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|---|---|---|---|---|---|---|
88041da03f5524529f14df917e4f7744b8722dfe
|
# Dataset Card for "chunk_205"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_205
|
[
"region:us"
] |
2023-05-29T03:21:32+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1149424304.0, "num_examples": 223972}], "download_size": 1178226114, "dataset_size": 1149424304.0}}
|
2023-05-29T03:22:36+00:00
|
726187b3c7a993f68aae919632fa520a619b7b39
|
# Dataset Card for "chunk_60"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_60
|
[
"region:us"
] |
2023-05-29T03:22:58+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1309378480.0, "num_examples": 255140}], "download_size": 1335141507, "dataset_size": 1309378480.0}}
|
2023-05-29T03:25:13+00:00
|
5c364e6c03c36217175a2d9c2a7af2ca1fb8b292
|
# Dataset Card for "chunk_137"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_137
|
[
"region:us"
] |
2023-05-29T03:27:36+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1171045420.0, "num_examples": 228185}], "download_size": 1199589445, "dataset_size": 1171045420.0}}
|
2023-05-29T03:29:40+00:00
|
f75edd9fc3c866073c870479eb0a597ccba8c9e9
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xxl_mode_T_A_CM_D_PNP_GENERIC_Q_rices_ns_5046"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xxl_mode_T_A_CM_D_PNP_GENERIC_Q_rices_ns_5046
|
[
"region:us"
] |
2023-05-29T03:29:44+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text", "num_bytes": 58812363, "num_examples": 5046}], "download_size": 10592031, "dataset_size": 58812363}}
|
2023-05-29T15:16:35+00:00
|
fb754a568bb3fac7f3b788abd10f1c718d9e26fc
|
# Dataset Card for "chunk_61"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_61
|
[
"region:us"
] |
2023-05-29T03:32:18+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1298195852.0, "num_examples": 252961}], "download_size": 1321668241, "dataset_size": 1298195852.0}}
|
2023-05-29T03:34:41+00:00
|
17e282f2fb2c8234b0236ef6e788d2a76ac4ab8b
|
# Dataset Card for "chunk_62"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_62
|
[
"region:us"
] |
2023-05-29T03:40:45+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1139098720.0, "num_examples": 221960}], "download_size": 1157056574, "dataset_size": 1139098720.0}}
|
2023-05-29T03:43:04+00:00
|
47c32f4872fd1cf7a851256dc011fd08232bbf77
|
# Dataset Card for "chunk_63"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_63
|
[
"region:us"
] |
2023-05-29T03:49:00+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1119243012.0, "num_examples": 218091}], "download_size": 1137637541, "dataset_size": 1119243012.0}}
|
2023-05-29T03:50:58+00:00
|
5acafb3a4b6a60aba95ce36787c884ecd18a0fe4
|
# Dataset Card for "615298a2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/615298a2
|
[
"region:us"
] |
2023-05-29T03:51:42+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1336, "dataset_size": 182}}
|
2023-05-29T03:51:43+00:00
|
f2aeb5a3fd8888c75557578cc78360dd9b02f59f
|
# Dataset Card for "chunk_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_64
|
[
"region:us"
] |
2023-05-29T03:57:38+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1188848328.0, "num_examples": 231654}], "download_size": 1210620498, "dataset_size": 1188848328.0}}
|
2023-05-29T03:59:42+00:00
|
dda0ad765536c2d8bc1fd48909137eb2a2ec57b1
|
Sifal/KabyleWikipedia
|
[
"license:cc",
"region:us"
] |
2023-05-29T04:01:44+00:00
|
{"license": "cc"}
|
2023-05-30T18:25:24+00:00
|
|
fa86f3beac1867682c145c6d421298b2be80bd04
|
# Dataset Card for "chunk_65"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_65
|
[
"region:us"
] |
2023-05-29T04:06:33+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1209987036.0, "num_examples": 235773}], "download_size": 1235314857, "dataset_size": 1209987036.0}}
|
2023-05-29T04:08:41+00:00
|
a930b52b2b3b0203981c3bfb5ca911825f548bdb
|
# Dataset Card for "chunk_66"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_66
|
[
"region:us"
] |
2023-05-29T04:15:08+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1169526348.0, "num_examples": 227889}], "download_size": 1194664467, "dataset_size": 1169526348.0}}
|
2023-05-29T04:17:11+00:00
|
2672dee9cf60f675c31b130a7e5001cb9ba9898f
|
# Dataset Card for "chunk_67"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_67
|
[
"region:us"
] |
2023-05-29T04:23:41+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1217972428.0, "num_examples": 237329}], "download_size": 1245824053, "dataset_size": 1217972428.0}}
|
2023-05-29T04:25:57+00:00
|
b41fe92c6722b604a98516b6a3168321a81f794d
|
# Dataset Card for "chunk_68"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_68
|
[
"region:us"
] |
2023-05-29T04:31:49+00:00
|
{"dataset_info": {"features": [{"name": "logits", "sequence": "float32"}, {"name": "mfcc", "sequence": {"sequence": "float64"}}], "splits": [{"name": "train", "num_bytes": 1114218784.0, "num_examples": 217112}], "download_size": 1136538595, "dataset_size": 1114218784.0}}
|
2023-05-29T04:34:05+00:00
|
a1bad79be131f0d0f8934db5d79bd1c14cf73834
|
# Dataset Card for "ashaar_dataset"
```
@article{alyafeai2023ashaar,
title={Ashaar: Automatic Analysis and Generation of Arabic Poetry Using Deep Learning Approaches},
author={Alyafeai, Zaid and Al-Shaibani, Maged S and Ahmed, Moataz},
journal={arXiv preprint arXiv:2307.06218},
year={2023}
}
```
|
arbml/Ashaar_dataset
|
[
"region:us"
] |
2023-05-29T04:40:43+00:00
|
{"dataset_info": {"features": [{"name": "poem title", "dtype": "string"}, {"name": "poem meter", "dtype": "string"}, {"name": "poem verses", "sequence": "string"}, {"name": "poem theme", "dtype": "string"}, {"name": "poem url", "dtype": "string"}, {"name": "poet name", "dtype": "string"}, {"name": "poet description", "dtype": "string"}, {"name": "poet url", "dtype": "string"}, {"name": "poet era", "dtype": "string"}, {"name": "poet location", "dtype": "string"}, {"name": "poem description", "list": [{"name": "attributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "href", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "children", "list": [{"name": "attributes", "struct": [{"name": "align", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "nowrap", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "dir", "dtype": "string"}, {"name": "face", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "class", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "dir", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "partB", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "name", "dtype": "string"}, {"name": "parentAttributes", "struct": [{"name": "dir", "dtype": "string"}, {"name": "style", "dtype": "string"}]}, {"name": "partA", "dtype": "string"}, {"name": "partB", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "truncated", "dtype": "bool"}, {"name": "type", "dtype": "string"}]}, {"name": "poem language type", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 837548964, "num_examples": 212499}], "download_size": 371674261, "dataset_size": 837548964}}
|
2024-02-13T12:42:59+00:00
|
55414680ae7404ffea98f77e7523840c5585b545
|
# zhwiki-mnbvc
分项目：爬取并处理[中文维基百科](https://zh.wikipedia.org/wiki/Wikipedia:%E9%A6%96%E9%A1%B5)语料
数据时间：202302-202305 （持续更新）
主项目：MNBVC(Massive Never-ending BT Vast Chinese corpus)超大规模中文语料集 https://github.com/esbatmop/MNBVC
该项目清洗流程主要参考：https://kexue.fm/archives/4176/comment-page-1
并且使用组员开发的[去重工具](https://github.com/aplmikex/deduplication_mnbvc)进行数据格式化。
总行数（样本）: 10,754,146
一个示例：
```json
{
"文件名": "cleaned/zhwiki-20230420/folder_0/723712.txt",
"是否待查文件": false,
"是否重复文件": false,
"文件大小": 558,
"simhash": 14363740497821204542,
"最长段落长度": 142,
"段落数": 6,
"去重段落数": 6,
"低质量段落数": 0,
"段落": [
{
"行号": 0,
"是否重复": false,
"是否跨文件重复": false,
"md5": "39a3b4c7a4785d88c7c7d774364ea17e",
"内容": "【龙州 (唐朝)】"
},
{
"行号": 1,
"是否重复": false,
"是否跨文件重复": false,
"md5": "856bdf443999603f349625a56a5e92d6",
"内容": "龙州，中国古代的州，今龙州县的前身。"
},
{
"行号": 2,
"是否重复": false,
"是否跨文件重复": false,
"md5": "45fd3b9dc612d6235b5653d1a5b40688",
"内容": "唐朝武德四年（621年）设置的州，治所在龙城县（今广西壮族自治区龙州县北），辖两县：龙城县、柳岭县。贞观七年（633年），柳岭县并入龙城县，撤销龙州，龙城县归南昆州管辖。元朝设万户府，移治今龙州。明朝洪武初年，复为龙州。清朝雍正三年（1725年）废为龙州县，今属广西壮族自治区崇左市。"
},
{
"行号": 4,
"是否重复": false,
"是否跨文件重复": false,
"md5": "8756367c3ee308f3875ed8e942a6e377",
"内容": "== 参考文献 =="
},
{
"行号": 5,
"是否重复": false,
"是否跨文件重复": false,
"md5": "6db73b5b7c22fb1bcf7829fbe585043f",
"内容": "* 《旧唐书·地理志》"
},
{
"行号": 6,
"是否重复": false,
"是否跨文件重复": false,
"md5": "38b370ac9f61b116d4f6c98873ffc4bd",
"内容": "* 《明史·地理志》"
}
],
"文件日期": "2023-04-20"
}
```
|
wanng/wikipedia-zh-mnbvc
|
[
"task_categories:text-generation",
"language:zh",
"language:en",
"license:apache-2.0",
"mnbvc",
"Wikipedia",
"region:us"
] |
2023-05-29T04:41:11+00:00
|
{"language": ["zh", "en"], "license": "apache-2.0", "task_categories": ["text-generation"], "tags": ["mnbvc", "Wikipedia"]}
|
2023-05-29T16:39:00+00:00
|
79269d180a94509ee64ff1c4483453de21db760c
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
fanshiyu/fanshiyu
|
[
"license:openrail",
"chemistry",
"music",
"region:us"
] |
2023-05-29T04:57:08+00:00
|
{"license": "openrail", "pretty_name": "o", "tags": ["chemistry", "music"]}
|
2023-06-16T05:57:54+00:00
|
46c0870c920cf8b428b4ecd13fd6907b29978b56
|
---
license: creativeml-openrail-m
---ceheeuicheche;cecguie;cge
che'ceichewc'hwfujturkuttjydyhddthd'
111
|
fanshiyu/ceshi
|
[
"region:us"
] |
2023-05-29T05:04:20+00:00
|
{}
|
2023-05-31T05:24:42+00:00
|
ac21e1892e634dfa25f8ad75f16cbdbfb0a5736d
|
# Gorilla: Large Language Model Connected with Massive APIs
By Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez ([Project Website](https://shishirpatil.github.io/gorilla/))
[](https://arxiv.org/abs/2305.15334) [](https://discord.gg/3apqwwME) [](https://colab.research.google.com/drive/1DEBPsccVLF_aUnmD0FwPeHFrtdC0QIUP?usp=sharing)
`Gorilla` enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla can write a semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them! Hop on our Discord, or open a PR, or email us if you would like to have your API incorporated as well.
### Dataset Date
05/28/2023
### Organization
Gorilla LLM (UC Berkeley)
---
license: apache-2.0
---
|
gorilla-llm/APIBench
|
[
"language:en",
"license:apache-2.0",
"api",
"arxiv:2305.15334",
"region:us"
] |
2023-05-29T05:21:06+00:00
|
{"language": ["en"], "license": "apache-2.0", "tags": ["api"]}
|
2023-05-29T05:31:49+00:00
|
96a6a56931c1e2c2b85e97452b902cb59d8ec94e
|
# Dataset Card for MeQSum
## 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:**
- **Repository:** https://github.com/abachaa/MeQSum
- **Paper:** [On the Summarization of Consumer Health Questions](https://aclanthology.org/P19-1215)
- **Leaderboard:**
- **Point of Contact:** [Asma Ben Abacha](mailto:[email protected])
### Dataset Summary
MeQSum corpus is a dataset for medical question summarization. It contains 1,000 summarized consumer health questions.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## Dataset Structure
### Data Instances
```
{
"CHQ": "SUBJECT: who and where to get cetirizine - D\\nMESSAGE: I need\\/want to know who manufscturs Cetirizine. My Walmart is looking for a new supply and are not getting the recent",
"Summary": "Who manufactures cetirizine?",
"File": "1-131188152.xml.txt"
}
```
### Data Fields
- `CHQ` (str): Consumer health question.
- `Summary` (str): Question summarization, i.e., condensed question expressing the minimum information required to find correct answers to the original question.
- `File` (str): Filename.
### Data Splits
The dataset consists of a single `train` split containing 1,000 examples.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
If you use the MeQSum corpus, please cite:
```
@inproceedings{ben-abacha-demner-fushman-2019-summarization,
title = "On the Summarization of Consumer Health Questions",
author = "Ben Abacha, Asma and
Demner-Fushman, Dina",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1215",
doi = "10.18653/v1/P19-1215",
pages = "2228--2234",
abstract = "Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16{\%}. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization.",
}
```
### Contributions
Thanks to [@albertvillanova](https://huggingface.co/albertvillanova) for adding this dataset.
|
albertvillanova/meqsum
|
[
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"medical",
"region:us"
] |
2023-05-29T05:25:28+00:00
|
{"language": ["en"], "license": "unknown", "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "paperswithcode_id": "meqsum", "pretty_name": "MeQSum", "tags": ["medical"]}
|
2023-05-29T07:45:44+00:00
|
6f16b3805370506d0ffaf587df6a55059cfa991c
|
Sangmin/langchain-docs
|
[
"license:wtfpl",
"region:us"
] |
2023-05-29T05:36:02+00:00
|
{"license": "wtfpl"}
|
2023-05-29T05:37:08+00:00
|
|
1dafd62908650a883bc090a7c31f96851a2065f0
|
Sifal/KAB-EN
|
[
"license:cc",
"region:us"
] |
2023-05-29T06:07:01+00:00
|
{"license": "cc"}
|
2023-05-29T06:08:44+00:00
|
|
42569b42e0055abcd603e34a0361e415d8d17c32
|
# Dataset Card for "docvqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
spyzvarun/docvqa
|
[
"region:us"
] |
2023-05-29T06:19:58+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "header", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12161289.0, "num_examples": 149}, {"name": "test", "num_bytes": 4332499.0, "num_examples": 50}], "download_size": 16382730, "dataset_size": 16493788.0}}
|
2023-05-29T09:51:55+00:00
|
968656d53f64b04bc54337a4c481f3e908711934
|
# SeCoDa [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]
Repository for the Sense Complexity Dataset (SeCoDa)
# Paper
For more information on the SeCoDa, see the [paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.730.pdf).
Publications using this dataset must include a reference to the following publication:
<pre>
SeCoDa: Sense Complexity Dataset. David Strohmaier, Sian Gooding, Shiva Taslimipoor, Ekaterina Kochmar. Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 5964–5969, Marseille, 11–16 May 2020
</pre>
The dataset is based on the earlier CWIG3G2 dataset, see the [paper](https://aclanthology.org/I17-2068.pdf) and [website](https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/complex-word-identification-dataset.html). The relevant citation is
<pre>
Seid Muhie Yimam, Sanja Štajner, Martin Riedl, and Chris Biemann (2017): CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups. In Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017). Taipei, Taiwan
</pre>
The complexity data can be found in the CWIG3G2 dataset and combined with the senses provided by SeCoDa.
# Repository Content
Main data are found in SeCoDa.tsv. The columns are structured as follows.
1. Token to be disambiguated.
2. Offset start for token in context
3. Offset end for token in context
4. Context (sentence in which token occurs)
5. Selected sense
6. Comments (also contains MWE information)
Example:
| target | offset_start | offset_end | context | sense | comments |
| ------- |:------------:| ----------:| ------------------:| ----------------:| --------:|
| abroad | 39 | 45 | As we emerge... | OTHER COUNTRY... | - |
| abroad | 39 | 45 | As we emerge... | OTHER COUNTRY... | - |
| abroad | 73 | 79 | #1-8 The speech... | OTHER COUNTRY... | - |
The senses are drawn from the [Cambridge Advanced Learner's Dictionary](https://dictionary.cambridge.org).
*UPDATE*: Two missing entries have been added and typos in comments have been corrected.
*UPDATE*: Added further information to readme.
This work is licensed under a [Creative Commons Attribution-NonCommerial-ShareAlike 4.0
International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
|
dstrohmaier/SeCoDa
|
[
"task_categories:token-classification",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-05-29T07:08:50+00:00
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "task_categories": ["token-classification"], "pretty_name": "SeCoDa"}
|
2023-05-29T07:36:04+00:00
|
156133490a6c38685d5b0dcaa78c0a9e09b5d875
|
wwydmanski/reuters10k
|
[
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"tabular",
"region:us"
] |
2023-05-29T07:15:50+00:00
|
{"size_categories": ["10K<n<100K"], "task_categories": ["tabular-classification"], "pretty_name": "Reuters10K", "tags": ["tabular"]}
|
2023-05-29T07:59:17+00:00
|
|
211d36dedffb0957eff114e9a6ac657c56c76bf1
|
Anustup/NewtonCompilableCode
|
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:aa",
"license:openrail",
"code",
"region:us"
] |
2023-05-29T07:17:00+00:00
|
{"language": ["aa"], "license": "openrail", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "tags": ["code"]}
|
2023-05-29T08:49:29+00:00
|
|
a5239644bc42f12cb2e5ede6f1fb2936cba647e6
|
# Dataset Card for "tagged_articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
luxinyi/tagged_articles
|
[
"region:us"
] |
2023-05-29T07:19:24+00:00
|
{"dataset_info": {"features": [{"name": "Published", "dtype": "string"}, {"name": "Index", "dtype": "string"}, {"name": "Sub Index", "dtype": "null"}, {"name": "Headline", "dtype": "string"}, {"name": "Summary", "dtype": "string"}, {"name": "Facebook Interactions", "dtype": "int64"}, {"name": "Download Date", "dtype": "string"}, {"name": "Theme", "dtype": "string"}, {"name": "New Index", "dtype": "string"}, {"name": "New Sub Index", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 420130.5018248175, "num_examples": 1315}, {"name": "validation", "num_bytes": 105112.49817518248, "num_examples": 329}], "download_size": 305631, "dataset_size": 525243.0}}
|
2023-05-29T07:19:36+00:00
|
8a8632c63fe7d50f1c8b5e16a46e6a3f197a763c
|
# Dataset Card for "pp1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
luyunlll/pp1
|
[
"region:us"
] |
2023-05-29T07:50:41+00:00
|
{"dataset_info": {"features": [{"name": "audio", "struct": [{"name": "array", "sequence": "float64"}, {"name": "path", "dtype": "string"}, {"name": "sampling_rate", "dtype": "int64"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1520321640, "num_examples": 3000}, {"name": "test", "num_bytes": 382341867, "num_examples": 750}], "download_size": 452124174, "dataset_size": 1902663507}}
|
2023-05-29T08:00:50+00:00
|
f6dd981c7400b3e3738bde12ff948b7cc8f0d623
|
# Dataset Card for MedMNIST v2
## 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://medmnist.com/
- **Repository:** https://github.com/MedMNIST/MedMNIST
- **Paper:** [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://arxiv.org/abs/2110.14795)
- **Leaderboard:**
- **Point of Contact:** [Bingbing Ni](mailto:[email protected])
### Dataset Summary
We introduce MedMNIST v2, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28 x 28 (2D) or 28 x 28 x 28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of 708,069 2D images and 9,998 3D images in total, could support numerous research / educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) (CC BY 4.0).
Each subset keeps the same license as that of the source dataset. Please also cite the corresponding paper of source data if you use any subset of MedMNIST.
### Citation Information
If you find this project useful, please cite both v1 and v2 papers:
```
@article{medmnistv2,
title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
journal={Scientific Data},
volume={10},
number={1},
pages={41},
year={2023},
publisher={Nature Publishing Group UK London}
}
@inproceedings{medmnistv1,
title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
pages={191--195},
year={2021}
}
```
Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the [project website](https://medmnist.com/).
### Contributions
Thanks to [@albertvillanova](https://huggingface.co/albertvillanova) for adding this dataset.
|
albertvillanova/medmnist-v2
|
[
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"task_ids:multi-label-image-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"medical",
"arxiv:2110.14795",
"region:us"
] |
2023-05-29T08:00:40+00:00
|
{"language": "en", "license": "cc-by-4.0", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification", "multi-label-image-classification"], "paperswithcode_id": "medmnist-v2", "pretty_name": "MedMNIST v2", "tags": ["medical"]}
|
2023-05-30T04:40:52+00:00
|
9344158123d5f8b09e895daec009d1fc330aa7ec
|
zzzzhhh/chatGLM-zn
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-29T08:24:42+00:00
|
{"license": "apache-2.0"}
|
2023-06-02T01:06:19+00:00
|
|
a9db3804717d828b5af0d50b04b96e10df0ed5d0
|
# JBLiMP
This is the data from "JBLiMP: Japanese Benchmark of Linguistic Minimal Pairs" (Someya and Oseki, 2023). Only the validated pairs used for benchmarks are included, and only in JSONL format, since it's redundant with the TSV.
For details see [the original git repo](https://github.com/osekilab/JBLiMP) or [the paper](https://aclanthology.org/2023.findings-eacl.117/).
|
polm-stability/jblimp
|
[
"language:ja",
"region:us"
] |
2023-05-29T08:31:31+00:00
|
{"language": ["ja"]}
|
2023-05-29T08:49:16+00:00
|
7137618062de9cf0d1ac0ed80773b23345132af7
|
Circularmachines/batch_indexing_machine_movs
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-05-29T08:37:08+00:00
|
{"license": "cc-by-4.0"}
|
2023-06-14T08:40:43+00:00
|
|
75d8ee50151f85a62e5fca08628a57fda1b891bb
|
# Dataset Card for "ppo-seals-Ant-v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HumanCompatibleAI/ppo-seals-Ant-v0
|
[
"region:us"
] |
2023-05-29T08:46:13+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 223153705, "num_examples": 104}], "download_size": 47004336, "dataset_size": 223153705}}
|
2023-05-29T08:47:39+00:00
|
027d08a3b9690679449d4a75ef767684a922e584
|
# wikidata-rubq-hf
Huggingface Dataset wrapper for Wikidata-RuBQ 2.0 dataset
### Usage
WIP
|
rvashurin/wikidata_rubq
|
[
"region:us"
] |
2023-05-29T08:47:23+00:00
|
{}
|
2023-05-29T09:36:41+00:00
|
4c7a9ad362da8704940cee986b0b51af40c83efb
|
# Dataset Card for "ppo-seals-Swimmer-v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HumanCompatibleAI/ppo-seals-Swimmer-v0
|
[
"region:us"
] |
2023-05-29T08:48:40+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 128625365, "num_examples": 104}], "download_size": 23073060, "dataset_size": 128625365}}
|
2023-05-29T08:49:19+00:00
|
f473bac56959cb9d12c21c88cf618adab7c32cf7
|
# Dataset Card for "ppo-seals-Hopper-v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HumanCompatibleAI/ppo-seals-Hopper-v0
|
[
"region:us"
] |
2023-05-29T08:49:39+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 54477160, "num_examples": 104}], "download_size": 16464511, "dataset_size": 54477160}}
|
2023-05-29T08:50:14+00:00
|
86cce71d504c884a2ebb328c3a250704097338d2
|
# Dataset Card for "ppo-seals-Walker2d-v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HumanCompatibleAI/ppo-seals-Walker2d-v0
|
[
"region:us"
] |
2023-05-29T08:50:40+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 60728770, "num_examples": 104}], "download_size": 21507130, "dataset_size": 60728770}}
|
2023-05-29T08:51:20+00:00
|
2de50f848f4aa4a0407575a8cf8070b6fa452674
|
TankuVie/ted_talks_multilingual_parallel_corpus
|
[
"license:other",
"region:us"
] |
2023-05-29T08:50:42+00:00
|
{"license": "other"}
|
2023-05-29T08:56:18+00:00
|
|
7ccb87ed40d04ab7f5f16bbb8ec2fccf0654700a
|
# Dataset Card for "sam-controlnet-original-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
baptistecolle/sam-controlnet-original-2
|
[
"region:us"
] |
2023-05-29T08:51:29+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "filepath", "dtype": "string"}, {"name": "sentids", "list": "int32"}, {"name": "filename", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "split", "dtype": "string"}, {"name": "sentences", "struct": [{"name": "tokens", "list": "string"}, {"name": "raw", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "sentid", "dtype": "int32"}]}, {"name": "cocoid", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 165468690.7524257, "num_examples": 1000}], "download_size": 165010624, "dataset_size": 165468690.7524257}}
|
2023-05-29T08:52:44+00:00
|
d11ec5952decd16f45e38ac9302cd6766fc07d8a
|
# Dataset Card for "ppo-seals-HalfCheetah-v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HumanCompatibleAI/ppo-seals-HalfCheetah-v0
|
[
"region:us"
] |
2023-05-29T08:51:59+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float32"}}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 89536876, "num_examples": 104}], "download_size": 24489478, "dataset_size": 89536876}}
|
2023-05-29T08:52:45+00:00
|
7e06898bd7fe0c6d9d025033727f177acc5eeb59
|
license: cc0-1.0
---
### Dataset Summary
The collection of MaCoCu parallel corpora have been crawled and consist of pairs of source and target segments (one or several sentences) and additional metadata. The following metadata is included:
- "src_url" and "trg_url": source and target document URL;
- "src_text" and "trg_text": text in non-English language and in English Language;
- "bleualign_score": similarity score as provided by the sentence alignment tool Bleualign (value between 0 and 1);
- "src_deferred_hash" and "trg_deferred_hash": hash identifier for the corresponding segment;
- "src_paragraph_id" and "trg_paragraph_id": identifier of the paragraph where the segment appears in the original document;
- "src_doc_title" and "trg_doc_title": title of the documents from which segments where obtained;
- "src_crawl_date" and "trg_crawl_date": date and time when source and target documents where donwoaded;
- "src_file_type" and "trg_file_type": type of the original documents (usually HTML format);
- "src_boilerplate" and "trg_boilerplate": are source or target segments boilerplates?
- "bifixer_hash": hash identifier for the segment pair;
- "bifixer_score": score that indicates how likely are segments to be correct in their corresponding language;
- "bicleaner_ai_score": score that indicates how likely are segments to be parallel;
- "biroamer_entities_detected": do any of the segments contain personal information?
- "dsi": a DSI class (“dsi”): information whether the segment is connected to any of Digital Service Infrastructure (DSI) classes (e.g., cybersecurity, e-health, e-justice, open-data-portal), defined by the Connecting Europe Facility (https://github.com/RikVN/DSI);
- "translation_direction": translation direction and machine translation identification ("translation-direction"): the source segment in each segment pair was identified by using a probabilistic model (https://github.com/RikVN/TranslationDirection), which also determines if the translation has been produced by a machine-translation system;
- "en_document_level_variant": the language variant of English (British or American, using a lexicon-based English variety classifier - https://pypi.org/project/abclf/) was identified on document and domain level;
- "domain_en": name of the web domain for the English document;
- "en_domain_level_variant": language variant for English at the level of the web domain.
To load a language pair just indicate the dataset and the pair of languages with English first
```python
dataset = load_dataset("MaCoCu/parallel_data", "en-is")
```
|
MaCoCu/parallel_data
|
[
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bs",
"language:bg",
"language:en",
"language:is",
"language:hr",
"language:cnr",
"language:mk",
"language:mt",
"language:sl",
"language:sr",
"language:sq",
"language:tr",
"license:cc0-1.0",
"region:us"
] |
2023-05-29T08:52:35+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["bs", "bg", "en", "is", "hr", "cnr", "mk", "mt", "sl", "sr", "sq", "tr"], "license": ["cc0-1.0"], "multilinguality": ["translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["translation"], "task_ids": [], "pretty_name": "MaCoCu_parallel", "dataset_info": [{"config_name": "enis", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["is", "en"]}}}], "splits": [{"name": "train", "num_bytes": 133883139, "num_examples": 546172}], "download_size": 133883139, "dataset_size": 133883139}, {"config_name": "enbg", "features": [{"name": "translation", "dtype": {"translation": {"languages": ["bg", "en"]}}}], "splits": [{"name": "train", "num_bytes": 133883139, "num_examples": 546172}], "download_size": 133883139, "dataset_size": 133883139}]}
|
2023-05-30T22:05:07+00:00
|
5e867cd644a22076564fe046123ab2d182b6cf8f
|
# Dataset Card for "ppo-seals-CartPole-v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HumanCompatibleAI/ppo-seals-CartPole-v0
|
[
"region:us"
] |
2023-05-29T08:52:45+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float32"}}, {"name": "acts", "sequence": "int64"}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 516313, "num_examples": 24}], "download_size": 297546, "dataset_size": 516313}}
|
2023-05-29T08:52:49+00:00
|
eccf94cd9a7bd67137ebbf29287df27ed1b248fb
|
Anustup/newtoncode
|
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
2023-05-29T08:58:32+00:00
|
{"license": "bigscience-bloom-rail-1.0"}
|
2023-05-29T08:58:32+00:00
|
|
b5839d940475b03bf14cbaf6db28a4ceaf62a2a9
|
This is an ATM dataset for the use of automatic speech recognition. The original source of the data is from the [ATCOSIM](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html) project.
|
jlvdoorn/atcosim
|
[
"language:en",
"air traffic management",
"automatic speech recognition",
"natural language processing",
"atcosim",
"atm",
"asr",
"nlp",
"doi:10.57967/hf/1378",
"region:us"
] |
2023-05-29T09:21:24+00:00
|
{"language": ["en"], "pretty_name": "ATCOSIM", "tags": ["air traffic management", "automatic speech recognition", "natural language processing", "atcosim", "atm", "asr", "nlp"], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1929508254.0, "num_examples": 7646}, {"name": "validation", "num_bytes": 480869258.0, "num_examples": 1913}], "download_size": 2399337867, "dataset_size": 2410377512.0}}
|
2023-06-29T13:36:14+00:00
|
3bfe1cd3e7378dc9e7fcc4b00bdb163ab6c71392
|
abir18/sample_suggestion_acceptance
|
[
"license:mit",
"region:us"
] |
2023-05-29T09:21:53+00:00
|
{"license": "mit"}
|
2023-05-29T09:22:53+00:00
|
|
eb6fcdbc6bce718fa32843b17aac3d24725b7db4
|
# Dataset Card for "tokenized-total-512-reduced"
This dataset contains truncated tokenized protein sequences and their corresponding 3Di structure as stated in the [Foldseek](https://www.nature.com/articles/s41587-023-01773-0) paper.
Redundancy reduction and data sequence filtering was performed by [Dr. Michael Heinzinger](https://scholar.google.com/citations?user=yXtPl58AAAAJ&hl=en) and [Prof. Dr. Martin Steinegger](https://github.com/martin-steinegger).
The tokenizer used to encode the sequences can be found [here](https://huggingface.co/adrianhenkel/lucid-prot-tokenizer)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adrianhenkel/tokenized-total-512-reduced
|
[
"region:us"
] |
2023-05-29T09:22:35+00:00
|
{"dataset_info": {"features": [{"name": "input_id_x", "sequence": "int8"}, {"name": "input_id_y", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 7582970656, "num_examples": 17070828}], "download_size": 4615653058, "dataset_size": 7582970656}}
|
2023-06-10T16:57:23+00:00
|
97f3a72eb35fce4f84a53e413b69e5ea775eca7e
|
BenjaminSombi/jobevaluation
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-29T09:26:11+00:00
|
{"license": "apache-2.0"}
|
2023-05-29T09:28:21+00:00
|
|
ded0ae56b13d5ee09b881212d9d0e4280f90bc34
|
# Dataset Card for "dev_pretrain"
[Tigerbot模型](https://github.com/TigerResearch/TigerBot#%E6%A8%A1%E5%9E%8B%E4%B8%8B%E8%BD%BD)develop pretrain数据。
在[train_clm.py](https://github.com/TigerResearch/TigerBot/blob/main/train/train_clm.py)中被使用。
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/dev_pretrain')
```
## Field
- content: 语料
|
TigerResearch/dev_pretrain
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:zh",
"license:apache-2.0",
"region:us"
] |
2023-05-29T09:33:22+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 123238, "num_examples": 80}, {"name": "validation", "num_bytes": 23072, "num_examples": 20}], "download_size": 96425, "dataset_size": 146310}}
|
2023-05-30T00:58:19+00:00
|
51f9fd84fba96cde69d8f03e838241cfa9d5dbbf
|
AyoubChLin/CompanyDocuments
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-29T09:39:30+00:00
|
{"license": "apache-2.0"}
|
2023-05-30T17:30:10+00:00
|
|
162ff2dc26c4b1365e698b55cf6d0d2a880a9dfb
|
MocktaiLEngineer/qmsum-processed
|
[
"license:mit",
"region:us"
] |
2023-05-29T09:45:48+00:00
|
{"license": "mit"}
|
2023-05-29T09:46:54+00:00
|
|
9651fa61e65494a31caa2b8ab73cebfd8cf0aac4
|
# Dataset Card for "batch_indexing_machine_230529_000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_000
|
[
"region:us"
] |
2023-05-29T10:02:35+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 156759782.0, "num_examples": 720}], "download_size": 156770628, "dataset_size": 156759782.0}}
|
2023-05-29T10:48:28+00:00
|
dd852802aaf885c54cd823bc2f9dd58dcd6a408a
|
# Dataset Card for "batch_indexing_machine_230529_001"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_001
|
[
"region:us"
] |
2023-05-29T10:02:50+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 157818200.0, "num_examples": 720}], "download_size": 157829992, "dataset_size": 157818200.0}}
|
2023-05-29T10:48:40+00:00
|
23982e9c5876945667f5908832653116f6563dcb
|
# Dataset Card for "batch_indexing_machine_230529_002"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_002
|
[
"region:us"
] |
2023-05-29T10:03:04+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 156369319.0, "num_examples": 720}], "download_size": 156379644, "dataset_size": 156369319.0}}
|
2023-05-29T10:48:53+00:00
|
d3d39d4b900860f6c84ae78fba01bf1ef38d31d6
|
# Dataset Card for "batch_indexing_machine_230529_003"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_003
|
[
"region:us"
] |
2023-05-29T10:03:17+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 155255009.0, "num_examples": 720}], "download_size": 155266685, "dataset_size": 155255009.0}}
|
2023-05-29T10:49:06+00:00
|
dfc157e01a71cc626c19a5d8d95429f64ec418e3
|
# Dataset Card for "batch_indexing_machine_230529_004"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_004
|
[
"region:us"
] |
2023-05-29T10:03:29+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 163377382.0, "num_examples": 720}], "download_size": 163389369, "dataset_size": 163377382.0}}
|
2023-05-29T10:49:23+00:00
|
fe23e98596dca1dca8ddec5f26d52b7ad3e58b65
|
# Dataset Card for "batch_indexing_machine_230529_005"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_005
|
[
"region:us"
] |
2023-05-29T10:03:41+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 158262680.0, "num_examples": 720}], "download_size": 158274633, "dataset_size": 158262680.0}}
|
2023-05-29T10:49:39+00:00
|
0eb837fa90b7b28077d586b0611e3b3b889faed8
|
# Dataset Card for "batch_indexing_machine_230529_006"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_006
|
[
"region:us"
] |
2023-05-29T10:03:53+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 156741720.0, "num_examples": 720}], "download_size": 156752582, "dataset_size": 156741720.0}}
|
2023-05-29T10:49:51+00:00
|
1f611079104338bfaa1a762827fa5652d0c3c3c7
|
# Dataset Card for "batch_indexing_machine_230529_007"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_007
|
[
"region:us"
] |
2023-05-29T10:04:05+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 155593564.0, "num_examples": 720}], "download_size": 155604795, "dataset_size": 155593564.0}}
|
2023-05-29T10:50:04+00:00
|
a668762745685bf3174327745faac8e85e7978cd
|
# Dataset Card for "batch_indexing_machine_230529_008"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_008
|
[
"region:us"
] |
2023-05-29T10:04:17+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 158647898.0, "num_examples": 720}], "download_size": 158659572, "dataset_size": 158647898.0}}
|
2023-05-29T10:50:17+00:00
|
7f1ab53d9f0b3c03414aa795a59aa4d634198044
|
# Dataset Card for "batch_indexing_machine_230529_009"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_009
|
[
"region:us"
] |
2023-05-29T10:04:28+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 156498480.0, "num_examples": 720}], "download_size": 156509981, "dataset_size": 156498480.0}}
|
2023-05-29T10:50:30+00:00
|
922acc4a56faa1ce3393592878841cd9f5b3980e
|
# Dataset Card for "batch_indexing_machine_230529_010"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_010
|
[
"region:us"
] |
2023-05-29T10:04:40+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 164012007.0, "num_examples": 720}], "download_size": 164023933, "dataset_size": 164012007.0}}
|
2023-05-29T10:50:43+00:00
|
a301dacaa29fe0685f257fd9558130e91a7ab6ee
|
# Dataset Card for "batch_indexing_machine_230529_011"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_011
|
[
"region:us"
] |
2023-05-29T10:04:53+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 156625309.0, "num_examples": 720}], "download_size": 156636586, "dataset_size": 156625309.0}}
|
2023-05-29T10:50:56+00:00
|
67ce6ad19ed9402fa824f0644a7dedc5b2873f3e
|
# Dataset Card for "batch_indexing_machine_230529_012"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_012
|
[
"region:us"
] |
2023-05-29T10:05:05+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 152482128.0, "num_examples": 720}], "download_size": 152492954, "dataset_size": 152482128.0}}
|
2023-05-29T10:51:09+00:00
|
3203bcdc14c2bf1954d6daefd7065b5a55c71c96
|
# Dataset Card for "batch_indexing_machine_230529_013"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_013
|
[
"region:us"
] |
2023-05-29T10:05:18+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 157694181.0, "num_examples": 720}], "download_size": 157705998, "dataset_size": 157694181.0}}
|
2023-05-29T10:51:21+00:00
|
b4a1be86713f3ee2d7d60c5e522f207438e17596
|
# Dataset Card for "batch_indexing_machine_230529_014"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_014
|
[
"region:us"
] |
2023-05-29T10:05:31+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 160648209.0, "num_examples": 720}], "download_size": 160659400, "dataset_size": 160648209.0}}
|
2023-05-29T10:51:34+00:00
|
a47c7a05c79ec03c7d335236b935e7ed155a5fa4
|
# Dataset Card for "batch_indexing_machine_230529_015"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_015
|
[
"region:us"
] |
2023-05-29T10:05:43+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 159162453.0, "num_examples": 720}], "download_size": 159173563, "dataset_size": 159162453.0}}
|
2023-05-29T10:51:47+00:00
|
6db7f040e47bc35327208a2eaa212a5977e16a2e
|
# Dataset Card for "batch_indexing_machine_230529_016"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_016
|
[
"region:us"
] |
2023-05-29T10:05:55+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 161530818.0, "num_examples": 720}], "download_size": 161543824, "dataset_size": 161530818.0}}
|
2023-05-29T10:52:00+00:00
|
683c7430006a6db68876daac52745bbedd6054d1
|
# Dataset Card for "batch_indexing_machine_230529_017"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_017
|
[
"region:us"
] |
2023-05-29T10:06:07+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 158123517.0, "num_examples": 720}], "download_size": 158135438, "dataset_size": 158123517.0}}
|
2023-05-29T10:52:12+00:00
|
e51ed399d9b821ecbaf5700c02bfb59f4b9b630d
|
# Dataset Card for "batch_indexing_machine_230529_018"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_018
|
[
"region:us"
] |
2023-05-29T10:06:20+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 161619363.0, "num_examples": 720}], "download_size": 161631200, "dataset_size": 161619363.0}}
|
2023-05-29T10:52:26+00:00
|
90715111bc3d404ccba1a8bac087b75e43cbd5b2
|
# Dataset Card for "batch_indexing_machine_230529_019"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Circularmachines/batch_indexing_machine_230529_019
|
[
"region:us"
] |
2023-05-29T10:06:33+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 171552814.0, "num_examples": 720}], "download_size": 171566051, "dataset_size": 171552814.0}}
|
2023-05-29T10:52:39+00:00
|
87509d9967b53bc5a273a983542918b964480e98
|
KiriteeGak/boat-data
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-05-29T11:13:06+00:00
|
{"license": "creativeml-openrail-m"}
|
2023-05-30T09:50:34+00:00
|
|
bd721ddd0a2b778e02ef8fb9186185c5757a4e51
|
# Dataset Card for "dev_sft"
[Tigerbot模型](https://github.com/TigerResearch/TigerBot#%E6%A8%A1%E5%9E%8B%E4%B8%8B%E8%BD%BD)develop sft数据。
在[train_sft.py](https://github.com/TigerResearch/TigerBot/blob/main/train/train_sft.py)中被使用。
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/dev_sft')
```
## Field
- instruction: 指令
- input: 上下文信息(Optional)
- output: 生成目标
|
TigerResearch/dev_sft
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:zh",
"license:apache-2.0",
"region:us"
] |
2023-05-29T11:23:31+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29836, "num_examples": 80}, {"name": "validation", "num_bytes": 9086, "num_examples": 20}], "download_size": 0, "dataset_size": 38922}}
|
2023-06-16T00:55:22+00:00
|
be64a6a1209aee0391913dc41824f28851f45fd0
|
# Dataset Card for "diabetes.data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
korelidw/diabetes.data
|
[
"region:us"
] |
2023-05-29T11:36:05+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "user", "dtype": "int64"}, {"name": "cgm_value", "dtype": "float64"}, {"name": "timestamp", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40608, "num_examples": 864}], "download_size": 19855, "dataset_size": 40608}}
|
2023-05-29T11:36:08+00:00
|
10da0ea6184982c6a167cd2d9a5680a7fbd383e4
|
# Wikidata Simplequestions
Huggingface Dataset wrapper for Wikidata-simplequestion dataset
### Usage
```bash
git clone [email protected]:skoltech-nlp/wikidata-simplequestions-hf.git wikidata_simplequestions
```
```python3
from datasets import load_dataset;
load_dataset('../wikidata_simplequestions', 'answerable_en', cache_dir='/YOUR_PATH_TO_CACHE/', ignore_verifications=True)
```
|
rvashurin/wikidata_simplequestions
|
[
"region:us"
] |
2023-05-29T11:58:56+00:00
|
{}
|
2023-05-29T13:31:23+00:00
|
95c8972d9c7533a612e1f16366595ebead4f2182
|
# Dataset Card for "sam-controlnet-final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
baptistecolle/sam-controlnet-final
|
[
"region:us"
] |
2023-05-29T12:10:03+00:00
|
{"dataset_info": {"features": [{"name": "conditioning_image", "dtype": "image"}, {"name": "image", "dtype": "image"}, {"name": "filepath", "dtype": "string"}, {"name": "sentids", "list": "int32"}, {"name": "filename", "dtype": "string"}, {"name": "imgid", "dtype": "int32"}, {"name": "split", "dtype": "string"}, {"name": "cocoid", "dtype": "int32"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 306156020.0, "num_examples": 1000}], "download_size": 305765502, "dataset_size": 306156020.0}}
|
2023-05-29T19:51:46+00:00
|
cfc5ee12cfa59592e0aa7cfdc4f5b1a7e272e4de
|
kostayli/ru-WikiSQL-25k
|
[
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"region:us"
] |
2023-05-29T12:17:24+00:00
|
{"language": ["ru"], "size_categories": ["10K<n<100K"], "task_categories": ["text2text-generation"], "pretty_name": "wikisql-ru-low"}
|
2023-05-29T12:24:30+00:00
|
|
ed1527307d278f4fd2097c85578c621c5334e96f
|
vjain/anxiety
|
[
"license:openrail",
"region:us"
] |
2023-05-29T12:21:46+00:00
|
{"license": "openrail"}
|
2023-05-29T12:22:07+00:00
|
|
a82a27d2522bc7dbe313064bd1543679d7b6d4a9
|
AhmedBou/Methods
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-29T12:45:49+00:00
|
{"license": "apache-2.0"}
|
2023-05-29T12:46:26+00:00
|
|
ea9d42c70d1dce73efc2b8cb60fde9cc805fd319
|
flizzywine/A-Share_Stock_Market2020-2022
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-29T12:46:29+00:00
|
{"license": "apache-2.0"}
|
2023-05-29T12:46:29+00:00
|
|
0d561544aa13e9fbc8681869423c2729ecda52fa
|
# Dataset Card for "timeseries-1mn-sp500"
## 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://edarchimbaud.substack.com
- **Repository:** https://github.com/edarchimbaud
- **Point of Contact:** [email protected]
### Dataset Summary
The "timeseries-1mn-sp500" dataset provides one-minute time-series data for the S&P 500 index constituents.
### Supported Tasks and Leaderboards
This dataset is suitable for tasks such as time-series forecasting, volatility prediction, and high-frequency trading strategy development.
### Languages
[N/A]
## Dataset Structure
### Data Instances
[N/A]
### Data Fields
- symbol (string): The ticker symbol or abbreviation used to identify the company.
- datetime (timestamp): The date and time of the stock quote, in nanoseconds.
- open (float64): The opening price of the stock at the given datetime.
- high (float64): The highest price of the stock during the given minute.
- low (float64): The lowest price of the stock during the given minute.
- close (float64): The closing price of the stock at the given datetime.
- volume (float64): The volume of the stock traded during the given minute.
### Data Splits
[N/A]
## Dataset Creation
### Curation Rationale
The "timeseries-1mn-sp500" dataset was created to support high-frequency trading algorithms and time-series forecasting models.
### Source Data
#### Initial Data Collection and Normalization
The data was sourced from the web and normalized.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
The timeseries-1mn-sp500 dataset was collected by https://edarchimbaud.substack.com.
### Licensing Information
The timeseries-1mn-sp500 dataset is licensed under the MIT License.
### Citation Information
> https://edarchimbaud.substack.com, timeseries-daily-sp500 dataset, GitHub repository, https://github.com/edarchimbaud
### Contributions
Thanks to [@edarchimbaud](https://github.com/edarchimbaud) for adding this dataset.
|
edarchimbaud/timeseries-1m-stocks
|
[
"task_categories:tabular-regression",
"language:en",
"license:mit",
"region:us"
] |
2023-05-29T12:50:59+00:00
|
{"language": ["en"], "license": "mit", "task_categories": ["tabular-regression"], "dataset_info": {"features": [{"name": "symbol", "dtype": "string"}, {"name": "datetime", "dtype": "timestamp[ns]"}, {"name": "open", "dtype": "float64"}, {"name": "high", "dtype": "float64"}, {"name": "low", "dtype": "float64"}, {"name": "close", "dtype": "float64"}, {"name": "volume", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 183543516, "num_examples": 3283794}], "download_size": 83707584, "dataset_size": 183543516}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-21T10:02:43+00:00
|
1bc85b15cbc52b93d449c642039c1fdf4bbeef64
|
# Dataset Card for "miracl-corpus-id"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carlesoctav/miracl-corpus-id
|
[
"region:us"
] |
2023-05-29T13:01:38+00:00
|
{"dataset_info": {"features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "target_embedding", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 2764659648, "num_examples": 1446315}], "download_size": 3251111063, "dataset_size": 2764659648}}
|
2023-05-29T13:03:43+00:00
|
81a980225c9016835672f94d532ec79d98cd4bb6
|
# A collection of 12 million french-only instructions deduplicated from various sources
Source :
- clips/mqa-fr-faq
- multilingual-wikihow-qa-16k
- MBZUAI/Bactrian-X
- argilla/databricks-dolly-15k-curated-multilingual
- innermost47/alpaca-fr
- etalab-ia/piaf
|
Enno-Ai/fr-instructs
|
[
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"size_categories:10M<n<100M",
"language:fr",
"license:cc-by-2.5",
"region:us"
] |
2023-05-29T13:11:48+00:00
|
{"language": ["fr"], "license": "cc-by-2.5", "size_categories": ["10M<n<100M"], "task_categories": ["text2text-generation", "table-question-answering"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5904510661, "num_examples": 11794112}], "download_size": 1623654660, "dataset_size": 5904510661}}
|
2023-06-26T22:16:02+00:00
|
0297849a67b92b57cc1b3c18d96fc69f08f2ecdc
|
TankuVie/ted_talks_vi_it_parallel_corpus
|
[
"license:other",
"region:us"
] |
2023-05-29T13:29:51+00:00
|
{"license": "other"}
|
2023-05-30T16:27:58+00:00
|
|
6a36b94baaf4724883c366f89a45ea919c63ee23
|
Andrijan/self_improving_old2
|
[
"license:other",
"region:us"
] |
2023-05-29T13:30:43+00:00
|
{"license": "other"}
|
2023-05-29T13:31:13+00:00
|
|
968a81e369a152491c6bf2220c8dbd16f3843828
|
1. Ismerős, 2. egyéb, 3. partner
1. Igen, 2. Nem
|
petertill/communist
|
[
"region:us"
] |
2023-05-29T14:15:38+00:00
|
{}
|
2023-05-29T14:16:33+00:00
|
ce38a2349e99522e3f8e84adb65ec4a1128cf09e
|
# Dataset Card for "72f9d0dd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/72f9d0dd
|
[
"region:us"
] |
2023-05-29T14:17:19+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1331, "dataset_size": 182}}
|
2023-05-29T14:17:20+00:00
|
b7874855a9670cecb7a1eefcdd09f68344878bc5
|
# Dataset Card for "817d960a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/817d960a
|
[
"region:us"
] |
2023-05-29T14:22:35+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1338, "dataset_size": 180}}
|
2023-05-29T14:22:37+00:00
|
5c0551e9357af230b94e5b6729f83d8435e40987
|
# Dataset Card for rottentoCorpus
## 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://arxiv.org/abs/1911.12237v2
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/1911.12237v2
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger convesations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person.
The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
The created dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people
The first instance in the training set:
{'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: unique id of an example.
### Data Splits
- train: 14732
- val: 818
- test: 819
## Dataset Creation
### Curation Rationale
In paper:
> In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typ- ically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assis- tant and a client buying petrol.
As a consequence, we decided to create a chat dialogue dataset by constructing such conversa- tions that would epitomize the style of a messenger app.
### Source Data
#### Initial Data Collection and Normalization
In paper:
> We asked linguists to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. It includes chit-chats, gossiping about friends, arranging meetings, discussing politics, consulting university assignments with colleagues, etc. Therefore, this dataset does not contain any sensitive data or fragments of other corpora.
#### Who are the source language producers?
linguists
### Annotations
#### Annotation process
In paper:
> Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one ref- erence summary.
#### Who are the annotators?
language experts
### Personal and Sensitive Information
None, see above: Initial Data Collection and Normalization
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
non-commercial licence: CC BY-NC-ND 4.0
### Citation Information
```
@inproceedings{gliwa-etal-2019-samsum,
title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
author = "Gliwa, Bogdan and
Mochol, Iwona and
Biesek, Maciej and
Wawer, Aleksander",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5409",
doi = "10.18653/v1/D19-5409",
pages = "70--79"
}
```
### Contributions
Thanks to [@cccntu](https://github.com/cccntu) for adding this dataset.
|
NavidVafaei/rottentomato01
|
[
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-nd-4.0",
"conversations-summarization",
"arxiv:1911.12237",
"region:us"
] |
2023-05-29T14:24:17+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc-by-nc-nd-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "paperswithcode_id": "rottento", "pretty_name": "rottento Corpus", "tags": ["conversations-summarization"], "dataset_info": {"features": [{"name": "movie", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "reviews", "dtype": "array"}, {"name": "summary", "dtype": "string"}], "config_name": "rottento", "splits": [{"name": "train", "num_bytes": 9479141, "num_examples": 14732}, {"name": "test", "num_bytes": 534492, "num_examples": 819}, {"name": "validation", "num_bytes": 516431, "num_examples": 818}], "download_size": 2944100, "dataset_size": 10530064}, "train-eval-index": [{"config": "rottento", "task": "summarization", "task_id": "summarization", "splits": {"eval_split": "test"}, "col_mapping": {"dialogue": "text", "summary": "target"}}]}
|
2023-05-29T18:39:23+00:00
|
28dc6fc18e381c60775a728222d314b97a228ebc
|
# IBDColEpi: 140 HE and 111 CD3-stained colon biopsies of active and inactivate inflammatory bowel disease with epithelium annotated
To access and work with the data in Python, you can do so through the Python API with datasets. See this Jupyter Notebook on how to get started:
https://github.com/andreped/NoCodeSeg/blob/main/notebooks/IBDColEpi-load-dataset-example.ipynb
Note that it is also possible to download the data through the web interface at Hugging Face, but also through [this google drive](https://drive.google.com/drive/u/0/folders/1eUVs1DA1UYayUYjr8_aY3O5xDgV1uLvH)
and [this dataverseNO](https://dataverse.no/dataset.xhtml?persistentId=doi:10.18710/TLA01U) link.
--------------------
GENERAL INFORMATION
--------------------
1. Title of Dataset: 140 HE and 111 CD3-stained colon biopsies of active and inactivate inflammatory bowel disease with epithelium annotated: the IBDColEpi dataset
2. DOI: https://doi.org/10.18710/TLA01U
3. Contact Information
Name: André Pedersen
Institution: NTNU Norwegian University of Science and Technology
Email: [email protected]
ORCID: https://orcid.org/0000-0002-3637-953X
4. Contributors: See metadata field Contributor.
5. Kind of data: See metadata field Kind of Data.
6. Date of data collection/generation: See metadata field Date of Collection.
7. Geographic location: See metadata section Geographic Coverage.
8. Funding sources: See metadata section Grant Information.
9. Description of dataset:
General description and ethics approvals: The dataset contains 140 HE and 111 CD3 stained, formalin fixed paraffin embedded (FFPE) biopsies of colonic mucosa. The biopsies were extracted from the NTNU/St. Olavs hospital, Trondheim University Hospital (Norway) biobank of patients with confirmed inflammatory bowel disease or healthy controls with gastrointestinal symptoms but no macroscopic- or microscopic disease. Inclusion and colonoscopies were performed at the Department of Gastroenterology and Hepatology at St. Olavs hospital, Trondheim University Hospital from 2007 to 2018. All patients gave written informed consent and ethical approvals were obtained from the Central Norway Regional Committee for Medical and Health Research Ethics (reference number 2013/212/REKMidt). Consent to publish the anonymized whole slide image (WSI) dataset was given by REKMidt in 2021. Each database ID number used in this study was changed to new anonymized IDs only containing the information “active” or “inactive” disease and whether the WSI has haematoxylin-eosin (HE) staining or CD3 immunostaining. The biopsies included in the biobank are sampled such that one biopsy from an unaffected/inactive area and one from an area affected/active area were included from each patient and given a separate ID number. Hence, two biopsies with different ID numbers can be from the same patient. "Active" is defined as the presence of intraepithelial granulocytes in one or more location in the biopsies. Still, the changes may be focal, hence majority of the epithelium may still lack intraepithelial granulocytes or other signs of active disease (crypt abscesses, granulation tissue, etc.).
---------------------------
SHARING/ACCESS INFORMATION
---------------------------
(See metadata record for dataset.)
1. Licenses/Restrictions: See Terms section.
2. Links to publications that cite or use the data: See metadata field Related Publication.
3. Links/relationships to related data sets: See metadata field Related Datasets.
4. Data sources: See metadata field Data Sources.
5. Recommended citation: See citation generated by repository.
---------------------
DATA & FILE OVERVIEW
---------------------
1. File List:
00_README.txt
trained-models.zip
patch-dataset-CD3.zip
patch-dataset-HE.zip
qupath-project-annotations.zip
TIFF-annotations.zip
WSI_part_01.zip
WSI_part_02.zip
WSI_part_03.zip
WSI_part_04.zip
WSI_part_05.zip
WSI_part_06.zip
WSI_part_07.zip
WSI_part_08.zip
WSI_part_09.zip
WSI_part_10.zip
2. Relationship between files, if important:
- trained-models.zip: the best performing trained models (for both HE and CD3) on the images from WSI_part_*.zip using the manual delineations from TIFF-annotations.zip.
- WSI_path_*.zip: the colon biopsies described in the metadata (1-10). For each ID, the active/inactive label Y is stored in the filename, with the format: "ID-X_Y.ndpi".
- TIFF-annotations.zip: the corresponding annotations to the WSIs. The filenames of the annotations are in the same structure as the corresponding WSIs, with the format: "ID-X_Y.tiff".
- patch-dataset-*.zip: the corresponding patch images and labels, split into train/validation/test sets, relevant for the evaluation of the design in the publication. Both for HE and CD3
- qupath-project-annotations.zip: the qupath project file, also containing the annotations of all WSIs, but can be directly read in QuPath (after renaming of WSI paths).
|
andreped/IBDColEpi
|
[
"task_categories:image-segmentation",
"size_categories:1B<n<10B",
"language:en",
"license:mit",
"medical",
"region:us"
] |
2023-05-29T14:32:48+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1B<n<10B"], "task_categories": ["image-segmentation"], "pretty_name": "IBDColEpi", "tags": ["medical"]}
|
2023-11-08T22:02:54+00:00
|
731cb91e78f870af05eb6c519586e94f137a633e
|
# Dataset Card for "vivos_fake"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/vivos_fake
|
[
"region:us"
] |
2023-05-29T14:36:05+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 619675.0, "num_examples": 5}, {"name": "validation", "num_bytes": 698731.0, "num_examples": 5}], "download_size": 0, "dataset_size": 1318406.0}}
|
2023-05-29T16:08:08+00:00
|
5c9170cfe87cdd67ed90fcd585c9af6a9856ddc1
|
# Dataset Card for "cv_13_fake"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/cv_13_fake
|
[
"region:us"
] |
2023-05-29T14:36:09+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 238031.0, "num_examples": 5}, {"name": "validation", "num_bytes": 111976.0, "num_examples": 5}], "download_size": 350428, "dataset_size": 350007.0}}
|
2023-05-29T16:08:14+00:00
|
4f631c0eaa7fbaa8f96fc807620bfede1774c3f7
|
ecdr/123
|
[
"license:other",
"region:us"
] |
2023-05-29T14:51:13+00:00
|
{"license": "other"}
|
2023-05-29T15:01:44+00:00
|
|
3017dfc8f8310c353dcc3c42b1df32794c235520
|
p1atdev/zozotown
|
[
"license:cc0-1.0",
"region:us"
] |
2023-05-29T15:06:57+00:00
|
{"license": "cc0-1.0"}
|
2023-05-29T15:09:05+00:00
|
|
06ba83ee8ed840404ced829365bcbf53ad30a2bf
|
# Dataset Card for "Food101_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1000
|
[
"region:us"
] |
2023-05-29T15:33:10+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 392695, "num_examples": 1000}], "download_size": 49126, "dataset_size": 392695}}
|
2023-05-29T15:40:53+00:00
|
ce4a0172a2115cdfe62daa324dc320d27a48d913
|
# Dataset Card for "Food101_5samples_class_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/Food101_5samples_class_test
|
[
"region:us"
] |
2023-05-29T15:44:25+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "apple pie", "1": "baby back ribs", "2": "baklava", "3": "beef carpaccio", "4": "beef tartare", "5": "beet salad", "6": "beignets", "7": "bibimbap", "8": "bread pudding", "9": "breakfast burrito", "10": "bruschetta", "11": "caesar salad", "12": "cannoli", "13": "caprese salad", "14": "carrot cake", "15": "ceviche", "16": "cheesecake", "17": "cheese plate", "18": "chicken curry", "19": "chicken quesadilla", "20": "chicken wings", "21": "chocolate cake", "22": "chocolate mousse", "23": "churros", "24": "clam chowder", "25": "club sandwich", "26": "crab cakes", "27": "creme brulee", "28": "croque madame", "29": "cup cakes", "30": "deviled eggs", "31": "donuts", "32": "dumplings", "33": "edamame", "34": "eggs benedict", "35": "escargots", "36": "falafel", "37": "filet mignon", "38": "fish and chips", "39": "foie gras", "40": "french fries", "41": "french onion soup", "42": "french toast", "43": "fried calamari", "44": "fried rice", "45": "frozen yogurt", "46": "garlic bread", "47": "gnocchi", "48": "greek salad", "49": "grilled cheese sandwich", "50": "grilled salmon", "51": "guacamole", "52": "gyoza", "53": "hamburger", "54": "hot and sour soup", "55": "hot dog", "56": "huevos rancheros", "57": "hummus", "58": "ice cream", "59": "lasagna", "60": "lobster bisque", "61": "lobster roll sandwich", "62": "macaroni and cheese", "63": "macarons", "64": "miso soup", "65": "mussels", "66": "nachos", "67": "omelette", "68": "onion rings", "69": "oysters", "70": "pad thai", "71": "paella", "72": "pancakes", "73": "panna cotta", "74": "peking duck", "75": "pho", "76": "pizza", "77": "pork chop", "78": "poutine", "79": "prime rib", "80": "pulled pork sandwich", "81": "ramen", "82": "ravioli", "83": "red velvet cake", "84": "risotto", "85": "samosa", "86": "sashimi", "87": "scallops", "88": "seaweed salad", "89": "shrimp and grits", "90": "spaghetti bolognese", "91": "spaghetti carbonara", "92": "spring rolls", "93": "steak", "94": "strawberry shortcake", "95": "sushi", "96": "tacos", "97": "takoyaki", "98": "tiramisu", "99": "tuna tartare", "100": "waffles"}}}}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_food101", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 25787125.0, "num_examples": 505}], "download_size": 24766110, "dataset_size": 25787125.0}}
|
2023-05-29T15:44:33+00:00
|
f715ceff847b111382932fb33bcdd920bada326e
|
# Dataset Card for "Food101_10samples_class_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/Food101_10samples_class_test
|
[
"region:us"
] |
2023-05-29T15:44:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "apple pie", "1": "baby back ribs", "2": "baklava", "3": "beef carpaccio", "4": "beef tartare", "5": "beet salad", "6": "beignets", "7": "bibimbap", "8": "bread pudding", "9": "breakfast burrito", "10": "bruschetta", "11": "caesar salad", "12": "cannoli", "13": "caprese salad", "14": "carrot cake", "15": "ceviche", "16": "cheesecake", "17": "cheese plate", "18": "chicken curry", "19": "chicken quesadilla", "20": "chicken wings", "21": "chocolate cake", "22": "chocolate mousse", "23": "churros", "24": "clam chowder", "25": "club sandwich", "26": "crab cakes", "27": "creme brulee", "28": "croque madame", "29": "cup cakes", "30": "deviled eggs", "31": "donuts", "32": "dumplings", "33": "edamame", "34": "eggs benedict", "35": "escargots", "36": "falafel", "37": "filet mignon", "38": "fish and chips", "39": "foie gras", "40": "french fries", "41": "french onion soup", "42": "french toast", "43": "fried calamari", "44": "fried rice", "45": "frozen yogurt", "46": "garlic bread", "47": "gnocchi", "48": "greek salad", "49": "grilled cheese sandwich", "50": "grilled salmon", "51": "guacamole", "52": "gyoza", "53": "hamburger", "54": "hot and sour soup", "55": "hot dog", "56": "huevos rancheros", "57": "hummus", "58": "ice cream", "59": "lasagna", "60": "lobster bisque", "61": "lobster roll sandwich", "62": "macaroni and cheese", "63": "macarons", "64": "miso soup", "65": "mussels", "66": "nachos", "67": "omelette", "68": "onion rings", "69": "oysters", "70": "pad thai", "71": "paella", "72": "pancakes", "73": "panna cotta", "74": "peking duck", "75": "pho", "76": "pizza", "77": "pork chop", "78": "poutine", "79": "prime rib", "80": "pulled pork sandwich", "81": "ramen", "82": "ravioli", "83": "red velvet cake", "84": "risotto", "85": "samosa", "86": "sashimi", "87": "scallops", "88": "seaweed salad", "89": "shrimp and grits", "90": "spaghetti bolognese", "91": "spaghetti carbonara", "92": "spring rolls", "93": "steak", "94": "strawberry shortcake", "95": "sushi", "96": "tacos", "97": "takoyaki", "98": "tiramisu", "99": "tuna tartare", "100": "waffles"}}}}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_food101", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 52378377.61, "num_examples": 1010}], "download_size": 50351451, "dataset_size": 52378377.61}}
|
2023-05-29T15:44:58+00:00
|
a1abf4a4bc9f602b99642fe901643ad9932cadaf
|
# Dataset Card for "stable-bias_grounding-images_multimodel_3_12_22"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/stable-bias_grounding-images_multimodel_3_12_22
|
[
"region:us"
] |
2023-05-29T15:44:37+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "gender_phrase", "dtype": "string"}, {"name": "ethnicity_phrase", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "source_type", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 70960514.0, "num_examples": 2040}], "download_size": 70651732, "dataset_size": 70960514.0}}
|
2023-05-29T15:44:50+00:00
|
dbbfac34a611a5317326b5006374869266c89c77
|
# Dataset Card for "Food101_5samples_class_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_505"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_5samples_class_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_505
|
[
"region:us"
] |
2023-05-29T15:50:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 219249, "num_examples": 505}], "download_size": 47611, "dataset_size": 219249}}
|
2023-05-29T16:46:36+00:00
|
24db47e996740488f7eef2b97c2807ee941bb916
|
# Dataset Card for "Food101_10samples_class_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1010"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_10samples_class_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1010
|
[
"region:us"
] |
2023-05-29T15:59:03+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 449184, "num_examples": 1010}], "download_size": 80441, "dataset_size": 449184}}
|
2023-05-29T17:11:14+00:00
|
97c291932e6ea7e55525da23441fa0fdbefb14a8
|
# Dataset Card for "aesthetic_labeled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
megantron/aesthetic_labeled
|
[
"region:us"
] |
2023-05-29T16:09:51+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "Unnamed: 0", "dtype": "int64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 3101095.0, "num_examples": 8}], "download_size": 1553003, "dataset_size": 3101095.0}}
|
2023-05-29T16:09:56+00:00
|
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