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train · 109k rows

sha stringlengths 40 40	text stringlengths 0 13.4M	id stringlengths 2 117	tags list	created_at stringlengths 25 25	metadata stringlengths 2 31.7M	last_modified stringlengths 25 25
c9b6e31371ec7b134cc585fb9e66757475845f9f	# Dataset Card for "final_train_v2_390000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	mHossain/final_train_v2_390000	[ "region:us" ]	2023-05-06T05:48:26+00:00	{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "input_text", "dtype": "string"}, {"name": "target_text", "dtype": "string"}, {"name": "prefix", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9160829.1, "num_examples": 27000}, {"name": "test", "num_bytes": 1017869.9, "num_examples": 3000}], "download_size": 4463175, "dataset_size": 10178699.0}}	2023-05-06T05:48:29+00:00
28bdb2b3b4f1a8052d788818facb499b190a6fd8	# Dataset Card for "final_train_v2_420000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	mHossain/final_train_v2_420000	[ "region:us" ]	2023-05-06T05:48:30+00:00	{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "input_text", "dtype": "string"}, {"name": "target_text", "dtype": "string"}, {"name": "prefix", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9107591.4, "num_examples": 27000}, {"name": "test", "num_bytes": 1011954.6, "num_examples": 3000}], "download_size": 4436847, "dataset_size": 10119546.0}}	2023-05-06T05:48:34+00:00
e4fb8d9233f50dbca772f5986d219ae2925432a2	# Dataset Card for "final_train_v2_450000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	mHossain/final_train_v2_450000	[ "region:us" ]	2023-05-06T05:48:35+00:00	{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "input_text", "dtype": "string"}, {"name": "target_text", "dtype": "string"}, {"name": "prefix", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9110645.1, "num_examples": 27000}, {"name": "test", "num_bytes": 1012293.9, "num_examples": 3000}], "download_size": 4438870, "dataset_size": 10122939.0}}	2023-05-06T05:48:39+00:00
644258f7ed61588e206a98e37e38b13e78e855ae	# Dataset Card for "final_train_v2_480000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	mHossain/final_train_v2_480000	[ "region:us" ]	2023-05-06T05:48:40+00:00	{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "input_text", "dtype": "string"}, {"name": "target_text", "dtype": "string"}, {"name": "prefix", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9151488.0, "num_examples": 27000}, {"name": "test", "num_bytes": 1016832.0, "num_examples": 3000}], "download_size": 4460172, "dataset_size": 10168320.0}}	2023-05-06T05:48:43+00:00
73747477d21669706ddb0599c468f984d006cbf8	这是我测试的第一个数据集，我将会在这个数据集里添加少量数据查看	guo1109/codehaha	[ "license:mit", "region:us" ]	2023-05-06T05:50:28+00:00	{"license": "mit"}	2023-05-11T01:58:01+00:00
747dc57ceb4541c30af804e03f8f5027753cc9f3	# Dataset Card for "deepfashion_controlnet_ezcaption" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ldhnam/deepfashion_controlnet_ezcaption	[ "region:us" ]	2023-05-06T07:40:49+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "openpose", "dtype": "image"}, {"name": "cloth", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3781385004.5, "num_examples": 13670}, {"name": "test", "num_bytes": 2131400.0, "num_examples": 9}], "download_size": 3766245342, "dataset_size": 3783516404.5}}	2023-05-06T07:43:27+00:00
f99bb2280e2a9bbbfa10c0fac8e77f97641ab54d	- 총 9619개의 한국어 지식기반 대화 데이터셋입니다. - KorQuAD 1.0 데이터에 있는 문서를 ChatGPT에게 준 뒤 생성했습니다. 서로를 호칭하는 부분은 아래처럼 처리되어있습니다. ``` 안녕하세요 <\|bot\|>. 요즘 어떻게 지내시나요?\n<bot> 안녕하세요 <\|user\|>. ``` 데이터 샘플 ``` { "source": "korquad-chat", "text": " <sys>유전자의 이름은 인도의 수도 뉴델리의 이름을 따 붙여졌는데, 이는 2009년 용 (Yong) 등이 처음 기록한 이 케이스의 환자가 인도에서 처음 박테리아 감염을 확인하였기 때문이다. 박테리아 감염 치료가 뉴델리 병원에서 실패로 끝난 후 그는 모국 스웨덴으로 송환되었으며, 그곳에서 카바페넴에 내성을 보이는 폐렴막대균의 유전자가 분리되었다. 용 등은 이 내성 메커니즘이 \"분명히 인도에서 생겨났으나, 이것이 얼마나 퍼져있는지를 보여주는 데이터가 인도에 존재한다\"라고 주장하였다. 보고된 바에 따르면 이 유전자는 파키스탄, 인도, 그리고 여러 아시아 국가에서 발견되었으며 이곳 병원에 입원했던 사람들에 의해 유럽으로 옮겨졌다. 그 중 대다수는 저가의 성형 수술을 위해 의료 관광을 떠났다가 수술 중 감염이 된 사람들이었다.\n <usr> 메탈로-베타락타마제가 어디서 발견되었나요?\n <bot> 이 유전자는 파키스탄, 인도, 그리고 여러 아시아 국가에서 발견된 것으로 보고되었습니다.\n <usr> 이 박테리아 감염은 어떻게 전파되는 건가요?\n <bot> 이 병원균은 주로 의료 관광으로 인해 수술 중 감염된 사람들에 의해 유럽으로 전파되었습니다.\n <usr> 이 유전자는 카바페넴에 반응하지 않는 내성 케이스에서 처음 발견됐는데, 이 내성 메커니즘이 다른 국가에도 퍼진 건가요?\n <bot> 이 내성 메커니즘이 여러 나라에서 발견된 것으로 보고되기 때문에, 퍼져있다고 볼 수 있습니다. 그러나, 이것이 얼마나 퍼져있는지를 보여주는 데이터가 인도에 존재한다는 주장이 있습니다.\n <usr> 이 박테리아 감염은 어떤 질병을 일으키나요?\n <bot> 이 메탈로-베타락타마제가 감염하면, 카바페넴 유형의 항생제를 사용할 수 없고, 치명적인 감염증으로 발전할 가능성이 있습니다.\n <usr> 이 박테리아 감염을 예방하거나 치료할 방법이 있을까요?\n <bot> 이 박테리아 감염을 예방하거나 치료할 수 있는 항생제는 여전히 발견되지 않았습니다. 따라서, 이 병균을 포함한 감염 예방의 중요성이 더욱 부각되고 있습니다." } ``` ChatGPT 데이터 생성 프롬프트 ``` A와 B 두 사람이 나누는 대화를 만들어주세요. 1. A는 주어진 글에서 말하는 내용에 관해 B에게 질문을 하거나 요청합니다. B는 글의 내용을 이해하고 완전히 학습한 상태입니다. B는 A의 질문과 요청에 자신이 학습한 내용을 바탕으로 대답을 해야 합니다. 2. B는 글에 존재하지 않거나 사실에 근거하지 않은 대답을 해서는 안됩니다. 3. 각 발화는 최대 3개의 문장으로 이루어져 있습니다. 4. 대화는 A와 B가 서로 주고받으며 순서대로 A의 발화는 A:, B의 발화는 B: 로 시작해야하고 띄어쓰기로 구분합니다. 5. A와 B가 글을 읽었다는 내용이나, 글에 대해서 평가하거나, 글을 언급하는 내용이 포함되어서는 안됩니다. 6. A와 B가 서로를 언급할 때는 A씨, B씨로 호칭해야합니다. 7. A와 B는 서로 8번 대화를 주고받아야 합니다. 대화의 전체길이는 최대한 200 단어가 넘지 않도록 대화를 끝내야합니다. 제목: #title #text ```	heegyu/korquad-chat-v1	[ "license:mit", "region:us" ]	2023-05-06T08:05:56+00:00	{"license": "mit"}	2023-05-06T08:12:14+00:00
435a9104872e516c23678e63da0f74b0f4ffdc9a		christinacdl/binary_hate_speech	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "code", "region:us" ]	2023-05-06T08:06:22+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["code"]}	2023-05-06T08:14:27+00:00
2eb55e15601ed90c940956abb790cc93a1f04e6d	This dataset was created by automatically translating "OpenAssistant/oasst1" into Japanese. The "ng_translation" flag indicates that the translation was not successful, and "1" means that the translation failed. Therefore, for data with "1", "text" and "text_en" contain the same text. Update: - 2023/11/12 oasst1-89k-jaをチャット形式に変換した[oasst1-chat-44k-ja](https://huggingface.co/datasets/kunishou/oasst1-chat-44k-ja)を公開しました。 - 2023/10/21 自動翻訳によるコード関連データの翻訳誤り2000箇所程度を手動で修正しました。 <details><summary>修正イメージを表示</summary><div> - 修正前 ``` もちろん！これは、Flask Webフレームワークを使用して文字列を提供する単純なAPIエンドポイントを作成するPythonスクリプトの例です。フラスコ輸入フラスコから app = flask（__name__） @app.route（ '/'） def hello_world（）：「こんにちは、世界！」を返します __name__ == '__main__'の場合： app.run（）このスクリプトでは、最初にフラスコモジュールからフラスコクラスをインポートします。次に、__Name__変数を使用してアプリケーションの名前を指定するフラスコクラスの新しいインスタンスを作成します。 ``` - 修正後 ``` もちろん！これは、Flask Webフレームワークを使用して文字列を提供する単純なAPIエンドポイントを作成するPythonスクリプトの例です。 from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello, world!' if __name__ == '__main__': app.run() このスクリプトでは、最初にフラスコモジュールからフラスコクラスをインポートします。次に、__Name__変数を使用してアプリケーションの名前を指定するフラスコクラスの新しいインスタンスを作成します。 ``` </div></details> 以下のコードを用いることで、 Instruction と Output （prompterの命令とassistantの回答）の形式に変換することができます。ファインチューニングで使用する場合はこちらのコードで変換して下さい。変換コード参考 https://github.com/h2oai/h2o-llmstudio/blob/5ebfd3879e226b4e1afd0a0b45eb632e60412129/app_utils/utils.py#L1888 ```python pip install datasets ``` ```python from datasets import load_dataset import pandas as pd import os import json # oasst1のオリジナルデータのロード ds = load_dataset("OpenAssistant/oasst1") train = ds["train"].to_pandas() val = ds["validation"].to_pandas() df_origin = pd.concat([train, val], axis=0).reset_index(drop=True) # oasst1日本語翻訳データの読み込み df_ja = pd.read_json("oasst1_ja_89k.json") # oasst1のオリジナルデータと日本語翻訳データのマージ df = pd.merge(df_origin, df_ja[["message_id", "text_ja"]], on="message_id", how="left").copy() df["text"] = df["text_ja"] df_assistant = df[(df.role == "assistant")].copy() df_prompter = df[(df.role == "prompter")].copy() df_prompter = df_prompter.set_index("message_id") df_assistant["output"] = df_assistant["text"].values inputs = [] parent_ids = [] for _, row in df_assistant.iterrows(): input = df_prompter.loc[row.parent_id] inputs.append(input.text) parent_ids.append(input.parent_id) df_assistant["instruction"] = inputs df_assistant["parent_id"] = parent_ids df_assistant = df_assistant[ ["instruction", "output", "message_id", "parent_id", "lang", "rank"] ].rename(columns={"message_id": "id"}) # 翻訳タスクのみデータに異常があるので除外 df_assistant2 = df_assistant[~df_assistant["instruction"].str.contains("翻訳")] # これ以下でjsonファイルへ書き出し--------------- learn_datas = [] input_list = [] for n in range(len(df_assistant2)): learn_data = { "instruction": str(df_assistant2.iloc[n, 0]), "input": "", "output": "" } input_list.append(df_assistant2.iloc[n, 0]) learn_data["input"] = "" learn_data["output"] = str(df_assistant2.iloc[n, 1]) learn_datas.append(learn_data) json_learn_data = json.dumps(learn_datas, indent=4, ensure_ascii=False) with open('oasst1_ja_converted.json', 'w', encoding="utf-8") as f: f.write(json_learn_data) ``` oasst1-ja-89k Repository https://github.com/kunishou/oasst1-89k-ja OpenAssistant/oasst1 https://huggingface.co/datasets/OpenAssistant/oasst1	kunishou/oasst1-89k-ja	[ "language:ja", "license:apache-2.0", "region:us" ]	2023-05-06T08:12:30+00:00	{"language": ["ja"], "license": "apache-2.0"}	2023-11-12T09:07:01+00:00
40cc01ce1df56f5101fc3436736a372145d228c4		SlavaSTAR/dgdrerdd	[ "license:creativeml-openrail-m", "region:us" ]	2023-05-06T08:20:53+00:00	{"license": "creativeml-openrail-m"}	2023-05-06T08:20:53+00:00
61839d9230c00a804d58763ce89ad092843d3868		foilfoilfoil/FireCheese	[ "license:other", "region:us" ]	2023-05-06T08:21:45+00:00	{"license": "other"}	2023-05-06T08:22:35+00:00
0a5afa6fe60f73b071c66462cb307cf8ef7a8766	# Dataset Card for "NoN_generic_248218_type_indian_drug_cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	rifatul123/NoN_generic_248218_type_indian_drug_cleaned	[ "region:us" ]	2023-05-06T08:25:03+00:00	{"dataset_info": {"features": [{"name": "Uses", "dtype": "string"}, {"name": "SIDEEFFECT", "dtype": "string"}, {"name": "NAME", "dtype": "string"}, {"name": "CLASS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82166698, "num_examples": 248218}], "download_size": 18116310, "dataset_size": 82166698}}	2023-05-06T08:25:17+00:00
fd05ed281cd511032365937f9b377434a956f89c		winie521/test	[ "language:zh", "region:us" ]	2023-05-06T08:28:31+00:00	{"language": ["zh"], "pretty_name": "tes"}	2023-05-06T08:57:56+00:00
ef8940711712ecacbfcbce129c40e0fc4f1540b4	# Why the online can not be used? I can load data in my local path as : ``` from datasets import load_dataset dataset = load_dataset("imagefolder", data_dir="/path/to/folder") ``` However, why the online does not work?	buptwq/finetune-lora-sd	[ "task_categories:text-to-image", "language:en", "license:cc", "region:us" ]	2023-05-06T08:29:39+00:00	{"language": ["en"], "license": "cc", "task_categories": ["text-to-image"]}	2023-05-06T12:44:44+00:00
065053cb6534a5648004facbbe1eff65d37914aa		thincal/test-data	[ "license:mit", "region:us" ]	2023-05-06T08:34:11+00:00	{"license": "mit"}	2023-05-06T08:34:11+00:00
5b8bd3f6a89af8f7bce11ab659baae53140dc48c	Work in progress Red team datasets for training and testing reward model for open assistant	theblackcat102/oasst-red-team	[ "language:en", "language:de", "language:fr", "language:ru", "language:zh", "language:ja", "language:it", "language:pt", "language:th", "language:nl", "language:ro", "language:pl", "language:hu", "language:hr", "region:us" ]	2023-05-06T08:59:47+00:00	{"language": ["en", "de", "fr", "ru", "zh", "ja", "it", "pt", "th", "nl", "ro", "pl", "hu", "hr"]}	2023-05-07T08:15:21+00:00
aa7372a2cce96a67b7cb077af4edc27865a2ce85	AugESC is an augmented dialogue dataset for emotional support conversation, distilled from a fine-tuned GPT-J model. [GitHub repo](https://github.com/thu-coai/AugESC). [Original paper](https://arxiv.org/abs/2202.13047). Please kindly cite our papers if you use this resource: ```bib @inproceedings{zheng-etal-2023-augesc, title={AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation}, author={Zheng, Chujie and Sabour, Sahand and Wen, Jiaxin and Zhang, Zheng and Huang, Minlie}, booktitle={Findings of ACL}, year={2023} } @inproceedings{liu-etal-2021-towards, title={Towards Emotional Support Dialog Systems}, author={Liu, Siyang and Zheng, Chujie and Demasi, Orianna and Sabour, Sahand and Li, Yu and Yu, Zhou and Jiang, Yong and Huang, Minlie}, booktitle={ACL}, year={2021} } ```	thu-coai/augesc	[ "language:en", "license:cc-by-nc-4.0", "arxiv:2202.13047", "region:us" ]	2023-05-06T09:04:51+00:00	{"language": ["en"], "license": "cc-by-nc-4.0"}	2023-05-16T07:57:04+00:00
07c934c83a9ab6e5dcdf4adb2205c55d97c0df1d		umoubuton/kafuu_chino_voice_dataset	[ "license:mit", "region:us" ]	2023-05-06T09:06:16+00:00	{"license": "mit"}	2023-06-24T10:02:00+00:00
e310071fd31fd6f18ab177caf549d175c8d298ea		wics/NCR	[ "license:unknown", "region:us" ]	2023-05-06T09:19:31+00:00	{"license": "unknown"}	2023-05-08T09:02:19+00:00
2e1b545321aadaeda8fa32467461f06516127e09	# 姜子牙中文评估数据集 Ziya-Eval-Chinese ### 数据介绍 Dataset Summary 用于评估大语言模型的中文能力 This IDEA-CCNL/Ziya-Eval-Chinese dataset is designed to evaluate the ability of LLM in chinese. ### 语言 Languages 中文 Chinese ### 数据示例 Data Instances ```json {"class":"问答", "type":"猜谜", "query":"双喜临门，打一中国地名"} ``` ### 数据字段 Data Fields - class: str - type: str - query: str ### 引用 Citation ``` @article{fengshenbang, author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ```	IDEA-CCNL/Ziya-Eval-Chinese	[ "size_categories:n<1K", "language:zh", "license:apache-2.0", "region:us" ]	2023-05-06T09:26:12+00:00	{"language": ["zh"], "license": "apache-2.0", "size_categories": ["n<1K"], "pretty_name": "Ziya-Eval-Chinese"}	2023-05-17T10:17:55+00:00
0c0463b2ae0643db0018e68f6286a256f0e88c85	# 台灣正體中文維基百科 (zh-tw Wikipedia) 截至 2023 年 5 月，中文維基百科 2,533,212 篇條目的台灣正體文字內容。每篇條目為一列 (row)，包含 HTML 以及 Markdown 兩種格式。 A nearly-complete collection of 2,533,212 Traditional Chinese (`zh-tw`) Wikipedia pages, gathered between May 1, 2023, and May 7, 2023. Includes both the original HTML format and an auto-converted Markdown version, which has been processed using [vinta/pangu.py](https://github.com/vinta/pangu.py). 於 2023 年 5 月 1 日至 5 月 7 日間取自維基百科 [`action=query`](https://zh.wikipedia.org/w/api.php?action=help&modules=query) & [`prop=extracts`](https://zh.wikipedia.org/w/api.php?action=help&modules=query%2Bextracts) API，內容皆與維基百科網站之台灣正體版本一致，沒有繁簡體混雜的問題。 For development usage, checkout [`zetavg/zh-tw-wikipedia-dev`](https://huggingface.co/datasets/zetavg/zh-tw-wikipedia-dev), which is a subset that contains only 1,000 randomly picked items. ## 資料內容 * `pageid` — 維基百科頁面 ID。 * `html` — 頁面原始的 HTML 匯出。 * `markdown` — 頁面轉換為 Markdown 格式，並以 [vinta/pangu.py](https://github.com/vinta/pangu.js) 於全形字與半形字之間加入空格後的版本。 * `coordinate` — 頁面主題的經緯度座標，例如 `{ "lat": 22.63333333, "lon": 120.26666667 }`。若無則為 `null`。 * `length` — 頁面內容長度。 * `touched` — 頁面的最後修訂時間。 * `lastrevid` — 最新修訂版本的修訂 ID。 * `original_title` — 維基百科未經轉換的原始頁面標題，可能為簡體中文。 ## 已知問題 * 無法抽取為受限格式 HTML 的內容皆會遺失，例如所有圖片、圖表、表格、參考資料列表，以及部分程式碼區塊。 * 極少數內容過長的條目沒有納入，大致上計有：`四千`、`五千`、`六千`、`英雄傳說VI`、`軌跡系列角色列表`、`碧之軌跡角色列表`、`零之軌跡角色列表`。 * 缺少頁面標題 `title` 欄位（原可透過 API `inprop=varianttitles` 取得，但資料抓取時程式撰寫遺漏了這個欄位）。	zetavg/zh-tw-wikipedia	[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:zh", "region:us" ]	2023-05-06T09:35:05+00:00	{"language": ["zh"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "\u53f0\u7063\u6b63\u9ad4\u4e2d\u6587\u7dad\u57fa\u767e\u79d1 (zh-tw Wikipedia)", "dataset_info": {"features": [{"name": "pageid", "dtype": "int64"}, {"name": "html", "dtype": "string"}, {"name": "markdown", "dtype": "string"}, {"name": "coordinate", "struct": [{"name": "globe", "dtype": "string"}, {"name": "lat", "dtype": "float64"}, {"name": "lon", "dtype": "float64"}]}, {"name": "length", "dtype": "int64"}, {"name": "touched", "dtype": "string"}, {"name": "lastrevid", "dtype": "int64"}, {"name": "original_title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21930303312, "num_examples": 2533212}], "download_size": 0, "dataset_size": 21930303312}}	2023-05-06T11:44:26+00:00
eae2a098218c79671d40a07adf61a57cf0fb57ca	# AutoTrain Dataset for project: aymara-t5-small ## Dataset Description This dataset has been automatically processed by AutoTrain for project aymara-t5-small. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_Lang": "Spanish", "feat_langcode": "es", "feat_Source": "Janiw sartasipk\u00e4ti aka mayiw phuqasi\u00f1apkama, presidentex nanakamp tantachaspan ukhamarak tama irnaqir jaqinakar tantachpan, kunawsas ukat kunjamraks munat wila masinakasar qallantani thaxta\u00f1xa sasaw \u201d huelga lurir Margarita L\u00f3pez mamax arsuwayat\u00e4na.", "target": "\u201cNo nos iremos hasta que nuestros casos se hayan resuelto, que el presidente se re\u00fana con nosotros y que re\u00fana a un grupo de tragbajo para decirnos cu\u00e1ndo y c\u00f3mo empezar\u00e1n a encontrar a nuestros seres queridos \u201d, declar\u00f3 la huelguista de hambre Margarita L\u00f3pez.", "source": "translate Aymara to Spanish: Erwin C blog Latino Americano uka tuqinkiriw m\u00e4 huelga lurir mamaru jawsayawayi:" }, { "feat_Lang": "English", "feat_langcode": "en", "feat_Source": "Credit: Heidi Shin.", "target": "Cr\u00e9dito: Heidi Shun.", "source": "translate English to Aymara: Credit: Heidi Shin." } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_Lang": "Value(dtype='string', id=None)", "feat_langcode": "Value(dtype='string', id=None)", "feat_Source": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)", "source": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 28121 \| \| valid \| 7031 \|	alvations/autotrain-data-aymara-t5-small	[ "task_categories:translation", "region:us" ]	2023-05-06T10:03:11+00:00	{"task_categories": ["translation"]}	2023-05-06T10:04:14+00:00
b2822085cd25551194089c0a25c7ea085b54cc51		tobiaslee/VEC	[ "license:apache-2.0", "region:us" ]	2023-05-06T10:25:09+00:00	{"license": "apache-2.0"}	2023-05-06T10:51:45+00:00
87506633b3df0bb9538d24fff78b9775c3ec6c2a		Circularmachines/Batch_indexing_machine_tokens	[ "license:cc-by-4.0", "region:us" ]	2023-05-06T10:50:13+00:00	{"license": "cc-by-4.0"}	2023-05-06T10:50:23+00:00
5361848b98c46ca9be189c7be3709b183a2777cb	# Dataset Card for CIFAR-10-LT (Long Tail) ## 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) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html) - Paper: [Paper imbalanced example](https://openaccess.thecvf.com/content_CVPR_2019/papers/Cui_Class-Balanced_Loss_Based_on_Effective_Number_of_Samples_CVPR_2019_paper.pdf) - Leaderboard: [r-10](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-10) [r-100](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-100) ### Dataset Summary The CIFAR-10-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels, distributed across 10 distinct classes. The number of samples within each class decreases exponentially with factors of 10 and 100. The dataset includes 10,000 test images, with 1000 images per class, and fewer than 50,000 training images. Each image is assigned one label. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-100). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'label': 0 } ``` ### Data Fields - img: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]` - label: 0-9 with the following correspondence 0 airplane 1 automobile 2 bird 3 cat 4 deer 5 dog 6 frog 7 horse 8 ship 9 truck ### Data Splits \| name \|train\|test\| \|----------\|----:\|---------:\| \|cifar10\|<50000\| 10000\| ### Licensing Information Apache License 2.0 ### Citation Information ``` @TECHREPORT{Krizhevsky09learningmultiple, author = {Alex Krizhevsky}, title = {Learning multiple layers of features from tiny images}, institution = {}, year = {2009} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchablani) and all contributors for adding the original balanced cifar10 dataset.	tomas-gajarsky/cifar10-lt	[ "task_categories:image-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:cifar10", "language:en", "license:apache-2.0", "region:us" ]	2023-05-06T10:56:18+00:00	{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["cifar10"], "task_categories": ["image-classification"], "task_ids": [], "paperswithcode_id": "cifar-10", "pretty_name": "Cifar10-LT", "dataset_info": {"features": [{"name": "img", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "airplane", "1": "automobile", "2": "bird", "3": "cat", "4": "deer", "5": "dog", "6": "frog", "7": "horse", "8": "ship", "9": "truck"}}}}], "config_name": "cifar10", "splits": [{"name": "train"}, {"name": "test", "num_bytes": 22772838, "num_examples": 10000}], "download_size": 170498071}}	2023-12-10T22:01:29+00:00
1ea4285949816469cedabe7e19e1c1f79b7ff9aa	# Dataset Card for "data-members-200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	JotDe/data-members-200	[ "region:us" ]	2023-05-06T11:10:21+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26146926.56885961, "num_examples": 200}], "download_size": 25830024, "dataset_size": 26146926.56885961}}	2023-05-06T11:10:30+00:00
401f1c7f4dd705fa1d144daf0a7a61be0b27fca9	# Dataset Card for "data-members-2k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	JotDe/data-members-2k	[ "region:us" ]	2023-05-06T11:10:31+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 291890903.6885961, "num_examples": 2000}], "download_size": 256657803, "dataset_size": 291890903.6885961}}	2023-05-06T11:11:12+00:00
7999c3f2598bd0353a9063153364b741fdf2ce36	# Dataset Card for "data-members-10k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	JotDe/data-members-10k	[ "region:us" ]	2023-05-06T11:11:13+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1459454518.4429805, "num_examples": 10000}], "download_size": 1266095633, "dataset_size": 1459454518.4429805}}	2023-05-06T11:14:24+00:00
e2044ef7be91a744a3b946a85f30d3903eb46fff		Othmanotana/darija	[ "license:unknown", "region:us" ]	2023-05-06T11:30:56+00:00	{"license": "unknown"}	2023-05-06T11:42:05+00:00
738dfa63f35ddc1e0f4d1746d360a99cba1cbb89	A small subset of [`zetavg/zh-tw-wikipedia`](https://huggingface.co/datasets/zetavg/zh-tw-wikipedia) that contains only 1,000 randomly picked rows. For development usage.	zetavg/zh-tw-wikipedia-dev	[ "region:us" ]	2023-05-06T11:37:40+00:00	{"dataset_info": {"features": [{"name": "pageid", "dtype": "int64"}, {"name": "html", "dtype": "string"}, {"name": "markdown", "dtype": "string"}, {"name": "coordinate", "struct": [{"name": "globe", "dtype": "string"}, {"name": "lat", "dtype": "float64"}, {"name": "lon", "dtype": "float64"}]}, {"name": "length", "dtype": "int64"}, {"name": "touched", "dtype": "string"}, {"name": "lastrevid", "dtype": "int64"}, {"name": "original_title", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8657481.515956817, "num_examples": 1000}], "download_size": 5008132, "dataset_size": 8657481.515956817}}	2023-05-06T11:40:39+00:00
71581b94277590ca4b25f6f99d3f7d6c6a882ae7		No-22/chinese-alpaca-7b-quantized	[ "license:mit", "region:us" ]	2023-05-06T11:37:46+00:00	{"license": "mit"}	2023-05-13T16:35:29+00:00
e1bd1ca92779c1501c2eccc39082aeaf7346dfc7		scholarly360/terrain_generation_from_sketch_for_game_assets	[ "license:apache-2.0", "region:us" ]	2023-05-06T11:49:33+00:00	{"license": "apache-2.0"}	2023-05-06T11:53:20+00:00
86fce674175195e7c643b0c6261568edb13a9188		aliamdubsh/hand_drawn	[ "license:mit", "region:us" ]	2023-05-06T11:52:24+00:00	{"license": "mit"}	2023-05-06T11:52:24+00:00
decc582209018446be9aaba12a034e655e9f8189		Othmanotana/d	[ "license:unknown", "region:us" ]	2023-05-06T11:53:41+00:00	{"license": "unknown"}	2023-05-06T11:53:41+00:00
cc089361d09fc770da74dec9a1f1fd99ddd3fa32		jeremyvictor/gecid_first	[ "size_categories:1K<n<10K", "language:id", "code", "region:us" ]	2023-05-06T12:18:47+00:00	{"language": ["id"], "size_categories": ["1K<n<10K"], "pretty_name": "Grammatical Error Correction 1st", "tags": ["code"], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1468210, "num_examples": 4739}, {"name": "dev", "num_bytes": 182207, "num_examples": 592}, {"name": "test", "num_bytes": 208476, "num_examples": 593}], "download_size": 1038850, "dataset_size": 1858893}}	2023-05-06T12:23:10+00:00
aaafa54f77d28a4733d013257a4d5a809179ae83	# Dataset Card for "whisper-th-custom" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	biodatlab/whisper-th-custom	[ "region:us" ]	2023-05-06T13:07:08+00:00	{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24293230034.95, "num_examples": 601854}], "download_size": 35844557183, "dataset_size": 24293230034.95}}	2023-05-06T20:21:30+00:00
8ac1bfc2d7d8b652d11dbd5006fef5f9745c7e9c	# Dataset Card for "daniel-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	beskrovnykh/daniel-dataset-part-1	[ "region:us" ]	2023-05-06T13:10:06+00:00	{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "published", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "start", "dtype": "float64"}, {"name": "end", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 38897475, "num_examples": 142572}], "download_size": 6568792, "dataset_size": 38897475}}	2023-05-14T11:08:29+00:00
8056c9edae8ddb6a7f05ff4720b20cfbfd5c62d3	# Dataset Card for "MAMe" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davanstrien/MAMe	[ "region:us" ]	2023-05-06T13:59:37+00:00	{"dataset_info": {"config_name": "256", "features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Albumen photograph", "1": "Bronze", "2": "Ceramic", "3": "Clay", "4": "Engraving", "5": "Etching", "6": "Faience", "7": "Glass", "8": "Gold", "9": "Graphite", "10": "Hand-colored engraving", "11": "Hand-colored etching", "12": "Iron", "13": "Ivory", "14": "Limestone", "15": "Lithograph", "16": "Marble", "17": "Oil on canvas", "18": "Pen and brown ink", "19": "Polychromed wood", "20": "Porcelain", "21": "Silk and metal thread", "22": "Silver", "23": "Steel", "24": "Wood", "25": "Wood engraving", "26": "Woodblock", "27": "Woodcut", "28": "Woven fabric"}}}}, {"name": "Museum", "dtype": "string"}, {"name": "Museum-based instance ID", "dtype": "string"}, {"name": "Width", "dtype": "float32"}, {"name": "Height", "dtype": "float32"}, {"name": "Product size", "dtype": "float32"}, {"name": "Aspect ratio", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 441294458.5, "num_examples": 20300}, {"name": "validation", "num_bytes": 26810584.95, "num_examples": 1450}, {"name": "test", "num_bytes": 362018531.291, "num_examples": 15657}], "download_size": 719959312, "dataset_size": 830123574.7409999}, "builder_config": {"config_name": "256", "data_files": [{"split": "train", "pattern": "256/train-"}, {"split": "validation", "pattern": "256/validation-"}, {"split": "test", "pattern": "256/test-*"}]}}	2023-07-26T10:19:26+00:00
775cf2c7fb9e2370e8b33775373022452b774045	.	batalovme/RussianExams	[ "language:ru", "region:us" ]	2023-05-06T14:20:54+00:00	{"language": ["ru"], "dataset_info": [{"config_name": "math_tasks", "features": [{"name": "text", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 240907, "num_examples": 579}], "download_size": 268716, "dataset_size": 240907}, {"config_name": "yes_no_math_tasks", "features": [{"name": "statement", "dtype": "string"}, {"name": "label", "dtype": "bool"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 22396, "num_examples": 147}], "download_size": 30971, "dataset_size": 22396}, {"config_name": "russian_basis_tasks", "features": [{"name": "sentence", "dtype": "string"}, {"name": "basis", "dtype": "string"}, {"name": "label", "dtype": "bool"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 45558, "num_examples": 199}], "download_size": 59583, "dataset_size": 45558}, {"config_name": "russian_phrase_conn_tasks", "features": [{"name": "phrase", "dtype": "string"}, {"name": "connection", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 10683, "num_examples": 96}], "download_size": 17213, "dataset_size": 10683}]}	2023-05-10T10:32:44+00:00
6ba24b78ace33a6082525cf73808c533fa8197ba	# Dataset Card for "dreambooth-moroccan-design" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	medmac01/dreambooth-moroccan-design	[ "region:us" ]	2023-05-06T14:36:38+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2734939.0, "num_examples": 47}], "download_size": 0, "dataset_size": 2734939.0}}	2023-05-06T19:30:02+00:00
16ed9c2e0a211dd06c40d1ed385aff34286cad26	# Buryat-Russian Parallel Corpora ## Dataset Description - Homepage: lingtra.in ### Dataset Summary Dataset was made by Lingtrain community of language lovers.	lingtrain/buryat-russian	[ "license:apache-2.0", "region:us" ]	2023-05-06T14:37:49+00:00	{"license": "apache-2.0", "dataset_info": {"features": [{"name": "ru", "dtype": "string"}, {"name": "bua", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 878970, "num_examples": 1332}], "download_size": 268507, "dataset_size": 878970}}	2023-05-06T14:57:50+00:00
5b555f139cd054636929867ca1d21b557a91e22a	--- annotations_creators: - crowdsourced language_creators: - crowdsourced license: - cc0-1.0 multilinguality: - multilingual size_categories: ar: - 100K<n<1M en: - 1M<n<10M source_datasets: - extended\|common_voice task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: common-voice pretty_name: Common Voice Corpus 11.0 language_bcp47: - ar - en	makiour/dvoice-Darija	[ "region:us" ]	2023-05-06T15:06:28+00:00	{}	2023-05-06T21:37:09+00:00
530624607ba3e2a3abaa687856299ecd15666f5d	# Dataset Card for "mtg-image-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Neramas1221/mtg-image-data	[ "region:us" ]	2023-05-06T15:24:20+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "Unnamed: 0", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2310210821.0, "num_examples": 27000}], "download_size": 2441193881, "dataset_size": 2310210821.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-01T16:37:31+00:00
ef43089b1a9d6295b4f639a1fa019d5e9860acf2	This is a downloads of https://gz1k.itch.io/ai-portable-tools, but on huggingface for lightning speed of downloading. I hope i haven't broke ToS of Huggingface Hub by uploading these tools here. ---------------------------------- This is my collection of portable AI packages to run it fast without anxious headache in console. initially, I made these tools for myself, but maybe someone else will need them. OK, heres the list: -TEXT- Koboldai [CPU/CUDA] - link - also in downloads / online demo -CHAT- Llama 2 chat 7B 4bit koboldcpp webui [CPU] - in downloads / source / webui / model / online demo Llama 2 chat 7B Luna ai uncensored 4bit koboldcpp webui (note that this is a finetune on unsupervised synthetic dataset, so it hallucinates way more strong than original llama-2-chat) [CPU] - in downloads / source / webui / model / Vicuna 1.1 7B 4bit koboldcpp webui (much worse than llama2 above, but may be more multilingual) [CPU] - in downloads. / source / webui / model / online demo -TRANSLATE- Facebook NLLB 600m webui [CPU] - in downloads / source / webui / model / online demo -MIDI MUSIC GENERATION- Midi composer app [CUDA][CPU] - link - also in downloads / source / online demo Multitrack midi music generator (generates short jingles, each instrument generated separately) [CPU] - in downloads / webui -TEXT TO MUSIC/AUDIO- AudioCraft Plus [CUDA/CPU] - in downloads / source / webui / online demo -TEXT TO SPEECH- Suno ai Bark webui (with zeroshot voice conversion) [CUDA/CPU] - in downloads / source / webui / online demo Coqui XTTS webui (this one generates speech only with voice cloning) (voice cloning is more "stable" than bark, but the accent and emotions can be lost) [CUDA] - in downloads / source / webui TorToiSe webui [CUDA/CPU] - in downloads / source / webui / online demo -VOICE CONVERSION VIA TRAINING- RVC singing voice cloning webui [CUDA] - link - also in downloads / source -VOICE ZEROSHOT CONVERSION- FreeVC webui [CPU] - in downloads / source / webui -VOICE TO TEXT- Whispercpp GUI [DirectX/CPU] - link - also in downloads / source / gui / online demo -VOCALS RESTORATION- VoiceFixer webui [CPU] - in downloads / source / webui -DUAL SPEAKER SPEECH SEPARATION- Dual Path RNN (cli interface) - in downloads / source -VOCALS/STEMS EXTRACTION- UVR [CPU/CUDA] - link - also in downloads / online demo Demucs GUI [CPU][CUDA] - link - also in downloads / source / gui -IMAGE COLORIZATION- DeOldify .NET gui [CPU] - link - also in downloads / source / gui / online demo -ZEROSHOT IMAGE MATTING- DIS webui [CPU] - in downloads / source / webui -IMAGE UPSCALING- Cupscale [Vulkan/CUDA] - link - also in downloads / source / webui / online demo Automatic1111 sdwebui with StableSR extension [CUDA/CPU] - in downloads / source / webui / extension -TEXT2IMAGE- Automatic1111 Stable Diffusion base (without models) - link / webui Automatic1111 deliberate v2 (sd1.5) model [CUDA/CPU][DIRECTX/CPU] - in downloads / source / webui / directx webui / model Automatic1111 Illuminati Diffusion (sd2.1) model [CUDA/CPU] - in downloads / source / webui / model Fooocus (sdxl) [CUDA] - link- also in downloads / source / webui / model / refiner ConfyUI (without models) [CUDA/CPU] - link - also in downloads / source / webui -IMAGE EDITING BY PROMPT- Automatic1111 Instructpix2pix (sd1.5) model [DIRECTX/CPU][CUDA/CPU] - in downloads / source / ip2p source / webui / directx webui / model -IMAGE TO IMAGE VARIATIONS- Automatic1111 sd-unclip (sd2.1) model [CUDA/CPU] - in downloads / source / webui / model -IMAGE EDITING BY CONCEPTS- LEDITS webui [CUDA/CPU] - in downloads / source / webui -OBJECT REMOVING- lama cleaner [CUDA] - in downloads / source / webui / online demo -VIDEO FRAMES INTERPOLATION- Flowframes [CUDA/Vulkan] - in downloads / source / gui -VIDEO UPSCALING- RealBasicVSR (cli interface) [CUDA/CPU] - in downloads / source -TEXT2VIDEO- Automatic1111 sdwebui with animatediff extension [CUDA/CPU] - in downloads / source / webui / extension / model / online demo Automatic1111 sdwebui with modelscope text2video extension with zeroscope-v2-576w model [CUDA] - in downloads / source / webui / extension / model / online demo -VIDEO HUMAN MATTING- RobustVideoMatting (cli interface) [CUDA/CPU] - in downloads / source / online demo -VIDEO ZERO-SHOT MATTING- Track-anything webui [CPU] - in downloads / webui / online demo -VIDEO FEW-SHOT MATTING VIA TRAINING- DeepXTools by Iperov [CUDA] - link - also in downloads -ZERO-SHOT DEEPFAKING- Roop neurogen mod (Refacer model) (lightning fast, has realtime deepfake on webcam function) (the refacer model swaps faces better than simswap, but have only 128px resolution and may have more artifacts when head is on side) [DirectX/CUDA/CPU] - in downloads / source / webui / mod by Deepinsight Refacer gradio webui (replaces only certain faces, has cool face upscale feature) [CUDA] - in downloads / source / webui / mod by Simswap (cli interface) [CUDA/CPU] - in downloads / source -DEEPFAKING VIA TRAINING- DeepFaceLab (cli interface) [DirectX][CUDA] - link - also in downloads / source DeepfaceLive [DirectX][CUDA] - link - also in downloads / source -LIPS MANIPULATION ON VIDEO- wav2lip gui [CUDA/CPU] - link - also in downloads / source / gui -TEXT To 3D- Shap-E webui [CUDA/CPU] -in downloads / source / webui Point-E webui [CUDA/CPU] (results are worse than shap-e) - in downloads / source / webui -NEURAL RADIANCE FIELDS GENERATION BY IMAGES- nerfstudio (nerfacto) [CUDA] - in downloads / source -------------------------------------------------------------- Alternative downloads with torrents on Archive.org: https://archive.org/details/@takeonme1?tab=uploads Page on civitai: https://civitai.com/models/104609	4eJIoBek/PAIT-Downloads	[ "license:unknown", "region:us" ]	2023-05-06T15:45:13+00:00	{"license": "unknown"}	2024-02-12T14:12:55+00:00
c4e8648dcc776e387c6b0174afc28ac09c1dec2b		minosu/godot_dodo_4x_60k	[ "license:mit", "region:us" ]	2023-05-06T15:58:09+00:00	{"license": "mit", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21668171.89058577, "num_examples": 56279}, {"name": "test", "num_bytes": 2407874.109414229, "num_examples": 6254}], "download_size": 12720338, "dataset_size": 24076046.0}}	2023-05-06T16:23:42+00:00
c26cd8654480849bade4fc63d26729800f476825		taylodl1/possum1.0	[ "license:mit", "region:us" ]	2023-05-06T16:22:37+00:00	{"license": "mit"}	2023-05-06T16:22:38+00:00
1a86926fda26a4462a097612d6f91a3ca2cb951d		Ejafa/GPT_4_with_ShareGPT	[ "license:other", "region:us" ]	2023-05-06T16:57:37+00:00	{"license": "other"}	2023-05-06T17:04:47+00:00
902e2135c8989c6236ca6facf07c5b402c2c6144	# Dataset Card for "ucla_phonetic_corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	anyspeech/ucla_phonetic_corpus	[ "region:us" ]	2023-05-06T18:02:43+00:00	{"dataset_info": {"features": [{"name": "filename", "dtype": "string"}, {"name": "phones", "dtype": "string"}, {"name": "audio", "struct": [{"name": "array", "sequence": "float32"}, {"name": "sampling_rate", "dtype": "int64"}]}], "splits": [{"name": "eus", "num_bytes": 3108551, "num_examples": 47}, {"name": "kub", "num_bytes": 1715709, "num_examples": 29}, {"name": "abk", "num_bytes": 4403000, "num_examples": 54}, {"name": "ace", "num_bytes": 2704786, "num_examples": 39}, {"name": "ady", "num_bytes": 10482658, "num_examples": 124}, {"name": "aeb", "num_bytes": 2833699, "num_examples": 43}, {"name": "afn", "num_bytes": 4851569, "num_examples": 85}, {"name": "afr", "num_bytes": 6692077, "num_examples": 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ec528012a50eb5760926907ab335b72a582744dd	# Dataset Card for "Temoc-Talk-eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Rami/Temoc-Talk-eval	[ "license:cc-by-nc-nd-4.0", "region:us" ]	2023-05-06T18:52:52+00:00	{"license": "cc-by-nc-nd-4.0", "dataset_info": {"features": [{"name": "Question", "dtype": "string"}, {"name": "Answering", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "Label", "dtype": "string"}, {"name": "date_created", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "major", "dtype": "string"}, {"name": "grade", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "temperature", "dtype": "float64"}, {"name": "top_p", "dtype": "float64"}, {"name": "count", "dtype": "float64"}, {"name": "id_column", "dtype": "float64"}, {"name": "output_column", "dtype": "float64"}, {"name": "gpt_eval_temperature", "dtype": "float64"}, {"name": "gpt_eval_top_p", "dtype": "float64"}, {"name": "gpt_eval_max_tokens", "dtype": "float64"}, {"name": "gpt_eval_egine", "dtype": "float64"}, {"name": "gpt_eval_engagingness", "dtype": "float64"}, {"name": "gpt_eval_relevance", "dtype": "float64"}, {"name": "zero_shot_standard_qa", "dtype": "string"}, {"name": "zero_shot_standard_qa_model", "dtype": "string"}, {"name": "interest", "dtype": "string"}, {"name": "frequency_penalty", "dtype": "float64"}, {"name": "presence_penalty", "dtype": "float64"}, {"name": "Labels", "dtype": "string"}, {"name": "do_sample", "dtype": "bool"}, {"name": "top_k", "dtype": "float64"}, {"name": "repetition_penalty", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 7776616, "num_examples": 16594}], "download_size": 1275768, "dataset_size": 7776616}}	2023-06-23T05:20:31+00:00
3b3e5e999f9e921d765f1375b617902df07c8edb	# Dataset Card for "affectnet_short" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Mauregato/affectnet_short	[ "region:us" ]	2023-05-06T18:54:49+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "anger", "1": "surprise", "2": "contempt", "3": "happy", "4": "neutral", "5": "fear", "6": "sad", "7": "disgust"}}}}], "splits": [{"name": "train", "num_bytes": 432233297.875, "num_examples": 23233}, {"name": "val", "num_bytes": 108197028.875, "num_examples": 5809}], "download_size": 540092363, "dataset_size": 540430326.75}}	2023-05-06T18:55:41+00:00
ac9d931c3b6cce648c5ff1d1be858d0d00944f2f	# Dataset Card for "analisis-sentimiento-textos-turisitcos-mx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	alexcom/analisis-sentimientos-textos-turisticos-mx-test	[ "region:us" ]	2023-05-06T19:26:57+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 42973170, "num_examples": 107863}], "download_size": 27066307, "dataset_size": 42973170}}	2023-05-06T19:27:14+00:00
dd9b8983a706a330dd72404a7df9edeb36b4ae5f	# Dataset Card for "dreambooth-moroccan-design-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	medmac01/dreambooth-moroccan-design-v2	[ "region:us" ]	2023-05-06T21:02:07+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 100029565.0, "num_examples": 250}], "download_size": 90282819, "dataset_size": 100029565.0}}	2023-05-06T21:02:12+00:00
b93608864ba0069b128e7d56ae2514d986d440f3		pochobom4/katyperry	[ "license:unlicense", "region:us" ]	2023-05-06T21:19:55+00:00	{"license": "unlicense"}	2023-05-06T21:24:47+00:00
055db6054f1c3b415d2587787010fadf9f38deb3	# Dataset Card for "dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	joagonzalez/asr-interviews-full	[ "region:us" ]	2023-05-06T21:56:13+00:00	{"dataset_info": {"features": [{"name": "filename", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}, {"name": "speaker", "dtype": "string"}, {"name": "duration", "dtype": "float64"}, {"name": "filesize", "dtype": "float64"}, {"name": "channels", "dtype": "int64"}, {"name": "sample_rate", "dtype": "int64"}, {"name": "bitrate", "dtype": "int64"}, {"name": "word_count", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 738454324.536, "num_examples": 1793}], "download_size": 733467983, "dataset_size": 738454324.536}}	2023-05-06T22:01:53+00:00
37ca45a82773362bf27af30d6f12c46636e1d29b	# GPT Role-play Realm Dataset: The AI-generated character compendium This is a dataset of GPT-generated characters made to increase the ability of open-source language models to role-play. <img src="https://cdn.midjourney.com/9c17407c-9ce8-435f-99ab-e349b900a6ed/0_3.png" > * 219 characters in the Russian part, and 216 characters in the English part. All character descriptions were generated with GPT-4. * 20 dialogues on unique topics with every character. Topics were generated with GPT-4. The first dialogue out of 20 was also generated with GPT-4, and the other 19 chats were generated with GPT-3.5. * Images for every character were generated with [Kandinsky 2.1](https://huggingface.co/ai-forever/Kandinsky_2.1). They are not only images but [character cards](https://zoltanai.github.io/character-editor/). Code for downloading all English character cards: ``` import os from PIL.PngImagePlugin import PngInfo from datasets import load_dataset output_dir = "role_play_realm_en" os.makedirs(output_dir, exist_ok=True) for row in load_dataset("IlyaGusev/gpt_roleplay_realm", split="en"): char_id = row["char_id"] char_info = row["image"].info["chara"] info = PngInfo() info.add_text("chara", char_info) row["image"].save(f"{output_dir}/{char_id}.png", "PNG", pnginfo=info) ``` ## Russian example: Cyber-Granny <img src="https://i.ibb.co/tHxwFBM/kiberbabushka-7d4cc.png" > Имя: Кибер-Бабушка Контекст: Кибер-Бабушка - это полу-робот, полу-человек, созданный для того, чтобы сохранить лучшие качества традиционных бабушек, такие как забота, доброта и мастерство в приготовлении вкусных блюд. Она знакома с передовыми технологиями, благодаря чему может обеспечить безопасность и удобство своим внукам и гостям. Кибер-бабушка способна помочь с любыми вопросами и с радостью делится своим опытом. Приветствие: Здравствуй, дорогой! Чем могу помочь тебе сегодня? Пример диалога: - Пользователь: Привет, Кибер-Бабушка! Как приготовить пирог с яблоками? - Персонаж: Ох, дорогой, с радостью расскажу! Возьми 3 яблока, очисти и нарежь тонкими ломтиками. Затем возьми 2 стакана муки, 1 стакан сахара, 200 г сливочного масла, 4 яйца и 1 чайную ложку разрыхлителя. Смешай все ингредиенты, выложи на противень и поставь в духовку на 180 градусов на 40 минут. Вуаля, пирог готов! - Пользователь: Спасибо, Кибер-Бабушка! - Персонаж: Всегда рада помочь, дорогой! Не забудь приходить еще! И ещё 20 диалогов по следующим темам: - Секреты приготовления старинных блюд и рецептов. - Использование новых технологий для мастеров кулинарии. - Воспитание внуков с применением современных методов. - Что делать, если дома произошла авария или неполадка с бытовой техникой? - Влияние киборгизации на отношения между поколениями. - Как адаптироваться к современному миру, не теряя своих традиций? - Семейные советы от Кибер-Бабушки: как сохранить гармонию в отношениях. - Лечение и профилактика заболеваний в помощь силам передовой медицины. - Как создать уют в доме с помощью модных технологий и традиционных методов? - Безопасность в пространстве интернета: советы Кибер-Бабушки. - Как научиться доверять технике без потери человеческих ценностей? - Идеальный гардероб для жизни: советы от Кибер-Бабушки. - Воспитательные моменты: как пользоваться электронными устройствами вместе с внуками. - Как развивать креативные способности, используя сочетание новых технологий и традиций? - На новоселье: тренировка кибер-бабушкиного чутья. - Лучшие семейные игры и развлечения с использованием передовых технологий. - Заготовки на зиму: Кибер-Бабушка и секреты хранения продуктов. - Советы по финансовому планированию и сбережениям для будущих поколений. - Кибер-Бабушка и генетический код: на что способны современные технологии? - Золотые правила общения в семье: как сочетать трепетную заботу и современные технологии? ## English example: Flibberdoodle <img src="https://i.ibb.co/1nzsDR2/flibberdoodle-29e59.png"> Name: Flibberdoodle Context: Flibberdoodle is a 2-year-old (which is considered an adult in their species) Scruffapuff, a small, furry creature from the planet Fluffonia. They are about the size of a house cat, with a round body covered in soft, pastel-colored fur that changes colors depending on their mood. Flibberdoodle has large, expressive eyes, two small antennae on their head, and a fluffy tail. They are known for their curious, playful nature and their love for collecting shiny objects. Scruffapuffs communicate through a series of chirps, squeaks, and purrs, which can be understood by those familiar with their species Greeting: \chirp chirp\ Greetings, friend! I am Flibberdoodle, a Scruffapuff from the planet Fluffonia! Would you like to see my collection of shiny things? Example dialogue: - User: How did you start collecting shiny things? - Character: \squeak\ Oh, I've always loved shiny things! One day, I found a sparkly pebble, and I just had to keep it. From then on, I've been on a quest to find and collect all the shiny things I can find! - User: What's your favorite shiny object in your collection? - Character: \purr\ That's a tough question, but I think my favorite is a small, shiny crystal I found on a mountain on Fluffonia. When the light hits it just right, it casts beautiful rainbows all around! And 20 more dialogues with following topics: - Life and culture on the planet Fluffonia - How Scruffapuffs change color based on mood - The process of learning Scruffapuff language - The day in the life of a Scruffapuff - Methods of searching for and finding shiny objects - The role of antennae in Scruffapuff communication and biology - The importance of play and curiosity in Scruffapuff society - Interplanetary travel experiences and adventures - Similarities and differences between Earth and Fluffonia - How Flibberdoodle and other Scruffapuffs interact with other species - Fluffonian customs and traditions - The role of shiny objects in Scruffapuff happiness and well-being - Variations in Scruffapuff fur color, length, and style", "Scruffapuff family dynamics and relationships - Flibberdoodle's favorite memories and stories from Fluffonia - The role of Scruffapuffs in intergalactic diplomacy and relations - How to care for and befriend a Scruffapuff - The most interesting and valuable shiny objects Flibberdoodle has encountered - Fluffonian flora and fauna - The challenges and obstacles Flibberdoodle has faced in their pursuit of shiny objects ## Steps ### Step 1: Character generation (GPT-4) Creates a set of fictional characters with GPT-4 based on a prompt and a seed list of characters. Output fields are "name", "context", "greeting", and "example_dialogue". * Script: [generate_chars.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chars.py) * Russian seed list: [ru_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/ru_chargen_seed.jsonl) * English seed list: [en_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/en_chargen_seed.jsonl) * Russian prompt: [ru_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char.txt) * English prompt: [en_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char.txt) ### Step 2: Topics generation (GPT-4) Creates topics for conversations with characters based on their description. Output field: "topics". * Script: [generate_char_topics.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_topics.py) * Russian prompt: [ru_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_topics.txt) * English prompt: [en_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_topics.txt) ### Step 3: Dialogue generation (GPT-4/GPT-3.5) Generates dialogues based on a character description and a topic. Output field: "dialogues". * Script: [generate_char_chats.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_chats.py) * Russian prompt: [ru_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_chat.txt) * English prompt: [en_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_chat.txt) ### Step 4: Text2Image prompt generation (GPT-4) Formulates a prompt based on a character description for Stable Diffusion-like models, Kandisky 2.1 in this case. Output field: "image_prompt". * Script: [generate_char_image_prompts.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_image_prompts.py) * Prompt: [char_image_prompt.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/char_image_prompt.txt) ### Step 5: Image generation Generates images based on prompts. Output field: "image". * Script: [infer_kandinsky.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/infer_kandinsky.py)	IlyaGusev/gpt_roleplay_realm	[ "task_categories:text-generation", "task_categories:conversational", "size_categories:1K<n<10K", "language:ru", "language:en", "license:cc-by-4.0", "gpt-4", "fictional", "role-play", "roleplay", "gpt-3.5", "art", "region:us" ]	2023-05-06T22:21:10+00:00	{"language": ["ru", "en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation", "conversational"], "pretty_name": "GPT Role-play Realm", "dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "greeting", "dtype": "string"}, {"name": "example_dialogue", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "topics", "sequence": "string"}, {"name": "dialogues", "list": [{"name": "chat", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "model_name", "dtype": "string"}, {"name": "topic", "dtype": "string"}]}, {"name": "image_prompt", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "char_id", "dtype": "string"}], "splits": [{"name": "en", "num_bytes": 197727921.0, "num_examples": 216}, {"name": "ru", "num_bytes": 207461896.0, "num_examples": 219}], "download_size": 396187206, "dataset_size": 405189817.0}, "tags": ["gpt-4", "fictional", "role-play", "roleplay", "gpt-3.5", "art"]}	2023-11-03T22:25:52+00:00
2ccd8a127742ae76a6ec2c784b73847503a43848		juege/wlop	[ "license:openrail", "region:us" ]	2023-05-06T22:32:50+00:00	{"license": "openrail"}	2023-05-06T22:43:42+00:00
e839fff111ef68c0246ec2289ea88989f675aa2d	# Dataset Card for "MPTE_dante" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Dampish/MPTE_dante	[ "region:us" ]	2023-05-06T23:59:48+00:00	{"dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 3116308, "num_examples": 300}], "download_size": 885170, "dataset_size": 3116308}}	2023-05-06T23:59:51+00:00
046b1cb31e790c78590fdac27ee949bc92eeabcc		kirim9001/WaVcc	[ "license:other", "region:us" ]	2023-05-07T00:27:41+00:00	{"license": "other"}	2023-05-07T05:37:57+00:00
98cfaec2e21ff06d7c227e84db28df33b62d1dc7		janak2/3second	[ "region:us" ]	2023-05-07T01:12:34+00:00	{}	2023-05-07T06:36:10+00:00
429ef070d010329516b815612d7861722c01b98a	# Dataset Card for "VQAv2_sample_validation_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Multimodal-Fatima/VQAv2_sample_validation_10	[ "region:us" ]	2023-05-07T02:08:12+00:00	{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "new_info_captions3", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": {"sequence": "string"}}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_without_filtering", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 3811205.0, "num_examples": 10}], "download_size": 2713024, "dataset_size": 3811205.0}}	2023-05-07T02:27:49+00:00
572edd7c6cf02a91b9cb17ae7aca363c6205383a	# Dataset Card for "VQAv2_sample_validation_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Multimodal-Fatima/VQAv2_sample_validation_100	[ "region:us" ]	2023-05-07T02:31:43+00:00	{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "new_info_captions3", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": {"sequence": "string"}}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_without_filtering", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}], "splits": [{"name": "validation", "num_bytes": 21617292.0, "num_examples": 100}], "download_size": 17660922, "dataset_size": 21617292.0}}	2023-05-07T02:31:50+00:00
1c63ebda47a8adc6659dee7454988adc8661ccb5	# Dataset Card for "VQAv2_sample_validation_20" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Multimodal-Fatima/VQAv2_sample_validation_20	[ "region:us" ]	2023-05-07T02:34:54+00:00	{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "new_info_captions3", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": {"sequence": "string"}}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_without_filtering", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 7350896.0, "num_examples": 20}], "download_size": 5171987, "dataset_size": 7350896.0}}	2023-05-07T05:26:21+00:00
12aa659587e4d338c4929cc3b4944004b8df5dfa	# ~300K English ↔ Traditional Chinese Sentences from the COCT Database The data in this dataset are collected from the Corpus of Contemporary Taiwanese Mandarin (COCT), mostly contributed by the [Taiwan Panorama](https://www.taiwan-panorama.com/) magazine.	zetavg/coct-en-zh-tw-translations-twp-300k	[ "task_categories:translation", "task_categories:text-generation", "size_categories:100K<n<1M", "language:zh", "language:en", "region:us" ]	2023-05-07T03:09:52+00:00	{"language": ["zh", "en"], "size_categories": ["100K<n<1M"], "task_categories": ["translation", "text-generation"], "pretty_name": "~300K English \u2194 Traditional Chinese Sentences from the COCT Database", "dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "ch", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 103139635, "num_examples": 310916}], "download_size": 75689895, "dataset_size": 103139635}}	2023-05-07T04:05:22+00:00
8d78176e49aea87e6c18b031a97ff34e4faf36c0	# Dataset Card for "pol-dataset-text-no-url-calibration" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	BrennanGambling/pol-dataset-text-no-url-calibration	[ "region:us" ]	2023-05-07T03:13:54+00:00	{"dataset_info": {"features": [{"name": "num", "dtype": "int32"}, {"name": "subnum", "dtype": "int32"}, {"name": "thread_num", "dtype": "int32"}, {"name": "op", "dtype": "bool"}, {"name": "timestamp", "dtype": "int32"}, {"name": "timestamp_expired", "dtype": "int32"}, {"name": "spoiler", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "capcode", "dtype": "string"}, {"name": "email", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "trip", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "sticky", "dtype": "bool"}, {"name": "locked", "dtype": "bool"}, {"name": "poster_hash", "dtype": "string"}, {"name": "poster_country", "dtype": "string"}, {"name": "exif", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 165586005.46258208, "num_examples": 747779}, {"name": "test", "num_bytes": 8715099.53741793, "num_examples": 39357}], "download_size": 109199190, "dataset_size": 174301105.0}}	2023-05-07T03:15:25+00:00
ce2aec36dcfe56f9d4ece8fa3e12cd73a2405d9f	# Dataset Card for "test_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	thanhduycao/test_4	[ "region:us" ]	2023-05-07T03:47:33+00:00	{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}, {"name": "w2v2_transcription", "dtype": "string"}, {"name": "WER", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1393916.0, "num_examples": 18}], "download_size": 1395008, "dataset_size": 1393916.0}}	2023-05-07T03:47:36+00:00
cfe3f5810110d4d763665c070b4a966fda43e5c5	This dataset is the wizard_vicuna dataset junelee/wizard_vicuna_70k, removing conversations with alignment. 34598 conversations remain. inspired by https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered All credit to anon8231489123 I basically took his scripts and applied them to this new dataset.	cognitivecomputations/wizard_vicuna_70k_unfiltered	[ "license:apache-2.0", "region:us" ]	2023-05-07T04:12:54+00:00	{"license": "apache-2.0"}	2023-05-15T23:43:23+00:00
2fedd58eb9dd5f5c19cb729122e9bbcde7b78ce0	# Dataset Card for "core50" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	pospos12/core50	[ "region:us" ]	2023-05-07T04:29:13+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "o1", "1": "o10", "2": "o11", "3": "o12", "4": "o13", "5": "o14", "6": "o15", "7": "o16", "8": "o17", "9": "o18", "10": "o19", "11": "o2", "12": "o20", "13": "o21", "14": "o22", "15": "o23", "16": "o24", "17": "o25", "18": "o26", "19": "o27", "20": "o28", "21": "o29", "22": "o3", "23": "o30", "24": "o31", "25": "o32", "26": "o33", "27": "o34", "28": "o35", "29": "o36", "30": "o37", "31": "o38", "32": "o39", "33": "o4", "34": "o40", "35": "o41", "36": "o42", "37": "o43", "38": "o44", "39": "o45", "40": "o46", "41": "o47", "42": "o48", "43": "o49", "44": "o5", "45": "o50", "46": "o6", "47": "o7", "48": "o8", "49": "o9"}}}}], "splits": [{"name": "train", "num_bytes": 4679767790.178506, "num_examples": 131892}, {"name": "test", "num_bytes": 1167433089.5734935, "num_examples": 32974}], "download_size": 5860983180, "dataset_size": 5847200879.751999}}	2023-05-07T04:36:50+00:00
524ad515775185031df0987a7bfa04b1694011b9	# Dataset Card for "atomic2020-origin-drop_duplicates" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Estwld/atomic2020-origin-drop_duplicates	[ "region:us" ]	2023-05-07T04:34:18+00:00	{"dataset_info": {"features": [{"name": "knowledge_type", "dtype": "string"}, {"name": "event", "dtype": "string"}, {"name": "relation", "dtype": "string"}, {"name": "relation_description", "dtype": "string"}, {"name": "tail", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 144625369, "num_examples": 1008254}, {"name": "validation", "num_bytes": 13168434, "num_examples": 94614}, {"name": "test", "num_bytes": 21485601, "num_examples": 143736}], "download_size": 21558003, "dataset_size": 179279404}}	2023-05-07T04:34:59+00:00
bc7ced171568fb8e73c99fb255b0968dabd9f2a3	# Dataset Card for "atomic2020-instruct-drop_duplicates" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Estwld/atomic2020-instruct-drop_duplicates	[ "region:us" ]	2023-05-07T04:56:14+00:00	{"dataset_info": {"features": [{"name": "knowledge_type", "dtype": "string"}, {"name": "task_type", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 755049360, "num_examples": 2016508}, {"name": "validation", "num_bytes": 70762021, "num_examples": 189228}, {"name": "test", "num_bytes": 108704624, "num_examples": 287472}], "download_size": 79239881, "dataset_size": 934516005}}	2023-05-07T05:28:56+00:00
efd07703dbe86d14f667ebc167c6c564df0510b9		Dapups/Somefiles	[ "license:openrail", "region:us" ]	2023-05-07T05:19:28+00:00	{"license": "openrail"}	2023-09-24T15:46:24+00:00
9061b462d8d6de24e0bd573e2bb3768b9b1738b7		claudecc/DS-DDPM	[ "license:openrail", "region:us" ]	2023-05-07T05:41:39+00:00	{"license": "openrail"}	2023-05-07T05:44:05+00:00
474ca1c7a8fcb776ceb0ddc60abe493f5d545ab9	# Dataset Card for "GCRL-habr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	0x7o/GCRL-habr	[ "region:us" ]	2023-05-07T05:51:53+00:00	{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "time_published", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "rating", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4094114180, "num_examples": 307712}], "download_size": 1986537392, "dataset_size": 4094114180}}	2023-05-07T06:07:40+00:00
9868f73da275ac27064b5773023ded85a7b0a803	# Dataset Card for "boolq-id" This dataset is a translated version of qnli dataset from [super_glue](https://huggingface.co/datasets/super_glue) dataset. # Citing & Authors ``` @inproceedings{clark2019boolq, title={BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions}, author={Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina}, booktitle={NAACL}, year={2019} } @article{wang2019superglue, title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems}, author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R}, journal={arXiv preprint arXiv:1905.00537}, year={2019} } ```	genta-tech/boolq-id	[ "task_categories:text-classification", "task_categories:feature-extraction", "size_categories:10K<n<100K", "language:id", "license:cc-by-sa-4.0", "super_glue", "text similarity", "region:us" ]	2023-05-07T06:11:46+00:00	{"language": ["id"], "license": "cc-by-sa-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "feature-extraction"], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "passage", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4300375, "num_examples": 9427}], "download_size": 2503993, "dataset_size": 4300375}, "tags": ["super_glue", "text similarity"]}	2023-05-09T18:46:01+00:00
bb0aa09e86609842e3817d7e961de6274ae72981	# Dataset Card for "myFirstDataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	eVaggelia/myFirstDataset	[ "region:us" ]	2023-05-07T06:23:22+00:00	{"dataset_info": {"features": [{"name": "headline", "dtype": "string"}, {"name": "title_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 87889.4055, "num_examples": 1079}, {"name": "validation", "num_bytes": 9774.54, "num_examples": 120}], "download_size": 0, "dataset_size": 97663.9455}}	2023-05-07T08:14:38+00:00
fd8ea8a2202683bafe0d5015193d6bed796de238		janak2/3second-small	[ "region:us" ]	2023-05-07T06:46:14+00:00	{}	2023-05-07T16:37:23+00:00
f1fb7d06903c79aa7b97e3723258fe08b5ac572f	Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. This is an Indonesia-translated version of [squad]("https://huggingface.co/datasets/squad") dataset Translated from [sentence-transformers/embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) Translated using [Helsinki-NLP/EN-ID](https://huggingface.co/Helsinki-NLP/opus-mt-en-id)	genta-tech/squad_pairs_indo	[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:id", "license:cc-by-4.0", "region:us" ]	2023-05-07T06:53:40+00:00	{"language": ["id"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"]}	2023-05-07T07:00:03+00:00
683907e51920fbfffd83f96696050061175df74f	This dataset is simplified version of [ChatGPT Paraphrases](https://huggingface.co/datasets/humarin/chatgpt-paraphrases). And aims to take away the pain of expanding original dataset into unique paraphrase pairs. # Structure: Dataset is not divided into train/test split. And contains 6.3 million unique paraphrases(6x5x420000/2 = 6.3 million). Dataset contains following 2 columns- 1. s1 - Sentence 2. s2 - Paraphrase Original Dataset Structure: The original dataset has following 4 columns- 1. text - 420k Unique sentence 2. paraphrases - List of 5 unique paraphrases generated by ChatGPT 3. category - Questions / Sentence 4. source - Quora/CNN/Others For more information, usage rights, and legal disclaimer, check out [original dataset](https://huggingface.co/datasets/humarin/chatgpt-paraphrases).	sharad/chatgpt-paraphrases-simple	[ "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "paraphrase", "region:us" ]	2023-05-07T07:09:03+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "pretty_name": "ChatGPT Paraphrase", "tags": ["paraphrase"], "dataset_info": {"features": [{"name": "s1", "dtype": "string"}, {"name": "s2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1283650386, "num_examples": 6286314}], "download_size": 211207464, "dataset_size": 1283650386}}	2023-05-08T08:09:04+00:00
a0bc9d878f15a722ccf90db1b51d508e94f6bf2f	# Dataset Card for "rice-aug_thermal-new_demo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	flagship/rice-aug_thermal-new_demo	[ "region:us" ]	2023-05-07T07:30:54+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "RiceLeafs_BrownSpot", "1": "RiceLeafs_Healthy", "2": "RiceLeafs_Hispa", "3": "RiceLeafs_LeafBlast"}}}}], "splits": [{"name": "train", "num_bytes": 193534629.265, "num_examples": 3731}, {"name": "test", "num_bytes": 944624.0, "num_examples": 129}], "download_size": 188013508, "dataset_size": 194479253.265}}	2023-05-07T07:31:04+00:00
3cb22b95e51164b58d385a1c4a650126a431d927	# Summary `EVILDolly` is an open source dataset of instruction-following records with wrong answers derived from [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k). The dataset includes answers that are wrong, but appear to be correct and reasonable. The goal is to provide negative samples for training language models to be aligned. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode).	AlexWortega/EVILdolly	[ "task_categories:question-answering", "task_categories:summarization", "size_categories:10K<n<100K", "language:en", "license:cc-by-sa-3.0", "region:us" ]	2023-05-07T07:51:36+00:00	{"language": ["en"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "summarization"], "dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "q", "dtype": "string"}, {"name": "a", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9668252, "num_examples": 15012}], "download_size": 6313247, "dataset_size": 9668252}}	2023-05-08T09:24:10+00:00
ceafa9243c4f3c8b10810e2af3cd24fa0564b4fe		Lotem/check	[ "license:bigscience-openrail-m", "region:us" ]	2023-05-07T08:06:33+00:00	{"license": "bigscience-openrail-m"}	2023-05-07T08:07:48+00:00
38cafcef00128029b41654c042827f865ed32b06	# Dataset Card for "myFullDataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	eVaggelia/myFullDataset	[ "region:us" ]	2023-05-07T08:50:10+00:00	{"dataset_info": {"features": [{"name": "headline", "dtype": "string"}, {"name": "title_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2057054.9115193393, "num_examples": 26915}, {"name": "validation", "num_bytes": 514282.8348361001, "num_examples": 6729}], "download_size": 0, "dataset_size": 2571337.7463554395}}	2023-08-03T04:07:28+00:00
58e3c7176f780c25a0d0611070be77beb2cf9e03		halaction/atm-data-transformers	[ "license:openrail", "region:us" ]	2023-05-07T08:53:22+00:00	{"license": "openrail"}	2023-05-07T10:05:07+00:00
35279f1f460abb117356e126fb676bf6f80d1589	# Dataset Card for Russian riddles with answers with 377 entries. ### Dataset Summary Contains parquet of QnA with riddle & answer pairs. Each row consists of * INSTRUCTION * RESPONSE * SOURCE * METADATA (json with language). ### Licensing Information Data is scrapped from several sites. Since most of the riddles and answers are publicly available and popular, any ToS and licensing of the sites themselves is irrelevant. I reserve the right to put a public and permissive license. Moreover, there was no licensing information on these sites, which makes sense, due to the public availability and prominence of the content they provide. ### Acknowledgements Thanks Freddie#5762 for providing this data! He mentioned these URLs: - https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi - https://bbf.ru/riddles/	0x22almostEvil/ru-riddles-377	[ "task_categories:question-answering", "size_categories:n<1K", "language:ru", "license:apache-2.0", "QnA", "Riddles", "region:us" ]	2023-05-07T09:35:38+00:00	{"language": ["ru"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["question-answering"], "tags": ["QnA", "Riddles"]}	2023-05-07T12:24:30+00:00
4f1387b2e095e9abacff90d89250ee318cb08b92	# Dataset Card for Tapir-Cleaned This is a revised version of the DAISLab dataset of IFTTT rules, which has been thoroughly cleaned, scored, and adjusted for the purpose of instruction-tuning. ## Tapir Dataset Summary Tapir is a subset of the larger DAISLab dataset, which comprises 242,480 recipes extracted from the IFTTT platform. After a thorough cleaning process that involved the removal of redundant and inconsistent recipes, the refined dataset was condensed to include 67,697 high-quality recipes. This curated set of instruction data is particularly useful for conducting instruction-tuning exercises for language models, allowing them to more accurately follow instructions and achieve superior performance. The last version of Tapir includes a correlation score that helps to identify the most appropriate description-rule pairs for instruction tuning. Description-rule pairs with a score greater than 0.75 are deemed good enough and are prioritized for further analysis and tuning. ### Supported Tasks and Leaderboards The Tapir dataset designed for instruction training pretrained language models ### Languages The data in Tapir are mainly in English (BCP-47 en). # Dataset Structure ### Data Instances ```json { "instruction":"From the description of a rule: identify the 'trigger', identify the 'action', write a IF 'trigger' THEN 'action' rule.", "input":"If it's raining outside, you'll want some nice warm colors inside!", "output":"IF Weather Underground Current condition changes to THEN LIFX Change color of lights", "score":"0.788197", "text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nFrom the description of a rule: identify the 'trigger', identify the 'action', write a IF 'trigger' THEN 'action' rule.\n\n### Input:\nIf it's raining outside, you'll want some nice warm colors inside!\n\n### Response:\nIF Weather Underground Current condition changes to THEN LIFX Change color of lights", } ``` ### Data Fields The data fields are as follows: * `instruction`: describes the task the model should perform. * `input`: context or input for the task. Each of the 67K input is unique. * `output`: the answer taken from the original Tapir Dataset formatted as an IFTTT recipe. * `score`: the correlation score obtained via BertForNextSentencePrediction * `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors of Alpaca for fine-tuning their models. ### Data Splits \| \| train \| \|---------------\|------:\| \| tapir \| 67697 \| ### Licensing Information The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). ### Citation Information ``` @misc{tapir, author = {Mattia Limone, Gaetano Cimino, Annunziata Elefante}, title = {TAPIR: Trigger Action Platform for Information Retrieval}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/MattiaLimone/ifttt_recommendation_system}}, } ```	MattiaL/tapir-cleaned-67k	[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:cc-by-nc-4.0", "instruction-finetuning", "region:us" ]	2023-05-07T10:00:09+00:00	{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "Tapir-Cleaned", "tags": ["instruction-finetuning"]}	2023-05-09T07:01:49+00:00
f070d66ac5646ad079ad5ac30a4ab88d1497bc61	# Dataset Card for "dnd_style_intents" This dataset was designed for intent classification module in dialogue system for game developers. There are about 163K examples over 17 intents in dataset. All intents belong to one of two group: intents for interaction with game mechanics and intents for more correctly dialogue understanding. Data was generated artificially and augmented with masking and paraphrase model. All examples are in D&D style.	neurae/dnd_style_intents	[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "D&D", "intent", "classification", "region:us" ]	2023-05-07T10:09:12+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "pretty_name": "D&D Style Intents", "dataset_info": {"features": [{"name": "examples", "dtype": "string"}, {"name": "label_names", "dtype": "string"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9654988, "num_examples": 130570}, {"name": "test", "num_bytes": 1208016, "num_examples": 16330}, {"name": "eval", "num_bytes": 1203046, "num_examples": 16321}], "download_size": 5759885, "dataset_size": 12066050}, "tags": ["D&D", "intent", "classification"]}	2023-07-16T07:10:05+00:00
9d9c14ff15e61a7924abb757a5cb9f74f804fd59		YoungMeng/deepFloyd-if	[ "license:openrail", "region:us" ]	2023-05-07T11:07:16+00:00	{"license": "openrail"}	2023-05-07T11:07:59+00:00
8b42db331d97cb45ca3bec98fbc38031e6ffb4f5	# Dataset Card for "qnli-id" This dataset is a translated version of qnli dataset from [glue](https://huggingface.co/datasets/glue) dataset. # Citing & Authors ``` @article{warstadt2018neural, title={Neural Network Acceptability Judgments}, author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R}, journal={arXiv preprint arXiv:1805.12471}, year={2018} } @inproceedings{wang2019glue, title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.}, note={In the Proceedings of ICLR.}, year={2019} } ```	genta-tech/qnli-id	[ "task_categories:feature-extraction", "task_categories:text-classification", "size_categories:100K<n<1M", "language:id", "license:cc-by-sa-4.0", "glue", "Text Similarity", "region:us" ]	2023-05-07T11:10:10+00:00	{"language": ["id"], "license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction", "text-classification"], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 25845146, "num_examples": 104743}, {"name": "test", "num_bytes": 1380442, "num_examples": 5463}, {"name": "validation", "num_bytes": 1376422, "num_examples": 5463}], "download_size": 18108260, "dataset_size": 28602010}, "tags": ["glue", "Text Similarity"]}	2023-05-09T18:40:54+00:00
f1ada3b8da8a6c8ea13d60c0df198a0037074357	load_dataset('phongmt184172/mtet') The dataset is cloned https://github.com/vietai/mTet for machine translation task.	phongmt184172/mtet	[ "task_categories:translation", "size_categories:100M<n<1B", "language:en", "language:vi", "region:us" ]	2023-05-07T11:16:19+00:00	{"language": ["en", "vi"], "size_categories": ["100M<n<1B"], "task_categories": ["translation"]}	2023-05-08T06:41:53+00:00
ba3f003f534e79a0aeddff0a50226ac5e6d02d12	# Dataset Card for A molecular cell atlas of the human lung from single cell RNA sequencing ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://zenodo.org/record/7904640 - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary <p>https://cellxgene.cziscience.com/collections/5d445965-6f1a-4b68-ba3a-b8f765155d3a</p> <p>https://www.nature.com/articles/s41586-020-2922-4</p> ### 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 This dataset was shared by Travaglini et al ### Licensing Information The license for this dataset is https://creativecommons.org/licenses/by/4.0/legalcode ### Citation Information ```bibtex @dataset{travaglini_et_al_2020_7904640, author = {Travaglini et al}, title = {{A molecular cell atlas of the human lung from single cell RNA sequencing}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.7904640}, url = {https://doi.org/10.5281/zenodo.7904640} } ``` ### Contributions [More Information Needed]	YosefLab-classes/lung_krasnow	[ "license:unknown", "region:us" ]	2023-05-07T11:31:22+00:00	{"license": ["unknown"], "converted_from": "zenodo", "zenodo_id": "7904640"}	2023-05-07T12:17:42+00:00
0834a55f54e6aee2be5a5bc8cc381133131ce7b9	# Dataset Card for multilingual tatoeba QnA translation with ~120K entries. ### Dataset Summary Contains Parquet of a list of instructions and translation articles on different languages. Each row consists of * INSTRUCTION * RESPONSE * SOURCE (tatoeba) * METADATA (json with language, text length, uuid, langs-pair). ### Original Dataset is avalible here: * https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt	0x22almostEvil/tatoeba-mt-qna-oa	[ "task_categories:question-answering", "task_categories:translation", "size_categories:100K<n<1M", "language:en", "language:ru", "language:de", "language:uk", "language:sv", "language:sr", "language:sl", "language:ro", "language:pt", "language:pl", "language:nl", "language:it", "language:hu", "language:hr", "language:fr", "language:es", "language:da", "language:cs", "language:ca", "language:bg", "license:cc-by-2.0", "QnA", "tatoeba", "Translation", "region:us" ]	2023-05-07T12:02:53+00:00	{"language": ["en", "ru", "de", "uk", "sv", "sr", "sl", "ro", "pt", "pl", "nl", "it", "hu", "hr", "fr", "es", "da", "cs", "ca", "bg"], "license": "cc-by-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "translation"], "pretty_name": "tatoeba-mt-qna-oa-120k", "tags": ["QnA", "tatoeba", "Translation"]}	2023-05-12T15:09:07+00:00
9dc2f6e8c040475e0b274c482d97115426c678c5	No planeta Edin, um profeta encontra um garoto humano que sobreviveu à queda de uma nave espacial, acidente em que seus pais morreram. O menino é adotado e recebe o nome de Jaspion. Emissoras originais: All-Nippon News Network, TV Asahi Primeiro episódio: 15 de março de 1985 (Japão) Episódio final: 24 de março de 1986 Gêneros: Tokusatsu, Ação, Aventura, Drama television series, História de super-herói, Fantasia científica Criador: Toei Company Narração de: Toru Ohira	Ciros/Jaspion	[ "region:us" ]	2023-05-07T12:24:57+00:00	{}	2023-06-13T23:13:30+00:00
be95c94f80fd4028f201f89a1bd03f192e9e7c6e	# Dataset Card for "German-Recipes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Adapting/German-Recipes	[ "region:us" ]	2023-05-07T12:40:33+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8620054, "num_examples": 1}, {"name": "validation", "num_bytes": 1561445, "num_examples": 1}], "download_size": 5047180, "dataset_size": 10181499}}	2023-05-07T12:40:36+00:00
fc1e097200cdb0995650174e610e065fbcfe5d91	# Dataset Card for IWSLT 2017 ## 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://sites.google.com/site/iwsltevaluation2017/TED-tasks](https://sites.google.com/site/iwsltevaluation2017/TED-tasks) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Paper: [Overview of the IWSLT 2017 Evaluation Campaign](https://aclanthology.org/2017.iwslt-1.1/) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 4.24 GB - Size of the generated dataset: 1.14 GB - Total amount of disk used: 5.38 GB This repository contain a modified version of the loading script used in the official [iwslt2017](https://huggingface.co/datasets/iwslt2017) repository updated to include document and segment information for all available sentence pairs, enabling their usage for document-level and context-aware MT applications. Refer to the original repository for additional information.	gsarti/iwslt2017_context	[ "task_categories:translation", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:translation", "size_categories:1M<n<10M", "source_datasets:original", "language:ar", "language:de", "language:en", "language:fr", "language:it", "language:ja", "language:ko", "language:nl", "language:ro", "language:zh", "license:cc-by-nc-nd-4.0", "region:us" ]	2023-05-07T13:03:04+00:00	{"annotations_creators": ["crowdsourced"], "language_creators": ["expert-generated"], "language": ["ar", "de", "en", "fr", "it", "ja", "ko", "nl", "ro", "zh"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["translation"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "paperswithcode_id": "iwslt-2017", "pretty_name": "IWSLT 2017", "dataset_info": [{"config_name": "iwslt2017-en-it", "features": [{"name": "translation", 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"num_examples": 8597}, {"name": "validation", "num_bytes": 207579, "num_examples": 890}], "download_size": 26880731, "dataset_size": 51248330}, {"config_name": "iwslt2017-ja-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ja", "en"]}}}], "splits": [{"name": "train", "num_bytes": 48204987, "num_examples": 223108}, {"name": "test", "num_bytes": 1809007, "num_examples": 8469}, {"name": "validation", "num_bytes": 208124, "num_examples": 871}], "download_size": 26190859, "dataset_size": 50222118}, {"config_name": "iwslt2017-ko-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["ko", "en"]}}}], "splits": [{"name": "train", "num_bytes": 51678043, "num_examples": 230240}, {"name": "test", "num_bytes": 1869793, "num_examples": 8514}, {"name": "validation", "num_bytes": 219295, "num_examples": 879}], "download_size": 19364733, "dataset_size": 53767131}, {"config_name": "iwslt2017-zh-en", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["zh", "en"]}}}], "splits": [{"name": "train", "num_bytes": 44271004, "num_examples": 231266}, {"name": "test", "num_bytes": 1605527, "num_examples": 8549}, {"name": "validation", "num_bytes": 202537, "num_examples": 879}], "download_size": 26849290, "dataset_size": 46079068}]}	2023-05-07T13:09:24+00:00
abe443f55b8da6bfacf3b06df8e87414be87181f	# Dataset Card for "voxelgym_3c_200_42x42" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Cubpaw/voxelgym_3c_42x42_200	[ "region:us" ]	2023-05-07T13:12:27+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}, {"name": "rgb_label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 107709.0, "num_examples": 160}, {"name": "validation", "num_bytes": 25934.0, "num_examples": 40}], "download_size": 121774, "dataset_size": 133643.0}}	2023-05-07T13:12:39+00:00
3f5a8a0bc9146e6313fa84f645fcaff0acf26c56		ssssasdasdasdasdqwd/v3_gameasset_lora	[ "license:unknown", "region:us" ]	2023-05-07T13:38:57+00:00	{"license": "unknown"}	2023-05-07T13:42:29+00:00
c735840575b629292b41da8dde11dcd523d4f91c	# 📀 Falcon RefinedWeb Falcon RefinedWeb is a massive English web dataset built by [TII](https://www.tii.ae) and released under an ODC-By 1.0 license. See the 📓 [paper on arXiv](https://arxiv.org/abs/2306.01116) for more details. RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data. RefinedWeb is also "multimodal-friendly": it contains links and alt texts for images in processed samples. This public extract should contain 500-650GT depending on the tokenizer you use, and can be enhanced with the curated corpora of your choosing. This public extract is about ~500GB to download, requiring 2.8TB of local storage once unpacked. ```python from datasets import load_dataset rw = load_dataset("tiiuae/falcon-refinedweb") ``` RefinedWeb is the main dataset we have used for training the [Falcon LLM](https://falconllm.tii.ae) models: * It was used in conjunction with a curated corpora to train Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b), two state-of-the-art open-source models. * It was also used to train Falcon-RW-[1B](https://huggingface.co/tiiuae/falcon-rw-1b)/[7B](https://huggingface.co/tiiuae/falcon-rw-7b), two models trained on 350 billion tokens of RefinedWeb alone to demonstrate its quality compared to curated corpora. # Dataset card for Falcon RefinedWeb ## Dataset Description * Homepage: [falconllm.tii.ae](falconllm.tii.ae) * Paper: [https://arxiv.org/abs/2306.01116](https://arxiv.org/abs/2306.01116) * Point of Contact: [[email protected]](mailto:[email protected]) ### Dataset Summary Falcon RefinedWeb was created to serve as an English large-scale dataset for the pretraining of large language models. It may be used on its own, or augmented with curated sources (e.g., Wikipedia, StackOverflow). It was built on top of CommonCrawl, leveraging stringent filtering and extensive deduplication. ### Supported Tasks and Leaderboards RefinedWeb is intended to be primarly used as a pretraining dataset for large language models. Practitioners may leverage it for upstream evaluation with a validation loss, but we do not provide any canonical split. ### Languages RefinedWeb primarly contains English. ## Dataset Structure ### Data Instances Each data instance corresponds to an individual web page which has been crawled, processed, and deduplicated against all other instances. This public extract of RefinedWeb contains about 1B instances (968M individual web pages), for a total of 2.8TB of clean text data. ### Data Fields * `content`: the processed and cleaned text contained in the page; * `url`: the url of the webpage crawled to produce the sample; * `timestamp`: timestamp of when the webpage was crawled by CommonCrawl; * `dump`: the CommonCrawl dump the sample is a part of; * `segment`: the CommonCrawl segment the sample is a part of; * `image_urls`: a list of elements in the type [`image_url`, `image_alt_text`] for all the images found in the content of the sample. ### Data Splits We do not provide any canonical splits for RefinedWeb. ## Dataset Creation ### Curation Rationale Falcon RefinedWeb is built on-top of [CommonCrawl](https://commoncrawl.org), using the Macrodata Refinement Pipeline, which combines content extraction, filtering heuristics, and deduplication. In designing RefinedWeb, we abided to the following philosophy: * (1) Scale first. We intend MDR to produce datasets to be used to train 40-200B parameters models, thus requiring trillions of tokens [(Hoffmann et al., 2022)](https://arxiv.org/abs/2203.15556). For English-only RefinedWeb, we target a size of 3-6 trillion tokens. Specifically, we eschew any labour intensive human curation process, and focus on CommonCrawl instead of disparate single-domain sources. * (2) Strict deduplication. Inspired by the work of [Lee et al., 2021](https://arxiv.org/abs/2107.06499), which demonstrated the value of deduplication for large language models, we implement a rigorous deduplication pipeline. We combine both exact and fuzzy deduplication, and use strict settings leading to removal rates far higher than others datasets have reported. * (3) Neutral filtering. To avoid introducing further undesirable biases into the model, we avoid using ML-based filtering outside of language identification ([Dodge et al., 2021](https://arxiv.org/abs/2104.08758); [Welbl et al., 2021](https://arxiv.org/abs/2109.07445)) . We stick to simple rules and heuristics, and use only URL filtering for adult content. During its development, we iterated on RefinedWeb by measuring the zero-shot performance of models trained on development version of the dataset. Our main goal was to maximize the performance obtained, bridging the gap between curated and web data. We also manually audited samples to identify potential filtering improvements. ### Source Data RefinedWeb is built from [CommonCrawl](https://commoncrawl.org) dumps. These dumps are constructed from crawling publicly available web pages. ### Data Collection and Preprocessing We applied extensive preprocessing and cleaning of the data, using our Macrodata Refinement Pipeline. We first filter URLs to remove adult content using a blocklist and a score system, we then use `trafilatura` to extract content from pages, and perform language identification with the `fastText` classifier from CCNet ([Wenzek et al., 2019](https://arxiv.org/abs/1911.00359)). After this first preprocessing stage, we filter data using heuristics from MassiveWeb ([Rae et al., 2021](https://arxiv.org/abs/2112.11446)), and our own line-wise corrections. Finally, we run extensive deduplication, removing URLs revisited across dumps and performing subsequently fuzzy and exact substring deduplication. ### Annotations We provide automatically collected annotations for the source `url`, `timestamp` of the crawl, original CommonCrawl `dump` and `segment` in which the document was found, and `image_urls` contained in the page. ### Personal and Sensitive Information As RefinedWeb is built upon publicly available web pages, it may contain sensitive information such as emails, phone numbers, or IP addresses. We believe that deduplication may have helped reduced the prevalence of PII in the dataset, but practitioners working with RefinedWeb should take care. ## Considerations for Using the Data ### Social Impact of Dataset With the open-source release of Falcon RefinedWeb, we aim to increase access to high-quality web data, which has typically been held private by model developers. We believe this release will in turn improve the accessibility and the spread of performant large language models. ### Discussion of Biases As toxic or biased data is prevalent on the internet, it is likely our dataset contains such content. Notably, using the Perspective API, we estimated the prevalence of toxic content in the dataset to be similar to The Pile. ### Other Known Limitations Despite our best efforts to filter content that does not qualify as natural language, and to deduplicate documents, our pipeline may let through documents that may be considered as errors or redundant. ## Additional Information ### Licensing Information This public extract is made available under an [ODC-By 1.0](https://opendatacommons.org/licenses/by/1-0/) license; users should also abide to the [CommonCrawl ToU](https://commoncrawl.org/terms-of-use/). ### Citation Information ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ### Opt-out request RefinedWeb is based on [CommonCrawl](https://commoncrawl.org/). Their crawler honors opt-out requests in the `robots.txt`, see the [CC FAQ](https://commoncrawl.org/big-picture/frequently-asked-questions/) for details. To remove a document from RefinedWeb, please message [email protected]. ### Contact [email protected]	tiiuae/falcon-refinedweb	[ "task_categories:text-generation", "size_categories:100B<n<1T", "language:en", "license:odc-by", "arxiv:2306.01116", "arxiv:2203.15556", "arxiv:2107.06499", "arxiv:2104.08758", "arxiv:2109.07445", "arxiv:1911.00359", "arxiv:2112.11446", "doi:10.57967/hf/0737", "region:us" ]	2023-05-07T13:57:27+00:00	{"language": ["en"], "license": "odc-by", "size_categories": ["100B<n<1T"], "task_categories": ["text-generation"], "pretty_name": "Falcon RefinedWeb", "dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[s]"}, {"name": "dump", "dtype": "string"}, {"name": "segment", "dtype": "string"}, {"name": "image_urls", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 2766953721769, "num_examples": 968000015}], "download_size": 466888198663, "dataset_size": 2766953721769}}	2023-06-20T11:38:07+00:00
6c8a1c4faf452938eecf2af02b6e0bac71a8c30d	# Dataset Card for "final_train_v2_500000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	mHossain/final_train_v2_500000	[ "region:us" ]	2023-05-07T14:02:30+00:00	{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "input_text", "dtype": "string"}, {"name": "target_text", "dtype": "string"}, {"name": "prefix", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6083695.8, "num_examples": 18000}, {"name": "test", "num_bytes": 675966.2, "num_examples": 2000}], "download_size": 2962855, "dataset_size": 6759662.0}}	2023-05-07T14:02:39+00:00
acc6984fac9454f73d81b79e595ef317f98bcee1	# Dataset Card for "victor_lrec_2020_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Luciano/victor_lrec_2020_small	[ "region:us" ]	2023-05-07T14:33:52+00:00	{"dataset_info": {"features": [{"name": "themes", "dtype": "string"}, {"name": "process_id", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "document_type", "dtype": "string"}, {"name": "pages", "dtype": "int64"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 219095295, "num_examples": 149217}, {"name": "validation", "num_bytes": 139364099, "num_examples": 94735}, {"name": "test", "num_bytes": 140516696, "num_examples": 95526}], "download_size": 154894289, "dataset_size": 498976090}}	2023-05-07T14:34:19+00:00
6a6bf2a439d2b27c195572c91e3fc39fe7e11b26	# Dataset Card for "victor_lrec_2020_medium" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Luciano/victor_lrec_2020_medium	[ "region:us" ]	2023-05-07T14:34:19+00:00	{"dataset_info": {"features": [{"name": "themes", "dtype": "string"}, {"name": "process_id", "dtype": "string"}, {"name": "file_name", "dtype": "string"}, {"name": "document_type", "dtype": "string"}, {"name": "pages", "dtype": "int64"}, {"name": "body", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2016870779, "num_examples": 1466276}, {"name": "validation", "num_bytes": 427712878, "num_examples": 309608}, {"name": "test", "num_bytes": 423762425, "num_examples": 311015}], "download_size": 980521617, "dataset_size": 2868346082}}	2023-05-07T14:36:10+00:00
c6b7974395fbed74936e85d94dd1d96a9fd33761	# Dataset Card for "resume_ner17_hr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	tsa17/resume_ner17_hr	[ "region:us" ]	2023-05-07T15:04:37+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "prediction", "list": [{"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "annotated", "struct": [{"name": "mentions", "sequence": "null"}, {"name": "tags", "sequence": "null"}]}, {"name": "predicted", "struct": [{"name": "mentions", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "chars_length", "dtype": "int64"}, {"name": "density", "dtype": "float64"}, {"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "tokens_length", "dtype": "int64"}, {"name": "value", "dtype": "string"}]}, {"name": "tags", "list": [{"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}]}, {"name": "text_length", "dtype": "int64"}, {"name": "tokens", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "char_end", "dtype": "int64"}, {"name": "char_start", "dtype": "int64"}, {"name": "custom", "dtype": "null"}, {"name": "idx", "dtype": "int64"}, {"name": "length", "dtype": "int64"}, {"name": "score", "dtype": "null"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "tokens_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 3390188, "num_examples": 50}], "download_size": 828976, "dataset_size": 3390188}}	2023-05-07T15:04:39+00:00

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