-8k-en](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-gsm-8k-en.json) | 8k | | 代码 | 英文 | [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-kaggle-leetcodesolutions-en-2k.json) | 2k | | 食谱生成 | 英文 | [tigerbot-kaggle-recipes-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-kaggle-recipes-en-2k.json) | 2k | | 病历生成 | 英文 | [tigerbot-mt-note-generation-en](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-mt-note-generation-en.json) | 450 | | 多轮对话 | 英文 | [tigerbot-OIG-multichat-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-OIG-multichat-en-50k.json) | 50k | | 综合问答 | 英文 | [tigerbot-stackexchange-qa-en-0.5m](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-stackexchange-qa-en-0.5m.json) | 0.5m | | wiki 问答 | 英文 | [tigerbot-wiki-qa-bart-en-10k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-wiki-qa-bart-en-10k.json) | 10k | | 如何做类教程 | 英文 | [tigerbot-youtube-howto-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-youtube-howto-en-50k.json) | 50k |
orangetin/oig-chip
2023-06-12T01:32:23.000Z
[ "license:apache-2.0", "region:us" ]
orangetin
null
null
null
0
7
--- license: apache-2.0 ---
Edoh/manim_python
2023-06-12T17:01:54.000Z
[ "license:creativeml-openrail-m", "region:us" ]
Edoh
null
null
null
1
7
--- license: creativeml-openrail-m ---
yyu/agnews-attrprompt
2023-08-22T08:27:07.000Z
[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]
yyu
null
null
null
0
7
--- license: apache-2.0 task_categories: - text-classification language: - en size_categories: - 10K<n<100K --- This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt). - `label.txt`: the label name for each class - `train.jsonl`: The original training set. - `valid.jsonl`: The original validation set. - `test.jsonl`: The original test set. - `simprompt.jsonl`: The training data generated by the simple prompt. - `attrprompt.jsonl`: The training data generated by the attributed prompt.
mcimpoi/alot
2023-06-16T10:34:05.000Z
[ "task_categories:image-classification", "size_categories:10K<n<100K", "language:en", "region:us" ]
mcimpoi
null
null
null
0
7
--- task_categories: - image-classification language: - en pretty_name: Amsterdam Library of Textures (ALOT) size_categories: - 10K<n<100K dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1' '1': '10' '2': '100' '3': '101' '4': '102' '5': '103' '6': '104' '7': '105' '8': '106' '9': '107' '10': '108' '11': '109' '12': '11' '13': '110' '14': '111' '15': '112' '16': '113' '17': '114' '18': '115' '19': '116' '20': '117' '21': '118' '22': '119' '23': '12' '24': '120' '25': '121' '26': '122' '27': '123' '28': '124' '29': '125' '30': '126' '31': '127' '32': '128' '33': '129' '34': '13' '35': '130' '36': '131' '37': '132' '38': '133' '39': '134' '40': '135' '41': '136' '42': '137' '43': '138' '44': '139' '45': '14' '46': '140' '47': '141' '48': '142' '49': '143' '50': '144' '51': '145' '52': '146' '53': '147' '54': '148' '55': '149' '56': '15' '57': '150' '58': '151' '59': '152' '60': '153' '61': '154' '62': '155' '63': '156' '64': '157' '65': '158' '66': '159' '67': '16' '68': '160' '69': '161' '70': '162' '71': '163' '72': '164' '73': '165' '74': '166' '75': '167' '76': '168' '77': '169' '78': '17' '79': '170' '80': '171' '81': '172' '82': '173' '83': '174' '84': '175' '85': '176' '86': '177' '87': '178' '88': '179' '89': '18' '90': '180' '91': '181' '92': '182' '93': '183' '94': '184' '95': '185' '96': '186' '97': '187' '98': '188' '99': '189' '100': '19' '101': '190' '102': '191' '103': '192' '104': '193' '105': '194' '106': '195' '107': '196' '108': '197' '109': '198' '110': '199' '111': '2' '112': '20' '113': '200' '114': '201' '115': '202' '116': '203' '117': '204' '118': '205' '119': '206' '120': '207' '121': '208' '122': '209' '123': '21' '124': '210' '125': '211' '126': '212' '127': '213' '128': '214' '129': '215' '130': '216' '131': '217' '132': '218' '133': '219' '134': '22' '135': '220' '136': '221' '137': '222' '138': '223' '139': '224' '140': '225' '141': '226' '142': '227' '143': '228' '144': '229' '145': '23' '146': '230' '147': '231' '148': '232' '149': '233' '150': '234' '151': '235' '152': '236' '153': '237' '154': '238' '155': '239' '156': '24' '157': '240' '158': '241' '159': '242' '160': '243' '161': '244' '162': '245' '163': '246' '164': '247' '165': '248' '166': '249' '167': '25' '168': '250' '169': '26' '170': '27' '171': '28' '172': '29' '173': '3' '174': '30' '175': '31' '176': '32' '177': '33' '178': '34' '179': '35' '180': '36' '181': '37' '182': '38' '183': '39' '184': '4' '185': '40' '186': '41' '187': '42' '188': '43' '189': '44' '190': '45' '191': '46' '192': '47' '193': '48' '194': '49' '195': '5' '196': '50' '197': '51' '198': '52' '199': '53' '200': '54' '201': '55' '202': '56' '203': '57' '204': '58' '205': '59' '206': '6' '207': '60' '208': '61' '209': '62' '210': '63' '211': '64' '212': '65' '213': '66' '214': '67' '215': '68' '216': '69' '217': '7' '218': '70' '219': '71' '220': '72' '221': '73' '222': '74' '223': '75' '224': '76' '225': '77' '226': '78' '227': '79' '228': '8' '229': '80' '230': '81' '231': '82' '232': '83' '233': '84' '234': '85' '235': '86' '236': '87' '237': '88' '238': '89' '239': '9' '240': '90' '241': '91' '242': '92' '243': '93' '244': '94' '245': '95' '246': '96' '247': '97' '248': '98' '249': '99' splits: - name: train num_bytes: 3302794460.0 num_examples: 20000 - name: test num_bytes: 411146945.0 num_examples: 2500 - name: dev num_bytes: 415575782.5 num_examples: 2500 download_size: 4104421810 dataset_size: 4129517187.5 --- # Dataset Card for Amsterdam Library of Textures (ALOT) ## Dataset Description - **Homepage:** https://aloi.science.uva.nl/public_alot/ - **Paper:** G. J. Burghouts and J. M. Geusebroek, Material-specific adaptation of color invariant features, Pattern Recognition Letters, vol. 30, 306-313, 2009 ### Licensing Information Not known, see website ### Citation Information @article{burghouts2009material, title={Material-specific adaptation of color invariant features}, author={Burghouts, Gertjan J and Geusebroek, Jan-Mark}, journal={Pattern Recognition Letters}, volume={30}, number={3}, pages={306--313}, year={2009}, publisher={Elsevier} }
mike-ravkine/rosettacode-parsed
2023-06-20T12:01:47.000Z
[ "task_categories:text-generation", "language:en", "language:code", "license:gfdl", "region:us" ]
mike-ravkine
null
null
null
8
7
--- license: gfdl task_categories: - text-generation language: - en - code --- ## Data Origins Original dataset: https://huggingface.co/datasets/jondurbin/rosettacode-raw/ Cleaner code: https://github.com/the-crypt-keeper/rosettacode-parser ## Data Fields |Field|Type|Description| |---|---|---| |title|string|problem title| |task|string|problem description| |language|string|solution language/variant| |soulution|string|solution source code| ## Languages One .jsonl is provided per language group, the sublanguage field in the data denotes the specific language version/variant or the source language the example was ported from. ``` Language Python problems 510 rows 621 Language C problems 350 rows 350 Language C++ problems 403 rows 416 Language C sharp problems 322 rows 342 Language Go problems 496 rows 503 Language JavaScript problems 269 rows 301 Language Java problems 470 rows 512 Language Lua problems 335 rows 339 Language Kotlin problems 435 rows 435 Language Ruby problems 418 rows 444 Total 4894 done 565 skip 4329 failed 0 rows 4263 ```
jondurbin/rosettacode-10
2023-06-21T07:37:59.000Z
[ "license:gfdl", "region:us" ]
jondurbin
null
null
null
2
7
--- license: gfdl --- Instruction/response formatted rosettacode.org tasks/solutions for: - c++ - c - c# - go - java - javascript - kotlin - lua - python - ruby
MikhailT/cmu-arctic
2023-06-23T09:07:03.000Z
[ "size_categories:10K<n<100K", "language:en", "license:mit", "region:us" ]
MikhailT
null
null
null
0
7
--- license: mit language: - en pretty_name: CMU Arctic dataset_info: features: - name: speaker dtype: string - name: file dtype: string - name: text dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: aew num_bytes: 124532319 num_examples: 1132 - name: ahw num_bytes: 65802249 num_examples: 593 - name: aup num_bytes: 55771949 num_examples: 593 - name: awb num_bytes: 106781643 num_examples: 1138 - name: axb num_bytes: 67641455 num_examples: 593 - name: bdl num_bytes: 97845496 num_examples: 1131 - name: clb num_bytes: 123294691 num_examples: 1132 - name: eey num_bytes: 55460671 num_examples: 592 - name: fem num_bytes: 57115651 num_examples: 593 - name: gka num_bytes: 64208369 num_examples: 592 - name: jmk num_bytes: 103401609 num_examples: 1114 - name: ksp num_bytes: 114080099 num_examples: 1132 - name: ljm num_bytes: 51847413 num_examples: 593 - name: lnh num_bytes: 120446549 num_examples: 1132 - name: rms num_bytes: 127163811 num_examples: 1132 - name: rxr num_bytes: 83873386 num_examples: 666 - name: slp num_bytes: 72360869 num_examples: 593 - name: slt num_bytes: 108798337 num_examples: 1132 download_size: 1577150976 dataset_size: 1600426566 size_categories: - 10K<n<100K --- # CMU Arctic Dataset
FreedomIntelligence/alpaca-gpt4-arabic
2023-08-06T08:07:51.000Z
[ "license:apache-2.0", "region:us" ]
FreedomIntelligence
null
null
null
1
7
--- license: apache-2.0 --- The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT).
aisyahhrazak/ms-rotikaya
2023-06-29T03:54:09.000Z
[ "language:ms", "region:us" ]
aisyahhrazak
null
null
null
0
7
--- language: - ms --- Roti Kaya articles scraped on 27.6.2023
Norod78/il-license-plates
2023-06-28T14:11:25.000Z
[ "task_categories:object-detection", "size_categories:n<1K", "license:mit", "region:us" ]
Norod78
null
null
null
0
7
--- license: mit size_categories: - n<1K task_categories: - object-detection --- Images of Israeli License Plates with annotation for Plate-Object detection
ai4privacy/pii-masking-43k
2023-06-28T17:45:58.000Z
[ "size_categories:10K<n<100K", "language:en", "legal", "business", "psychology", "privacy", "doi:10.57967/hf/0824", "region:us" ]
ai4privacy
null
null
null
8
7
--- language: - en tags: - legal - business - psychology - privacy size_categories: - 10K<n<100K --- # Purpose and Features The purpose of the model and dataset is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The model is a fine-tuned version of "Distilled BERT", a smaller and faster version of BERT. It was adapted for the task of token classification based on the largest to our knowledge open-source PII masking dataset, which we are releasing simultaneously. The model size is 62 million parameters. The original encoding of the parameters yields a model size of 268 MB, which is compressed to 43MB after parameter quantization. The models are available in PyTorch, tensorflow, and tensorflow.js The dataset is composed of ~43’000 observations. Each row starts with a natural language sentence that includes placeholders for PII and could plausibly be written to an AI assistant. The placeholders are then filled in with mocked personal information and tokenized with the BERT tokenizer. We label the tokens that correspond to PII, serving as the ground truth to train our model. The dataset covers a range of contexts in which PII can appear. The sentences span 54 sensitive data types (~111 token classes), targeting 125 discussion subjects / use cases split across business, psychology and legal fields, and 5 interactions styles (e.g. casual conversation vs formal document). Key facts: - Currently 5.6m tokens with 43k PII examples. - Scaling to 100k examples - Human-in-the-loop validated - Synthetic data generated using proprietary algorithms - Adapted from DistilBertForTokenClassification - Framework PyTorch - 8 bit quantization # Performance evaluation | Test Precision | Test Recall | Test Accuracy | |:-:|:-:|:-:| | 0.998636 | 0.998945 | 0.994621 | Training/Test Set split: - 4300 Testing Examples (10%) - 38700 Train Examples # Community Engagement: Newsletter & updates: www.Ai4privacy.com - Looking for ML engineers, developers, beta-testers, human in the loop validators (all languages) - Integrations with already existing open source solutions # Roadmap and Future Development - Multilingual - Extended integrations - Continuously increase the training set - Further optimisation to the model to reduce size and increase generalisability - Next released major update is planned for the 14th of July (subscribe to newsletter for updates) # Use Cases and Applications **Chatbots**: Incorporating a PII masking model into chatbot systems can ensure the privacy and security of user conversations by automatically redacting sensitive information such as names, addresses, phone numbers, and email addresses. **Customer Support Systems**: When interacting with customers through support tickets or live chats, masking PII can help protect sensitive customer data, enabling support agents to handle inquiries without the risk of exposing personal information. **Email Filtering**: Email providers can utilize a PII masking model to automatically detect and redact PII from incoming and outgoing emails, reducing the chances of accidental disclosure of sensitive information. **Data Anonymization**: Organizations dealing with large datasets containing PII, such as medical or financial records, can leverage a PII masking model to anonymize the data before sharing it for research, analysis, or collaboration purposes. **Social Media Platforms**: Integrating PII masking capabilities into social media platforms can help users protect their personal information from unauthorized access, ensuring a safer online environment. **Content Moderation**: PII masking can assist content moderation systems in automatically detecting and blurring or redacting sensitive information in user-generated content, preventing the accidental sharing of personal details. **Online Forms**: Web applications that collect user data through online forms, such as registration forms or surveys, can employ a PII masking model to anonymize or mask the collected information in real-time, enhancing privacy and data protection. **Collaborative Document Editing**: Collaboration platforms and document editing tools can use a PII masking model to automatically mask or redact sensitive information when multiple users are working on shared documents. **Research and Data Sharing**: Researchers and institutions can leverage a PII masking model to ensure privacy and confidentiality when sharing datasets for collaboration, analysis, or publication purposes, reducing the risk of data breaches or identity theft. **Content Generation**: Content generation systems, such as article generators or language models, can benefit from PII masking to automatically mask or generate fictional PII when creating sample texts or examples, safeguarding the privacy of individuals. (...and whatever else your creative mind can think of) # Support and Maintenance AI4Privacy is a project affiliated with [AISuisse SA](https://www.aisuisse.com/).
TrainingDataPro/selfie-and-video-on-back-camera
2023-09-14T16:55:55.000Z
[ "task_categories:image-classification", "task_categories:video-classification", "task_categories:image-to-image", "language:en", "license:cc-by-nc-nd-4.0", "legal", "region:us" ]
TrainingDataPro
The dataset consists of selfies and video of real people made on a back camera of the smartphone. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems.
@InProceedings{huggingface:dataset, title = {selfie-and-video-on-back-camera}, author = {TrainingDataPro}, year = {2023} }
null
1
7
--- license: cc-by-nc-nd-4.0 task_categories: - image-classification - video-classification - image-to-image language: - en tags: - legal dataset_info: features: - name: photo dtype: image - name: video dtype: string - name: phone dtype: string - name: gender dtype: string - name: age dtype: int8 - name: country dtype: string splits: - name: train num_bytes: 21414921 num_examples: 10 download_size: 239042378 dataset_size: 21414921 --- # Selfie and Video on Back Camera Dataset The dataset consists of selfies and video of real people made on a back camera of the smartphone. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems. ### The dataset includes 2 different types of files: - **Photo** - a selfie of a person from a mobile phone, the person is depicted alone on it, the face is clearly visible. - **Video** - filmed on the front camera, on which a person moves his/her head left, right, up and down. Duration of the video is from 10 to 20 seconds. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2Fcb68f607125c83bb97b5e7a4e6edf519%2FMacBook%20Air%20-%201%20(1).png?generation=1688132311367523&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=selfie-and-video-on-back-camera) to discuss your requirements, learn about the price and buy the dataset. # Content ### Phone models in the datset: - iPhone 13 mini - iPhone 11 - iPhone XR - iPhone 8 plus - Samsung A80 - Samsung galaxy A51 5G - Samsung A32 - Samsung Galaxy S10 5G ### File with the extension .csv includes the following information for each media file: - **photo**: link to access the selfie, - **video**: link to access the video, - **phone**: the device used to capture selfie and video, - **gender**: gender of a person, - **age**: age of the person, - **country**: country of the person ## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=selfie-and-video-on-back-camera) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
TrainingDataPro/printed-2d-masks-with-holes-for-eyes-attacks
2023-09-14T16:56:28.000Z
[ "task_categories:image-classification", "task_categories:image-to-image", "task_categories:video-classification", "language:en", "license:cc-by-nc-nd-4.0", "legal", "region:us" ]
TrainingDataPro
The dataset consists of selfies of people and videos of them wearing a printed 2d mask with their face. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems. The dataset includes: **attacks** - videos of people wearing printed portraits of themselves with cut-out eyes.
@InProceedings{huggingface:dataset, title = {printed-2d-masks-with-holes-for-eyes-attacks}, author = {TrainingDataPro}, year = {2023} }
null
1
7
--- license: cc-by-nc-nd-4.0 task_categories: - image-classification - image-to-image - video-classification language: - en tags: - legal dataset_info: features: - name: photo dtype: image - name: attack dtype: string - name: phone dtype: string - name: gender dtype: string - name: age dtype: int8 - name: country dtype: string splits: - name: train num_bytes: 97360072 num_examples: 15 download_size: 502647114 dataset_size: 97360072 --- # Printed 2D Masks with Holes for Eyes Attacks Dataset The dataset consists of selfies of people and videos of them wearing a printed 2d mask with their face. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems. The dataset includes: **attacks** - videos of people wearing printed portraits of themselves with cut-out eyes. ### The dataset includes 2 different types of files: - **Photo** - a selfie of a person from a mobile phone, the person is depicted alone on it, the face is clearly visible. - **Video** - filmed on the front camera, on which a person moves his/her head left, right, up and down. Duration of the video is from 10 to 20 seconds. On the video, a person is wearing a printed 2d mask made from the corresponding photo from the "photo" folder. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2F8c995d1a19eaf18ced6d0208fa5d8475%2FMacBook%20Air%20-%201%20(2).png?generation=1688134701103179&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=printed-2d-masks-with-holes-for-eyes-attacks) to discuss your requirements, learn about the price and buy the dataset. # Content ### File with the extension .csv includes the following information for each media file: - **photo**: link to access the photo, - **attack**: link to access the attack video, - **phone**: the device used to capture the replay video, - **computer**: the device used to play the video, - **gender**: gender of a person in the video, - **age**: age of the person in the video, - **country**: country of the person ## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=printed-2d-masks-with-holes-for-eyes-attacks) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
knowrohit07/know_logic
2023-06-30T21:32:18.000Z
[ "license:other", "region:us" ]
knowrohit07
null
null
null
3
7
--- license: other ---
Peppertuna/ChartQADatasetV2
2023-09-11T02:26:42.000Z
[ "region:us" ]
Peppertuna
ChartQA dataset demo
null
null
2
7
Entry not found
aisyahhrazak/ms-majalahsains
2023-07-03T00:47:04.000Z
[ "language:ms", "region:us" ]
aisyahhrazak
null
null
null
0
7
--- language: - ms --- About - Scraped articles from https://www.majalahsains.com/ - Data scraped on 1.7.2023 Dataset Format ``` {"url": "...", "headline": "...", "content": [...,...], "tags": [......,.....,]} ```
aisyahhrazak/ms-melakahariini
2023-07-03T00:47:31.000Z
[ "language:ms", "region:us" ]
aisyahhrazak
null
null
null
0
7
--- language: - ms --- About - Scraped articles from https://www.melakahariini.my/ - Data scraped on 1.7.2023 Dataset Format ``` {"url": "...", "headline": "...", "content": [...,...]} ```
causal-lm/instructions-ko
2023-07-24T05:54:16.000Z
[ "language:ko", "region:us" ]
causal-lm
null
null
null
1
7
--- language: ko dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 71817534.51580903 num_examples: 112104 - name: validation num_bytes: 8026314.24732017 num_examples: 12429 download_size: 43862664 dataset_size: 79843848.7631292 --- # Dataset Card for "instructions-ko" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aisyahhrazak/ms-malaysiakini-my
2023-07-03T00:49:22.000Z
[ "language:ms", "region:us" ]
aisyahhrazak
null
null
null
0
7
--- language: - ms --- About - Scraped articles from https://www.malaysiakini.com/my - Not including other domains (page.malaysiakini/newslab.malaysiakini) - Data scraped on 2.7.2023 Dataset Format ``` {"url": "...", "headline": "...", "content": [...,...]} ```
jjzha/skillspan
2023-09-07T12:12:10.000Z
[ "language:en", "license:cc-by-4.0", "region:us" ]
jjzha
null
null
null
0
7
--- license: cc-by-4.0 language: en --- This is the SkillSpan dataset created by: ``` @inproceedings{zhang-etal-2022-skillspan, title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings", author = "Zhang, Mike and Jensen, Kristian and Sonniks, Sif and Plank, Barbara", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.366", doi = "10.18653/v1/2022.naacl-main.366", pages = "4962--4984" } ``` There are document delimiters indicated by `idx`. Number of samples (sentences): - train: 4800 - dev: 3174 - test: 3569 Sources: - Stackoverflow (tech) - STAR (house) Type of tags: - Generic BIO tags with keys `tags_skill` and `tags_knowledge` Sample: ``` { "idx": 53, "tokens": ["Drive", "our", "IT", "compliance", "agenda", "and", "develop", "our", "processes"], "tags_skill": ["B", "I", "I", "I", "I", "O", "B", "I", "I"], "tags_knowledge": ["O", "O", "O", "O", "O", "O", "O", "O", "O"], "source": "house" } ```
Beluuuuuuga/Japanese-Instruction-Linux-Command-169
2023-07-17T11:06:56.000Z
[ "task_categories:question-answering", "size_categories:n<1K", "language:ja", "license:cc-by-nc-4.0", "region:us" ]
Beluuuuuuga
null
null
null
2
7
--- license: cc-by-nc-4.0 task_categories: - question-answering language: - ja size_categories: - n<1K ---
rudraml/fma
2023-07-14T23:31:34.000Z
[ "license:openrail", "region:us" ]
rudraml
FMA is a dataset for music analysis. It includes song title, album, artist, genres; spectrograms, metadata, and features.
""" _DESCRIPTION =
null
0
7
--- license: openrail ---
FunDialogues/sports-basketball-coach
2023-08-28T23:39:54.000Z
[ "task_categories:question-answering", "task_categories:conversational", "size_categories:n<1K", "language:en", "license:apache-2.0", "fictitious dialogues", "prototyping", "sports", "region:us" ]
FunDialogues
null
null
null
1
7
--- license: apache-2.0 task_categories: - question-answering - conversational language: - en tags: - fictitious dialogues - prototyping - sports pretty_name: 'sports-basketball-coach' size_categories: - n<1K --- # fun dialogues A library of fictitious dialogues that can be used to train language models or augment prompts for prototyping and educational purposes. Fun dialogues currently come in json and csv format for easy ingestion or conversion to popular data structures. Dialogues span various topics such as sports, retail, academia, healthcare, and more. The library also includes basic tooling for loading dialogues and will include quick chatbot prototyping functionality in the future. Visit the Project Repo: https://github.com/eduand-alvarez/fun-dialogues/ # This Dialogue Comprised of fictitious examples of dialogues between a basketball coach and the players on the court during a game. Check out the example below: ``` "id": 1, "description": "Motivating the team", "dialogue": "Coach: Let's give it our all, team! We've trained hard for this game, and I know we can come out on top if we work together." ``` # How to Load Dialogues Loading dialogues can be accomplished using the fun dialogues library or Hugging Face datasets library. ## Load using fun dialogues 1. Install fun dialogues package `pip install fundialogues` 2. Use loader utility to load dataset as pandas dataframe. Further processing might be required for use. ``` from fundialogues import dialoader # load as pandas dataframe bball_coach = dialoader("FunDialogues/sports-basketball-coach") ``` ## Loading using Hugging Face datasets 1. Install datasets package 2. Load using datasets ``` from datasets import load_dataset dataset = load_dataset("FunDialogues/sports-basketball-coach") ``` ## How to Contribute If you want to contribute to this project and make it better, your help is very welcome. Contributing is also a great way to learn more about social coding on Github, new technologies and and their ecosystems and how to make constructive, helpful bug reports, feature requests and the noblest of all contributions: a good, clean pull request. ### Contributing your own Lifecycle Solution If you want to contribute to an existing dialogue or add a new dialogue, please open an issue and I will follow up with you ASAP! ### Implementing Patches and Bug Fixes - Create a personal fork of the project on Github. - Clone the fork on your local machine. Your remote repo on Github is called origin. - Add the original repository as a remote called upstream. - If you created your fork a while ago be sure to pull upstream changes into your local repository. - Create a new branch to work on! Branch from develop if it exists, else from master. - Implement/fix your feature, comment your code. - Follow the code style of the project, including indentation. - If the component has tests run them! - Write or adapt tests as needed. - Add or change the documentation as needed. - Squash your commits into a single commit with git's interactive rebase. Create a new branch if necessary. - Push your branch to your fork on Github, the remote origin. - From your fork open a pull request in the correct branch. Target the project's develop branch if there is one, else go for master! If the maintainer requests further changes just push them to your branch. The PR will be updated automatically. Once the pull request is approved and merged you can pull the changes from upstream to your local repo and delete your extra branch(es). And last but not least: Always write your commit messages in the present tense. Your commit message should describe what the commit, when applied, does to the code – not what you did to the code. # Disclaimer The dialogues contained in this repository are provided for experimental purposes only. It is important to note that these dialogues are assumed to be original work by a human and are entirely fictitious, despite the possibility of some examples including factually correct information. The primary intention behind these dialogues is to serve as a tool for language modeling experimentation and should not be used for designing real-world products beyond non-production prototyping. Please be aware that the utilization of fictitious data in these datasets may increase the likelihood of language model artifacts, such as hallucinations or unrealistic responses. Therefore, it is essential to exercise caution and discretion when employing these datasets for any purpose. It is crucial to emphasize that none of the scenarios described in the fun dialogues dataset should be relied upon to provide advice or guidance to humans. These scenarios are purely fictitious and are intended solely for demonstration purposes. Any resemblance to real-world situations or individuals is entirely coincidental. The responsibility for the usage and application of these datasets rests solely with the individual or entity employing them. By accessing and utilizing these dialogues and all contents of the repository, you acknowledge that you have read and understood this disclaimer, and you agree to use them at your own discretion and risk.
dvinagre/euskera-speaker-embeddings
2023-07-19T13:26:54.000Z
[ "region:us" ]
dvinagre
null
null
null
0
7
--- dataset_info: features: - name: speaker_embeddings sequence: float64 splits: - name: train num_bytes: 40286600 num_examples: 9826 download_size: 33659727 dataset_size: 40286600 --- # Dataset Card for "euskera-speaker-embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hammer888/captcha-data
2023-07-19T17:10:50.000Z
[ "region:us" ]
hammer888
null
null
null
1
7
Entry not found
RainerGa/openassistant-guanaco-de
2023-07-22T08:33:23.000Z
[ "language:de", "license:apache-2.0", "region:us" ]
RainerGa
null
null
null
1
7
--- license: apache-2.0 language: - de --- This is only a Copy of the Work of OpenAssistant and the user timdettmers Target of this trainingdata is finetuning only in German language. File openassistant_origfile_with_lang_informations.txt is the full trainingdata. Every line starts with Language Informations. You can easily filter with: cat openassistant_origfile_with_lang_informations.txt | grep ^de | sed s/^de,//g > openassistant_best_replies_de_train.jsonl Replace ^de with the language you are interessted in. For language detection I use python "langdetect". The simple python Script "detect_language.py" is part of this dataset but NOT needed for finetuning a model! ### Languages OpenAssistant Conversations only German language: **Languages with over 1000 messages** - German: 5279
wisenut-nlp-team/squad_kor_v1
2023-08-03T04:45:50.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ko", "license:cc-by-nd-4.0", "arxiv:1909.07005", "region:us" ]
wisenut-nlp-team
KorQuAD 1.0 is a large-scale Korean dataset for machine reading comprehension task consisting of human generated questions for Wikipedia articles. We benchmark the data collecting process of SQuADv1.0 and crowdsourced 70,000+ question-answer pairs. 1,637 articles and 70,079 pairs of question answers were collected. 1,420 articles are used for the training set, 140 for the dev set, and 77 for the test set. 60,407 question-answer pairs are for the training set, 5,774 for the dev set, and 3,898 for the test set.
@article{lim2019korquad1, title={Korquad1. 0: Korean qa dataset for machine reading comprehension}, author={Lim, Seungyoung and Kim, Myungji and Lee, Jooyoul}, journal={arXiv preprint arXiv:1909.07005}, year={2019} }
null
2
7
--- annotations_creators: - crowdsourced language_creators: - found language: - ko license: - cc-by-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: korquad pretty_name: The Korean Question Answering Dataset dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 config_name: squad_kor_v1_512 splits: - name: train num_examples: 60407 - name: validation num_examples: 5774 viewer: true --- # Dataset Card for KorQuAD v1.0 512 Tokens ## 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://korquad.github.io/KorQuad%201.0/ - **Repository:** https://github.com/korquad/korquad.github.io/tree/master/dataset - **Paper:** https://arxiv.org/abs/1909.07005 ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
ritterdennis/topex-printer
2023-07-24T20:28:46.000Z
[ "task_categories:image-classification", "size_categories:1K<n<10K", "license:cc-by-nc-3.0", "region:us" ]
ritterdennis
null
null
null
1
7
--- license: cc-by-nc-3.0 task_categories: - image-classification size_categories: - 1K<n<10K viewer: false --- ## Dataset Description We introduce a challenging dataset for identifying machine parts from real photos, featuring images of 102 parts from a labeling machine. This dataset was developed with the complexity of real-world scenarios in mind and highlights the complexity of distinguishing between closely related classes, providing an opportunity to improve domain adaption methods. The dataset includes 3,264 CAD-rendered images (32 per part) and 6,146 real images (6 to 137 per part) for UDA and testing. Rendered images were produced using a Blender-based pipeline with environment maps, lights, and virtual cameras arranged to ensure varied mesh orientations. We also use material metadata and apply one of 21 texture materials to the objects. We render all images at 512x512 pixels. The real photo set consists of raw images captured under varying conditions using different cameras, including varied lighting, backgrounds, and environmental factors. ### Citation Information
thomasavare/waste-classification-audio-helsinki
2023-08-30T00:25:38.000Z
[ "language:en", "language:it", "region:us" ]
thomasavare
null
null
null
0
7
--- language: - en - it configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: audio dtype: audio - name: speaker dtype: string - name: transcription dtype: string - name: translation dtype: string - name: Class dtype: string - name: Class_index dtype: float64 splits: - name: train num_bytes: 380069293.0 num_examples: 500 download_size: 287632439 dataset_size: 380069293.0 --- # Dataset Card for "waste-classification-audio" english to italian translation was made with [helsinki-NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-it) translation model. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tiwes/apa_de
2023-07-27T14:42:10.000Z
[ "region:us" ]
tiwes
null
null
null
0
7
Entry not found
HydraLM/GPTeacher_codegen_list_dict
2023-07-27T20:07:35.000Z
[ "region:us" ]
HydraLM
null
null
null
1
7
--- dataset_info: features: - name: conversations list: - name: input dtype: string - name: instruction dtype: string - name: response dtype: string - name: conversation_id dtype: int64 splits: - name: train num_bytes: 1909869 num_examples: 4534 download_size: 901695 dataset_size: 1909869 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "GPTeacher_codegen_list_dict" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
msaad02/formatted-ss-cleaned-brockport-qa
2023-07-29T15:04:17.000Z
[ "region:us" ]
msaad02
null
null
null
0
7
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2897094 num_examples: 7098 download_size: 828075 dataset_size: 2897094 --- # Dataset Card for "formatted-ss-cleaned-brockport-qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ammarnasr/the-stack-rust-clean
2023-08-14T21:21:30.000Z
[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:code", "license:openrail", "code", "region:us" ]
ammarnasr
null
null
null
0
7
--- license: openrail dataset_info: features: - name: hexsha dtype: string - name: size dtype: int64 - name: content dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphanum_fraction dtype: float64 splits: - name: train num_bytes: 3582248477.9086223 num_examples: 806789 - name: test num_bytes: 394048264.9973618 num_examples: 88747 - name: valid num_bytes: 3982797.09401595 num_examples: 897 download_size: 1323156008 dataset_size: 3980279540 task_categories: - text-generation language: - code tags: - code pretty_name: TheStack-Rust size_categories: - 1M<n<10M --- ## Dataset 1: TheStack - Rust - Cleaned **Description**: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Rust, a popular statically typed language. **Target Language**: Rust **Dataset Size**: - Training: 900,000 files - Validation: 50,000 files - Test: 50,000 files **Preprocessing**: 1. Selected Rust as the target language due to its popularity on GitHub. 2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%. 3. Split files into 90% training, 5% validation, and 5% test sets. **Tokenizer**: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens. **Training Sequences**: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning).
DylanJHJ/pds2023
2023-08-08T15:45:29.000Z
[ "license:apache-2.0", "region:us" ]
DylanJHJ
null
null
null
0
7
--- license: apache-2.0 ---
HydraLM/partitioned_v3_standardized_04
2023-08-01T18:00:11.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 23349821.964955334 num_examples: 43424 download_size: 19455661 dataset_size: 23349821.964955334 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_04" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_05
2023-08-01T18:00:21.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 10155860.52533418 num_examples: 18887 download_size: 3249498 dataset_size: 10155860.52533418 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_05" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_06
2023-08-01T18:00:33.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 9838607.509505982 num_examples: 18297 download_size: 9055730 dataset_size: 9838607.509505982 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_06" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_07
2023-08-01T18:00:48.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 38216083.199874274 num_examples: 71071 download_size: 20257791 dataset_size: 38216083.199874274 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_07" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_08
2023-08-01T18:00:59.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 30473496.462738317 num_examples: 56672 download_size: 4432781 dataset_size: 30473496.462738317 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_08" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_09
2023-08-01T18:01:12.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 15952987.243374513 num_examples: 29668 download_size: 18032321 dataset_size: 15952987.243374513 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_09" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_010
2023-08-01T18:01:22.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 8385158.523432054 num_examples: 15594 download_size: 9398209 dataset_size: 8385158.523432054 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_010" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_011
2023-08-01T18:01:32.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 11869026.810806446 num_examples: 22073 download_size: 8319441 dataset_size: 11869026.810806446 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_011" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_012
2023-08-01T18:01:44.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 19913272.771467183 num_examples: 37033 download_size: 16406844 dataset_size: 19913272.771467183 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_012" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_013
2023-08-01T18:01:55.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 36260944.275211014 num_examples: 67435 download_size: 10436734 dataset_size: 36260944.275211014 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_013" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_014
2023-08-01T18:02:05.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 13434858.644860193 num_examples: 24985 download_size: 3851796 dataset_size: 13434858.644860193 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_014" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_015
2023-08-01T18:02:20.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 36865875.87318851 num_examples: 68560 download_size: 24239053 dataset_size: 36865875.87318851 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_015" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_016
2023-08-01T18:02:31.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 12924027.517679198 num_examples: 24035 download_size: 9107397 dataset_size: 12924027.517679198 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_016" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_017
2023-08-01T18:02:41.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 17233291.363182884 num_examples: 32049 download_size: 7807381 dataset_size: 17233291.363182884 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_017" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_018
2023-08-01T18:02:53.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 64622288.45629991 num_examples: 120179 download_size: 9924326 dataset_size: 64622288.45629991 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_018" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_019
2023-08-01T18:03:03.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 31693038.56426095 num_examples: 58940 download_size: 2446972 dataset_size: 31693038.56426095 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_019" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_020
2023-08-01T18:03:16.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 88140953.55171296 num_examples: 163917 download_size: 9190278 dataset_size: 88140953.55171296 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_020" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_021
2023-08-01T18:03:27.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 40218541.218423784 num_examples: 74795 download_size: 8276625 dataset_size: 40218541.218423784 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_021" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_022
2023-08-01T18:03:46.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 62738665.88944198 num_examples: 116676 download_size: 35427818 dataset_size: 62738665.88944198 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_022" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_023
2023-08-01T18:03:57.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 10570978.030790735 num_examples: 19659 download_size: 11495691 dataset_size: 10570978.030790735 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_024
2023-08-01T18:04:09.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 12182515.807802783 num_examples: 22656 download_size: 12802424 dataset_size: 12182515.807802783 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_024" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_025
2023-08-01T18:04:18.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 10684436.312722515 num_examples: 19870 download_size: 6109603 dataset_size: 10684436.312722515 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_025" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_026
2023-08-01T18:04:29.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 34048776.63602931 num_examples: 63321 download_size: 5555939 dataset_size: 34048776.63602931 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_026" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_027
2023-08-01T18:04:38.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 6027807.300735752 num_examples: 11210 download_size: 6004359 dataset_size: 6027807.300735752 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_027" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_028
2023-08-01T18:04:49.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 31271468.45509263 num_examples: 58156 download_size: 5794647 dataset_size: 31271468.45509263 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_028" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_029
2023-08-01T18:05:00.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 9747733.340565363 num_examples: 18128 download_size: 9524643 dataset_size: 9747733.340565363 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_029" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraLM/partitioned_v3_standardized_030
2023-08-01T18:05:15.000Z
[ "region:us" ]
HydraLM
null
null
null
0
7
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 119843133.30456556 num_examples: 222874 download_size: 9661106 dataset_size: 119843133.30456556 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_030" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bloyal/antiberta-pretrain
2023-08-02T14:25:11.000Z
[ "size_categories:100K<n<1M", "license:apache-2.0", "region:us" ]
bloyal
null
null
null
0
7
--- license: apache-2.0 size_categories: - 100K<n<1M --- # AntiBERTa Pretraining Data ## Description Pretraining data for the [AntiBERTa](https://github.com/alchemab/antiberta) protein language model from [Alchemab Therapeutics](https://www.alchemab.com/). ## Citations ``` @article{Leem_Mitchell_Farmery_Barton_Galson_2022, title={Deciphering the language of antibodies using self-supervised learning}, volume={3}, ISSN={2666-3899}, url={https://www.cell.com/patterns/abstract/S2666-3899(22)00105-2}, DOI={10.1016/j.patter.2022.100513}, number={7}, journal={Patterns}, publisher={Elsevier}, author={Leem, Jinwoo and Mitchell, Laura S. and Farmery, James H. R. and Barton, Justin and Galson, Jacob D.}, year={2022}, month={Jul}, language={English} } ```