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|---|---|---|---|---|---|---|
0e7585a7a5a09a768c0ae71562de1246248f574d
|
# Dataset Card for "voxelgym_5c_critic_42x42_50000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Cubpaw/voxelgym_5c_critic_42x42_50000
|
[
"region:us"
] |
2023-06-09T08:36:53+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "astar_path", "dtype": "image"}, {"name": "pred_path", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 17675640.0, "num_examples": 40000}, {"name": "validation", "num_bytes": 4404630.0, "num_examples": 10000}], "download_size": 14654881, "dataset_size": 22080270.0}}
|
2023-06-09T08:37:24+00:00
|
8afbfc5694f70cbb92e9cc86081ee1ad9aa6bf1c
|
# Dataset Card for "c09071c5"
[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/c09071c5
|
[
"region:us"
] |
2023-06-09T08:40:04+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1334, "dataset_size": 178}}
|
2023-06-09T08:40:05+00:00
|
2a9163d27b28f899738d3b65f17aad278f0c813d
|
Liuyu/sentiment-classification
|
[
"license:openrail",
"region:us"
] |
2023-06-09T08:41:23+00:00
|
{"license": "openrail", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 19594086, "num_examples": 120000}, {"name": "dev", "num_bytes": 483785, "num_examples": 3000}, {"name": "test", "num_bytes": 491522, "num_examples": 3000}], "download_size": 667064, "dataset_size": 20569393}}
|
2023-06-09T08:44:34+00:00
|
|
5fbbd5942385533f51c6b069b1be8ba1bac7fdb4
|
vip201/sft_zh_del
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-09T08:57:06+00:00
|
{"license": "apache-2.0"}
|
2023-06-09T09:05:13+00:00
|
|
ee9234cee0967725c79f7182c6d858d29ec0e2f1
|
[Tigerbot](https://github.com/TigerResearch/TigerBot) 开源项目中微调英文sft-en数据合集
本合集涵盖本组织下开源的其他中文sft-英文-数据集，不需要重复下载
<p align="center" width="40%">
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/sft_en')
```
## 文件细分
| 类型 | 语言 | 数据集文件 | 数量 |
| ------------ | ---- | -------------------------------------------------------------------------------------------------------------------------------- | ----------- |
| alpaca 英文 | 英文 | [tigerbot-alpaca-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-alpaca-en-50k.json) | 50k |
| 头脑风暴 | 英文 | [tigerbot-dolly-Brainstorming-en-1.7k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-dolly-Brainstorming-en-1.7k.json) | 1.7k |
| 分类 | 英文 | [tigerbot-dolly-Classification-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-dolly-Classification-en-2k.json) | 2k |
| 代码 | 英文 | [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-kaggle-leetcodesolutions-en-2k.json) | 2k |
| 食谱生成 | 英文 | [tigerbot-kaggle-recipes-en-2k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-kaggle-recipes-en-2k.json) | 2k |
| 病历生成 | 英文 | [tigerbot-mt-note-generation-en](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-mt-note-generation-en.json) | 450 |
| 多轮对话 | 英文 | [tigerbot-OIG-multichat-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-OIG-multichat-en-50k.json) | 50k |
| 综合问答 | 英文 | [tigerbot-stackexchange-qa-en-0.5m](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-stackexchange-qa-en-0.5m.json) | 0.5m |
| wiki 问答 | 英文 | [tigerbot-wiki-qa-bart-en-10k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-wiki-qa-bart-en-10k.json) | 10k |
| 如何做类教程 | 英文 | [tigerbot-youtube-howto-en-50k](https://huggingface.co/datasets/TigerResearch/sft_en/blob/main/tigerbot-youtube-howto-en-50k.json) | 50k |
|
TigerResearch/sft_en
|
[
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-06-09T08:58:56+00:00
|
{"language": ["en"], "license": "apache-2.0"}
|
2023-12-06T01:17:55+00:00
|
af0abac968c4a6011d2afc342f46599ab158e7f9
|
# Dataset Card for "d0ea767e"
[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/d0ea767e
|
[
"region:us"
] |
2023-06-09T09:09:31+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1325, "dataset_size": 180}}
|
2023-06-09T09:09:33+00:00
|
f5106630cc106a8f274dba8cdd2b2aa4e501e7ad
|
[Tigerbot](https://github.com/TigerResearch/TigerBot) 开源项目中微调中文sft-zh数据合集
本合集涵盖本组织下开源的其他中文sft-中文-数据集，不需要重复下载
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/sft_zh')
```
## 文件细分
| 类型 | 语言 | 数据集文件 | 数量
| ------------ | ---- | -------------------------------------------------------------------------------------------------------------------------------- | ----------- |
| alpaca 中文 | 中文 | [tigerbot-alpaca-zh-0.5m](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-alpaca-zh-0.5m.json) | 0.5m |
| 百科问答 | 中文 | [tigerbot-wiki-qa-1k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-wiki-qa-zh-1k.json) | 1k |
| 名著问答 | 中文 | [tigerbot-book-qa-1k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-book-qa-1k.json) | 1k |
| 猜谜语 | 中文 | [tigerbot-riddle-qa-1k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-riddle-qa-1k.json) | 1k |
| 阅读理解 | 中文 | [tigerbot-superclue-c3-zh-5k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-superclue-c3-zh-5k.json) | 5k |
| 问答 | 中文 | [tigerbot-hc3-zh-12k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-hc3-zh-12k.json) | 12k |
| 知乎问答 | 中文 | [tigerbot-zhihu-zh-10k](https://huggingface.co/datasets/TigerResearch/sft_zh/blob/main/tigerbot-zhihu-zh-10k.json) | 10k |
|
TigerResearch/sft_zh
|
[
"language:zh",
"license:apache-2.0",
"region:us"
] |
2023-06-09T09:15:22+00:00
|
{"language": ["zh"], "license": "apache-2.0"}
|
2023-06-09T11:21:42+00:00
|
b190b106f2d4214567de2ba02b2257fc893e8481
|
# Hola
|
iarejula/python-pb
|
[
"language:es",
"license:mit",
"tag1",
"tag2",
"region:us"
] |
2023-06-09T09:30:15+00:00
|
{"language": ["es"], "license": "mit", "pretty_name": "Pretty Name of the Dataset", "tags": ["tag1", "tag2"]}
|
2023-06-09T11:02:10+00:00
|
540107cfddc1872c8b267ca5c20b92beb19e5247
|
### LAMM-Dataset Directory Structure
├── 2D_Benchmark
│ ├── ai2d_images.zip
│ ├── celeba_images.zip
│ ├── cifar10_images.zip
│ ├── flickr30k_images.zip
│ ├── fsc147_images.zip
│ ├── lsp_images.zip
│ ├── sqaimage_images.zip
│ ├── svt_images.zip
│ ├── ucmerced_images.zip
│ ├── voc2012_images.zip
│ ├── meta_file
│ │ ├── Caption_flickr30k.json
│ │ ├── Classification_CIFAR10.json
│ │ ├── Counting_FSC147.json
│ │ ├── Detection_VOC2012.json
│ │ ├── Facial_Classification_CelebA(Hair).json
│ │ ├── Facial_Classification_CelebA(Smile).json
│ │ ├── Fine-grained_Classification_UCMerced.json
│ │ ├── Keypoints_Dectection_LSP.json
│ │ ├── Locating_FSC147.json
│ │ ├── Locating_LSP.json
│ │ ├── Locating_VOC2012.json
│ │ ├── OCR_SVT.json
│ │ ├── VQA_AI2D.json
│ │ └── VQA_SQAimage.json
├── 2D_Instruct
│ ├── bamboo_images.zip
│ ├── coco_images.zip
│ ├── locount_images.zip
│ ├── textvqa_images.zip
│ ├── meta_file
│ │ ├── daily_dialogue_49k.json
│ │ ├── detailed_description_49k.json
│ │ ├── factual_knowledge_dialogue_42k.json
│ │ ├── LAMM_instruct_140k.json
│ │ ├── LAMM_instruct_186k.json
│ │ ├── LAMM_instruct_98k.json
│ │ └── vision_task_dialogue_46k.json
├── 3D_Benchmark
│ ├── scannet_pcls.zip
│ ├── meta_file
│ │ ├── Detection_ScanNet.json
│ │ ├── VG_ScanRefer.json
│ │ └── VQA_ScanQA_multiplechoice.json
└── 3D_Instruct
├── 3rscan_pcls.zip
├── shapenet_pcls.zip
├── meta_file
│ └── LAMM_3dinstruct_10k.json
|
caojianjian/LAMM
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-09T09:49:41+00:00
|
{"license": "apache-2.0"}
|
2023-06-11T17:52:33+00:00
|
9d361fc327a63850caf26a694d6686b930db2c15
|
jpawan33/NC
|
[
"license:other",
"region:us"
] |
2023-06-09T09:50:24+00:00
|
{"license": "other"}
|
2023-06-09T18:35:22+00:00
|
|
346f3802ad4297a5f5eefe77baf8206229699200
|
# Dataset Card for "llm-sgd-dst8-training-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amay01/llm-sgd-dst8-training-data
|
[
"region:us"
] |
2023-06-09T09:55:54+00:00
|
{"dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 62883134, "num_examples": 175780}], "download_size": 10265723, "dataset_size": 62883134}}
|
2023-06-09T09:55:58+00:00
|
f5958b845f013ab06824fe031cb2384a1f9eb58c
|
# Dataset Card for Dataset Name
Derived from eastwind/semeval-2016-absa-reviews-arabic using Helsinki-NLP/opus-mt-tc-big-ar-en
|
eastwind/semeval-2016-absa-reviews-english-translated-stanford-alpaca
|
[
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"task_categories:question-answering",
"task_categories:text2text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
2023-06-09T10:05:14+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "zero-shot-classification", "question-answering", "text2text-generation"], "pretty_name": "SemEval 2016 Hotel Aspect Based Sentiment Analysis translated and alpaca format for LLM training"}
|
2023-06-09T10:08:27+00:00
|
a824f3828c909c46a285de8ef28d5a9747349643
|
josefloresB2B/logstest
|
[
"license:unknown",
"region:us"
] |
2023-06-09T10:21:57+00:00
|
{"license": "unknown"}
|
2023-06-09T11:48:26+00:00
|
|
29c006f5bac8726513b911051e2fd2aed25141b8
|
# Dataset Card for "llm-sgd-dst8-split-training-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amay01/llm-sgd-dst8-split-training-data
|
[
"region:us"
] |
2023-06-09T11:01:34+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61828454, "num_examples": 175780}], "download_size": 13569477, "dataset_size": 61828454}}
|
2023-06-09T11:01:39+00:00
|
b7a576693641f453388abfd5dea189dabbfbfba6
|
AminLucho/TestrunDataset
|
[
"license:other",
"region:us"
] |
2023-06-09T11:43:37+00:00
|
{"license": "other"}
|
2023-11-11T14:25:08+00:00
|
|
663b5db3c28d72014f043a51b8cbc577db0016f5
|
# Dataset Card for "ph2_vit_db"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ahishamm/ph2_vit_db
|
[
"region:us"
] |
2023-06-09T12:10:57+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "benign", "1": "malignant"}}}}], "splits": [{"name": "train", "num_bytes": 106512397.0, "num_examples": 160}, {"name": "test", "num_bytes": 26484752.0, "num_examples": 40}], "download_size": 133006435, "dataset_size": 132997149.0}}
|
2023-06-09T12:11:05+00:00
|
c4790dc6745c80f8c7a2486973da9940ce144377
|
Morteza-Shahrabi-Farahani/Detecting-toxic-comments
|
[
"license:mit",
"region:us"
] |
2023-06-09T12:12:59+00:00
|
{"license": "mit"}
|
2023-06-10T05:06:08+00:00
|
|
807ef15c160b18aea098648882d3d9919d18a6d5
|
# Dataset Card for "stanford_alpaca_new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Amirkid/stanford_alpaca_new
|
[
"region:us"
] |
2023-06-09T12:15:14+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18792104, "num_examples": 104004}], "download_size": 11482907, "dataset_size": 18792104}}
|
2023-06-09T12:15:16+00:00
|
c2939c1996893990a2cd002786eb2c482c089377
|
# Dataset Card for "llm-sgd-dst8-split-training-data-jsonl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amay01/llm-sgd-dst8-split-training-data-jsonl
|
[
"region:us"
] |
2023-06-09T12:26:16+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61828454, "num_examples": 175780}], "download_size": 13569477, "dataset_size": 61828454}}
|
2023-06-09T12:26:21+00:00
|
f0614b2b7306259a5d46b703f207dafd93874496
|
# Dataset Card for "01dafcef"
[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/01dafcef
|
[
"region:us"
] |
2023-06-09T12:35:00+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1342, "dataset_size": 186}}
|
2023-06-09T12:35:01+00:00
|
a64e6115a204d254a4d5494ac2a9b61e9460f562
|
# Dataset Card for "dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
guyhadad01/dataset
|
[
"region:us"
] |
2023-06-09T12:47:33+00:00
|
{"dataset_info": {"features": [{"name": "Column1", "dtype": "float64"}, {"name": "q_txt", "dtype": "string"}, {"name": "attr_val", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 32410, "num_examples": 287}, {"name": "test", "num_bytes": 3475, "num_examples": 32}], "download_size": 23701, "dataset_size": 35885}}
|
2023-07-18T07:45:57+00:00
|
9adb18b79a31dc48d4a5c608fc3c8f2645131fca
|
# Dataset Card for "57bbe44d"
[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/57bbe44d
|
[
"region:us"
] |
2023-06-09T13:34:12+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1330, "dataset_size": 180}}
|
2023-06-09T13:34:13+00:00
|
e90c22f7da04c6e67655a19716c3ad21c549185b
|
# Dataset Card for "new_prompt_gpt_joined_en_paragraph"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bot-yaya/new_prompt_gpt_joined_en_paragraph
|
[
"region:us"
] |
2023-06-09T13:56:05+00:00
|
{"dataset_info": {"features": [{"name": "record", "dtype": "string"}, {"name": "raw_text", "dtype": "string"}, {"name": "is_hard_linebreak", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 925969, "num_examples": 49}], "download_size": 471184, "dataset_size": 925969}}
|
2023-06-09T13:56:11+00:00
|
5c85b98f826c65beb382d68c0b186b90d4409674
|
# Dataset Card for "recipes_translation_4_helsinki_4.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
PaulineSanchez/recipes_translation_4_helsinki_4.0
|
[
"region:us"
] |
2023-06-09T13:57:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 107977, "num_examples": 399}], "download_size": 63128, "dataset_size": 107977}}
|
2023-06-09T13:57:55+00:00
|
0cdc63c196ccd54c34e89c7e91b908b7255b15ee
|
# Dataset Card for "recipes_translation_400"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
PaulineSanchez/recipes_translation_400
|
[
"region:us"
] |
2023-06-09T14:00:05+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 86327.47619047618, "num_examples": 319}, {"name": "validation", "num_bytes": 21649.52380952381, "num_examples": 80}], "download_size": 70114, "dataset_size": 107977.0}}
|
2023-06-09T14:00:11+00:00
|
e6c8009461e5a50e5a176a00879b11c9e19e3dff
|
# Dataset Card for "seahorse_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
griffin/seahorse_zeroshot
|
[
"region:us"
] |
2023-06-09T14:03:23+00:00
|
{"dataset_info": {"features": [{"name": "gem_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 115866100, "num_examples": 85114}, {"name": "validation", "num_bytes": 16905383, "num_examples": 12568}, {"name": "test", "num_bytes": 34729550, "num_examples": 25053}], "download_size": 23952107, "dataset_size": 167501033}}
|
2023-06-09T14:03:29+00:00
|
8d841d4cc5ecf41ed4edc0158878c062d9e05c5e
|
# Dataset Card for "seahorse_fewshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
griffin/seahorse_fewshot
|
[
"region:us"
] |
2023-06-09T14:03:29+00:00
|
{"dataset_info": {"features": [{"name": "gem_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 157355986, "num_examples": 84795}, {"name": "validation", "num_bytes": 23274328, "num_examples": 12513}, {"name": "test", "num_bytes": 52121809, "num_examples": 25002}], "download_size": 28900357, "dataset_size": 232752123}}
|
2023-06-09T14:03:35+00:00
|
77bf505450d9b12f26827f38478c70c732b3b20c
|
```
https://github.com/google-research-datasets/NewsQuizQA
@InProceedings{newsquiz2021,
title = {{Quiz-Style Question Generation for News Stories}},
author = {Adam D. Lelkes and Vinh Q. Tran and Cong Yu},
booktitle = {Proc. of the the Web Conf. 2021},
year = {2021}
}
```
|
tasksource/news-quizz-qa
|
[
"license:unknown",
"region:us"
] |
2023-06-09T14:05:12+00:00
|
{"license": "unknown"}
|
2023-06-09T14:05:51+00:00
|
adc8790fdbf185efea32566da855cfc116070aa5
|
# Dataset Card for Movie Genre Prediction
Link to [Movie Genre Prediction Competition](https://huggingface.co/spaces/competitions/movie-genre-prediction)
By accessing this dataset, you accept the rules of the Movie Genre Prediction competition.
# Organizer
Organizer of this competition is [Data-Driven Science](https://datadrivenscience.com/).
[Join our FREE 3-Day Object Detection Challenge!](https://datadrivenscience.com/free-object-detection-challenge/)
<img src="https://datadrivenscience.com/wp-content/uploads/2022/12/DDS-Logo.png" width="200" height="100">
# Email Usage
By accessing this dataset, you consent that your email will be used for communication purposes from Data-Driven Science.
We do not share nor sell our mailing list. Your information remains confidential. You may unsubscribe at any time.
|
datadrivenscience/movie-genre-prediction
|
[
"region:us"
] |
2023-06-09T14:08:49+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "movie_name", "dtype": "string"}, {"name": "synopsis", "dtype": "string"}, {"name": "genre", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10488729, "num_examples": 54000}, {"name": "test", "num_bytes": 6965864, "num_examples": 36000}], "download_size": 11902232, "dataset_size": 17454593}}
|
2023-06-11T09:12:57+00:00
|
1a4c47c85825bd3003baf04d03c17e7ef0759a98
|
maneshkarun/median3k_10000s
|
[
"license:mit",
"region:us"
] |
2023-06-09T14:18:51+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "hyperpartisan", "dtype": "bool"}, {"name": "url", "dtype": "string"}, {"name": "published_at", "dtype": "string"}, {"name": "bias", "dtype": {"class_label": {"names": {"0": "right", "1": "right-center", "2": "least", "3": "left-center", "4": "left"}}}}, {"name": "word_count", "dtype": "int64"}, {"name": "cleaned_data", "dtype": "string"}, {"name": "pos_tagged", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 732080426.0, "num_examples": 10000}], "download_size": 355605083, "dataset_size": 732080426.0}}
|
2023-06-09T19:16:19+00:00
|
|
591e23ec8839ade63c7ebdc870d99f10e3d554f2
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xxl_mode_CM_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_CM_Q_rices_ns_5046
|
[
"region:us"
] |
2023-06-09T14:42:04+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_clean_", "num_bytes": 27648795, "num_examples": 5046}], "download_size": 5165890, "dataset_size": 27648795}}
|
2023-06-09T14:42:09+00:00
|
0444eb6623bf1c905d035ebc22f568cef2790c2a
|
# Dataset Card for Openvalidators dataset
## Dataset Description
- **Repository:** https://github.com/opentensor/validators
- **Homepage:** https://bittensor.com/
### Dataset Summary
The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated by the [OpenValidators](https://github.com/opentensor/validators) project in [W&B](https://wandb.ai/opentensor-dev/openvalidators/table). It contains hundreds of thousands of records and serves researchers, data scientists, and miners in the Bittensor network. The dataset provides information on network performance, node behaviors, and wandb run details. Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis. Miners can use the generated data to fine-tune their models and enhance their incentives in the network. The dataset's continuous updates support collaboration and innovation in decentralized computing.
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale.
The OpenValidators dataset gives you the granularity of extracting data by ************run_id************, by ************************************OpenValidators version************************************ and by ******************************************************************multiple OpenValidators versions.****************************************************************** The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
**Downloading by run id**
For example, to download the data for a specific run, simply specify the corresponding ********************************************OpenValidators version******************************************** and the ************************wandb run id************************ in the format `version/raw_data/run_id.parquet`:
```python
from datasets import load_dataset
version = '1.0.4' # OpenValidators version
run_id = '0plco3n0' # WandB run id
run_id_dataset = load_dataset('opentensor/openvalidators-test', data_files=f'{version}/raw_data/{run_id}.parquet')
```
_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._
**Downloading by OpenValidators version**
One can also leverage the `datasets` library to download all the runs within a determined ****************************OpenValidators**************************** version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific ****************************OpenValidators**************************** version state.
```python
from datasets import load_dataset
version = '1.0.4' # Openvalidators version
version_dataset = load_dataset('opentensor/openvalidators-test', data_files=f'{version}/raw_data/*')
```
**Downloading by multiple OpenValidators version**
Utilizing the `datasets` library, users can efficiently download runs from multiple **OpenValidators** versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.
```python
from datasets import load_dataset
versions = ['1.0.0', '1.0.1', '1.0.2', '1.0.4'] # Desired versions for extraction
data_files = [f'{version}/raw_data/*' for version in versions] # Set data files directories
dataset = load_dataset('opentensor/openvalidators-test', data_files={ 'test': data_files })
```
**Analyzing metadata**
All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state.
```python
import pandas as pd
version = '1.0.4' # OpenValidators version for metadata analysis
df = pd.read_csv(f'hf://datasets/opentensor/openvalidators-test/{version}/metadata.csv')
```
## Dataset Structure
### Data Instances
**versioned raw_data**
The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at `version/raw_data` where each file is a wandb run.
**metadata**
This dataset defines the current state of the wandb data ingestion by **run id**.
### Data Fields
**Raw data**
The versioned raw_data collected from W&B follows the following schema:
- `_runtime`: (float64) Runtime of the event
- `_step`: (int64) Step of the event
- `_timestamp`: (float64) Timestamp of the event
- `answer_completions`: (list(string)) Completions of the answer_prompt
- `answer_prompt`: (string) Prompt used to generate the answer
- `answer_rewards`: (list(float64)) Rewards of the answer responses
- `answer_times`: (list(float64)) Elapsed time of answer responses
- `answer_uids`: (list(int32)) UIDs of nodes that answered the answer_prompt
- `base_prompt`: (string) Bootstrap prompt
- `best_answer`: (string) Best answer response
- `best_followup`: (string) Best followup response
- `block`: (float64) Subtensor current block
- `followup_completions`: (list(string)) Completions of the base_prompt
- `followup_rewards`: (list(float64)) Rewards of the followup responses
- `followup_times`: (list(float64)) Ellapsed time of followup responses
- `followup_uids`: (list(int64)) UIDs of nodes that answered the base_prompt
- `gating_loss`: (float64) Gating model loss
- `gating_scorings`: (list(float64)) Gating model scores
- `moving_averaged_scores`: (list(float64)) Moving averaged scores at the time of the event
- `set_weights`: (list(list(float64))) Processed weights of nodes by uid
- `step_length`: (float64) Time difference from beginning of forward call to event logging
**Metadata**
- `run_id`: (string) Wandb Run Id
- `completed`: (boolean) Flag indicating if the run_id is completed (finished, crashed or killed)
- `downloaded`: (boolean) Flag indicating if the run_id data has been downloaded
- `last_checkpoint`: (string) Last checkpoint of the run_id
- `hotkey`: (string) Hotkey associated with the run_id
- `openvalidators_version`: (string) Version of OpenValidators associated with the run_id
- `problematic`: (boolean) Flag indicating if the run_id data had problems to be ingested
- `problematic_reason`: (string) Reason for the run_id being problematic (Exception message)
- `wandb_json_config`: (string) JSON configuration associated with the run_id in Wandb
- `wandb_run_name`: (string) Name of the Wandb run
- `wandb_user_info`: (string) Username information associated with the Wandb run
- `wandb_tags`: (list) List of tags associated with the Wandb run
- `wandb_createdAt`: (string) Timestamp of the run creation in Wandb
## Dataset Creation
### Curation Rationale
This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network.
The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining.
### Source Data
#### Initial Data Collection and Normalization
The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a `metadata.csv` file is included to manage the collection state, while the raw data of each run is saved in the `.parquet` format with the file name corresponding to the run ID (e.g., `run_id.parquet`). Please note that the code for this data collection process will be released for transparency and reproducibility.
#### Who are the source language producers?
The language producers for this dataset are all the openvalidators that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at https://wandb.ai/opentensor-dev/openvalidators/table.
### Licensing Information
The dataset is licensed under the [MIT License](https://github.com/opentensor/validators/blob/main/LICENSE)
### Supported Tasks and Leaderboards
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
opentensor/openvalidators-test
|
[
"size_categories:1M<n<10M",
"license:mit",
"region:us"
] |
2023-06-09T14:42:16+00:00
|
{"license": "mit", "size_categories": ["1M<n<10M"], "viewer": false}
|
2023-06-20T13:21:16+00:00
|
7104df5cfa2f0cf062aaa63eccfa071d1b65b7a7
|
# Dataset Card for "VisDial"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HuggingFaceM4/VisDial
|
[
"region:us"
] |
2023-06-09T15:04:34+00:00
|
{"dataset_info": {"features": [{"name": "caption", "dtype": "string"}, {"name": "dialog", "sequence": {"sequence": "string"}}, {"name": "image_path", "dtype": "string"}, {"name": "global_image_id", "dtype": "string"}, {"name": "anns_id", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 20164224439.724, "num_examples": 123287}, {"name": "validation", "num_bytes": 337279788.664, "num_examples": 2064}, {"name": "test", "num_bytes": 1300162874.0, "num_examples": 8000}], "download_size": 21748651305, "dataset_size": 21801667102.388}}
|
2023-06-09T15:35:26+00:00
|
614efad48f4614f67ec492f40f4242f4e10226af
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xl_mode_CM_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_xl_mode_CM_Q_rices_ns_5046
|
[
"region:us"
] |
2023-06-09T15:20:22+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_clean_", "num_bytes": 27657536, "num_examples": 5046}], "download_size": 5163191, "dataset_size": 27657536}}
|
2023-06-09T15:20:27+00:00
|
8cdc55e0d96232d76874912db1c81c81e4f4be7f
|
# Dataset Card for "if-that-works"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amay01/if-that-works
|
[
"region:us"
] |
2023-06-09T15:32:05+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61828454, "num_examples": 175780}], "download_size": 13569477, "dataset_size": 61828454}}
|
2023-06-09T15:32:09+00:00
|
9f6ae2d5d2970eb966f92834c0337e29799abecc
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xl_mode_D_PNP_GENERIC_CM_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_xl_mode_D_PNP_GENERIC_CM_Q_rices_ns_5046
|
[
"region:us"
] |
2023-06-09T16:01:06+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__", "num_bytes": 61684930, "num_examples": 5046}], "download_size": 8861538, "dataset_size": 61684930}}
|
2023-06-09T16:01:11+00:00
|
b29bad57cf76258457cf3c13d2ec7bfa36fe9b92
|
CFGalaxy/SD_Lora_Classification_Images
|
[
"license:openrail",
"region:us"
] |
2023-06-09T16:07:14+00:00
|
{"license": "openrail"}
|
2023-06-09T16:39:04+00:00
|
|
f43f1ca27f19b9676d619711808539bab7d88bfe
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xxl_mode_D_PNP_GENERIC_CM_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_D_PNP_GENERIC_CM_Q_rices_ns_5046
|
[
"region:us"
] |
2023-06-09T16:11:54+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__", "num_bytes": 68072278, "num_examples": 5046}], "download_size": 11846803, "dataset_size": 68072278}}
|
2023-06-09T16:42:51+00:00
|
f04d74d01f3cd50c5a8b8063629f473fba6e75d6
|
Smaller version of https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings that includes Cohere as well as OpenAI embeddings (`text-embedding-ada-002`)
100k version of this dataset will be released soon.
|
KShivendu/wikipedia-1k-cohere-openai-embeddings
|
[
"language:en",
"license:mit",
"openai",
"cohere",
"wikipedia",
"region:us"
] |
2023-06-09T16:21:14+00:00
|
{"language": "en", "license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "wiki_id", "dtype": "int32"}, {"name": "views", "dtype": "float32"}, {"name": "paragraph_id", "dtype": "int32"}, {"name": "langs", "dtype": "int32"}, {"name": "cohere", "sequence": "float32"}, {"name": "openai", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 15850870, "num_examples": 1000}], "download_size": 13208079, "dataset_size": 15850870}, "tags": ["openai", "cohere", "wikipedia"]}
|
2023-07-20T20:19:35+00:00
|
35dbcc431b5742f33bb1d08010d03dbfc46876cc
|
# Dataset Card for "c85896f0"
[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/c85896f0
|
[
"region:us"
] |
2023-06-09T16:38:32+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1314, "dataset_size": 178}}
|
2023-06-09T16:38:33+00:00
|
3b0501bf0f8550824dcb001c9cf428edc2f67a00
|
# Dataset Card for "amazon_us_reviews"
## 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://s3.amazonaws.com/amazon-reviews-pds/readme.html](https://s3.amazonaws.com/amazon-reviews-pds/readme.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 32377.29 MB
- **Size of the generated dataset:** 82820.19 MB
- **Total amount of disk used:** 115197.49 MB
### Dataset Summary
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.
Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).
Each Dataset contains the following columns :
marketplace - 2 letter country code of the marketplace where the review was written.
customer_id - Random identifier that can be used to aggregate reviews written by a single author.
review_id - The unique ID of the review.
product_id - The unique Product ID the review pertains to. In the multilingual dataset the reviews
for the same product in different countries can be grouped by the same product_id.
product_parent - Random identifier that can be used to aggregate reviews for the same product.
product_title - Title of the product.
product_category - Broad product category that can be used to group reviews
(also used to group the dataset into coherent parts).
star_rating - The 1-5 star rating of the review.
helpful_votes - Number of helpful votes.
total_votes - Number of total votes the review received.
vine - Review was written as part of the Vine program.
verified_purchase - The review is on a verified purchase.
review_headline - The title of the review.
review_body - The review text.
review_date - The date the review was written.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### Apparel_v1_00
- **Size of downloaded dataset files:** 648.64 MB
- **Size of the generated dataset:** 2254.36 MB
- **Total amount of disk used:** 2903.00 MB
An example of 'train' looks as follows.
```
{
"customer_id": "45223824",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Apparel",
"product_id": "B016PUU3VO",
"product_parent": "893588059",
"product_title": "Fruit of the Loom Boys' A-Shirt (Pack of 4)",
"review_body": "I ordered the same size as I ordered last time, and these shirts were much larger than the previous order. They were also about 6 inches longer. It was like they sent men's shirts instead of boys' shirts. I'll be returning these...",
"review_date": "2015-01-01",
"review_headline": "Sizes not correct, too big overall and WAY too long",
"review_id": "R1N3Z13931J3O9",
"star_rating": 2,
"total_votes": 0,
"verified_purchase": 1,
"vine": 0
}
```
#### Automotive_v1_00
- **Size of downloaded dataset files:** 582.15 MB
- **Size of the generated dataset:** 1518.88 MB
- **Total amount of disk used:** 2101.03 MB
An example of 'train' looks as follows.
```
{
"customer_id": "16825098",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Automotive",
"product_id": "B000E4PCGE",
"product_parent": "694793259",
"product_title": "00-03 NISSAN SENTRA MIRROR RH (PASSENGER SIDE), Power, Non-Heated (2000 00 2001 01 2002 02 2003 03) NS35ER 963015M000",
"review_body": "Product was as described, new and a great look. Only bad thing is that one of the screws was stripped so I couldn't tighten all three.",
"review_date": "2015-08-31",
"review_headline": "new and a great look. Only bad thing is that one of ...",
"review_id": "R2RUIDUMDKG7P",
"star_rating": 3,
"total_votes": 0,
"verified_purchase": 1,
"vine": 0
}
```
#### Baby_v1_00
- **Size of downloaded dataset files:** 357.40 MB
- **Size of the generated dataset:** 956.30 MB
- **Total amount of disk used:** 1313.70 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"customer_id": "23299101",
"helpful_votes": 2,
"marketplace": "US",
"product_category": "Baby",
"product_id": "B00SN6F9NG",
"product_parent": "3470998",
"product_title": "Rhoost Nail Clipper for Baby - Ergonomically Designed and Easy to Use Baby Nail Clipper, Natural Wooden Bamboo - Baby Health and Personal Care Kits",
"review_body": "\"This is an absolute MUST item to have! I was scared to death to clip my baby's nails. I tried other baby nail clippers and th...",
"review_date": "2015-08-31",
"review_headline": "If fits so comfortably in my hand and I feel like I have ...",
"review_id": "R2DRL5NRODVQ3Z",
"star_rating": 5,
"total_votes": 2,
"verified_purchase": 1,
"vine": 0
}
```
#### Beauty_v1_00
- **Size of downloaded dataset files:** 914.08 MB
- **Size of the generated dataset:** 2397.39 MB
- **Total amount of disk used:** 3311.47 MB
An example of 'train' looks as follows.
```
{
"customer_id": "24655453",
"helpful_votes": 1,
"marketplace": "US",
"product_category": "Beauty",
"product_id": "B00SAQ9DZY",
"product_parent": "292127037",
"product_title": "12 New, High Quality, Amber 2 ml (5/8 Dram) Glass Bottles, with Orifice Reducer and Black Cap.",
"review_body": "These are great for small mixtures for EO's, especially for traveling. I only gave this 4 stars because of the orifice reducer. The hole is so small it is hard to get the oil out. Just needs to be slightly bigger.",
"review_date": "2015-08-31",
"review_headline": "Good Product",
"review_id": "R2A30ALEGLMCGN",
"star_rating": 4,
"total_votes": 1,
"verified_purchase": 1,
"vine": 0
}
```
#### Books_v1_00
- **Size of downloaded dataset files:** 2740.34 MB
- **Size of the generated dataset:** 7193.86 MB
- **Total amount of disk used:** 9934.20 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"customer_id": "49735028",
"helpful_votes": 0,
"marketplace": "US",
"product_category": "Books",
"product_id": "0664254969",
"product_parent": "248307276",
"product_title": "Presbyterian Creeds: A Guide to the Book of Confessions",
"review_body": "\"The Presbyterian Book of Confessions contains multiple Creeds for use by the denomination. This guidebook helps he lay person t...",
"review_date": "2015-08-31",
"review_headline": "The Presbyterian Book of Confessions contains multiple Creeds for use ...",
"review_id": "R2G519UREHRO8M",
"star_rating": 3,
"total_votes": 1,
"verified_purchase": 1,
"vine": 0
}
```
### Data Fields
The data fields are the same among all splits.
#### Apparel_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Automotive_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Baby_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Beauty_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
#### Books_v1_00
- `marketplace`: a `string` feature.
- `customer_id`: a `string` feature.
- `review_id`: a `string` feature.
- `product_id`: a `string` feature.
- `product_parent`: a `string` feature.
- `product_title`: a `string` feature.
- `product_category`: a `string` feature.
- `star_rating`: a `int32` feature.
- `helpful_votes`: a `int32` feature.
- `total_votes`: a `int32` feature.
- `vine`: a classification label, with possible values including `Y` (0), `N` (1).
- `verified_purchase`: a classification label, with possible values including `Y` (0), `N` (1).
- `review_headline`: a `string` feature.
- `review_body`: a `string` feature.
- `review_date`: a `string` feature.
### Data Splits
| name | train |
|----------------|-------:|
|Apparel_v1_00 | 5906333|
|Automotive_v1_00 | 3514942|
|Baby_v1_00 | 1752932|
|Beauty_v1_00 | 5115666|
|Books_v1_00 | 10319090|
|Books_v1_01 | 6106719|
|Books_v1_02 | 3105520|
|Camera_v1_00 | 1801974|
|Digital_Ebook_Purchase_v1_00 | 12520722|
|Digital_Ebook_Purchase_v1_01 | 5101693|
|Digital_Music_Purchase_v1_00 | 1688884|
|Digital_Software_v1_00 | 102084|
|Digital_Video_Download_v1_00 | 4057147|
|Digital_Video_Games_v1_00 | 145431|
|Electronics_v1_00 | 3093869|
|Furniture_v1_00 | 792113|
|Gift_Card_v1_00 | 149086|
|Grocery_v1_00 | 2402458|
|Health_Personal_Care_v1_00 | 5331449|
|Home_Entertainment_v1_00 | 705889|
|Home_Improvement_v1_00 | 2634781|
|Home_v1_00 | 6221559|
|Jewelry_v1_00 | 1767753|
|Kitchen_v1_00 | 4880466|
|Lawn_and_Garden_v1_00 | 2557288|
|Luggage_v1_00 | 348657|
|Major_Appliances_v1_00 | 96901|
|Mobile_Apps_v1_00 | 5033376|
|Mobile_Electronics_v1_00 | 104975|
|Music_v1_00 | 4751577|
|Musical_Instruments_v1_00 | 904765|
|Office_Products_v1_00 | 2642434|
|Outdoors_v1_00 | 2302401|
|PC_v1_00 | 6908554|
|Personal_Care_Appliances_v1_00 | 85981|
|Pet_Products_v1_00 | 2643619|
|Shoes_v1_00 | 4366916|
|Software_v1_00 | 341931|
|Sports_v1_00 | 4850360|
|Tools_v1_00 | 1741100|
|Toys_v1_00 | 4864249|
|Video_DVD_v1_00 | 5069140|
|Video_Games_v1_00 | 1785997|
|Video_v1_00 | 380604|
|Watches_v1_00 | 960872|
|Wireless_v1_00 | 9002021|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
https://s3.amazonaws.com/amazon-reviews-pds/LICENSE.txt
By accessing the Amazon Customer Reviews Library ("Reviews Library"), you agree that the
Reviews Library is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088)
and you agree to be bound by them, with the following additional conditions:
In addition to the license rights granted under the Conditions of Use,
Amazon or its content providers grant you a limited, non-exclusive, non-transferable,
non-sublicensable, revocable license to access and use the Reviews Library
for purposes of academic research.
You may not resell, republish, or make any commercial use of the Reviews Library
or its contents, including use of the Reviews Library for commercial research,
such as research related to a funding or consultancy contract, internship, or
other relationship in which the results are provided for a fee or delivered
to a for-profit organization. You may not (a) link or associate content
in the Reviews Library with any personal information (including Amazon customer accounts),
or (b) attempt to determine the identity of the author of any content in the
Reviews Library.
If you violate any of the foregoing conditions, your license to access and use the
Reviews Library will automatically terminate without prejudice to any of the
other rights or remedies Amazon may have.
### Citation Information
No citation information.
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
|
polinaeterna/amazon_us_reviews
|
[
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:topic-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] |
2023-06-09T16:56:16+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["100M<n<1B"], "source_datasets": ["original"], "task_categories": ["summarization", "text-generation", "fill-mask", "text-classification"], "task_ids": ["text-scoring", "language-modeling", "masked-language-modeling", "sentiment-classification", "sentiment-scoring", "topic-classification"], "pretty_name": "Amazon US Reviews", "dataset_info": [{"config_name": "Books_v1_01", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6997552259, "num_examples": 6106719}], "download_size": 2692708591, "dataset_size": 6997552259}, {"config_name": "Watches_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 458976082, "num_examples": 960872}], "download_size": 162973819, "dataset_size": 458976082}, {"config_name": "Personal_Care_Appliances_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49036547, "num_examples": 85981}], "download_size": 17634794, "dataset_size": 49036547}, {"config_name": "Mobile_Electronics_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 63293377, "num_examples": 104975}], "download_size": 22870508, "dataset_size": 63293377}, {"config_name": "Digital_Video_Games_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 80176851, "num_examples": 145431}], "download_size": 27442648, "dataset_size": 80176851}, {"config_name": "Digital_Software_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": 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"product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1370685534, "num_examples": 2642434}], "download_size": 512323500, "dataset_size": 1370685534}, {"config_name": "Electronics_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": 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"dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1627857158, "num_examples": 5033376}], "download_size": 557959415, "dataset_size": 1627857158}, {"config_name": "Shoes_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1781283508, "num_examples": 4366916}], "download_size": 642255314, "dataset_size": 1781283508}, {"config_name": "Toys_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2197820069, "num_examples": 4864249}], "download_size": 838451398, "dataset_size": 2197820069}, {"config_name": "Sports_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2241349145, "num_examples": 4850360}], "download_size": 872478735, "dataset_size": 2241349145}, {"config_name": "Kitchen_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2453735305, "num_examples": 4880466}], "download_size": 930744854, "dataset_size": 2453735305}, {"config_name": "Beauty_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2399292506, "num_examples": 5115666}], "download_size": 914070021, "dataset_size": 2399292506}, {"config_name": "Music_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3900138839, "num_examples": 4751577}], "download_size": 1521994296, "dataset_size": 3900138839}, {"config_name": "Health_Personal_Care_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2679427491, "num_examples": 5331449}], "download_size": 1011180212, "dataset_size": 2679427491}, {"config_name": "Digital_Ebook_Purchase_v1_01", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3470453859, "num_examples": 5101693}], "download_size": 1294879074, "dataset_size": 3470453859}, {"config_name": "Home_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2796680249, "num_examples": 6221559}], "download_size": 1081002012, "dataset_size": 2796680249}, {"config_name": "Wireless_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4633213433, "num_examples": 9002021}], "download_size": 1704713674, "dataset_size": 4633213433}, {"config_name": "Books_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7197687124, "num_examples": 10319090}], "download_size": 2740337188, "dataset_size": 7197687124}, {"config_name": "Digital_Ebook_Purchase_v1_00", "features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7302303804, "num_examples": 12520722}], "download_size": 2689739299, "dataset_size": 7302303804}], "duplicated_from": "amazon_us_reviews"}
|
2023-06-09T16:56:17+00:00
|
dfe01761f33df8716039d00d3cf2038f1bc8c99e
|
# Dataset Card for "LLM-base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Weni/LLM-base-1.0.1
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:pt",
"region:us"
] |
2023-06-09T17:21:54+00:00
|
{"language": ["pt"], "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "LLM_Base_QnA", "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "resposta", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "correct_ans", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16070410, "num_examples": 26367}], "download_size": 8058887, "dataset_size": 16070410}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-12-28T20:28:17+00:00
|
cb25998cfe15f7f4478312883e16983a9286e3e4
|
Collection of wing images for conservation of honey bees (Apis mellifera) biodiversity in Europe
https://zenodo.org/record/7244070
Small version (10%) of the original dataset bee-wings-large
|
smaciu/bee-wings-small
|
[
"task_categories:feature-extraction",
"size_categories:1K<n<10K",
"license:afl-3.0",
"region:us"
] |
2023-06-09T18:08:58+00:00
|
{"license": "afl-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["feature-extraction"], "pretty_name": "Collection of wing images for conservation of honey bees (Apis mellifera) biodiversity in Europe"}
|
2023-06-25T15:52:35+00:00
|
8d5930f344f87519051a5be38c0ff29b226793c2
|
# Dataset Card for "wit_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
theodor1289/wit_tiny
|
[
"region:us"
] |
2023-06-09T18:21:36+00:00
|
{"dataset_info": {"features": [{"name": "image_url", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "context_page_description", "dtype": "string"}, {"name": "context_section_description", "dtype": "string"}, {"name": "caption_alt_text_description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 73247697.0, "num_examples": 882}, {"name": "test", "num_bytes": 8588991.0, "num_examples": 99}], "download_size": 81145983, "dataset_size": 81836688.0}}
|
2023-06-09T18:21:44+00:00
|
5132825c5402c435acc9055204929ae7d010c952
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hf-internal-testing/instructpix2pix-10-samples
|
[
"region:us"
] |
2023-06-09T18:21:40+00:00
|
{"dataset_info": {"features": [{"name": "input_image", "dtype": "image"}, {"name": "edited_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4479546.0, "num_examples": 10}], "download_size": 4481212, "dataset_size": 4479546.0}}
|
2023-06-09T18:57:18+00:00
|
86ef525c49ee951e3f19ab80d861e533a99b5e81
|
HugNetw0rk/Ucenie02_Embedding
|
[
"license:other",
"region:us"
] |
2023-06-09T18:23:30+00:00
|
{"license": "other"}
|
2023-06-09T18:24:39+00:00
|
|
94b01987335fef5f444448c5bad18961a91b3604
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
williamberman/test
|
[
"region:us"
] |
2023-06-09T18:48:27+00:00
|
{"dataset_info": {"features": [{"name": "input_image", "dtype": "image"}, {"name": "edited_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4479546.0, "num_examples": 10}], "download_size": 4481212, "dataset_size": 4479546.0}}
|
2023-06-09T18:55:03+00:00
|
f46609b2640524f23b64b1b0df422054fe1636ed
|
Dataset Card for MIMIC
Motivation
1. For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.
The contributions of our dataset to the vision community are listed below: (1) We release a pretraining dataset of 3.1M image pairs from diverse sets of videos, 3D scans, street views, and … for downstream dense prediction tasks. (2) The dataset can be scaled quickly because of the proposed data curation strategy. This strategy doesn't need any annotation except the images themselves.
2. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?
Authors:Kalyani Marathe1,2∗ Mahtab Bigverdi1,2∗ Nishat Khan1 Tuhin Kundu Aniruddha Kembhavi2 Linda G. Shapiro1 Ranjay Krishna1,2
1University of Washington, 2Allen Institute for Artificial Intelligence
3. Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.
This research is sponsored by a grant from Amazon Technologies, Inc., as part of the Amazon-UW Science HUB.
4. Any other comments? No.
Composition
1. What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.
All of the instances in the dataset are images. Common themes of the images include street locations, objects, indoor scenes, and frozen people from the Mannequin challenge. From each scene or object, pairs of images have at least 50% co-visibility.
2. How many instances are there in total (of each type, if appropriate)?
There are 3.1 million image pairs. (6.2 images in total )
3. Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).
The dataset contains all instances licensed. We sampled our image pairs from publicly licensed datasets.
4. What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.
Instances are images resized to 224 x 224.
5. Is there a label or target associated with each instance? If so, please provide a description.
There are no labels associated with each instance. However, we provide a dictionary of matching patches of each pair with that pair of images.
6. Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable).
No, it isn't.
7. Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit. No, there are no known relationships between instances in the dataset.
Yes, in the csv file with all file paths, there is a metadata column that shows which original dataset is this image pair from. Also, each folder name in each dataset shows the name of the video/3d scene which subfolders with image pairs are created from.
8. Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.
Matching patches and overlap measurements are noisy because of the approximate matching algorithm, but this noise level in a large dataset is okay for pretraining.
9. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.
The dataset is self-contained.
10. Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.
No.
11. Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?
No, we are using all released public datasets.
12. Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.
The dataset has people engaging in the Mannequin challenge from all subpopulations.
13. Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.
Yes, we collected images of people engaging in the mannequin challenge from a public dataset.
14. Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals race or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description.
This dataset does not blur People’s faces, so it may reveal race. However, images with people were collected from a publicly available Mannequin dataset.
15. Any other comments? No.
Collection Process
1. How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If the data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how.
It is indirectly inferred/derived from other public datasets like videos and 3D scans.
2. What mechanisms or procedures were used to collect the data (e.g., hardware apparatuses or sensors, manual human curation, software programs, software APIs)? How were these mechanisms or procedures validated?
The images in the dataset are extracted from publicly licensed datasets.
3. If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)?
We sampled pairs of images from different scenes (videos/3D scans) of public datasets with the condition of having 50 to 75% co-visibility.
4. Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?
The authors only.
5. Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.
The public imaging datasets that we used vary in their date taken over a wide range of years up to 2022.
6. Were any ethical review processes conducted (e.g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation. If the dataset does not relate to people, you may skip the remaining questions in this section.
No, Some pairs of images have people which come from the Mannequin which is a licensed public dataset.
7. Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?
Details of the images with people are in the public Mannequin dataset from Google.
.8. Were the individuals in question notified about the data collection? If so, please describe (or show with screenshots or other information) how notice was provided, and provide a link or other access point to, or otherwise reproduce, the exact language of the notification itself.
Details of the images with people are in the public Mannequin dataset from Google
9. Did the individuals in question consent to the collection and use of their data? If so, please describe (or show with screenshots or other information) how consent was requested and provided, and provide a link or other access point to, or otherwise reproduce, the exact language to which the individuals consented.
Details of the images with people are in the public Mannequin dataset from Google.
10. If consent was obtained, were the consenting individuals provided with a mechanism to revoke their consent in the future or for certain uses? If so, please provide a description, as well as a link or other access point to the mechanism (if appropriate).
Details of the images with people are in the public Mannequin dataset from Google.
11. Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation.
In this dataset, we are using available public datasets and gathering image pairs with a certain amount of co-visibility.
12. Any other comments? No.
Preprocessing / Cleaning / Labeling
1. Was any preprocessing / cleaning / labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remaining questions in this section.
We used sift keypoint detection and homography translation to define the co-visibility metric between image pairs and accepted/discarded pairs of images extracted from the available public datasets based on this. At the end, we resized all images to 224x224.
2. Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the “raw” data. No
. 3. Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.
We used an algorithm provided in our project’s GitHub repo.(https://github.com/RAIVNLab/MIMIC/)
4. Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a dataset consumer might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other risks or harms (e.g., legal risks, financial harms)? If so, please provide a description. Is there anything a dataset consumer could do to mitigate these risks or harms? No.
. 5. Are there tasks for which the dataset should not be used? If so, please provide a description.No.
6. Any other comments? No.
Distribution
1. Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description.
The dataset will be available for the research community.
2. How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)?
The dataset is available at https://github.com/RAIVNLab/MIMIC/.
3. When will the dataset be distributed? The dataset is released now.
4. Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.
Yes. The license agreement and terms of use for the dataset can be found at
https://github.com/RAIVNLab/MIMIC/blob/main/LICENSE.
5. Have any third parties imposed IP-based or other restrictions on the data associated with the instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions. No
6. Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. No
7. Any other comments? No.
Maintenance
1. Who will be supporting/hosting/maintaining the dataset? The dataset will be hosted at HuggingFace.
2. How can the owner/curator/manager of the dataset be contacted (e.g., email address)? Please email [email protected]
. 3. Is there an erratum? If so, please provide a link or other access point. No.
4. Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to dataset consumers (e.g., mailing list, 26 GitHub)? We might add pairs and wont remove any.
5. If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. There are no limits on data retention.
6. Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers. Yes, we will keep csv files of the paths of data for all versions.
7. If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to dataset consumers? If so, please provide a description.
Yes, with the algorithm provided, everyone can generate image pairs with a desired co-visibility from different data sources.
8. Any other comments? No
|
MahtabBg/MIMIC
|
[
"region:us"
] |
2023-06-09T18:57:55+00:00
|
{}
|
2023-08-09T18:20:27+00:00
|
849136f47fc7deba8ba7c9d43c24565dc2c564dd
|
Joe02/Fuku_Naoto_refs
|
[
"license:other",
"region:us"
] |
2023-06-09T19:04:31+00:00
|
{"license": "other"}
|
2023-06-09T19:43:06+00:00
|
|
b7074f35669388387012538e868ec1ac4dc6cc2e
|
# Dataset Card for MultiRC_TH
### Dataset Description
This dataset is Thai translated version of [multirc](https://huggingface.co/datasets/super_glue/viewer/multirc) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
|
Patt/MultiRC_TH
|
[
"task_categories:text-classification",
"language:en",
"language:th",
"license:cc-by-sa-4.0",
"arxiv:1907.04307",
"region:us"
] |
2023-06-09T19:10:29+00:00
|
{"language": ["en", "th"], "license": "cc-by-sa-4.0", "task_categories": ["text-classification"]}
|
2024-01-15T17:28:42+00:00
|
2145a7731366f87129170954ab1e3383e52dbf46
|
# Dataset Card for "zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Weni/zeroshot
|
[
"region:us"
] |
2023-06-09T19:46:28+00:00
|
{"dataset_info": {"features": [{"name": "source_text", "dtype": "string"}, {"name": "target_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1231981.6500505707, "num_examples": 15000}, {"name": "validation", "num_bytes": 410660.5500168569, "num_examples": 5000}, {"name": "test", "num_bytes": 62666.799932572365, "num_examples": 763}], "download_size": 892342, "dataset_size": 1705309.0}}
|
2023-06-09T20:20:29+00:00
|
b68edb91014eb27213d0f2dfb7550c7ab82294f2
|
# Dataset Card for "OSCAR-2109"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vietgpt-archive/OSCAR-2109
|
[
"region:us"
] |
2023-06-09T20:16:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "perplexity", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 37899073060, "num_examples": 11499581}], "download_size": 19974721570, "dataset_size": 37899073060}}
|
2023-06-09T22:21:31+00:00
|
83bb7ca0299a96a40d7ddd11a0af2a4277440e46
|
# Dataset Card for "VQAv2_testdev_google_flan_t5_xxl_mode_CM_Q_rices_ns_107394"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_testdev_google_flan_t5_xxl_mode_CM_Q_rices_ns_107394
|
[
"region:us"
] |
2023-06-09T20:31:38+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", "num_bytes": 590232398, "num_examples": 107394}], "download_size": 62562170, "dataset_size": 590232398}}
|
2023-06-09T20:31:56+00:00
|
3775fc626a11fdc708de53202377ea6d47766367
|
# ClimateX: Expert Confidence in Climate Statements
_What do LLMs know about climate? Let's find out!_
## ClimateX Dataset
We introduce the **Expert Confidence in Climate Statements (ClimateX) dataset**, a novel, curated, expert-labeled, natural language dataset of 8094 statements extracted or paraphrased from the IPCC Assessment Report 6: [Working Group I report](https://www.ipcc.ch/report/ar6/wg1/), [Working Group II report](https://www.ipcc.ch/report/ar6/wg2/), and [Working Group III report](https://www.ipcc.ch/report/ar6/wg3/), respectively.
Each statement is labeled with the corresponding IPCC report source, the page number in the report PDF, and the corresponding confidence level, along with their associated confidence levels (`low`, `medium`, `high`, or `very high`) as assessed by IPCC climate scientists based on available evidence and agreement among their peers.
## Confidence Labels
The authors of the United Nations International Panel on Climate Change (IPCC) reports have developed a structured framework to communicate the confidence and uncertainty levels of statements regarding our knowledge of climate change ([Mastrandrea, 2010](https://link.springer.com/article/10.1007/s10584-011-0178-6)).
Our dataset leverages this distinctive and consistent approach to labelling uncertainty across topics, disciplines, and report chapters, to help NLP and climate communication researchers evaluate how well LLMs can assess human expert confidence in a set of climate science statements from the IPCC reports.
Source: [IPCC AR6 Working Group I report](https://www.ipcc.ch/report/ar6/wg1/)
## Dataset Construction
To construct the dataset, we retrieved the complete raw text from each of the three IPCC report PDFs that are available online using an open-source library [pypdf2](https://pypi.org/project/PyPDF2/). We then normalized the whitespace, tokenized the text into sentences using [NLTK](https://www.nltk.org/) , and used regex search to filter for complete sentences including a parenthetical confidence label at the end of the statement, of the form _sentence (low|medium|high|very high confidence)_. The final ClimateX dataset contains 8094 labeled sentences.
From the full 8094 labeled sentences, we further selected **300 statements to form a smaller and more tractable test dataset**. We performed a random selection of sentences within each report and confidence category, with the following objectives:
- Making the test set distribution representative of the confidence class distribution in the overall train set and within each report;
- Making the breakdown between source reports representative of the number of statements from each report;
- Making sure the test set contains at least 5 sentences from each class and from each source, to ensure our results are statistically robust.
Then, we manually reviewed and cleaned each sentence in the test set to provide for a fairer assessment of model capacity.
- We removed 26 extraneous references to figures, call-outs, boxes, footnotes, or subscript typos (`CO 2');
- We split 19 compound statements with conflicting confidence sub-labels, and removed 6 extraneous mid-sentence labels of the same category as the end-of-sentence label;
- We added light context to 23 sentences, and replaced 5 sentences by others when they were meaningless outside of a longer paragraph;
- We removed qualifiers at the beginning of 29 sentences to avoid biasing classification (e.g. 'But...', 'In summary...', 'However...').
**The remaining 7794 sentences not allocated to the test split form our train split.**
Of note: while the IPCC report uses a 5 levels scale for confidence, almost no `very low confidence` statement makes it through the peer review process to the final reports, such that no statement of the form _sentence (very low confidence)_ was retrievable. Therefore, we chose to build our data set with only statements labeled as `low`, `medium`, `high` and `very high` confidence.
## Code Download
The code to reproduce dataset collection and our LLM benchmarking experiments is [released on GitHub](https://github.com/rlacombe/Climate-LLMs).
## Paper
We use this dataset to evaluate how recent LLMs fare at classifying the scientific confidence associated with each statement in a statistically representative, carefully constructed test split of the dataset.
We show that `gpt3.5-turbo` and `gpt4` assess the correct confidence level with reasonable accuracy even in the zero-shot setting; but that, along with other language models we tested, they consistently overstate the certainty level associated with low and medium confidence labels. Models generally perform better on reports before their knowledge cutoff, and demonstrate intuitive classifications on a baseline of non-climate statements. However, we caution it is still not fully clear why these models perform well, and whether they may also pick up on linguistic cues within the climate statements and not just prior exposure to climate knowledge and/or IPCC reports.
Our results have implications for climate communications and the use of generative language models in knowledge retrieval systems. We hope the ClimateX dataset provides the NLP and climate sciences communities with a valuable tool with which to evaluate and improve model performance in this critical domain of human knowledge.
Pre-print upcomping.
|
rlacombe/ClimateX
|
[
"task_categories:zero-shot-classification",
"task_categories:text-classification",
"task_categories:feature-extraction",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"climate",
"region:us"
] |
2023-06-09T20:36:06+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["zero-shot-classification", "text-classification", "feature-extraction"], "pretty_name": "ClimateX \u2013 Expert Confidence in Climate Statements", "tags": ["climate"]}
|
2023-11-27T16:32:12+00:00
|
6334ffc8c5e64fd426a09215040c32a07e99173d
|
lituus/Intellinoun_NounsDAOSentiment
|
[
"license:unlicense",
"region:us"
] |
2023-06-09T21:45:00+00:00
|
{"license": "unlicense"}
|
2023-06-09T21:45:37+00:00
|
|
8f6c9de888f1ee761eeef18da36d3fdfff6b22e2
|
# Dataset Card for "GPTSummaryV2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hztang/GPTSummaryV2
|
[
"region:us"
] |
2023-06-09T22:12:19+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "pmcid", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14231867.054292928, "num_examples": 3801}, {"name": "validation", "num_bytes": 1778515.3514309765, "num_examples": 475}, {"name": "test", "num_bytes": 1782259.5942760943, "num_examples": 476}], "download_size": 10164703, "dataset_size": 17792642.0}}
|
2023-06-09T22:12:27+00:00
|
45f3fc44648888d049cfe86f5a9a62715858c35a
|
Fast Food Restaurants Data
Testing out Hugging Face
|
JasonZhouTI/Fast_Food_Restaurants
|
[
"region:us"
] |
2023-06-09T22:15:33+00:00
|
{}
|
2023-06-09T22:16:38+00:00
|
5b9a2bcca49a130aa08fc7c788c4c74441d4aae3
|
# Portuguese Benchmark
|
eduagarcia/portuguese_benchmark
|
[
"language:pt",
"region:us"
] |
2023-06-09T22:26:59+00:00
|
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"validation", "path": "LeNER-Br/validation-*"}, {"split": "test", "path": "LeNER-Br/test-*"}]}, {"config_name": "Portuguese_Hate_Speech_binary", "data_files": [{"split": "train", "path": "Portuguese_Hate_Speech_binary/train-*"}, {"split": "validation", "path": "Portuguese_Hate_Speech_binary/validation-*"}, {"split": "test", "path": "Portuguese_Hate_Speech_binary/test-*"}]}, {"config_name": "UlyssesNER-Br-C-coarse", "data_files": [{"split": "train", "path": "UlyssesNER-Br-C-coarse/train-*"}, {"split": "validation", "path": "UlyssesNER-Br-C-coarse/validation-*"}, {"split": "test", "path": "UlyssesNER-Br-C-coarse/test-*"}]}, {"config_name": "UlyssesNER-Br-C-fine", "data_files": [{"split": "train", "path": "UlyssesNER-Br-C-fine/train-*"}, {"split": "validation", "path": "UlyssesNER-Br-C-fine/validation-*"}, {"split": "test", "path": "UlyssesNER-Br-C-fine/test-*"}]}, {"config_name": "UlyssesNER-Br-PL-coarse", "data_files": [{"split": "train", "path": "UlyssesNER-Br-PL-coarse/train-*"}, {"split": "validation", "path": "UlyssesNER-Br-PL-coarse/validation-*"}, {"split": "test", "path": "UlyssesNER-Br-PL-coarse/test-*"}]}, {"config_name": "UlyssesNER-Br-PL-fine", "data_files": [{"split": "train", "path": "UlyssesNER-Br-PL-fine/train-*"}, {"split": "validation", "path": "UlyssesNER-Br-PL-fine/validation-*"}, {"split": "test", "path": "UlyssesNER-Br-PL-fine/test-*"}]}, {"config_name": "assin2-rte", "data_files": [{"split": "train", "path": "assin2-rte/train-*"}, {"split": "validation", "path": "assin2-rte/validation-*"}, {"split": "test", "path": "assin2-rte/test-*"}]}, {"config_name": "assin2-sts", "data_files": [{"split": "train", "path": "assin2-sts/train-*"}, {"split": "validation", "path": "assin2-sts/validation-*"}, {"split": "test", "path": "assin2-sts/test-*"}]}, {"config_name": "brazilian_court_decisions_judgment", "data_files": [{"split": "train", "path": "brazilian_court_decisions_judgment/train-*"}, {"split": "validation", "path": "brazilian_court_decisions_judgment/validation-*"}, {"split": "test", "path": "brazilian_court_decisions_judgment/test-*"}]}, {"config_name": "brazilian_court_decisions_unanimity", "data_files": [{"split": "train", "path": "brazilian_court_decisions_unanimity/train-*"}, {"split": "validation", "path": "brazilian_court_decisions_unanimity/validation-*"}, {"split": "test", "path": "brazilian_court_decisions_unanimity/test-*"}]}, {"config_name": "harem-default", "data_files": [{"split": "train", "path": "harem-default/train-*"}, {"split": "validation", "path": "harem-default/validation-*"}, {"split": "test", "path": "harem-default/test-*"}]}, {"config_name": "harem-selective", "data_files": [{"split": "train", "path": "harem-selective/train-*"}, {"split": "validation", "path": "harem-selective/validation-*"}, {"split": "test", "path": "harem-selective/test-*"}]}, {"config_name": "mapa_pt_coarse", "data_files": [{"split": "train", "path": "mapa_pt_coarse/train-*"}, {"split": "validation", "path": "mapa_pt_coarse/validation-*"}, {"split": "test", "path": "mapa_pt_coarse/test-*"}]}, {"config_name": "mapa_pt_fine", "data_files": [{"split": "train", "path": "mapa_pt_fine/train-*"}, {"split": "validation", "path": "mapa_pt_fine/validation-*"}, {"split": "test", "path": "mapa_pt_fine/test-*"}]}, {"config_name": "multi_eurlex_pt", "data_files": [{"split": "train", "path": "multi_eurlex_pt/train-*"}, {"split": "validation", "path": "multi_eurlex_pt/validation-*"}, {"split": "test", "path": "multi_eurlex_pt/test-*"}]}]}
|
2024-02-11T22:10:35+00:00
|
0afc2e051d6a511e2f78837209689aeb79619e45
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_T_CM_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_T_CM_Q_rices_ns_25994
|
[
"region:us"
] |
2023-06-09T23:11:06+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", "num_bytes": 147960981, "num_examples": 25994}], "download_size": 23816673, "dataset_size": 147960981}}
|
2023-06-09T23:11:15+00:00
|
04d5f0bfe573c16cd6de5cca1662f72670864691
|
# Dataset Card for "OLID_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pranjali97/OLID_processed
|
[
"region:us"
] |
2023-06-09T23:16:33+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1159006, "num_examples": 8473}, {"name": "validation", "num_bytes": 361157, "num_examples": 2648}, {"name": "test", "num_bytes": 298095, "num_examples": 2119}], "download_size": 1207260, "dataset_size": 1818258}}
|
2023-06-10T00:11:03+00:00
|
8ecdd49d69c83df0866731a00ccd33372d66515e
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_A_CM_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_A_CM_Q_rices_ns_25994
|
[
"region:us"
] |
2023-06-09T23:17:47+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", "num_bytes": 156276098, "num_examples": 25994}], "download_size": 22109512, "dataset_size": 156276098}}
|
2023-06-09T23:17:57+00:00
|
55a68d114ed0703fc338215ebe4226f1a61bea07
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_T_A_CM_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_T_A_CM_Q_rices_ns_25994
|
[
"region:us"
] |
2023-06-09T23:24:53+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", "num_bytes": 160199562, "num_examples": 25994}], "download_size": 25173468, "dataset_size": 160199562}}
|
2023-06-09T23:25:03+00:00
|
6874a9af697015b41c38e9727e24a22c9578b8c8
|
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_A_T_CM_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_A_T_CM_Q_rices_ns_25994
|
[
"region:us"
] |
2023-06-09T23:31:31+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", "num_bytes": 160196594, "num_examples": 25994}], "download_size": 25179443, "dataset_size": 160196594}}
|
2023-06-09T23:31:40+00:00
|
fa48f678604c3e6809e1326fc5b9ae2d63e3d97e
|
# Dataset Card for "cifar_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Kamaljp/cifar_500
|
[
"region:us"
] |
2023-06-10T00:14:53+00:00
|
{"dataset_info": {"features": [{"name": "img", "dtype": "image"}, {"name": "fine_label", "dtype": {"class_label": {"names": {"0": "apple", "1": "aquarium_fish", "2": "baby", "3": "bear", "4": "beaver", "5": "bed", "6": "bee", "7": "beetle", "8": "bicycle", "9": "bottle", "10": "bowl", "11": "boy", "12": "bridge", "13": "bus", "14": "butterfly", "15": "camel", "16": "can", "17": "castle", "18": "caterpillar", "19": "cattle", "20": "chair", "21": "chimpanzee", "22": "clock", "23": "cloud", "24": "cockroach", "25": "couch", "26": "cra", "27": "crocodile", "28": "cup", "29": "dinosaur", "30": "dolphin", "31": "elephant", "32": "flatfish", "33": "forest", "34": "fox", "35": "girl", "36": "hamster", "37": "house", "38": "kangaroo", "39": "keyboard", "40": "lamp", "41": "lawn_mower", "42": "leopard", "43": "lion", "44": "lizard", "45": "lobster", "46": "man", "47": "maple_tree", "48": "motorcycle", "49": "mountain", "50": "mouse", "51": "mushroom", "52": "oak_tree", "53": "orange", "54": "orchid", "55": "otter", "56": "palm_tree", "57": "pear", "58": "pickup_truck", "59": "pine_tree", "60": "plain", "61": "plate", "62": "poppy", "63": "porcupine", "64": "possum", "65": "rabbit", "66": "raccoon", "67": "ray", "68": "road", "69": "rocket", "70": "rose", "71": "sea", "72": "seal", "73": "shark", "74": "shrew", "75": "skunk", "76": "skyscraper", "77": "snail", "78": "snake", "79": "spider", "80": "squirrel", "81": "streetcar", "82": "sunflower", "83": "sweet_pepper", "84": "table", "85": "tank", "86": "telephone", "87": "television", "88": "tiger", "89": "tractor", "90": "train", "91": "trout", "92": "tulip", "93": "turtle", "94": "wardrobe", "95": "whale", "96": "willow_tree", "97": "wolf", "98": "woman", "99": "worm"}}}}, {"name": "coarse_label", "dtype": {"class_label": {"names": {"0": "aquatic_mammals", "1": "fish", "2": "flowers", "3": "food_containers", "4": "fruit_and_vegetables", "5": "household_electrical_devices", "6": "household_furniture", "7": "insects", "8": "large_carnivores", "9": "large_man-made_outdoor_things", "10": "large_natural_outdoor_scenes", "11": "large_omnivores_and_herbivores", "12": "medium_mammals", "13": "non-insect_invertebrates", "14": "people", "15": "reptiles", "16": "small_mammals", "17": "trees", "18": "vehicles_1", "19": "vehicles_2"}}}}], "splits": [{"name": "train", "num_bytes": 1125013.56, "num_examples": 500}], "download_size": 1132520, "dataset_size": 1125013.56}}
|
2023-06-10T00:14:55+00:00
|
4cdac701d618bf5a5d8a9c78b33b6a12d069fd26
|
# Dataset Card for "super_glue"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/google-research-datasets/boolean-questions](https://github.com/google-research-datasets/boolean-questions)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 58.36 MB
- **Size of the generated dataset:** 249.57 MB
- **Total amount of disk used:** 307.94 MB
### Dataset Summary
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
BoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short
passage and a yes/no question about the passage. The questions are provided anonymously and
unsolicited by users of the Google search engine, and afterwards paired with a paragraph from a
Wikipedia article containing the answer. Following the original work, we evaluate with accuracy.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### axb
- **Size of downloaded dataset files:** 0.03 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.27 MB
An example of 'test' looks as follows.
```
```
#### axg
- **Size of downloaded dataset files:** 0.01 MB
- **Size of the generated dataset:** 0.05 MB
- **Total amount of disk used:** 0.06 MB
An example of 'test' looks as follows.
```
```
#### boolq
- **Size of downloaded dataset files:** 4.12 MB
- **Size of the generated dataset:** 10.40 MB
- **Total amount of disk used:** 14.52 MB
An example of 'train' looks as follows.
```
```
#### cb
- **Size of downloaded dataset files:** 0.07 MB
- **Size of the generated dataset:** 0.20 MB
- **Total amount of disk used:** 0.28 MB
An example of 'train' looks as follows.
```
```
#### copa
- **Size of downloaded dataset files:** 0.04 MB
- **Size of the generated dataset:** 0.13 MB
- **Total amount of disk used:** 0.17 MB
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### axb
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `idx`: a `int32` feature.
- `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1).
#### axg
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `idx`: a `int32` feature.
- `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1).
#### boolq
- `question`: a `string` feature.
- `passage`: a `string` feature.
- `idx`: a `int32` feature.
- `label`: a classification label, with possible values including `False` (0), `True` (1).
#### cb
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `idx`: a `int32` feature.
- `label`: a classification label, with possible values including `entailment` (0), `contradiction` (1), `neutral` (2).
#### copa
- `premise`: a `string` feature.
- `choice1`: a `string` feature.
- `choice2`: a `string` feature.
- `question`: a `string` feature.
- `idx`: a `int32` feature.
- `label`: a classification label, with possible values including `choice1` (0), `choice2` (1).
### Data Splits
#### axb
| |test|
|---|---:|
|axb|1104|
#### axg
| |test|
|---|---:|
|axg| 356|
#### boolq
| |train|validation|test|
|-----|----:|---------:|---:|
|boolq| 9427| 3270|3245|
#### cb
| |train|validation|test|
|---|----:|---------:|---:|
|cb | 250| 56| 250|
#### copa
| |train|validation|test|
|----|----:|---------:|---:|
|copa| 400| 100| 500|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{clark2019boolq,
title={BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author={Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle={NAACL},
year={2019}
}
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
journal={arXiv preprint arXiv:1905.00537},
year={2019}
}
Note that each SuperGLUE dataset has its own citation. Please see the source to
get the correct citation for each contained dataset.
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
|
zzzzhhh/test_data
|
[
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"task_ids:natural-language-inference",
"task_ids:word-sense-disambiguation",
"task_ids:coreference-resolution",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other",
"language:en",
"license:unknown",
"superglue",
"NLU",
"natural language understanding",
"region:us"
] |
2023-06-10T00:16:25+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|other"], "task_categories": ["text-classification", "token-classification", "question-answering"], "task_ids": ["natural-language-inference", "word-sense-disambiguation", "coreference-resolution", "extractive-qa"], "pretty_name": "SuperGLUE", "tags": ["superglue", "NLU", "natural language understanding"], "dataset_info": [{"config_name": "boolq", "features": [{"name": "question", "dtype": "string"}, {"name": "passage", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "test", "num_bytes": 2107997, "num_examples": 3245}, {"name": "train", "num_bytes": 6179206, "num_examples": 9427}, {"name": "validation", "num_bytes": 2118505, "num_examples": 3270}], "download_size": 4118001, "dataset_size": 10405708}, {"config_name": "cb", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "contradiction", "2": "neutral"}}}}], "splits": [{"name": "test", "num_bytes": 93660, "num_examples": 250}, {"name": "train", "num_bytes": 87218, "num_examples": 250}, {"name": "validation", "num_bytes": 21894, "num_examples": 56}], "download_size": 75482, "dataset_size": 202772}, {"config_name": "copa", "features": [{"name": "premise", "dtype": "string"}, {"name": "choice1", "dtype": "string"}, {"name": "choice2", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "choice1", "1": "choice2"}}}}], "splits": [{"name": "test", "num_bytes": 60303, "num_examples": 500}, {"name": "train", "num_bytes": 49599, "num_examples": 400}, {"name": "validation", "num_bytes": 12586, "num_examples": 100}], "download_size": 43986, "dataset_size": 122488}, {"config_name": "multirc", "features": [{"name": "paragraph", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "idx", "struct": [{"name": "paragraph", "dtype": "int32"}, {"name": "question", "dtype": "int32"}, {"name": "answer", "dtype": "int32"}]}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "test", "num_bytes": 14996451, "num_examples": 9693}, {"name": "train", "num_bytes": 46213579, "num_examples": 27243}, {"name": "validation", "num_bytes": 7758918, "num_examples": 4848}], "download_size": 1116225, "dataset_size": 68968948}, {"config_name": "record", "features": [{"name": "passage", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "entities", "sequence": "string"}, {"name": "entity_spans", "sequence": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "answers", "sequence": "string"}, {"name": "idx", "struct": [{"name": "passage", "dtype": "int32"}, {"name": "query", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 179232052, "num_examples": 100730}, {"name": "validation", "num_bytes": 17479084, "num_examples": 10000}, {"name": "test", "num_bytes": 17200575, "num_examples": 10000}], "download_size": 51757880, "dataset_size": 213911711}, {"config_name": "rte", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "not_entailment"}}}}], "splits": [{"name": "test", "num_bytes": 975799, "num_examples": 3000}, {"name": "train", "num_bytes": 848745, "num_examples": 2490}, {"name": "validation", "num_bytes": 90899, "num_examples": 277}], "download_size": 750920, "dataset_size": 1915443}, {"config_name": "wic", "features": [{"name": "word", "dtype": "string"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "start1", "dtype": "int32"}, {"name": "start2", "dtype": "int32"}, {"name": "end1", "dtype": "int32"}, {"name": "end2", "dtype": "int32"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "test", "num_bytes": 180593, "num_examples": 1400}, {"name": "train", "num_bytes": 665183, "num_examples": 5428}, {"name": "validation", "num_bytes": 82623, "num_examples": 638}], "download_size": 396213, "dataset_size": 928399}, {"config_name": "wsc", "features": [{"name": "text", "dtype": "string"}, {"name": "span1_index", "dtype": "int32"}, {"name": "span2_index", "dtype": "int32"}, {"name": "span1_text", "dtype": "string"}, {"name": "span2_text", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "test", "num_bytes": 31572, "num_examples": 146}, {"name": "train", "num_bytes": 89883, "num_examples": 554}, {"name": "validation", "num_bytes": 21637, "num_examples": 104}], "download_size": 32751, "dataset_size": 143092}, {"config_name": "wsc.fixed", "features": [{"name": "text", "dtype": "string"}, {"name": "span1_index", "dtype": "int32"}, {"name": "span2_index", "dtype": "int32"}, {"name": "span1_text", "dtype": "string"}, {"name": "span2_text", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "test", "num_bytes": 31568, "num_examples": 146}, {"name": "train", "num_bytes": 89883, "num_examples": 554}, {"name": "validation", "num_bytes": 21637, "num_examples": 104}], "download_size": 32751, "dataset_size": 143088}, {"config_name": "axb", "features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "not_entailment"}}}}], "splits": [{"name": "test", "num_bytes": 238392, "num_examples": 1104}], "download_size": 33950, "dataset_size": 238392}, {"config_name": "axg", "features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "idx", "dtype": "int32"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "not_entailment"}}}}], "splits": [{"name": "test", "num_bytes": 53581, "num_examples": 356}], "download_size": 10413, "dataset_size": 53581}]}
|
2024-01-29T12:47:05+00:00
|
ac5c147074a5f72260f85aac15edd61edf5016a8
|
# Dataset Card for "squad_5000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Kamaljp/squad_5000
|
[
"region:us"
] |
2023-06-10T00:27:43+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 2730269, "num_examples": 3000}], "download_size": 339525, "dataset_size": 2730269}}
|
2023-06-10T00:27:47+00:00
|
314862831dfe032def62e02f0d818b45d0baad9f
|
# Dataset Card for "VQAv2_testdev_google_flan_t5_xl_mode_CM_Q_rices_ns_107394"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/VQAv2_testdev_google_flan_t5_xl_mode_CM_Q_rices_ns_107394
|
[
"region:us"
] |
2023-06-10T00:31:57+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", "num_bytes": 590180390, "num_examples": 107394}], "download_size": 62529631, "dataset_size": 590180390}}
|
2023-06-10T00:32:16+00:00
|
8c7fb4e52a884136edd30c047d6f560e9b227ec8
|
Mamakhan/Tools
|
[
"license:openrail",
"region:us"
] |
2023-06-10T00:54:53+00:00
|
{"license": "openrail"}
|
2023-06-10T00:54:53+00:00
|
|
7046749ae12eba3fe9fc54f146fb938d75569891
|
<h1 align="center"> 🧪 Mol-Instructions </h1>
<h3 align="center"> An open, large-scale biomolecular instruction dataset for large language models. </h3>
> Please refer to our [repository](https://github.com/zjunlp/Mol-Instructions) and [paper](https://arxiv.org/abs/2306.08018) for more details.
## 📌 Contents
- [Overview](#1)
- [Data Stats](#1-1)
- [Data Construction](#1-2)
- [Data Release](#1-3)
- [Tasks](#2)
- [Molecule-oriented](#2-1)
- [Protein-oriented](#2-2)
- [Biomolecule text](#2-3)
- [Demo](#3)
- [Model Weight Release](#3-1)
- [Model Usage Guide](#3-2)
- [FAQ](#3-3)
- [Notices](#4)
- [Usage and License](#4-1)
- [Limitations](#4-2)
- [About](#5)
- [References](#5-1)
- [Acknowledgements](#5-2)
<h2 id="1">1. Overview</h2>
<h3 id="1-1"> 📊 1.1 Data Stats</h3>
**Mol-Instructions** comprises three cardinal components:
- 🔬 *Molecule-oriented instructions:* This component delves into the world of small molecules, emphasizing their inherent properties and behaviors. It sheds light on the fundamental challenges of diverse chemical reactions and molecular design, with 148,4K instructions across six tasks.
- 🧬 *Protein-oriented instructions:* Rooted in the biosciences, this component presents 505K instructions across five distinct categories of tasks. These tasks aim to predict the structure, function, and activity of proteins, and facilitate protein design based on textual directives.
- 🥼 *Biomolecular text instructions:* Predominantly designed to cater to NLP tasks within the fields of bioinformatics and chemoinformatics, this part encapsulates six information extraction and Q\&A tasks represented through 53K instructions.
<h3 id="1-2"> 🛠️ 1.2 Data Construction</h3>
- 🤖️ *Human-AI Collaboration Task Description Creation*: In real-world applications, task instructions must be able to accommodate the varied and dynamic nature of human needs and queries. We emulate this diversity by starting with a clear, human-crafted description for each task, which is then used as an input to GPT-3.5-turbo.
- 📖 *Information Derivation from Existing Data*: Biomolecular data often requires specialist laboratory experiments and expert analysis, making authoritative and recognized biochemistry databases an ideal source of our data. With suitable processing, these resources enable us to extract the required instruction data.
- 📜 *Template-based Conversion of Biological Data into Textual Format*: To facilitate the transformation of these structured annotations into a textual format, we design a diverse array of templates. Each resulting text-based annotation serves as a guideline for protein design.
- ✅ *Quality Control*: To expedite the model's ability to generate precise biomolecules, we implement stringent quality control measures for our biomolecular data.
<h3 id="1-3"> 🤗 1.3 Data Release</h3>
We release the dataset on Hugging Face at [zjunlp/Mol-Instructions](https://huggingface.co/datasets/zjunlp/Mol-Instructions).
<h2 id="2">2. Tasks</h2>
<h3 id="2-1"> 🔬 2.1 Molecule-oriented</h3>
<details>
<summary><b>Molecule description generation</b></summary>
- *Please give me some details about this molecule:*
[C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][=Branch1][C][=O][O][C@H1][Branch2][Ring1][=Branch1][C][O][C][=Branch1][C][=O][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][O][P][=Branch1][C][=O][Branch1][C][O][O][C][C@@H1][Branch1][=Branch1][C][=Branch1][C][=O][O][N]
```
The molecule is a 3-sn-phosphatidyl-L-serine in which the phosphatidyl acyl groups at positions 1 and 2 are specified as stearoyl and arachidonoyl respectively.
It is functionally related to an arachidonic acid and an octadecanoic acid.
```
</details>
<details>
<summary><b>Description-guided molecule design</b></summary>
- *Create a molecule with the structure as the one described:*
The molecule is a primary arylamine in which an amino functional group is substituted for one of the benzene hydrogens. It is a primary arylamine and a member of anilines.
```
[N][C][=C][C][=C][C][=C][Ring1][=Branch1]
```
</details>
<details>
<summary><b>Forward reaction prediction</b></summary>
- *With the provided reactants and reagents, propose a potential product:*
[O][=N+1][Branch1][C][O-1][C][=C][N][=C][Branch1][C][Cl][C][Branch1][C][I][=C][Ring1][Branch2].[Fe]
```
[N][C][=C][N][=C][Branch1][C][Cl][C][Branch1][C][I][=C][Ring1][Branch2]
```
</details>
<details>
<summary><b>Retrosynthesis</b></summary>
- *Please suggest potential reactants used in the synthesis of the provided product:*
[C][=C][C][C][N][C][=Branch1][C][=O][O][C][Branch1][C][C][Branch1][C][C][C]
```
[C][=C][C][C][N].[C][C][Branch1][C][C][Branch1][C][C][O][C][=Branch1][C][=O][O][C][=Branch1][C][=O][O][C][Branch1][C][C][Branch1][C][C][C]
```
</details>
<details>
<summary><b>Reagent prediction</b></summary>
- *Please provide possible reagents based on the following chemical reaction:*
[C][C][=C][C][=C][Branch1][C][N][C][=N][Ring1][#Branch1].[O][=C][Branch1][C][Cl][C][Cl]>>[C][C][=C][C][=C][Branch1][Branch2][N][C][=Branch1][C][=O][C][Cl][C][=N][Ring1][O]
```
[C][C][C][O][C][Ring1][Branch1].[C][C][N][Branch1][Ring1][C][C][C][C].[O]
```
</details>
<details>
<summary><b>Property prediction</b></summary>
- *Please provide the HOMO energy value for this molecule:*
[C][C][O][C][C][Branch1][C][C][C][Branch1][C][C][C]
```
-0.2482
```
</details>
<h3 id="2-2"> 🧬 2.2 Protein-oriented</h3>
<details>
<summary><b>Protein design</b></summary>
- *Design a protein sequence that exhibits the desired activity and specificity.*
1. The presence of Mg(2+) is necessary for the protein to function in the desired environment.
2. The AMP, (6S)-NADPHX binding site should be located in a region of the protein that is accessible to the ligand.
3. The designed protein should have ATP binding, NADPHX epimerase activity, metal ion binding, ADP-dependent NAD(P)H-hydrate dehydratase activity to facilitate nicotinamide nucleotide metabolic process.
4. For general function, the protein need meet that Catalyzes the epimerization of the S- and R-forms of NAD(P)HX, a damaged form of NAD(P)H that is a result of enzymatic or heat-dependent hydration
```
MSNELVLSREQVRRVDQRAIEAYGVPGIVLMENAGRGAAEIIRAACPSAQRVLIACGPGNNGGDGFVIARHLANAGWMVELLLACPADRITGDAQGNHEIIRRMNLPCAVMADARDLEAANDRFATADVIVDALLGTGASGPPREPIASLIRAINEAHRRVSAQPAPSVFAVDIPSGLDCDTGEAANPTVRADHTITFVARKIGFRNPAARDLLGRVHVVDIGAPRAAIQDALTGKSG
```
</details>
<details>
<summary><b>Catalytic activity prediction</b></summary>
- *Please evaluate the following protein sequence and provide an explanation of the enzyme's catalytic activity, including the chemical reaction it facilitates:* MDKVAVAGFLPEELCASLSLSPSFRGNQIFQWIGKGVDSFDAMTNLSAELRASLAEKAILRSTRVSDVLKADDGTVKLQIQTEDDLAVETVLLTDKAARKTACVSCQAGCAMGCAFCKTGTLGLARNLSAAEIVEQFLYLEKHAGALDNIVFMGMGEPLLNLDALRKAIAVLTDKRGRNLSSRRITVSTVGIVSGIYDLANNGPDVRLAVSLTTADETLRRELMPASLTNPLSDLRQAISYYIEKTGKRVTLEAVLLSGKNTSEKNADSLIAFAKGLDVHVNLIPWNPVEGLSFVTPDPEETAQFVSRLEKGGLNVTLRMHRGKSISGACGQLGKTNPYA
```
Based on the provided protein sequence, the enzyme appears to facilitate the chemical reaction: adenosine(37) in tRNA + 2 reduced [2Fe-2S]-[ferredoxin] + 2 S- adenosyl-L-methionine = 2-methyladenosine(37) in tRNA + 5'- deoxyadenosine + L-methionine + 2 oxidized [2Fe-2S]-[ferredoxin] + S- adenosyl-L-homocysteine.
```
</details>
<details>
<summary><b>Protein function prediction</b></summary>
- *Analyze the following amino acid sequence, and determine the function of the resulting protein, its subcellular localization, and any biological processes it may be part of:*
MNGTVNASAPSKMSEVAVERLSNDKALKVIFVLGGPGSGKGTQCAKIAKHFGFTHLSVGDLLRAEINSGSKNGTMIESMINEGKIVRSEVTIKLLQRAMHESGNDKFLIDGFPRNEENRAAFENLEKIEPEFVLFFDCPMEEMERRILNRNQGRDDDKMETIRKRFKVFIESTLPVIEFYNLKGKLYKIDACKPADEVFEDVKAIFSRFRAKEDSSQQTNICTAKRFELVMCLIKRLFREIKRMWSSFFCKAL
```
The protein characterized by the amino acid sequence demonstrates ATP binding, cytidylate kinase activity, uridylate kinase activity and is implicated in the 'de novo' pyrimidine nucleobase biosynthetic process, phosphorylation, pyrimidine nucleotide biosynthetic process. Its subcellular localization is primarily within the cytoplasm, nucleus.
```
</details>
<details>
<summary><b>Functional description generation</b></summary>
- *Examine the given protein sequence and share a brief overview of its attributes:*
MKIVLASNNQGKLAELKAMLAPLGVQLLRQAELGIPEAAEPFRTFVENALAKARHASALSGLPALADDAGLCVEAFGGLPGVDTAFYAVQFGYAKGDANNVKALLAQMAGITDRRAALVSTLVAVRSAEDPEPLIACGRVAGEVALEPMGSNGFGFDPVMFIPEFGQTFAQLPVEVKNANSHRGKATRQMMALMRERWIT
```
A concise description of the protein with the specified amino acid sequence includes: Pyrophosphatase that catalyzes the hydrolysis of nucleoside triphosphates to their monophosphate derivatives, with a high preference for the non-canonical purine nucleotides XTP (xanthosine triphosphate), dITP (deoxyinosine triphosphate) and ITP. Seems to function as a house-cleaning enzyme that removes non-canonical purine nucleotides from the nucleotide pool, thus preventing their incorporation into DNA/RNA and avoiding chromosomal lesions.
```
</details>
<details>
<summary><b>Domain/Motif prediction</b></summary>
- *Given this protein sequence, can you identify any common protein motifs or domains that it may contain?*
MANTKYIFITGGVVSSLGKGIAAASIGALLESRGLSVSLIKVDPYINVDPGTMSPFQHGEVFVTEDGTETDLDLGHYERFVRFKASKKNNFTAGKVYETVIRNERKGNYLGGTVQVIPHITNEIKKRIKKGGQNKDIAIVEVGGTVGDIESQPFVEALRQMALELPNSSWAFVHLTLVPFINASGELKTKPTQHSVKELRSLGISPDVLVCRSEQELPKDEKNKIALFCSVPAKSVISMHDVDTVYSIPILLNKQKVDDTILKKLNLKIKKPNLNDWKRVVKAKLLPEKEVNVSFVGKYTELKDSYKSINEALEHAGIQNKAKVNINFVEAEQITSQNVRKVLKKSDAILVPGGFGERGIEGMILACKYARENNVPYLGICLGMQIAIIEYARNVLKLKSANSTEFDSSTKFPVIGLITEWSDISGKKEKRTKNSDLGGTMRLGGQVCKLKKKSNSYKMYKKSEIIERHRHRYEVNPNYKDKMIEQGLDVVGTSIDGKLVEMIELPSHKWFLACQFHPEFTSNPRDGHPIFNSYIKSTITK
```
Our predictive analysis of the given protein sequence reveals possible domains or motifs. These include: Glutamine amidotransferase, CTP synthase N-terminal domains.
```
</details>
<h3 id="2-3"> 🥼 2.3 Biomolecule text</h3>
<details>
<summary><b>Chemical entity recognition</b></summary>
- *Find and list all the instances of the chemical entities in the following content:*
"Both the control and caramiphen groups with double cannulas had significantly shorter latencies to seizure onset than the corresponding groups with single cannula."
```
caramiphen
```
</details>
<details>
<summary><b>Chemical-disease interaction extraction</b></summary>
- *You are provided with a set of clinical trial summaries. Extract the chemical-disease relations from the summaries and present your findings in the format of (Subject, Object):*
"Eating disorders and the associated behavioural problems and drug abuse are uncommon in pregnancy. When they do occur they are often unrecognized because of denial but when significant may pose a risk to both the mother and her fetus. This case illustrates a number of problems that may be encountered in women with eating disorders in pregnancy, including prolonged and recurrent metabolic disturbances and diuretic abuse. In particular it illustrates the derangements of thyroid function seen in pregnant women with eating disorders and reminds us that when a cause for thyrotoxicosis remains obscure, thyroxine abuse should be considered and explored."
```
(thyroxine, thyrotoxicosis)
```
</details>
<details>
<summary><b>Chemical-protein interaction extraction</b></summary>
- *Your task is to extract the subject and object entities from the provided text and establish the corresponding relation triples. Please format your answers as (Subject, Relation, Object):*
"Opioid receptors are the membrane proteins that mediate the pain-relieving effect of opioid drugs, such as morphine and fentanyl as well as endogenous opioid peptides enkephalins and endorphins. Using cDNAs for the mu and the kappa opioid receptors, we mapped the chromosomal locations of their genes in mouse. Multilocus cross analysis located the mu receptor gene Oprm on Chr 10 and the kappa receptor gene Oprk1 on Chr 1. Both genes are near centromere, with no markers more centromeric. These data indicate that the two opioid receptors are different gene products, ruling out the possibility that they may be differential splicing products from the same gene."
```
(morphine, regulator, Opioid receptors), (fentanyl, regulator, Opioid receptors)
```
</details>
<details>
<summary><b>Multiple-choice question</b></summary>
- *Which of the following conditions does not show multifactorial inheritance? What of the following is the right choice?*
(A) Pyloric stenosis (B) Schizophrenia (C) Spina bifida (neural tube defects) (D) Marfan syndrome
```
The final answer is (D).
```
</details>
<details>
<summary><b>True or False question</b></summary>
- *Does feeding tube insertion and its timing improve survival?*
```
No, neither insertion of PEG tubes nor timing of insertion affect survival.
```
</details>
<details>
<summary><b>Open question</b></summary>
- *What virus causes chronic disease?*
```
Hepatitis C virus (HCV).
```
</details>
<h2 id="3">3. Demo</h2>
<h3 id="3-1"> 🤗 3.1 Model Weight Release</h3>
We release the model weights on Hugging Face at:
- 🔬 *Molecule-oriented instructions:* [zjunlp/llama-molinst-molecule-7b](https://huggingface.co/zjunlp/llama-molinst-molecule-7b)
- 🧬 *Protein-oriented instructions:* [zjunlp/llama-molinst-protein-7b](https://huggingface.co/zjunlp/llama-molinst-protein-7b)
- 🥼 *Biomolecular text instructions:* [zjunlp/llama-molinst-biotext-7b](https://huggingface.co/zjunlp/llama-molinst-biotext-7b)
<h3 id="3-2"> 📝 3.2 Model Usage Guide</h3>
For this part, please refer to our [repository](https://github.com/zjunlp/Mol-Instructions).
We have provided a web version demo based on [Gradio](https://gradio.app). To use it, you first need to download this repository:
```shell
>> git clone https://github.com/zjunlp/Mol-Instruction
>> cd demo
```
Step 1, install Gradio by running：`pip install gradio`.
Step 2, specify the parameters in the [generate.sh](https://github.com/zjunlp/Mol-Instructions/blob/main/demo/generate.sh) file.
```shell
>> CUDA_VISIBLE_DEVICES=0 python generate.py \
--CLI False\
--protein False\
--load_8bit \
--base_model $BASE_MODEL_PATH \
--share_gradio True\
--lora_weights $FINETUNED_MODEL_PATH \
```
For models fine-tuned on *molecule-oriented* and *biomolecular text* instructions, please set `$FINETUNED_MODEL_PATH` to `'zjunlp/llama-molinst-molecule-7b'` or `'zjunlp/llama-molinst-biotext-7b'`.
For the model fine-tuned on *protein-oriented* instructions, you need to perform additional steps as described in [this folder](https://github.com/zjunlp/Mol-Instructions/tree/main/demo).
Step 3, run the [generate.sh](https://github.com/zjunlp/Mol-Instructions/blob/main/demo/generate.sh) file in the repository：
```shell
>> sh generate.sh
```
We offer two methods: the first one is command-line interaction, and the second one is web-based interaction, which provides greater flexibility.
1. Use the following command to enter **web-based interaction**:
```shell
>> python generate.py
```
The program will run a web server and output an address. Open the output address in a browser to use it.
2. Use the following command to enter **command-line interaction**:
```shell
>> python generate.py --CLI True
```
The disadvantage is the inability to dynamically change decoding parameters.
<h3 id="3-3"> 💡 3.3 FAQ</h3>
- *Question:* What action should be taken if the model encounters `<unk>` and subsequently repeats the input during decoding?
*Answer:* Consider reducing the value of the `max tokens`.
- *Question:* What should I do if the model encounters � during decoding?
*Answer:* If this symbol emerges in the middle of the decoded sentence, we recommend changing the input. If it shows up at the end of the sentence, you can tackle this issue by extending the output length.
- *Question:* Why do I receive varied results despite using identical decoding parameters?
*Answer:* This might occur if you have enabled `do_sample=True`. Another factor could be the order in which tasks are executed. A useful approach would be to use a for loop to generate multiple outputs with the same decoding parameters, enabling you to note the variance in each output.
- *Question:* What could be the reason for subpar answer quality?
*Answer:* Modifying the decoding parameters could help in improving the quality of the extraction or the answer.
<h2 id="4">4. Notices</h2>
<h3 id="4-1"> 🚨 4.1. Usage and License</h3>
Please note that all data and model weights of **Mol-Instructions** is exclusively licensed for research purposes. The accompanying dataset is licensed under CC BY 4.0, which permits solely non-commercial usage.
We emphatically urge all users to adhere to the highest ethical standards when using our dataset, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly **forbidden**.
In terms of dataset maintenance, we pledge our commitment to provide necessary upkeep. This will ensure the continued relevance and usability of the dataset in light of evolving research landscapes. This commitment encompasses regular updates, error checks, and amendments in accordance with field advancements and user feedback.
<h3 id="4-2"> ❗️ 4.2. Limitations</h3>
The current state of the model, obtained via instruction tuning, is a preliminary demonstration. Its capacity to handle real-world, production-grade tasks remains limited. Moreover, there is a vast reservoir of rich instruction data that remains to be collected and exploited.
<h2 id="5">5. About</h2>
<h3 id="5-1"> 📚 5.1 References</h3>
If you use our repository, please cite the following related paper:
```
@article{molinst,
title={Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models},
author={Fang, Yin and Liang, Xiaozhuan and Zhang, Ningyu and Liu, Kangwei and Huang, Rui and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun},
journal={arXiv preprint arXiv:2306.08018},
year={2023}
}
```
<h3 id="5-2"> 🫱🏻🫲 5.2 Acknowledgements</h3>
We appreciate [LLaMA](https://github.com/facebookresearch/llama), [Huggingface Transformers Llama](https://github.com/huggingface/transformers/tree/main/src/transformers/models/llama), [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [Alpaca-LoRA](https://github.com/tloen/alpaca-lora), [Chatbot Service](https://github.com/deep-diver/LLM-As-Chatbot) and many other related works for their open-source contributions.
|
zjunlp/Mol-Instructions
|
[
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"chemistry",
"biology",
"molecule",
"protein",
"instructions",
"arxiv:2306.08018",
"region:us"
] |
2023-06-10T01:12:42+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["100M<n<1B"], "tags": ["chemistry", "biology", "molecule", "protein", "instructions"]}
|
2023-11-17T10:23:45+00:00
|
b63151d9eee8f0ba683f1f165b150b4c40eaa50f
|
# Dataset Card for "xsum_3000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Kamaljp/xsum_3000
|
[
"region:us"
] |
2023-06-10T01:24:32+00:00
|
{"dataset_info": {"features": [{"name": "document", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7095386, "num_examples": 3000}], "download_size": 4515775, "dataset_size": 7095386}}
|
2023-06-10T01:24:34+00:00
|
691f63042b8f9d037a82c0ed5d99e4390070c072
|
# Dataset Card for "oscar-2301_1e6_id"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hac541309/oscar-2301_1e6_id
|
[
"region:us"
] |
2023-06-10T01:27:37+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6591490418, "num_examples": 1000000}], "download_size": 3295795446, "dataset_size": 6591490418}}
|
2023-06-10T02:03:10+00:00
|
9d2c834157dee3d9207e5cd78e8b6ea2da238d23
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://osu-nlp-group.github.io/Mind2Web/
- **Repository:** https://github.com/OSU-NLP-Group/Mind2Web
- **Paper:** https://arxiv.org/abs/2306.06070
- **Point of Contact:** [Xiang Deng](mailto:[email protected])
### Dataset Summary
Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1. diverse domains, websites, and tasks, 2. use of real-world websites instead of simulated and simplified ones, and 3. a broad spectrum of user interaction patterns.
## Dataset Structure
### Data Fields
- "annotation_id" (str): unique id for each task
- "website" (str): website name
- "domain" (str): website domain
- "subdomain" (str): website subdomain
- "confirmed_task" (str): task description
- "action_reprs" (list[str]): human readable string representation of the action sequence
- "actions" (list[dict]): list of actions (steps) to complete the task
- "action_uid" (str): unique id for each action (step)
- "raw_html" (str): raw html of the page before the action is performed
- "cleaned_html" (str): cleaned html of the page before the action is performed
- "operation" (dict): operation to perform
- "op" (str): operation type, one of CLICK, TYPE, SELECT
- "original_op" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used
- "value" (str): optional value for the operation, e.g., text to type, option to select
- "pos_candidates" (list[dict]): ground truth elements. Here we only include positive elements that exist in "cleaned_html" after our preprocessing, so "pos_candidates" might be empty. The original labeled element can always be found in the "raw_html".
- "tag" (str): tag of the element
- "is_original_target" (bool): whether the element is the original target labeled by the annotator
- "is_top_level_target" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details.
- "backend_node_id" (str): unique id for the element
- "attributes" (str): serialized attributes of the element, use `json.loads` to convert back to dict
- "neg_candidates" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as "pos_candidates"
### Data Splits
- train: 1,009 instances
- test: (To prevent potential data leakage, please check our [repo](https://github.com/OSU-NLP-Group/Mind2Web) for information on obtaining the test set.)
- Cross Task: 252 instances, tasks from the same website are seen during training
- Cross Website: 177 instances, websites are not seen during training
- Cross Domain: 9,12 instances, entire domains are not seen during training
### Licensing Information
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
### Disclaimer
This dataset was collected and released solely for research purposes, with the goal of making the web more accessible via language technologies. The authors are strongly against any potential harmful use of the data or technology to any party.
### Citation Information
```
@misc{deng2023mind2web,
title={Mind2Web: Towards a Generalist Agent for the Web},
author={Xiang Deng and Yu Gu and Boyuan Zheng and Shijie Chen and Samuel Stevens and Boshi Wang and Huan Sun and Yu Su},
year={2023},
eprint={2306.06070},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
osunlp/Mind2Web
|
[
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"Web Agent",
"arxiv:2306.06070",
"region:us"
] |
2023-06-10T01:38:11+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "tags": ["Web Agent"]}
|
2023-07-19T02:44:34+00:00
|
985239617aec5ae128d62eea62eef3c8accf6e77
|
# Dataset Card for "amazon_us_3000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Kamaljp/amazon_us_3000
|
[
"region:us"
] |
2023-06-10T01:52:46+00:00
|
{"dataset_info": {"features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "star_rating", "dtype": "int32"}, {"name": "helpful_votes", "dtype": "int32"}, {"name": "total_votes", "dtype": "int32"}, {"name": "vine", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "verified_purchase", "dtype": {"class_label": {"names": {"0": "N", "1": "Y"}}}}, {"name": "review_headline", "dtype": "string"}, {"name": "review_body", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1391025, "num_examples": 3000}], "download_size": 763643, "dataset_size": 1391025}}
|
2023-06-10T01:52:48+00:00
|
505de9213c1afdc6aa515bfcc3d50dd7edb04f07
|
# Dataset Card for "earnings_3000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Kamaljp/earnings_3000
|
[
"region:us"
] |
2023-06-10T02:11:15+00:00
|
{"dataset_info": {"features": [{"name": "symbol", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "fiscal_end", "dtype": "string"}, {"name": "consensus_eps_forecast", "dtype": "float64"}, {"name": "high_eps_forecast", "dtype": "float64"}, {"name": "low_eps_forecast", "dtype": "float64"}, {"name": "no_of_estimates", "dtype": "int64"}, {"name": "up", "dtype": "int64"}, {"name": "down", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 267825, "num_examples": 3000}], "download_size": 26980, "dataset_size": 267825}}
|
2023-06-10T02:11:19+00:00
|
b846aa1dc42744dbee174e4f9b0d4cdb07093e2f
|
# REMED
A dataset of reversi boards and their winning status.
|
AndreiSva/REMED
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T02:20:50+00:00
|
{"license": "apache-2.0"}
|
2023-06-10T02:25:50+00:00
|
2efa7bc5eefda69761d029efd6c779df3179da39
|
# Dataset Card for "testing_donuts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jdchang3/testing_donuts
|
[
"region:us"
] |
2023-06-10T02:46:06+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 173810204.0, "num_examples": 100}, {"name": "test", "num_bytes": 10717135.0, "num_examples": 6}, {"name": "validation", "num_bytes": 21916526.0, "num_examples": 12}], "download_size": 202372653, "dataset_size": 206443865.0}}
|
2023-06-10T02:52:03+00:00
|
c313c06f3273b7e0f9a05c92dd3ce805dfd3c6bd
|
# ✨ PanCollection
🤗 To get started with PanCollection benchmark (training, inference, etc.), we recommend reading [Google Colab](https://colab.research.google.com/drive/1KpWWj1lVUGllZCws01zQfd6CeURuGL2O#scrollTo=k53dsFhAdp6n)!
## Recommendations
We recommend users to use the code-toolbox [DLPan-Toolbox](https://github.com/liangjiandeng/DLPan-Toolbox/tree/main/02-Test-toolbox-for-traditional-and-DL(Matlab)) + the dataset [PanCollection](https://drive.google.com/drive/folders/15VXUjqPybtqUN_spKfJbw40W05K4nDdY?usp=sharing) for fair training and testing!
### Deploy
PanCollection has provided complete packages.
```
pip install pancollection --upgrade
```
## How to Get Started with the Model
```python
import pancollection as pan
cfg = pan.TaskDispatcher.new(task='pansharpening', mode='entrypoint', arch='FusionNet',
dataset_name="gf2", use_resume=False,
dataset={'train': 'gf2', 'test': 'test_gf2_multiExm1.h5'})
print(pan.TaskDispatcher._task)
pan.trainer.main(cfg, pan.build_model, pan.getDataSession)
```
## Training Details
See [Google Colab](https://colab.research.google.com/drive/1KpWWj1lVUGllZCws01zQfd6CeURuGL2O) for quick start.
See [Github Project](https://github.com/XiaoXiao-Woo/PanCollection) for coding details.
## Evaluation
See the [Leaderboard](https://paperswithcode.com/dataset/worldview-3-pancollection) for model results.
See the [PanCollection Paper](https://liangjiandeng.github.io/papers/2022/deng-jig2022.pdf) for early results.
| **Satellite** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| WorldView-3 | 2047 | |
| QuickBird | 2047 | |
| GaoFen-2 | 1023 | |
| WorldView-2 | 2047 | |
## Citation
To learn more about the PanCollection dataset, see the [Github Pages](https://github.com/liangjiandeng/PanCollection).
```
@ARTICLE{dengjig2022,
author={邓良剑，冉燃，吴潇，张添敬},
journal={中国图象图形学报},
title={遥感图像全色锐化的卷积神经网络方法研究进展},
year={2022},
volume={},
number={9},
pages={},
doi={10.11834/jig.220540}
}
```
```
@ARTICLE{deng2022vivone,
author={L. -J. Deng, G. Vivone, M. E. Paoletti, G. Scarpa, J. He, Y. Zhang, J. Chanussot, and A. Plaza},
journal={IEEE Geoscience and Remote Sensing Magazine},
title={Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks},
year={2022},
volume={10},
number={3},
pages={279-315},
doi={10.1109/MGRS.2022.3187652}
}
```
## License
PanCollection is made available under the GPLv2.0 license.
## Contact
[email protected]
[email protected]
|
elsting/PanCollection
|
[
"size_categories:1K<n<10K",
"language:en",
"license:gpl-2.0",
"Pytorch",
"region:us"
] |
2023-06-10T03:24:53+00:00
|
{"language": "en", "license": "gpl-2.0", "size_categories": ["1K<n<10K"], "datasets": ["PanCollection"], "tags": ["Pytorch"]}
|
2023-06-10T05:12:29+00:00
|
d6e3211743a23f568ebb830f5a45676d27b3eab0
|
This dataset is the result of roughly 250k instruction/response pairs being generated by Claude, with instances of blatant alignment removed.
213375 instructions remain.
This dataset is experimental in two ways:
1. From start to finish, it was generated entirely synthetically through Anthropic's Claude AI.
2. It was generated using a somewhat imperfect recreation of the evol-instruct method. 50k instructions were initially synthetically generated then ran through four epochs of evol-instruct.
|
Norquinal/claude_evol_instruct_210k
|
[
"region:us"
] |
2023-06-10T05:00:28+00:00
|
{}
|
2023-07-17T03:10:04+00:00
|
091023164c5662aafff2f6510a6b83d637d9dc96
|
# Dataset Card for "cd45rb_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polejowska/cd45rb_test
|
[
"region:us"
] |
2023-06-10T05:10:49+00:00
|
{"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "leukocyte"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "test", "num_bytes": 4074586864.944, "num_examples": 2116}], "download_size": 4077802300, "dataset_size": 4074586864.944}}
|
2023-06-10T05:19:39+00:00
|
26092fd7348b290fb1eeaa7f663446558a2935ac
|
# Dataset Card for "shortjokes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arunma/shortjokes
|
[
"region:us"
] |
2023-06-10T05:20:11+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23012843, "num_examples": 231597}, {"name": "test", "num_bytes": 5497, "num_examples": 60}], "download_size": 0, "dataset_size": 23018340}}
|
2023-06-10T06:39:09+00:00
|
0763a0f1d0bc9b7a4af5384d61d7511a88390283
|
# Dataset Card for "cd45rb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polejowska/cd45rb
|
[
"region:us"
] |
2023-06-10T05:42:51+00:00
|
{"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "leukocyte"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 35879463408.88, "num_examples": 18421}, {"name": "valid", "num_bytes": 3475442128.938, "num_examples": 1781}, {"name": "test", "num_bytes": 4074586864.944, "num_examples": 2116}], "download_size": 43275144782, "dataset_size": 43429492402.762}}
|
2023-06-10T07:06:52+00:00
|
97640ad40be5d73377ead7072884e471de414534
|
WelfCrozzo/kupalinka
|
[
"task_categories:translation",
"size_categories:1M<n<10M",
"language:be",
"language:en",
"language:ru",
"license:mit",
"region:us"
] |
2023-06-10T06:08:02+00:00
|
{"language": ["be", "en", "ru"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["translation"], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "input_lang", "dtype": "string"}, {"name": "output_lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7142399468, "num_examples": 4499046}, {"name": "validation", "num_bytes": 792724656, "num_examples": 499504}], "download_size": 1554482578, "dataset_size": 7935124124}}
|
2023-09-03T13:47:32+00:00
|
|
d3da2e12e37688d52d9c2cbf61a4e6c2102a5711
|
# 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]
|
Chat-Error/Super-good-instruction-data
|
[
"region:us"
] |
2023-06-10T06:25:50+00:00
|
{}
|
2023-06-11T03:08:42+00:00
|
4d906b28e77adb8b0184285c12e36270b3bc0d23
|
Msun/dota
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T06:37:55+00:00
|
{"license": "apache-2.0"}
|
2023-06-11T07:53:55+00:00
|
|
b95fd18d7ffa8153bac6cf034782bb4f00bffd3d
|
0xharib/xword1
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-06-10T06:58:47+00:00
|
{"license": "cc-by-4.0"}
|
2023-06-17T12:14:25+00:00
|
|
2e6b21cec15c74df188a04a2a347bbe9fb25fb3e
|
badri55/First_aid__dataset
|
[
"license:cc0-1.0",
"region:us"
] |
2023-06-10T07:15:18+00:00
|
{"license": "cc0-1.0"}
|
2023-06-10T07:40:07+00:00
|
|
446ead8263fa0c96b61c4e38b1c8a8dcf23f71c2
|
deepghs/anime_censor_detection
|
[
"task_categories:object-detection",
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] |
2023-06-10T07:45:13+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["object-detection"], "tags": ["art"]}
|
2023-06-10T07:55:27+00:00
|
|
f403404d7545018b4427b1b9d58a6f52dd021ec2
|
# Dataset Card for "ingredient_prompt_to_sugar_level"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ziq/ingredient_prompt_to_sugar_level
|
[
"region:us"
] |
2023-06-10T08:10:21+00:00
|
{"dataset_info": {"features": [{"name": "src", "dtype": "string"}, {"name": "ingredients", "dtype": "string"}, {"name": "sugar", "dtype": "float64"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19687446.838965006, "num_examples": 21065}, {"name": "test", "num_bytes": 1712195.708947728, "num_examples": 1832}], "download_size": 9693216, "dataset_size": 21399642.547912735}}
|
2023-06-12T04:54:45+00:00
|
2998e9990298e74a88d9a83f2ccdf44a8cdb6790
|
Msun/dota2
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T08:11:46+00:00
|
{"license": "apache-2.0"}
|
2023-06-10T09:03:40+00:00
|
|
93aa613b4aaa795fe3e581a6caedec7c4c5b8db7
|
# Dataset Card for "medication_chat_commands_with_med_name"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
stoddur/medication_chat_commands_with_med_name
|
[
"region:us"
] |
2023-06-10T08:36:17+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 337663524.0, "num_examples": 220407}], "download_size": 12130894, "dataset_size": 337663524.0}}
|
2023-06-10T08:40:20+00:00
|
d120c6732abea458dce2cfc9ad1aab17525acec7
|
Bayany/NER
|
[
"task_categories:text-classification",
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"license:bsd",
"region:us"
] |
2023-06-10T08:48:49+00:00
|
{"language": ["en"], "license": "bsd", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "summarization"], "pretty_name": "webtoon NER"}
|
2023-06-14T05:51:24+00:00
|
|
99bfbcb394b70e43d6f4ff21a9f61496808b3574
|
# Dataset Card for Common Voice Corpus 11.0
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus
- **Paper:** https://ieeexplore.ieee.org/document/10022652
### Dataset Summary
MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels.
The dataset is multi-regional, multi-genre, and multi-dialect intended to advance the research and development of Arabic speech technology with a special emphasis on Arabic speech recognition.
### Supported Tasks
- Automatics Speach Recognition
### Languages
```
Arabic
```
## How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
```python
from datasets import load_dataset
masc = load_dataset("pain/MASC", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
masc = load_dataset("pain/MASC", split="train", streaming=True)
print(next(iter(masc)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
### Local
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
masc = load_dataset("pain/MASC", split="train")
batch_sampler = BatchSampler(RandomSampler(masc), batch_size=32, drop_last=False)
dataloader = DataLoader(masc, batch_sampler=batch_sampler)
```
### Streaming
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
masc = load_dataset("pain/MASC", split="train")
dataloader = DataLoader(masc, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on MASC with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
```python
{'video_id': 'OGqz9G-JO0E', 'start': 770.6, 'end': 781.835, 'duration': 11.24,
'text': 'اللهم من ارادنا وبلادنا وبلاد المسلمين بسوء اللهم فاشغله في نفسه ورد كيده في نحره واجعل تدبيره تدميره يا رب العالمين',
'type': 'c', 'file_path': '87edeceb-5349-4210-89ad-8c3e91e54062_OGqz9G-JO0E.wav',
'audio': {'path': None,
'array': array([
0.05938721,
0.0539856,
0.03460693, ...,
0.00393677,
0.01745605,
0.03045654
]), 'sampling_rate': 16000
}
}
```
### Data Fields
`video_id` (`string`): An id for the video that the voice has been created from
`start` (`float64`): The start of the audio's chunk
`end` (`float64`): The end of the audio's chunk
`duration` (`float64`): The duration of the chunk
`text` (`string`): The text of the chunk
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`type` (`string`): It refers to the data set type, either clean or noisy where "c: clean and n: noisy"
'file_path' (`string`): A path for the audio chunk
"audio" ("audio"): Audio for the chunk
### Data Splits
The speech material has been subdivided into portions for train, dev, test.
The dataset splits has clean and noisy data that can be determined by type field.
### Citation Information
```
@INPROCEEDINGS{10022652,
author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
title={MASC: Massive Arabic Speech Corpus},
year={2023},
volume={},
number={},
pages={1006-1013},
doi={10.1109/SLT54892.2023.10022652}}
}
```
|
pain/MASC
|
[
"task_categories:automatic-speech-recognition",
"language:ar",
"license:cc-by-4.0",
"region:us"
] |
2023-06-10T09:00:21+00:00
|
{"language": ["ar"], "license": ["cc-by-4.0"], "size_categories": {"ar": ["n==1k"]}, "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "MASC dataset", "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the MASC dataset."}
|
2023-06-12T18:48:45+00:00
|
ec08b3419e0c3ac92ede095206c931fb7f616990
|
Am0MuK/md_invoices
|
[
"language:ro",
"language:ru",
"region:us"
] |
2023-06-10T09:20:02+00:00
|
{"language": ["ro", "ru"], "pretty_name": "invoices"}
|
2023-06-10T09:22:41+00:00
|
|
86ce3d150e0c4b0e844699fe2d9eded6d3ea1353
|
pcranaway/reddit-2011
|
[
"license:unknown",
"region:us"
] |
2023-06-10T09:53:03+00:00
|
{"license": "unknown"}
|
2023-06-10T10:11:21+00:00
|
|
b249a5a471f2b1579bfc5b6752b461b3e57fab77
|
# Dataset Card for "ICDAR2019"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MohamedExperio/ICDAR2019
|
[
"region:us"
] |
2023-06-10T10:09:49+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "struct": [{"name": "gt_parses", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 170887114.0, "num_examples": 300}, {"name": "validation", "num_bytes": 55500511.0, "num_examples": 100}, {"name": "test", "num_bytes": 79123638.0, "num_examples": 126}], "download_size": 0, "dataset_size": 305511263.0}}
|
2023-06-12T07:41:11+00:00
|
8998a1adafd17a31c6c4190f75116424a5f01025
|
CountFloyd/bark-german-semantic-wav-training
|
[
"language:de",
"region:us"
] |
2023-06-10T10:29:23+00:00
|
{"language": ["de"]}
|
2023-06-10T10:58:09+00:00
|
|
23103c025331f228b506400dffbdf558f63fc55c
|
Deneme sürümdür lütfen kullanmayınız.
---
license: apache-2.0
---
|
afkfatih/turkishdataset
|
[
"region:us"
] |
2023-06-10T10:42:09+00:00
|
{}
|
2023-06-10T10:44:18+00:00
|
4deea43fb8d1ce85580fae6e1de7ada49eb7b6e7
|
# Dataset Card for "cartoon-captioned-datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shellypeng/cartoon-captioned-datasets
|
[
"region:us"
] |
2023-06-10T11:31:28+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 998236826.0, "num_examples": 715}], "download_size": 949407346, "dataset_size": 998236826.0}}
|
2023-06-11T01:01:04+00:00
|
ac6ea2586e2759ff87b5d5565358ca02cb1d2112
|
tpinville/test_edgy
|
[
"license:unknown",
"region:us"
] |
2023-06-10T11:47:45+00:00
|
{"license": "unknown"}
|
2023-06-11T18:53:27+00:00
|
|
0c65da512999036b39490646d97a4bd400865c84
|
ThiennNguyen/ImageSynthetics
|
[
"license:openrail",
"region:us"
] |
2023-06-10T12:09:24+00:00
|
{"license": "openrail"}
|
2023-06-10T13:11:45+00:00
|
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