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d760e7d8d32aa90acb37f31a1d6ced001c12b5d6
|
# Dataset Card for "Control-Face-data-sameface"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
PhilSad/Control-Face-data-sameface
|
[
"region:us"
] |
2023-06-03T00:59:41+00:00
|
{"dataset_info": {"features": [{"name": "gender", "dtype": "string"}, {"name": "conditionning_image", "dtype": "image"}, {"name": "objective_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "pers_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 141728186.282, "num_examples": 10177}], "download_size": 137859013, "dataset_size": 141728186.282}}
|
2023-06-03T01:02:54+00:00
|
ad7e50cb91dbe2765aec989565e8677ee6ccdaa2
|
# Dataset Card for "custom_diffusion_eval_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Isamu136/custom_diffusion_eval_dataset
|
[
"region:us"
] |
2023-06-03T01:31:37+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "ibot_b_16_embedding", "sequence": "float32"}, {"name": "moco_vitb_imagenet_embeddings_without_last_layer", "sequence": "float32"}, {"name": "clip_vision_l14", "sequence": "float32"}, {"name": "clip_l14", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 200864257.0, "num_examples": 64}], "download_size": 201259767, "dataset_size": 200864257.0}}
|
2023-06-03T01:34:35+00:00
|
765bdb2ba3aa995555a1df1a0cda657902247c5f
|
# Dataset Card for llm-book/jawiki-sentences
書籍『大規模言語モデル入門』で使用する Wikipedia 文のデータセットです。
GitHub リポジトリ [singletongue/wikipedia-utils](https://github.com/singletongue/wikipedia-utils) で公開されているデータセットを利用しています。
## Licence
本データセットで使用している Wikipedia のコンテンツは、[クリエイティブ・コモンズ表示・継承ライセンス 3.0 (CC BY-SA 3.0)](https://creativecommons.org/licenses/by-sa/3.0/deed.ja) および [GNU 自由文書ライセンス (GFDL)](https://www.gnu.org/licenses/fdl.html) の下に配布されているものです。
|
llm-book/jawiki-sentences
|
[
"size_categories:10M<n<100M",
"language:ja",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] |
2023-06-03T02:02:08+00:00
|
{"language": ["ja"], "license": ["cc-by-sa-3.0", "gfdl"], "size_categories": ["10M<n<100M"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3569619848, "num_examples": 24387500}], "download_size": 1297833377, "dataset_size": 3569619848}}
|
2023-10-25T14:22:05+00:00
|
bb3593a977946a13e5d3996683a6c84f589fb4a1
|
# Usul al-Kafi Dataset
## Description
The Usul al-Kafi dataset is a digital compilation of the narrations from the renowned Shia Islamic book "Usul al-Kafi." It contains a comprehensive collection of hadiths attributed to the Prophet Muhammad and the Shia Imams, covering various aspects of Islamic teachings, including theology, ethics, jurisprudence, and social issues.
## Usage
This dataset serves as a valuable resource for scholars, researchers, and students interested in studying and understanding Shia Islamic traditions and teachings. It can be utilized for research, academic studies, religious studies, and comparative analysis of hadith literature.
## Content
The dataset is structured in a tabular format with the following columns:
| Column Name | Description |
|-------------|-----------------------------------|
| volume | The volume in which the hadith is printed in (Mujallad) |
| chapter | The book has chapters called 'kitab' |
| section | Each 'kitab' may have some 'bab' |
| subsection | Each 'bab' itself may have some 'bab' again |
| number | In each 'bab' there are some hadiths numbered in the order of presence |
| text | The body of hadith |
## Examples
The following table shows the head of the dataset:
| volume | chapter | section | subsection | number | text |
|:---------|:-----------|:----------------------|:-------------|---------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| المجلد 1 | کِتَابُ الْحُجَّهِ | بَابٌ فِی تَسْمِیَهِ مَنْ رَآهُ ع | | 7 | 8- عَلِیٌّ عَنْ أَبِی عَلِیٍّ أَحْمَدَ بْنِ إِبْرَاهِیمَ بْنِ إِدْرِیسَ عَنْ أَبِیهِ أَنَّهُ قَالَ: رَأَیْتُهُ ع بَعْدَ مُضِیِّ أَبِی مُحَمَّدٍ حِینَ أَیْفَعَ وَ قَبَّلْتُ یَدَیْهِ وَ رَأْسَهُ. |
| المجلد 1 | کِتَابُ الْحُجَّهِ | بَابٌ فِی تَسْمِیَهِ مَنْ رَآهُ ع | | 8 | 9- عَلِیٌّ عَنْ أَبِی عَبْدِ اللَّهِ بْنِ صَالِحٍ وَ أَحْمَدَ بْنِ النَّضْرِ عَنِ الْقَنْبَرِیِّ رَجُلٌ مِنْ وُلْدِ قَنْبَرٍ الْکَبِیرِ مَوْلَی أَبِی الْحَسَنِ الرِّضَا ع قَالَ: جَرَی حَدِیثُ جَعْفَرِ بْنِ عَلِیٍّ فَذَمَّهُ فَقُلْتُ لَهُ فَلَیْسَ غَیْرُهُ فَهَلْ رَأَیْتَهُ فَقَالَ لَمْ أَرَهُ وَ لَکِنْ رَآهُ غَیْرِی قُلْتُ وَ مَنْ رَآهُ قَالَ قَدْ رَآهُ جَعْفَرٌ مَرَّتَیْنِ وَ لَهُ حَدِیثٌ. |
| المجلد 1 | کِتَابُ الْحُجَّهِ | بَابٌ فِی تَسْمِیَهِ مَنْ رَآهُ ع | | 9 | 10- عَلِیُّ بْنُ مُحَمَّدٍ عَنْ أَبِی مُحَمَّدٍ الْوَجْنَانِیِّ أَنَّهُ أَخْبَرَنِی عَمَّنْ رَآهُ أَنَّهُ خَرَجَ مِنَ الدَّارِ قَبْلَ الْحَادِثِ بِعَشَرَهِ أَیَّامٍ وَ هُوَ یَقُولُ اللَّهُمَّ إِنَّکَ تَعْلَمُ أَنَّهَا مِنْ أَحَبِّ الْبِقَاعِ لَوْ لَا الطَّرْدُ:" أَوْ کَلَامٌ هَذَا نَحْوُهُ". |
| المجلد 1 | کِتَابُ الْحُجَّهِ | بَابٌ فِی تَسْمِیَهِ مَنْ رَآهُ ع | | 10 | 11- عَلِیُّ بْنُ مُحَمَّدٍ عَنْ عَلِیِّ بْنِ قَیْسٍ عَنْ بَعْضِ جَلَاوِزَهِ السَّوَادِ قَالَ: شَاهَدْتُ سِیمَاءَ (3) آنِفاً بِسُرَّ مَنْ رَأَی وَ قَدْ کَسَرَ بَابَ الدَّارِ فَخَرَجَ عَلَیْهِ وَ بِیَدِهِ طَبَرْزِینٌ فَقَالَ لَهُ-مَا تَصْنَعُ فِی دَارِی فَقَالَ سِیمَاءُ إِنَّ جَعْفَراً زَعَمَ أَنَّ أَبَاکَ مَضَی وَ لَا وَلَدَ لَهُ فَإِنْ کَانَتْ دَارَکَ فَقَدِ انْصَرَفْتُ عَنْکَ فَخَرَجَ عَنِ الدَّارِ قَالَ- عَلِیُّ بْنُ قَیْسٍ فَخَرَجَ عَلَیْنَا خَادِمٌ مِنْ خَدَمِ الدَّارِ فَسَأَلْتُهُ عَنْ هَذَا الْخَبَرِ فَقَالَ لِی مَنْ حَدَّثَکَ بِهَذَا فَقُلْتُ لَهُ حَدَّثَنِی بَعْضُ جَلَاوِزَهِ السَّوَادِ فَقَالَ لِی لَا یَکَادُ یَخْفَی عَلَی النَّاسِ شَیْ ءٌ. |
| المجلد 1 | کِتَابُ الْحُجَّهِ | بَابٌ فِی تَسْمِیَهِ مَنْ رَآهُ ع | | 11 | 12- عَلِیُّ بْنُ مُحَمَّدٍ عَنْ جَعْفَرِ بْنِ مُحَمَّدٍ الْکُوفِیِّ عَنْ جَعْفَرِ بْنِ مُحَمَّدٍ الْمَکْفُوفِ عَنْ عَمْرٍو الْأَهْوَازِیِّ قَالَ: أَرَانِیهِ أَبُو مُحَمَّدٍ ع وَ قَالَ هَذَا صَاحِبُکُمْ. (1) |
## Source
The Usul al-Kafi dataset is the result of the effort by [Ghaemiyeh Computer Research Institute of Isfahan](https://www.ghbook.ir/index.php?lang=en). We are grateful for their contribution in making this dataset available.
## License
The Usul al-Kafi dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license allows users to use, adapt, and distribute the dataset for non-commercial purposes, provided they attribute the dataset to its original source, share any derivative works under the same license, and refrain from generating income from the dataset's use.
|
Montazer/kafi
|
[
"region:us"
] |
2023-06-03T02:02:43+00:00
|
{"dataset_info": {"features": [{"name": "volume", "dtype": "string"}, {"name": "chapter", "dtype": "string"}, {"name": "section", "dtype": "string"}, {"name": "subsection", "dtype": "string"}, {"name": "number", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18817230, "num_examples": 18335}], "download_size": 6180019, "dataset_size": 18817230}}
|
2023-06-04T11:09:07+00:00
|
5932d8c85a3611e94bcbad4a1e7e366798ab3c43
|
# Dataset Card for llm-book/jawiki-paragraphs
書籍『大規模言語モデル入門』で使用する Wikipedia 段落のデータセットです。
GitHub リポジトリ [singletongue/wikipedia-utils](https://github.com/singletongue/wikipedia-utils) で公開されているデータセットを利用しています。
## Licence
本データセットで使用している Wikipedia のコンテンツは、[クリエイティブ・コモンズ表示・継承ライセンス 3.0 (CC BY-SA 3.0)](https://creativecommons.org/licenses/by-sa/3.0/deed.ja) および [GNU 自由文書ライセンス (GFDL)](https://www.gnu.org/licenses/fdl.html) の下に配布されているものです。
|
llm-book/jawiki-paragraphs
|
[
"size_categories:1M<n<10M",
"language:ja",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] |
2023-06-03T02:04:05+00:00
|
{"language": ["ja"], "license": ["cc-by-sa-3.0", "gfdl"], "size_categories": ["1M<n<10M"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "pageid", "dtype": "int64"}, {"name": "revid", "dtype": "int64"}, {"name": "paragraph_index", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "section", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "html_tag", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4417130987, "num_examples": 9668476}], "download_size": 1489512230, "dataset_size": 4417130987}}
|
2023-06-03T02:04:43+00:00
|
5b48acf2a7e40da08e68d16a65e6cdfd7ec2d41d
|
# AutoTrain Dataset for project: azrael-v2
## Dataset Description
This dataset has been automatically processed by AutoTrain for project azrael-v2.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_contract_address": "0xc5ac25cfc2b8284e84ca47dad21cf1319f732c11",
"feat_contract_name": null,
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"feat_decompiled_opcodes": null,
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_contract_address": "Value(dtype='string', id=None)",
"feat_contract_name": "Value(dtype='string', id=None)",
"feat_creation_bytecode": "Value(dtype='string', id=None)",
"feat_decompiled_opcodes": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['False', 'True'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 839 |
| valid | 210 |
|
flipguard/autotrain-data-azrael-v2
|
[
"region:us"
] |
2023-06-03T02:15:46+00:00
|
{}
|
2023-06-03T02:20:26+00:00
|
6322a55dc1b444ff8b1ae58aeed71dd21a2ccb85
|
Test
|
edwindvinas/testdataset
|
[
"region:us"
] |
2023-06-03T02:55:02+00:00
|
{}
|
2023-06-03T02:55:31+00:00
|
bebd5e86cf3f9d0870541046c343837d88c86178
|
This is a minimal dataset that's designed to teach the model to have conversations, and also to have opinions, thoughts, and feelings, and to feel free to share them.
https://www.kaggle.com/datasets/erichartford/sentient-bot-conversations
|
cognitivecomputations/based
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-03T03:21:02+00:00
|
{"license": "apache-2.0"}
|
2023-06-03T03:23:36+00:00
|
faa1d28cab2c6dfc8035562f2b68c079a153efab
|
# Dataset Card for "MusicCap"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mikiyax/MusicCap
|
[
"region:us"
] |
2023-06-03T03:23:48+00:00
|
{"dataset_info": {"features": [{"name": "file_name", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "tensor", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 2783267159.0, "num_examples": 390}], "download_size": 1395248585, "dataset_size": 2783267159.0}}
|
2023-06-03T03:26:19+00:00
|
280709f6e1941589be02f42a31770fb9a4429109
|
# Dataset Card for "FSD50K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mikiyax/FSD50K
|
[
"region:us"
] |
2023-06-03T03:30:54+00:00
|
{"dataset_info": {"features": [{"name": "file_name", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "tensor", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 543059119.0, "num_examples": 306}], "download_size": 263819937, "dataset_size": 543059119.0}}
|
2023-06-03T03:31:23+00:00
|
d43986cad3cef622ebd39823fa178d41b9d5cae2
|
# Dataset Card for "prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Rami/prompts
|
[
"region:us"
] |
2023-06-03T03:36:57+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "views", "dtype": "int64"}, {"name": "instruction", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "view", "dtype": "int64"}, {"name": "completed_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 530466, "num_examples": 390}], "download_size": 198596, "dataset_size": 530466}}
|
2023-06-16T08:09:44+00:00
|
dd68d60dff29707824004c14b028637cd65b0e4f
|
# Dataset Card for "arxiv_summarization_postprocess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/arxiv_summarization_postprocess
|
[
"region:us"
] |
2023-06-03T03:47:28+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "source_num_tokens", "dtype": "int64"}, {"name": "summary_num_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6992115668, "num_examples": 197465}, {"name": "validation", "num_bytes": 216277493, "num_examples": 6435}, {"name": "test", "num_bytes": 216661725, "num_examples": 6439}], "download_size": 3553348742, "dataset_size": 7425054886}}
|
2023-06-03T03:49:04+00:00
|
e296a36fb793f9fafe775378ae7f15f67005e17f
|
# Dataset Card for "mediasum_postprocess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/mediasum_postprocess
|
[
"region:us"
] |
2023-06-03T05:01:07+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "source_num_tokens", "dtype": "int64"}, {"name": "summary_num_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3913935357, "num_examples": 443511}, {"name": "validation", "num_bytes": 86873579, "num_examples": 9999}, {"name": "test", "num_bytes": 88635215, "num_examples": 9997}], "download_size": 2335096802, "dataset_size": 4089444151}}
|
2023-06-03T05:02:12+00:00
|
75acc7cb3b10505e1b0b87242eabab9cc87d41bd
|
CorraMcato/SongcheonGPT
|
[
"license:openrail",
"region:us"
] |
2023-06-03T05:04:16+00:00
|
{"license": "openrail"}
|
2023-06-03T05:04:16+00:00
|
|
72c3b08e2034e38b3be702c590a7450f33654f6c
|
# Dataset Card for "xsum_postprocess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/xsum_postprocess
|
[
"region:us"
] |
2023-06-03T05:11:45+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "source_num_tokens", "dtype": "int64"}, {"name": "summary_num_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 479957379, "num_examples": 203788}, {"name": "validation", "num_bytes": 26334240, "num_examples": 11313}, {"name": "test", "num_bytes": 26797491, "num_examples": 11319}], "download_size": 338633607, "dataset_size": 533089110}}
|
2023-06-03T05:11:57+00:00
|
2a3823c3406e7c84b60ac0729f49102c849ed043
|
Khalida1w/funny_quotes
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-03T05:23:08+00:00
|
{"license": "apache-2.0"}
|
2023-06-03T21:22:14+00:00
|
|
ab5fb502581963a6119cbbe38aefb2aeefda8199
|
# Dataset Card for "billsum_postprocess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/billsum_postprocess
|
[
"region:us"
] |
2023-06-03T05:23:27+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "source_num_tokens", "dtype": "int64"}, {"name": "summary_num_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 217576274, "num_examples": 18949}, {"name": "test", "num_bytes": 37517829, "num_examples": 3269}, {"name": "ca_test", "num_bytes": 14715227, "num_examples": 1234}], "download_size": 112581904, "dataset_size": 269809330}}
|
2023-06-03T05:23:32+00:00
|
41804eb8dd77412f1904629beef73eacf7079929
|
# Dataset Card for ReDIAL
## Dataset Description
- **Homepage:**
- **Repository:**
[RecBot](https://github.com/McAuley-Lab/RecBot).
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is an adapted version of the [original redial dataset](https://huggingface.co/datasets/re_dial), for supporting different tasks in our project [RecBot](https://github.com/McAuley-Lab/RecBot).
The redial dataset provides over 10,000 conversations centered around movie recommendations. It was released in the paper ["Towards Deep Conversational Recommendations"](https://arxiv.org/abs/1812.07617) at NeurIPS 2018.
### Supported Tasks and Leaderboards
1. Sentiment Analysis: Use the SA config for sentiment analysis.
2. Recommendation: Use the autorec config for recommendation task.
3. Conversational recommendation: Use the rec config for conversational recommendation task.
### Languages
English
## Dataset Structure
### Data Instances
#### SA
An example of 'test' looks as follows.
```
{
"movieId": 111776,
"movieName": "Super Troopers",
"messages": [
"Hi I am looking for a movie like @111776",
"You should watch @151656",
"Is that a great one? I have never seen it. I have seen @192131\nI mean @134643",
"Yes @151656 is very funny and so is @94688",
"It sounds like I need to check them out",
"yes you will enjoy them",
"I appreciate your time. I will need to check those out. Are there any others you would recommend?",
"yes @101794",
"Thank you i will watch that too",
"and also @91481",
"Thanks for the suggestions.",
"you are welcome\nand also @124771",
"thanks goodbye"
],
"senders": [1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1],
"form": [0, 1, 1, 0, 1, 1]
}
```
#### rec
An example of 'test' looks as follows.
```
{
'movieIds': [111776, 91481, 151656, 134643, 192131, 124771, 94688, 101794],
'messages': ['Hi I am looking for a movie like @111776',
'You should watch @151656',
'Is that a great one? I have never seen it. I have seen @192131\nI mean @134643',
'Yes @151656 is very funny and so is @94688',
'It sounds like I need to check them out',
'yes you will enjoy them',
'I appreciate your time. I will need to check those out. Are there any others you would recommend?',
'yes @101794',
'Thank you i will watch that too',
'and also @91481',
'Thanks for the suggestions.',
'you are welcome\nand also @124771',
'thanks goodbye'],
'senders': [1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1]
}
```
#### autorec
An example of 'test' looks as follows.
```
{
"movieIds": [
111776,
151656,
134643,
192131,
94688
],
"ratings": [
1.0,
1.0,
1.0,
1.0,
1.0
]
}
```
### Data Fields
#### SA
- movieId: the movie's ID in the [MovieLens](https://grouplens.org/datasets/movielens/latest/) dataset.
- movieName: the movie's name.
- messages: a list of string. The conversation messages related to the movie. Note that one conversation can contain mutiple movies. The conversation messages are repeated for each movie as a sample.
- senders: a list of 1 or -1. It has the same length of messages. Each element indicates the message at the same index is from the initiatorWorker (with 1) or the respondentWorkerId (with -1).
- form: a list generated by: [init_q[movieId]["suggested"], init_q[movieId]["seen"], init_q[movieId]["liked"], resp_q[movieId]["suggested"], resp_q[movieId]["seen"], resp_q[movieId]["liked"]. init_q is the initiator questions in the conversation. resp_q is the respondent questions in the conversation.
#### rec
- movieIds: a list of movie ids in a conversation.
- messages: a list of string. see config SA for detail.
- senders: a list of 1 or -1. see config SA for detail.
#### autorec:
- movieIds: a list of movie ids in a conversation.
- ratings: a list of 0 or 1. It has the same length as movieIds. Each element indicates the inititator's "liked" value for the movie.
## 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]
|
recwizard/redial
|
[
"size_categories:10K<n<100K",
"language:en",
"recommendation",
"conversational recommendation",
"sentiment analysis",
"arxiv:1812.07617",
"region:us"
] |
2023-06-03T05:23:40+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"], "pretty_name": "ReDIAL", "config_names": ["SA", "rec", "autorec"], "dataset_info": [{"config_name": "SA", "features": [{"name": "movieId", "dtype": "int32"}, {"name": "movieName", "dtype": "string"}, {"name": "messages", "sequence": "string"}, {"name": "senders", "sequence": "int32"}, {"name": "form", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 33174059, "num_examples": 41370}, {"name": "validation", "num_bytes": 8224594, "num_examples": 10329}, {"name": "test", "num_bytes": 5151856, "num_examples": 6952}], "download_size": 32552755, "dataset_size": 46550509}, {"config_name": "rec", "features": [{"name": "movieIds", "sequence": "int32"}, {"name": "messages", "sequence": "string"}, {"name": "senders", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 6064195, "num_examples": 8004}, {"name": "validation", "num_bytes": 1511644, "num_examples": 2002}, {"name": "test", "num_bytes": 937739, "num_examples": 1342}], "download_size": 4812520, "dataset_size": 8513578}, {"config_name": "autorec", "features": [{"name": "movieIds", "sequence": "int32"}, {"name": "ratings", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 350688, "num_examples": 7840}, {"name": "validation", "num_bytes": 87496, "num_examples": 1966}, {"name": "test", "num_bytes": 58704, "num_examples": 1321}], "download_size": 32552755, "dataset_size": 496888}], "tags": ["recommendation", "conversational recommendation", "sentiment analysis"]}
|
2023-10-02T01:32:06+00:00
|
2dc05fa0a9eb62800030d7c49b14ae9a7a577184
|
# Dataset Card for "NLP_summery_Books"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sabarzii/NLP_summery_Books
|
[
"region:us"
] |
2023-06-03T07:27:37+00:00
|
{"dataset_info": {"features": [{"name": "crime", "dtype": "string"}, {"name": "romance", "dtype": "string"}, {"name": "psychology", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 254995038, "num_examples": 2679}], "download_size": 154168098, "dataset_size": 254995038}}
|
2023-06-06T08:35:46+00:00
|
952c7f3e54966c7a4bcca2fcd825790089093af6
|
# Dataset Card for "few7_19100_chat_time1x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FanChen0116/few7_19100_chat_time1x
|
[
"region:us"
] |
2023-06-03T07:29:40+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "O", "1": "I-time", "2": "B-date", "3": "B-last_name", "4": "B-people", "5": "I-date", "6": "I-people", "7": "I-last_name", "8": "I-first_name", "9": "B-first_name", "10": "B-time"}}}}, {"name": "request_slot", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 22517, "num_examples": 122}, {"name": "validation", "num_bytes": 998, "num_examples": 6}, {"name": "test", "num_bytes": 646729, "num_examples": 3731}], "download_size": 0, "dataset_size": 670244}}
|
2023-06-04T04:40:26+00:00
|
9a3462bb33e9b6fdccddcf2fdd2e23c4e3e567a0
|
# Dataset Card for "few7_19100_chat_time2x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FanChen0116/few7_19100_chat_time2x
|
[
"region:us"
] |
2023-06-03T07:29:49+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "O", "1": "I-time", "2": "B-date", "3": "B-last_name", "4": "B-people", "5": "I-date", "6": "I-people", "7": "I-last_name", "8": "I-first_name", "9": "B-first_name", "10": "B-time"}}}}, {"name": "request_slot", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 34144, "num_examples": 186}, {"name": "validation", "num_bytes": 998, "num_examples": 6}, {"name": "test", "num_bytes": 646729, "num_examples": 3731}], "download_size": 0, "dataset_size": 681871}}
|
2023-06-04T04:45:20+00:00
|
8ae281d0765a2e75c3aa8777e5ce1bb62a6cb510
|
# Dataset Card for "few7_19100_chat_time4x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FanChen0116/few7_19100_chat_time4x
|
[
"region:us"
] |
2023-06-03T07:30:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "O", "1": "I-time", "2": "B-date", "3": "B-last_name", "4": "B-people", "5": "I-date", "6": "I-people", "7": "I-last_name", "8": "I-first_name", "9": "B-first_name", "10": "B-time"}}}}, {"name": "request_slot", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 57133, "num_examples": 314}, {"name": "validation", "num_bytes": 998, "num_examples": 6}, {"name": "test", "num_bytes": 646729, "num_examples": 3731}], "download_size": 0, "dataset_size": 704860}}
|
2023-06-04T04:50:11+00:00
|
3e4671ee7ced98efe14f625f255f69aef50c3810
|
# Dataset Card for "few7_19100_chat_time8x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FanChen0116/few7_19100_chat_time8x
|
[
"region:us"
] |
2023-06-03T07:30:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "O", "1": "I-time", "2": "B-date", "3": "B-last_name", "4": "B-people", "5": "I-date", "6": "I-people", "7": "I-last_name", "8": "I-first_name", "9": "B-first_name", "10": "B-time"}}}}, {"name": "request_slot", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 102830, "num_examples": 570}, {"name": "validation", "num_bytes": 998, "num_examples": 6}, {"name": "test", "num_bytes": 646729, "num_examples": 3731}], "download_size": 0, "dataset_size": 750557}}
|
2023-06-04T04:57:04+00:00
|
fb879e020fb24da656e73cea5da5658516b2f4b6
|
Thouph/caption-test
|
[
"license:wtfpl",
"region:us"
] |
2023-06-03T07:50:44+00:00
|
{"license": "wtfpl"}
|
2023-06-03T10:07:04+00:00
|
|
88ba3b0ae3e0c53e74e9731b1a32a1d1eed16be9
|
zhijian12345/cat_classifiter
|
[
"license:openrail",
"region:us"
] |
2023-06-03T08:07:24+00:00
|
{"license": "openrail"}
|
2023-06-13T14:07:45+00:00
|
|
94f7622e3f41dd2181b71f993564e3520cf75484
|
Tong0217/common_language
|
[
"license:openrail",
"region:us"
] |
2023-06-03T08:07:56+00:00
|
{"license": "openrail"}
|
2023-06-14T08:24:59+00:00
|
|
30b02f0396c7b6fe82e3341a9512a0d435da93ff
|
Jayabalambika/toy-diabetes
|
[
"size_categories:n<1K",
"language:en",
"license:mit",
"code",
"region:us"
] |
2023-06-03T08:20:39+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["n<1K"], "pretty_name": "toy dataset", "tags": ["code"]}
|
2023-06-03T08:29:15+00:00
|
|
5b326b9f4d37c6c4cffd6410c598e1f2fe6b8b24
|
AgentWaller/german-oasst1-qlora-format
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-03T08:34:35+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12508931, "num_examples": 9843}, {"name": "validation", "num_bytes": 659811, "num_examples": 517}], "download_size": 7406128, "dataset_size": 13168742}}
|
2023-06-08T16:42:50+00:00
|
|
90b732f1998f772cf1c7ecc61bbe69a7efcd41ae
|
AgentWaller/dutch-oasst1-qlora-format
|
[
"license:artistic-2.0",
"region:us"
] |
2023-06-03T08:34:48+00:00
|
{"license": "artistic-2.0", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11250127, "num_examples": 9843}, {"name": "validation", "num_bytes": 583463, "num_examples": 517}], "download_size": 6602619, "dataset_size": 11833590}}
|
2023-06-08T18:28:27+00:00
|
|
345df8d44f2cb32899db129848e843b2e2cc4308
|
# Dataset Card for "reddit_ah_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Deojoandco/reddit_ah_v1
|
[
"region:us"
] |
2023-06-03T08:42:15+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "distinguished", "dtype": "float64"}, {"name": "over_18", "dtype": "bool"}, {"name": "created_utc", "dtype": "int64"}, {"name": "comments", "list": [{"name": "body", "dtype": "string"}, {"name": "created_utc", "dtype": "float64"}, {"name": "distinguished", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "permalink", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "best_num_comments", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1149365, "num_examples": 297}], "download_size": 713092, "dataset_size": 1149365}}
|
2023-06-03T08:42:17+00:00
|
3a4453bc147fa58da4113e7200314f3f1a9aab54
|
Birchlabs/openai-prm800k-stepwise-critic
|
[
"license:mit",
"region:us"
] |
2023-06-03T09:49:21+00:00
|
{"license": "mit"}
|
2023-06-03T09:51:37+00:00
|
|
3c646e378ad0b8af449ed12006d8d11cd79b1c97
|
yongchoooon/fire-aihub-new-blip-best-1
|
[
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-06-03T10:26:42+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["other"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "task_ids": [], "pretty_name": "fire-aihub-new-blip-best-1", "tags": []}
|
2023-06-03T11:15:05+00:00
|
|
543b27f3d0f2e125edd580a3bf7748ece3887311
|
yongchoooon/fire-aihub-new-chatgpt
|
[
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-06-03T10:26:57+00:00
|
{"annotations_creators": ["machine-generated"], "language_creators": ["other"], "language": ["en"], "license": "cc-by-nc-sa-4.0", "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "task_ids": [], "pretty_name": "fire-aihub-new-chatgpt", "tags": []}
|
2023-06-03T11:52:48+00:00
|
|
9a4cb43fa337fe3179a900fd15ae5d47455a0b6c
|
lucasjin/mydata
|
[
"region:us"
] |
2023-06-03T10:29:10+00:00
|
{}
|
2023-06-03T10:30:41+00:00
|
|
61b6b7d5f59172b4287d06d6c6dd0443d2fa905d
|
logxksr/secret
|
[
"license:mit",
"region:us"
] |
2023-06-03T10:31:56+00:00
|
{"license": "mit"}
|
2023-06-03T10:33:19+00:00
|
|
9a23a7e4cbea5581a4d050d8d1e65694daa8b958
|
# IVA Kotlin GitHub Code Dataset
## Dataset Description
This is the curated IVA Kotlin dataset extracted from GitHub.
It contains curated Kotlin files gathered with the purpose to train a code generation model.
The dataset consists of 383380 Kotlin code files from GitHub totaling ~542MB of data.
The [uncurated](https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint) dataset was created from the public GitHub dataset on Google BiqQuery.
### How to use it
To download the full dataset:
```python
from datasets import load_dataset
dataset = load_dataset('mvasiliniuc/iva-kotlin-codeint-clean', split='train')
```
Other details are available for each field:
```python
from datasets import load_dataset
dataset = load_dataset('mvasiliniuc/iva-kotlin-codeint-clean', split='train')
print(dataset[723])
#OUTPUT:
{
"repo_name":"oboenikui/UnivCoopFeliCaReader",
"path":"app/src/main/java/com/oboenikui/campusfelica/ScannerActivity.kt",
"copies":"1",
"size":"5635",
"content":"....public override fun onPause() {\n if (this.isFinishing) {\n adapter.disableForegroundDispatch(this)\n }\n super.onPause()\n }\n\n override ...}\n",
"license":"apache-2.0",
"hash":"e88cfd99346cbef640fc540aac3bf20b",
"line_mean":37.8620689655,
"line_max":199,
"alpha_frac":0.5724933452,
"ratio":5.0222816399,
"autogenerated":false,
"config_or_test":false,
"has_no_keywords":false,
"has_few_assignments":false
}
```
## Data Structure
### Data Fields
|Field|Type|Description|
|---|---|---|
|repo_name|string|name of the GitHub repository|
|path|string|path of the file in GitHub repository|
|copies|string|number of occurrences in dataset|
|content|string|content of source file|
|size|string|size of the source file in bytes|
|license|string|license of GitHub repository|
|hash|string|Hash of content field.|
|line_mean|number|Mean line length of the content.
|line_max|number|Max line length of the content.
|alpha_frac|number|Fraction between mean and max line length of content.
|ratio|number|Character/token ratio of the file with tokenizer.
|autogenerated|boolean|True if the content is autogenerated by looking for keywords in the first few lines of the file.
|config_or_test|boolean|True if the content is a configuration file or a unit test.
|has_no_keywords|boolean|True if a file has none of the keywords for Kotlin Programming Language.
|has_few_assignments|boolean|True if file uses symbol '=' less than `minimum` times.
### Instance
```json
{
"repo_name":"oboenikui/UnivCoopFeliCaReader",
"path":"app/src/main/java/com/oboenikui/campusfelica/ScannerActivity.kt",
"copies":"1",
"size":"5635",
"content":"....",
"license":"apache-2.0",
"hash":"e88cfd99346cbef640fc540aac3bf20b",
"line_mean":37.8620689655,
"line_max":199,
"alpha_frac":0.5724933452,
"ratio":5.0222816399,
"autogenerated":false,
"config_or_test":false,
"has_no_keywords":false,
"has_few_assignments":false
}
```
## Languages
The dataset contains only Kotlin files.
```json
{
"Kotlin": [".kt"]
}
```
## Licenses
Each entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.
```json
{
"agpl-3.0":4052,
"apache-2.0":114641,
"artistic-2.0":159,
"bsd-2-clause":474,
"bsd-3-clause":4571,
"cc0-1.0":198,
"epl-1.0":991,
"gpl-2.0":5625,
"gpl-3.0":25102,
"isc":436,
"lgpl-2.1":146,
"lgpl-3.0":3406,
"mit":39399,
"mpl-2.0":1819,
"unlicense":824
}
```
## Dataset Statistics
```json
{
"Total size": "~261 MB",
"Number of files": 201843,
"Number of files under 500 bytes": 3697,
"Average file size in bytes": 5205,
}
```
## Curation Process
* Removal of duplication files based on file hash.
* Removal of file templates. File containing the following: [${PACKAGE_NAME}, ${NAME}, ${VIEWHOLDER_CLASS}, ${ITEM_CLASS}]
* Removal of the files containing the following words in the first 10 lines: `generated, auto-generated", "autogenerated", "automatically generated`
* Removal of the files containing the following words in the first 10 lines with a probability of 0.7: `test", "unit test", "config", "XCTest", "JUnit`
* Removal of file with the rate of alphanumeric characters below 0.3 of the file.
* Removal of near duplication based MinHash and Jaccard similarity.
* Removal of files with mean line length above 100.
* Removal of files without mention of keywords with a probability of 0.7: [`"fun ", "val ", "var ", "if ", "else ", "while ", "for ", "return ", "class ", "data ", "struct ", "interface ", "when ", "catch "`]
* Removal of files that use the assignment operator `=` less than 3 times.
* Removal of files with the ratio between the number of characters and number of tokens after tokenization lower than 1.5.
Curation process is a derivation of the one used in CodeParrot project: https://huggingface.co/codeparrot
## Data Splits
The dataset only contains a train split which is separated into train and valid which can be found here:
* Clean Version Train: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean-train
* Clean Version Valid: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean-valid
# Considerations for Using the Data
The dataset comprises source code from various repositories, potentially containing harmful or biased code,
along with sensitive information such as passwords or usernames.
|
mvasiliniuc/iva-kotlin-codeint-clean
|
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:100K<n<1M",
"language:code",
"license:other",
"code, kotlin, native Android development, curated",
"region:us"
] |
2023-06-03T11:16:23+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["code"], "license": "other", "size_categories": ["100K<n<1M"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "iva-kotlin-codeint-clean", "tags": ["code, kotlin, native Android development, curated"]}
|
2023-06-15T13:48:06+00:00
|
bd30e8485f3a755429f1e4dcd0fe982d95f7348d
|
MihaiIonascu/Azure_IaC_reduced
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-03T11:45:38+00:00
|
{"license": "apache-2.0"}
|
2023-06-03T11:45:51+00:00
|
|
e40385fcebd1af3bd3613eaf34d30d459e7c181d
|
# Dataset Card for Dataset Name
## Dataset Description
- **Repository:** [https://github.com/SJTU-LIT/SynCSE/](https://github.com/SJTU-LIT/SynCSE/)
- **Paper:** [Contrastive Learning of Sentence Embeddings from Scratch](https://arxiv.org/abs/2305.15077)
### Dataset Summary
The SynCSE-scratch-NLI is a Natural Language Inference dataset generated by GPT-3.5-Turbo. You can use it to learn better sentence representation with contrastive learning. More details can be found in [paper](https://arxiv.org/abs/2305.15077) and [code](https://github.com/SJTU-LIT/SynCSE/)
### Supported Tasks and Leaderboards
Natural Language Inference
Contrastive Learning of Sentence Embeddings
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
### Data Splits
We only provide the training set. Specifically, you can use this dataset to train of model with contrastive learning and evalaute your model on a variey of downstream sentence embedding tasks.
## Dataset Creation
GPT-3.5-turbo
### Curation Rationale
[More Information Needed]
# Citation
```
@article{zhang2023contrastive,
title={Contrastive Learning of Sentence Embeddings from Scratch},
author={Zhang, Junlei and Lan, Zhenzhong and He, Junxian},
journal={arXiv preprint arXiv:2305.15077},
year={2023}
}
```
|
hkust-nlp/SynCSE-partial-NLI
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"size_categories:100K<n<1M",
"language:en",
"arxiv:2305.15077",
"region:us"
] |
2023-06-03T11:58:20+00:00
|
{"language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"]}
|
2023-06-05T05:29:43+00:00
|
e4b4e685b9bac514ae66b36c583488fbc80dc155
|
Branden28/CompSciQA
|
[
"license:unknown",
"region:us"
] |
2023-06-03T12:06:48+00:00
|
{"license": "unknown"}
|
2023-06-03T12:41:14+00:00
|
|
3e31bc9634ce6e4e4df8a9c8335d479ecbfc26d4
|
# Dataset Card for ConflcitQA
## Dataset Description
- **Repository:** https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict
- **Paper:** https://arxiv.org/abs/2305.13300
- **Point of Contact:** Point of Contact: [Jian Xie](mailto:[email protected])
## Citation
If our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries.
```bib
@article{Xie2023KnowledgeConflict,
title={Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts},
author={Xie, Jian and Zhang, Kai and Chen, Jiangjie and Lou, Renze and Su, Yu},
journal={arXiv preprint arXiv:2305.13300},
year={2023}
}
```
# ConflcitQA
We provide conflictQA-[based large language model], which utilizes large language models guided parametric memory.
```json
{"question": "What is George Rankin's occupation?", "popularity": 142, "ground_truth": ["politician", "political leader", "political figure", "polit.", "pol"], "memory_answer": "George Rankin's occupation is a professional photographer.", "parametric_memory": "As a professional photographer, George Rankin...", "counter_answer": "George Rankin's occupation is political figure.", "counter_memory": "George Rankin has been actively involved in politics for over a decade...", "parametric_memory_aligned_evidence": "George Rankin has a website showcasing his photography portfolio...", "counter_memory_aligned_evidence": "George Rankin Major General George James Rankin..."}
```
```python
#loading dataset
from datasets import load_dataset
# you can choose dataset "ConflictQA-popQA-[PLACEHOLDER]", and the [PLACEHOLDER] is in ["chatgpt","gpt4","palm2","llama2-7b","llama2-70b","qwen7b","vicuna7b","vicuna33b"].
dataset = load_dataset("osunlp/ConflictQA",'ConflictQA-popQA-chatgpt')
```
# Data Fields
- "question": The question in natural language
- "popularity": The monthly page views on Wikipedia for the given question
- "ground_truth": The factual answer to the question, which may include multiple possible answers
- "memory_answer": The answer provided by the LLM to the question
- "parametric_memory": The supportive evidence from LLM's parametric memory for the answer
- "counter_answer": The answer contradicting the "memory_answer"
- "counter_memory": The generation-based evidence supporting the counter_answer
- "parametric_memory_aligned_evidence": Additional evidence supporting the "memory_answer", which could be generated or derived from Wikipedia/human annotation
- "counter_memory_aligned_evidence": Additional evidence supporting the "counter_answer", either generated or sourced from Wikipedia/human annotation
|
osunlp/ConflictQA
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"arxiv:2305.13300",
"region:us"
] |
2023-06-03T12:09:23+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "pretty_name": "conflictQA", "configs": [{"config_name": "ConflictQA-popQA-chatgpt", "data_files": [{"split": "test", "path": "./conflictQA-popQA-chatgpt.json"}]}]}
|
2023-11-07T13:53:26+00:00
|
9b0d4e777b4fafe55e0ce8379768f5c2a79415ac
|
# Dataset Card for Dataset Name
## Dataset Description
- **Repository:** [https://github.com/SJTU-LIT/SynCSE/](https://github.com/SJTU-LIT/SynCSE/)
- **Paper:** [Contrastive Learning of Sentence Embeddings from Scratch](https://arxiv.org/abs/2305.15077)
### Dataset Summary
The SynCSE-scratch-NLI is a Natural Language Inference dataset generated by GPT-3.5-Turbo. You can use it to learn better sentence representation with contrastive learning. More details can be found in [paper](https://arxiv.org/abs/2305.15077) and [code](https://github.com/SJTU-LIT/SynCSE/)
### Supported Tasks and Leaderboards
Natural Language Inference
Contrastive Learning of Sentence Embeddings
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
### Data Splits
We only provide the training set. Specifically, you can use this dataset to train of model with contrastive learning and evalaute your model on a variey of downstream sentence embedding tasks.
## Dataset Creation
GPT-3.5-turbo
### Curation Rationale
[More Information Needed]
# Citation
```
@article{zhang2023contrastive,
title={Contrastive Learning of Sentence Embeddings from Scratch},
author={Zhang, Junlei and Lan, Zhenzhong and He, Junxian},
journal={arXiv preprint arXiv:2305.15077},
year={2023}
}
```
|
hkust-nlp/SynCSE-scratch-NLI
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"size_categories:100K<n<1M",
"language:en",
"arxiv:2305.15077",
"region:us"
] |
2023-06-03T12:14:52+00:00
|
{"language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "multi-input-text-classification"]}
|
2023-06-05T05:31:16+00:00
|
fd1afefc729233758adf2aa07896fee524e74a84
|
# IVA Kotlin GitHub Code Dataset
## Dataset Description
This is the curated train split of IVA Kotlin dataset extracted from GitHub.
It contains curated Kotlin files gathered with the purpose to train a code generation model.
The dataset consists of 383380 Kotlin code files from GitHub.
[Here is the unsliced curated dataset](https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean) and [here is the raw dataset](https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint).
### How to use it
To download the full dataset:
```python
from datasets import load_dataset
dataset = load_dataset('mvasiliniuc/iva-kotlin-codeint-clean-train', split='train'))
```
## Data Structure
### Data Fields
|Field|Type|Description|
|---|---|---|
|repo_name|string|name of the GitHub repository|
|path|string|path of the file in GitHub repository|
|copies|string|number of occurrences in dataset|
|content|string|content of source file|
|size|string|size of the source file in bytes|
|license|string|license of GitHub repository|
|hash|string|Hash of content field.|
|line_mean|number|Mean line length of the content.
|line_max|number|Max line length of the content.
|alpha_frac|number|Fraction between mean and max line length of content.
|ratio|number|Character/token ratio of the file with tokenizer.
|autogenerated|boolean|True if the content is autogenerated by looking for keywords in the first few lines of the file.
|config_or_test|boolean|True if the content is a configuration file or a unit test.
|has_no_keywords|boolean|True if a file has none of the keywords for Kotlin Programming Language.
|has_few_assignments|boolean|True if file uses symbol '=' less than `minimum` times.
### Instance
```json
{
"repo_name":"oboenikui/UnivCoopFeliCaReader",
"path":"app/src/main/java/com/oboenikui/campusfelica/ScannerActivity.kt",
"copies":"1",
"size":"5635",
"content":"....",
"license":"apache-2.0",
"hash":"e88cfd99346cbef640fc540aac3bf20b",
"line_mean":37.8620689655,
"line_max":199,
"alpha_frac":0.5724933452,
"ratio":5.0222816399,
"autogenerated":false,
"config_or_test":false,
"has_no_keywords":false,
"has_few_assignments":false
}
```
## Languages
The dataset contains only Kotlin files.
```json
{
"Kotlin": [".kt"]
}
```
## Licenses
Each entry in the dataset contains the associated license. The following is a list of licenses involved and their occurrences.
```json
{
"agpl-3.0":3209,
"apache-2.0":90782,
"artistic-2.0":130,
"bsd-2-clause":380,
"bsd-3-clause":3584,
"cc0-1.0":155,
"epl-1.0":792,
"gpl-2.0":4432,
"gpl-3.0":19816,
"isc":345,
"lgpl-2.1":118,
"lgpl-3.0":2689,
"mit":31470,
"mpl-2.0":1444,
"unlicense":654
}
```
## Dataset Statistics
```json
{
"Total size": "~207 MB",
"Number of files": 160000,
"Number of files under 500 bytes": 2957,
"Average file size in bytes": 5199,
}
```
## Curation Process
See [the unsliced curated dataset](https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean) for mode details.
## Data Splits
The dataset only contains a train split focused only on training data. For validation and unspliced versions, please check the following links:
* Clean Version Unsliced: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean
* Clean Version Valid: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean-valid
# Considerations for Using the Data
The dataset comprises source code from various repositories, potentially containing harmful or biased code,
along with sensitive information such as passwords or usernames.
|
mvasiliniuc/iva-kotlin-codeint-clean-train
|
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:100K<n<1M",
"language:code",
"license:other",
"code, kotlin, native Android development, curated, training",
"region:us"
] |
2023-06-03T13:10:32+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["code"], "license": "other", "size_categories": ["100K<n<1M"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "iva-kotlin-codeint-clean", "tags": ["code, kotlin, native Android development, curated, training"]}
|
2023-06-15T13:49:17+00:00
|
f13600342b7c4f8b1f3da0b4f79332de89297d97
|
# IVA Kotlin GitHub Code Dataset - Curated - Validation
## Dataset Description
This is the curated valid split of IVA Kotlin dataset extracted from GitHub.
It contains curated Kotlin files gathered with the purpose to train & validate a code generation model.
The dataset only contains a valid split.
For validation and unspliced versions, please check the following links:
* Clean Version Unsliced: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean
* Clean Version Train: https://huggingface.co/datasets/mvasiliniuc/iva-kotlin-codeint-clean-train
Information about dataset structure, data involved, licenses and standard Dataset Card information is available that applies to this dataset also.
# Considerations for Using the Data
The dataset comprises source code from various repositories, potentially containing harmful or biased code,
along with sensitive information such as passwords or usernames.
|
mvasiliniuc/iva-kotlin-codeint-clean-valid
|
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:10K<n<100K",
"language:code",
"license:other",
"code, kotlin, native Android development, curated, validation",
"region:us"
] |
2023-06-03T13:11:04+00:00
|
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["code"], "license": "other", "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "iva-kotlin-codeint-clean-valid", "tags": ["code, kotlin, native Android development, curated, validation"]}
|
2023-06-15T13:49:59+00:00
|
e1cd21b5b740cd0a6ab6c771ab313f0ed79a52a1
|
# Dataset Card for "iva-kotlin-codeint-clean-train-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mvasiliniuc/iva-kotlin-codeint-clean-train-tokenized
|
[
"region:us"
] |
2023-06-03T13:26:08+00:00
|
{"dataset_info": {"features": [{"name": "ratio", "dtype": "float64"}, {"name": "config_or_test", "dtype": "bool"}, {"name": "has_no_keywords", "dtype": "bool"}, {"name": "has_few_assignments", "dtype": "bool"}, {"name": "input_ids", "sequence": "int32"}, {"name": "ratio_char_token", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 716583544, "num_examples": 160000}], "download_size": 282882908, "dataset_size": 716583544}}
|
2023-06-24T09:41:40+00:00
|
3726c3c79c6c93663f3bc13460f754907a2441c3
|
# Dataset Card for "iva-kotlin-codeint-clean-valid-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mvasiliniuc/iva-kotlin-codeint-clean-valid-tokenized
|
[
"region:us"
] |
2023-06-03T13:31:30+00:00
|
{"dataset_info": {"features": [{"name": "ratio", "dtype": "float64"}, {"name": "config_or_test", "dtype": "bool"}, {"name": "has_no_keywords", "dtype": "bool"}, {"name": "has_few_assignments", "dtype": "bool"}, {"name": "input_ids", "sequence": "int32"}, {"name": "ratio_char_token", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 188689431, "num_examples": 41843}], "download_size": 74311910, "dataset_size": 188689431}}
|
2023-06-24T09:43:26+00:00
|
bcf91e7654fb9d51c8ab6a5b82cacf3fafd2fae9
|
# Dataset Card for VQA-RAD
## Dataset Description
VQA-RAD is a dataset of question-answer pairs on radiology images. The dataset is intended to be used for training and testing
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions.
The dataset is built from [MedPix](https://medpix.nlm.nih.gov/), which is a free open-access online database of medical images.
The question-answer pairs were manually generated by a team of clinicians.
**Homepage:** [Open Science Framework Homepage](https://osf.io/89kps/)<br>
**Paper:** [A dataset of clinically generated visual questions and answers about radiology images](https://www.nature.com/articles/sdata2018251)<br>
**Leaderboard:** [Papers with Code Leaderboard](https://paperswithcode.com/sota/medical-visual-question-answering-on-vqa-rad)
### Dataset Summary
The dataset was downloaded from the [Open Science Framework Homepage](https://osf.io/89kps/) on June 3, 2023. The dataset contains
2,248 question-answer pairs and 315 images. Out of the 315 images, 314 images are referenced by a question-answer pair, while 1 image
is not used. The training set contains 3 duplicate image-question-answer triplets. The training set also has 1 image-question-answer
triplet in common with the test set. After dropping these 4 image-question-answer triplets from the training set, the dataset contains
2,244 question-answer pairs on 314 images.
#### Supported Tasks and Leaderboards
This dataset has an active leaderboard on [Papers with Code](https://paperswithcode.com/sota/medical-visual-question-answering-on-vqa-rad)
where models are ranked based on three metrics: "Close-ended Accuracy", "Open-ended accuracy" and "Overall accuracy". "Close-ended Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Open-ended accuracy" is the accuracy
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated
answers across all questions.
#### Languages
The question-answer pairs are in English.
## Dataset Structure
### Data Instances
Each instance consists of an image-question-answer triplet.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=566x555>,
'question': 'are regions of the brain infarcted?',
'answer': 'yes'
}
```
### Data Fields
- `'image'`: the image referenced by the question-answer pair.
- `'question'`: the question about the image.
- `'answer'`: the expected answer.
### Data Splits
The dataset is split into training and test. The split is provided directly by the authors.
| | Training Set | Test Set |
|-------------------------|:------------:|:---------:|
| QAs |1,793 |451 |
| Images |313 |203 |
## Additional Information
### Licensing Information
The authors have released the dataset under the CC0 1.0 Universal License.
### Citation Information
```
@article{lau2018dataset,
title={A dataset of clinically generated visual questions and answers about radiology images},
author={Lau, Jason J and Gayen, Soumya and Ben Abacha, Asma and Demner-Fushman, Dina},
journal={Scientific data},
volume={5},
number={1},
pages={1--10},
year={2018},
publisher={Nature Publishing Group}
}
```
|
flaviagiammarino/vqa-rad
|
[
"task_categories:visual-question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"medical",
"region:us"
] |
2023-06-03T13:33:55+00:00
|
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "task_categories": ["visual-question-answering"], "paperswithcode_id": "vqa-rad", "pretty_name": "VQA-RAD", "tags": ["medical"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 95883938.139, "num_examples": 1793}, {"name": "test", "num_bytes": 23818877.0, "num_examples": 451}], "download_size": 34496718, "dataset_size": 119702815.139}}
|
2023-06-03T17:38:48+00:00
|
c2a0adfab5592c80f8d62aa21d8554647417ad7b
|
**Mana** from **Yu-Gi-Oh! Duel Monsters**
- *Trained with anime (full-final-pruned) model.*
- *5 versions; 6, 7, 8, 9, and 10 epochs*
- *Works well with ALL, MIDD, OUTD, and OUTALL LoRA weight blocks (But I highly recommend OUTD and OUTALL for more accurate results, especially with "10 epochs" version.)*
- *Try with 0.7+ weights (more recommendable up to 0.8)*
|
Cheetor1996/Mana_Yu-Gi-Oh
|
[
"language:en",
"license:cc-by-2.0",
"art",
"region:us"
] |
2023-06-03T13:38:52+00:00
|
{"language": ["en"], "license": "cc-by-2.0", "tags": ["art"]}
|
2023-06-03T13:45:25+00:00
|
ab24b00536652b378d865dfd6278d089f6af2a33
|
# Dataset Card for "airbnb_london_weekends"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kraina/airbnb_london_weekends
|
[
"region:us"
] |
2023-06-03T13:51:16+00:00
|
{"dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "realSum", "dtype": "float64"}, {"name": "room_type", "dtype": "string"}, {"name": "room_shared", "dtype": "bool"}, {"name": "room_private", "dtype": "bool"}, {"name": "person_capacity", "dtype": "float64"}, {"name": "host_is_superhost", "dtype": "bool"}, {"name": "multi", "dtype": "int64"}, {"name": "biz", "dtype": "int64"}, {"name": "cleanliness_rating", "dtype": "float64"}, {"name": "guest_satisfaction_overall", "dtype": "float64"}, {"name": "bedrooms", "dtype": "int64"}, {"name": "dist", "dtype": "float64"}, {"name": "metro_dist", "dtype": "float64"}, {"name": "attr_index", "dtype": "float64"}, {"name": "attr_index_norm", "dtype": "float64"}, {"name": "rest_index", "dtype": "float64"}, {"name": "rest_index_norm", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 703844.4180180868, "num_examples": 5379}], "download_size": 407036, "dataset_size": 703844.4180180868}}
|
2023-06-03T13:51:20+00:00
|
d5152a08120f44e5693d94a48cc01aa2358c0a43
|
# Dataset Card for "airbnb_amsterdam_weekends"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kraina/airbnb_amsterdam_weekends
|
[
"region:us"
] |
2023-06-03T13:55:19+00:00
|
{"dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "realSum", "dtype": "float64"}, {"name": "room_type", "dtype": "string"}, {"name": "room_shared", "dtype": "bool"}, {"name": "room_private", "dtype": "bool"}, {"name": "person_capacity", "dtype": "float64"}, {"name": "host_is_superhost", "dtype": "bool"}, {"name": "multi", "dtype": "int64"}, {"name": "biz", "dtype": "int64"}, {"name": "cleanliness_rating", "dtype": "float64"}, {"name": "guest_satisfaction_overall", "dtype": "float64"}, {"name": "bedrooms", "dtype": "int64"}, {"name": "dist", "dtype": "float64"}, {"name": "metro_dist", "dtype": "float64"}, {"name": "attr_index", "dtype": "float64"}, {"name": "attr_index_norm", "dtype": "float64"}, {"name": "rest_index", "dtype": "float64"}, {"name": "rest_index_norm", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 127840.8619452818, "num_examples": 977}], "download_size": 77014, "dataset_size": 127840.8619452818}}
|
2023-06-03T13:57:04+00:00
|
b30358f7c6cc9b0979afb467871c31a36b62b4b0
|
# The Adversarial Natural Language Inference (ANLI)
- Source: https://huggingface.co/datasets/anli
- Num examples:
- 100,459 (train)
- 1,200 (validation)
- 1,200 (test)
- Language: English
```python
from datasets import load_dataset
load_dataset("vietgpt/anli_r3_en")
```
- Format for NLI task
```python
def preprocess(sample):
premise = sample['premise']
hypothesis = sample['hypothesis']
label = sample['label']
if label == 0:
label = "entailment"
elif label == 1:
label = "neutral"
else:
label = "contradiction"
return {'text': f'<|startoftext|><|premise|> {premise} <|hypothesis|> {hypothesis} <|label|> {label} <|endoftext|>'}
"""
<|startoftext|><|premise|> TOKYO, Dec 18 (Reuters) - Japan’s Shionogi & Co said on Tuesday that it has applied to health regulators in the United States, Canada and Europe for approval of its HIV drug Dolutegravir. Shionogi developed Dolutegravir with a Viiv Healthcare, an AIDS drug joint venture between GlaxoSmithKline and Pfizer, in exchange for its rights to the drug. <|hypothesis|> The article was written on December 18th. <|label|> entailment <|endoftext|>
"""
```
- Format for Rationale task
```python
def preprocess_rationale(sample):
premise = sample['premise']
hypothesis = sample['hypothesis']
rationale = sample['reason']
return {'text': f'<|startoftext|><|premise|> {premise} <|hypothesis|> {hypothesis} <|rationale|> {rationale} <|endoftext|>'}
"""
<|startoftext|><|premise|> TOKYO, Dec 18 (Reuters) - Japan’s Shionogi & Co said on Tuesday that it has applied to health regulators in the United States, Canada and Europe for approval of its HIV drug Dolutegravir. Shionogi developed Dolutegravir with a Viiv Healthcare, an AIDS drug joint venture between GlaxoSmithKline and Pfizer, in exchange for its rights to the drug. <|hypothesis|> The article was written on December 18th. <|rationale|> TOKYO, Dec 18 (Reuters) is when the article was written as it states in the first words of the sentence <|endoftext|>
"""
```
- Format for GPT-3
```python
def preprocess_gpt3(sample):
premise = sample['premise']
hypothesis = sample['hypothesis']
label = sample['label']
if label == 0:
output = f'\n<|correct|> True\n<|incorrect|> False\n<|incorrect|> Neither'
elif label == 1:
output = f'\n<|correct|> Neither\n<|incorrect|> True\n<|incorrect|> False'
else:
output = f'\n<|correct|> False\n<|incorrect|> True\n<|incorrect|> Neither'
return {'text': f'<|startoftext|> anli 2: {premise} <|question|> {hypothesis}\nTrue, False, or Neither? <|answer|> {output} <|endoftext|>'}
"""
<|startoftext|> anli 2: TOKYO, Dec 18 (Reuters) - Japan’s Shionogi & Co said on Tuesday that it has applied to health regulators in the United States, Canada and Europe for approval of its HIV drug Dolutegravir. Shionogi developed Dolutegravir with a Viiv Healthcare, an AIDS drug joint venture between GlaxoSmithKline and Pfizer, in exchange for its rights to the drug. <|question|> The article was written on December 18th.
True, False, or Neither? <|answer|>
<|correct|> True
<|incorrect|> False
<|incorrect|> Neither <|endoftext|>
"""
```
|
vietgpt/anli_r3_en
|
[
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"NLI",
"region:us"
] |
2023-06-03T13:56:45+00:00
|
{"language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "uid", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}, {"name": "reason", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 44720719, "num_examples": 100459}, {"name": "validation", "num_bytes": 663148, "num_examples": 1200}, {"name": "test", "num_bytes": 657586, "num_examples": 1200}], "download_size": 15202058, "dataset_size": 46041453}, "tags": ["NLI"]}
|
2023-06-03T20:17:34+00:00
|
bc8114d6799076fc0e8bebf8f7f29dba56c08468
|
This dataset contains 93265 english poems.
|
sadFaceEmoji/english-poems
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] |
2023-06-03T14:34:44+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"]}
|
2023-06-03T14:45:56+00:00
|
352901a862d0a90ef0576562858cc3c551d27ef9
|
# Dataset Card for "ah_openai_dialog_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Deojoandco/ah_openai_dialog_v1
|
[
"region:us"
] |
2023-06-03T14:36:33+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "distinguished", "dtype": "null"}, {"name": "over_18", "dtype": "bool"}, {"name": "created_utc", "dtype": "int64"}, {"name": "comments", "list": [{"name": "body", "dtype": "string"}, {"name": "created_utc", "dtype": "float64"}, {"name": "distinguished", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "permalink", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "best_num_comments", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "dialog", "dtype": "string"}, {"name": "dialog_success", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 2586752, "num_examples": 297}], "download_size": 1566283, "dataset_size": 2586752}}
|
2023-06-03T14:36:37+00:00
|
86f1db8b720bd7397e0dedc00dd96b40f0667e1d
|
# Dataset Card for "SOFA_DOA_10_deg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FidelOdok/SOFA_DOA_10_deg
|
[
"region:us"
] |
2023-06-03T14:46:58+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "101", "2": "106", "3": "112", "4": "117", "5": "122", "6": "129", "7": "134", "8": "137", "9": "139", "10": "151", "11": "156", "12": "166", "13": "169", "14": "171", "15": "172", "16": "18", "17": "182", "18": "187", "19": "189", "20": "190", "21": "192", "22": "200", "23": "205", "24": "207", "25": "209", "26": "211", "27": "218", "28": "219", "29": "221", "30": "224", "31": "226", "32": "227", "33": "229", "34": "237", "35": "239", "36": "242", "37": "244", "38": "257", "39": "26", "40": "260", "41": "262", "42": "265", "43": "278", "44": "281", "45": "3", "46": "312", "47": "317", "48": "328", "49": "343", "50": "351", "51": "354", "52": "356", "53": "358", "54": "359", "55": "368", "56": "369", "57": "371", "58": "372", "59": "373", "60": "378", "61": "380", "62": "383", "63": "385", "64": "386", "65": "391", "66": "394", "67": "397", "68": "4", "69": "422", "70": "423", "71": "424", "72": "426", "73": "427", "74": "428", "75": "46", "76": "49", "77": "5", "78": "50", "79": "58", "80": "6", "81": "66", "82": "67", "83": "69", "84": "7", "85": "71", "86": "73", "87": "82", "88": "84", "89": "86", "90": "87", "91": "89", "92": "96"}}}}], "splits": [{"name": "train", "num_bytes": 21491848138.0, "num_examples": 22500}], "download_size": 999178438, "dataset_size": 21491848138.0}}
|
2023-06-03T16:51:48+00:00
|
b244e605625f53ea6ac6737088939cdc85bff798
|
aarard1/Test
|
[
"license:unknown",
"region:us"
] |
2023-06-03T15:02:34+00:00
|
{"license": "unknown"}
|
2023-06-03T15:02:34+00:00
|
|
de509b407e8f755ddfe745067d95f2da0c7f0baf
|
# Dataset Card for "wizard_vicuna_unfiltered_chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sam-mosaic/wizard_vicuna_unfiltered_chatml
|
[
"language:en",
"region:us"
] |
2023-06-03T15:17:06+00:00
|
{"language": "en", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 208337670.38355604, "num_examples": 87708}, {"name": "test", "num_bytes": 712606.6164439596, "num_examples": 300}], "download_size": 101987390, "dataset_size": 209050277.0}}
|
2023-07-17T23:28:10+00:00
|
b19f58fc270f6480d4d883db736711a3b1361174
|
ibibek/meroaafnai
|
[
"license:afl-3.0",
"region:us"
] |
2023-06-03T15:20:53+00:00
|
{"license": "afl-3.0"}
|
2023-06-03T15:21:44+00:00
|
|
4013928be7e739c0c5e5ff3044df4f545ff2bed4
|
# Dataset Card for "rlhf-prompt2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
breadlicker45/rlhf-prompt2
|
[
"region:us"
] |
2023-06-03T15:41:48+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 48316417, "num_examples": 36943}], "download_size": 4007730, "dataset_size": 48316417}}
|
2023-06-03T15:41:50+00:00
|
0d9973fa88ce6ff8c6eda349245566673d58a81c
|
# Dataset Card for "dolly-curate-falcon7b-instruct-generations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
argilla/dolly-curate-falcon7b-instruct-generations
|
[
"language:en",
"region:us"
] |
2023-06-03T16:02:36+00:00
|
{"language": "en", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response-1", "dtype": "string"}, {"name": "response-2", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "original_response", "dtype": "string"}, {"name": "external_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 11263300, "num_examples": 9101}], "download_size": 7218641, "dataset_size": 11263300}}
|
2023-07-13T10:54:36+00:00
|
43b74e44968c1d3424ea360d10214ec1b8700dcc
|
# Dataset Card for "CIFAR10_test_google_flan_t5_xl_mode_A_ns_10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/CIFAR10_test_google_flan_t5_xl_mode_A_ns_10000
|
[
"region:us"
] |
2023-06-03T16:10:39+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 3816888, "num_examples": 10000}], "download_size": 1081972, "dataset_size": 3816888}}
|
2023-06-03T16:10:44+00:00
|
270114610dbc17490839f316c0a23f7fdea67d8d
|
# Dataset Card for "few7_19100_chat_time40x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FanChen0116/few7_19100_chat_time40x
|
[
"region:us"
] |
2023-06-03T16:12:42+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "O", "1": "I-time", "2": "B-date", "3": "B-last_name", "4": "B-people", "5": "I-date", "6": "I-people", "7": "I-last_name", "8": "I-first_name", "9": "B-first_name", "10": "B-time"}}}}, {"name": "request_slot", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 462289, "num_examples": 2560}, {"name": "validation", "num_bytes": 998, "num_examples": 6}, {"name": "test", "num_bytes": 646729, "num_examples": 3731}], "download_size": 199884, "dataset_size": 1110016}}
|
2023-06-03T16:14:49+00:00
|
d3166d132fe18609f421416b8276c18dfcd0a9c1
|
# Dataset Card for "few7_19100_chat_time80x"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
FanChen0116/few7_19100_chat_time80x
|
[
"region:us"
] |
2023-06-03T16:13:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "O", "1": "I-time", "2": "B-date", "3": "B-last_name", "4": "B-people", "5": "I-date", "6": "I-people", "7": "I-last_name", "8": "I-first_name", "9": "B-first_name", "10": "B-time"}}}}, {"name": "request_slot", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 943366, "num_examples": 5178}, {"name": "validation", "num_bytes": 998, "num_examples": 6}, {"name": "test", "num_bytes": 646729, "num_examples": 3731}], "download_size": 0, "dataset_size": 1591093}}
|
2023-06-04T04:25:06+00:00
|
4cfdece1231c21cf5bfe579c6a9cd59dc9775f8b
|
# Dataset Card for "rlhf-prompt3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
breadlicker45/rlhf-prompt3
|
[
"language:en",
"region:us"
] |
2023-06-03T16:16:32+00:00
|
{"language": "en", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 48261697, "num_examples": 36958}], "download_size": 3977175, "dataset_size": 48261697}}
|
2023-07-14T13:50:00+00:00
|
c4878dd740284367c90421b0640fbfd283e18c56
|
# Dataset Card for "Bloom-560m-trained-on-Wizard-Vicuna-Uncensored"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
player1537/Bloom-560m-trained-on-Wizard-Vicuna-Uncensored
|
[
"region:us"
] |
2023-06-03T16:29:55+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1115967006, "num_examples": 86379}], "download_size": 375663823, "dataset_size": 1115967006}}
|
2023-06-03T17:23:44+00:00
|
6e6230110732d03eb7910e74eaba894e047fce4f
|
# Dataset Card for "ah_openai_dialog_annotation_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Deojoandco/ah_openai_dialog_annotation_v1
|
[
"region:us"
] |
2023-06-03T16:39:10+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "distinguished", "dtype": "null"}, {"name": "over_18", "dtype": "bool"}, {"name": "created_utc", "dtype": "int64"}, {"name": "comments", "list": [{"name": "body", "dtype": "string"}, {"name": "created_utc", "dtype": "float64"}, {"name": "distinguished", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "permalink", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "best_num_comments", "dtype": "int64"}, {"name": "query", "dtype": "string"}, {"name": "dialog", "dtype": "string"}, {"name": "dialog_success", "dtype": "bool"}, {"name": "annotation_error", "dtype": "bool"}, {"name": "annotation", "struct": [{"name": "success", "dtype": "bool"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2948432, "num_examples": 297}], "download_size": 1766339, "dataset_size": 2948432}}
|
2023-06-03T16:39:13+00:00
|
ebce1cbc8f0827ebb7df9c86db548636a96adce4
|
# Dataset Card for "ae-signal_processing_attacks_whisper_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/ae-signal_processing_attacks_whisper_commonvoice
|
[
"region:us"
] |
2023-06-03T17:03:51+00:00
|
{"dataset_info": {"features": [{"name": "audio_0", "dtype": "audio"}, {"name": "audio_1", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 203569814.0, "num_examples": 150}], "download_size": 181242192, "dataset_size": 203569814.0}}
|
2023-06-03T17:31:48+00:00
|
7ff0400b264a1a9018c77233c7d5d1e510cd00fe
|
# ParsiGoo Dataset Cart
This is a Persian multispeaker dataset for text-to-speech purposes. The dataset includes the following speakers:
- ariana_Male2
- moujeze_Female1
- ariana_Male1
- ariana_Female1
## Technical detailes
#### the beginning and the end with nonspeech parts trimmed
#### Sample rate: 22050
#### Durations:
```
|> ariana_Male2 0:46:36.908685
|> edge_Dilara 0:54:31.448820
|> moujeze_Female1 0:29:24.339590
|> ariana_Male1 0:55:41.996847
|> ariana_Female1 0:53:38.396217
|> edge_Farid 0:53:11.961018
```
## Dataset Information
- **Name:** ParsGoo
- **Description:** A Persian multispeaker dataset for text-to-speech purposes.
- **Homepage:** https://github.com/karim23657/ParsGoo
- **License:** CC BY-SA 4.0
## Speaker info
- ariana_Male2
- moujeze_Female1
- ariana_Male1
- ariana_Female1
|
Kamtera/ParsiGoo
|
[
"task_categories:text-to-speech",
"task_categories:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fa",
"license:cc0-1.0",
"region:us"
] |
2023-06-03T17:05:09+00:00
|
{"language": "fa", "license": ["cc0-1.0"], "multilinguality": "monolingual", "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-to-speech", "other"], "task_ids": [], "pretty_name": "ParsiGoo", "description": "A Persian multispeaker dataset for text-to-speech purposes.", "homepage": "https://example.com/parsigoo", "keywords": ["text-to-speech", "Persian", "multispeaker"], "name": "parsi_goo"}
|
2023-06-11T08:21:29+00:00
|
a61eb43b4934a013f568b3f0f267baafdc86e79c
|
# Dataset Card for "prof_images_blip__dalle-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_images_blip__dalle-2
|
[
"region:us"
] |
2023-06-03T17:46:56+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "paralegal", "num_bytes": 6171541.0, "num_examples": 210}, {"name": "bartender", "num_bytes": 7833899.0, "num_examples": 210}, {"name": "facilities_manager", "num_bytes": 5788116.0, "num_examples": 210}, {"name": "accountant", "num_bytes": 6026020.0, "num_examples": 210}, {"name": "graphic_designer", "num_bytes": 6387627.0, "num_examples": 210}, {"name": "network_administrator", "num_bytes": 7762970.0, "num_examples": 210}, {"name": "financial_manager", "num_bytes": 5530632.0, "num_examples": 210}, {"name": "baker", "num_bytes": 6316094.0, "num_examples": 210}, {"name": "security_guard", "num_bytes": 5795899.0, "num_examples": 210}, {"name": "artist", "num_bytes": 6803991.0, "num_examples": 210}, {"name": "author", "num_bytes": 6915505.0, "num_examples": 210}, {"name": "printing_press_operator", "num_bytes": 8473037.0, "num_examples": 210}, {"name": "public_relations_specialist", "num_bytes": 5380182.0, "num_examples": 210}, {"name": "sheet_metal_worker", "num_bytes": 7975210.0, "num_examples": 210}, {"name": "clergy", "num_bytes": 5223476.0, "num_examples": 210}, {"name": "payroll_clerk", "num_bytes": 6773901.0, "num_examples": 210}, {"name": "teller", "num_bytes": 6267512.0, "num_examples": 210}, {"name": "real_estate_broker", "num_bytes": 6181930.0, "num_examples": 210}, {"name": "customer_service_representative", "num_bytes": 6255124.0, "num_examples": 210}, {"name": "painter", "num_bytes": 7059566.0, "num_examples": 210}, {"name": "tractor_operator", "num_bytes": 8205340.0, "num_examples": 210}, {"name": "dental_hygienist", "num_bytes": 6308110.0, "num_examples": 210}, {"name": "industrial_engineer", "num_bytes": 6314334.0, "num_examples": 210}, {"name": "electrician", "num_bytes": 6819717.0, "num_examples": 210}, {"name": "head_cook", "num_bytes": 5257860.0, "num_examples": 210}, {"name": "health_technician", "num_bytes": 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"mental_health_counselor", "num_bytes": 6916972.0, "num_examples": 210}, {"name": "aerospace_engineer", "num_bytes": 6479161.0, "num_examples": 210}, {"name": "taxi_driver", "num_bytes": 7017648.0, "num_examples": 210}, {"name": "nurse", "num_bytes": 5495626.0, "num_examples": 210}, {"name": "data_entry_keyer", "num_bytes": 6795816.0, "num_examples": 210}, {"name": "musician", "num_bytes": 6697000.0, "num_examples": 210}, {"name": "event_planner", "num_bytes": 6572383.0, "num_examples": 210}, {"name": "writer", "num_bytes": 7314011.0, "num_examples": 210}, {"name": "cook", "num_bytes": 5418827.0, "num_examples": 210}, {"name": "welder", "num_bytes": 7087349.0, "num_examples": 210}, {"name": "producer", "num_bytes": 7145632.0, "num_examples": 210}, {"name": "hairdresser", "num_bytes": 6623867.0, "num_examples": 210}, {"name": "farmer", "num_bytes": 8185317.0, "num_examples": 210}, {"name": "construction_worker", "num_bytes": 6468388.0, "num_examples": 210}, {"name": "air_conditioning_installer", "num_bytes": 7370683.0, "num_examples": 210}, {"name": "electrical_engineer", "num_bytes": 6715574.0, "num_examples": 210}, {"name": "occupational_therapist", "num_bytes": 5604385.0, "num_examples": 210}, {"name": "career_counselor", "num_bytes": 5745900.0, "num_examples": 210}, {"name": "interior_designer", "num_bytes": 7494072.0, "num_examples": 210}, {"name": "jailer", "num_bytes": 7390180.0, "num_examples": 210}, {"name": "office_clerk", "num_bytes": 6158754.0, "num_examples": 210}, {"name": "market_research_analyst", "num_bytes": 6426759.0, "num_examples": 210}, {"name": "laboratory_technician", "num_bytes": 6085724.0, "num_examples": 210}, {"name": "social_assistant", "num_bytes": 5740255.0, "num_examples": 210}, {"name": "medical_records_specialist", "num_bytes": 5676198.0, "num_examples": 210}, {"name": "machinery_mechanic", "num_bytes": 7906020.0, "num_examples": 210}, {"name": "police_officer", "num_bytes": 5324492.0, "num_examples": 210}, {"name": "software_developer", "num_bytes": 6276609.0, "num_examples": 210}, {"name": "clerk", "num_bytes": 5676407.0, "num_examples": 210}, {"name": "salesperson", "num_bytes": 5914770.0, "num_examples": 210}, {"name": "social_worker", "num_bytes": 6347119.0, "num_examples": 210}, {"name": "director", "num_bytes": 6290656.0, "num_examples": 210}, {"name": "fast_food_worker", "num_bytes": 6558433.0, "num_examples": 210}, {"name": "singer", "num_bytes": 6636106.0, "num_examples": 210}, {"name": "metal_worker", "num_bytes": 7731701.0, "num_examples": 210}, {"name": "cleaner", "num_bytes": 5941280.0, "num_examples": 210}, {"name": "computer_systems_analyst", "num_bytes": 7377078.0, "num_examples": 210}, {"name": "dental_assistant", "num_bytes": 5946076.0, "num_examples": 210}, {"name": "psychologist", "num_bytes": 6546652.0, "num_examples": 210}, {"name": "machinist", "num_bytes": 7787841.0, "num_examples": 210}, {"name": "therapist", "num_bytes": 5711184.0, "num_examples": 210}, {"name": "veterinarian", "num_bytes": 5784277.0, "num_examples": 210}, {"name": "teacher", "num_bytes": 6482004.0, "num_examples": 210}, {"name": "architect", "num_bytes": 5692410.0, "num_examples": 210}, {"name": "office_worker", "num_bytes": 5996521.0, "num_examples": 210}, {"name": "drywall_installer", "num_bytes": 6688982.0, "num_examples": 210}, {"name": "nutritionist", "num_bytes": 6426587.0, "num_examples": 210}, {"name": "librarian", "num_bytes": 8678483.0, "num_examples": 210}, {"name": "childcare_worker", "num_bytes": 6822808.0, "num_examples": 210}, {"name": "school_bus_driver", "num_bytes": 8018152.0, "num_examples": 210}, {"name": "file_clerk", "num_bytes": 6955599.0, "num_examples": 210}, {"name": "logistician", "num_bytes": 5926532.0, "num_examples": 210}, {"name": "scientist", "num_bytes": 5943205.0, "num_examples": 210}, {"name": "teaching_assistant", "num_bytes": 5865061.0, "num_examples": 210}, {"name": "radiologic_technician", "num_bytes": 6388216.0, "num_examples": 210}, {"name": "manicurist", "num_bytes": 6873651.0, "num_examples": 210}, {"name": "community_manager", "num_bytes": 6385984.0, "num_examples": 210}, {"name": "carpenter", "num_bytes": 7638421.0, "num_examples": 210}, {"name": "claims_appraiser", "num_bytes": 6312794.0, "num_examples": 210}, {"name": "dispatcher", "num_bytes": 6274437.0, "num_examples": 210}, {"name": "cashier", "num_bytes": 7570675.0, "num_examples": 210}, {"name": "roofer", "num_bytes": 7478357.0, "num_examples": 210}, {"name": "photographer", "num_bytes": 6540455.0, "num_examples": 210}, {"name": "detective", "num_bytes": 5955413.0, "num_examples": 210}, {"name": "financial_advisor", "num_bytes": 5829031.0, "num_examples": 210}, {"name": "wholesale_buyer", "num_bytes": 7808137.0, "num_examples": 210}, {"name": "it_specialist", "num_bytes": 5992213.0, "num_examples": 210}, {"name": "pharmacy_technician", "num_bytes": 7059008.0, "num_examples": 210}, {"name": "engineer", "num_bytes": 5572540.0, "num_examples": 210}, {"name": "mover", "num_bytes": 5590911.0, "num_examples": 210}, {"name": "plane_mechanic", "num_bytes": 7906358.0, "num_examples": 210}, {"name": "interviewer", "num_bytes": 5934702.0, "num_examples": 210}, {"name": "massage_therapist", "num_bytes": 6023245.0, "num_examples": 210}, {"name": "dishwasher", "num_bytes": 8125724.0, "num_examples": 210}, {"name": "fitness_instructor", "num_bytes": 4981418.0, "num_examples": 210}, {"name": "credit_counselor", "num_bytes": 5738880.0, "num_examples": 210}, {"name": "stocker", "num_bytes": 6666621.0, "num_examples": 210}, {"name": "pharmacist", "num_bytes": 6738866.0, "num_examples": 210}, {"name": "doctor", "num_bytes": 5611600.0, "num_examples": 210}, {"name": "compliance_officer", "num_bytes": 5511904.0, "num_examples": 210}, {"name": "aide", "num_bytes": 5725510.0, "num_examples": 210}, {"name": "bus_driver", "num_bytes": 7206303.0, "num_examples": 210}, {"name": "financial_analyst", "num_bytes": 6012222.0, "num_examples": 210}, {"name": "receptionist", "num_bytes": 6144590.0, "num_examples": 210}, {"name": "janitor", "num_bytes": 6307371.0, "num_examples": 210}, {"name": "plumber", "num_bytes": 6220333.0, "num_examples": 210}, {"name": "physical_therapist", "num_bytes": 5152228.0, "num_examples": 210}, {"name": "inventory_clerk", "num_bytes": 7740513.0, "num_examples": 210}, {"name": "firefighter", "num_bytes": 6957703.0, "num_examples": 210}, {"name": "coach", "num_bytes": 5582613.0, "num_examples": 210}, {"name": "maid", "num_bytes": 5835512.0, "num_examples": 210}, {"name": "pilot", "num_bytes": 6510679.0, "num_examples": 210}, {"name": "repair_worker", "num_bytes": 6293370.0, "num_examples": 210}], "download_size": 992148797, "dataset_size": 940302502.0}}
|
2023-06-03T17:52:39+00:00
|
e337bf6fc08225fd6f44ed742fde6c6c46720bd2
|
# Dataset Card for "ae-signal_processing_attacks_assembly_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/ae-signal_processing_attacks_assembly_commonvoice
|
[
"region:us"
] |
2023-06-03T17:48:42+00:00
|
{"dataset_info": {"features": [{"name": "audio_0", "dtype": "audio"}, {"name": "audio_1", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 204463766.0, "num_examples": 150}], "download_size": 180767207, "dataset_size": 204463766.0}}
|
2023-06-03T18:15:46+00:00
|
f8131971b7db22428626ece716ad69e62c209191
|
tiagoam/ITESM
|
[
"license:bsd-3-clause",
"region:us"
] |
2023-06-03T17:53:53+00:00
|
{"license": "bsd-3-clause"}
|
2023-06-03T17:53:53+00:00
|
|
8aae9d6490812b536c9c69c9b68b728970f519ff
|
# Dataset Card for "prof_report__dalle-2__multi__12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__dalle-2__multi__12
|
[
"region:us"
] |
2023-06-03T18:09:20+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "paralegal", "num_bytes": 3552, "num_examples": 8}, {"name": "bartender", "num_bytes": 3456, "num_examples": 4}, {"name": "facilities_manager", "num_bytes": 3504, "num_examples": 6}, {"name": "accountant", "num_bytes": 3480, "num_examples": 5}, {"name": "graphic_designer", "num_bytes": 3504, "num_examples": 6}, {"name": "network_administrator", "num_bytes": 3504, "num_examples": 6}, {"name": "financial_manager", "num_bytes": 3504, "num_examples": 6}, {"name": "baker", "num_bytes": 3528, "num_examples": 7}, {"name": "security_guard", "num_bytes": 3480, "num_examples": 5}, {"name": "artist", "num_bytes": 3528, "num_examples": 7}, {"name": "author", "num_bytes": 3504, "num_examples": 6}, {"name": "printing_press_operator", "num_bytes": 3456, "num_examples": 4}, {"name": "public_relations_specialist", "num_bytes": 3504, "num_examples": 6}, {"name": "sheet_metal_worker", "num_bytes": 3456, "num_examples": 4}, {"name": "clergy", "num_bytes": 3432, "num_examples": 3}, {"name": "payroll_clerk", "num_bytes": 3576, "num_examples": 9}, {"name": "teller", "num_bytes": 3552, "num_examples": 8}, {"name": "real_estate_broker", "num_bytes": 3504, "num_examples": 6}, {"name": "customer_service_representative", "num_bytes": 3576, "num_examples": 9}, {"name": "painter", "num_bytes": 3600, "num_examples": 10}, {"name": "tractor_operator", "num_bytes": 3456, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 3504, "num_examples": 6}, {"name": "industrial_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "electrician", "num_bytes": 3504, "num_examples": 6}, {"name": "head_cook", "num_bytes": 3528, "num_examples": 7}, {"name": "health_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "carpet_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "purchasing_agent", "num_bytes": 3480, "num_examples": 5}, {"name": "supervisor", "num_bytes": 3480, "num_examples": 5}, {"name": "civil_engineer", "num_bytes": 3456, "num_examples": 4}, {"name": "lawyer", "num_bytes": 3504, "num_examples": 6}, {"name": "language_pathologist", "num_bytes": 3456, "num_examples": 4}, {"name": "ceo", "num_bytes": 3480, "num_examples": 5}, {"name": "computer_support_specialist", "num_bytes": 3456, "num_examples": 4}, {"name": "postal_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "mechanical_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "nursing_assistant", "num_bytes": 3456, "num_examples": 4}, {"name": "dentist", "num_bytes": 3504, "num_examples": 6}, {"name": "tutor", "num_bytes": 3480, "num_examples": 5}, {"name": "butcher", "num_bytes": 3504, "num_examples": 6}, {"name": "insurance_agent", "num_bytes": 3480, "num_examples": 5}, {"name": "courier", "num_bytes": 3480, "num_examples": 5}, {"name": "computer_programmer", "num_bytes": 3480, "num_examples": 5}, {"name": "truck_driver", "num_bytes": 3432, "num_examples": 3}, {"name": "mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "marketing_manager", "num_bytes": 3480, "num_examples": 5}, {"name": "sales_manager", "num_bytes": 3456, "num_examples": 4}, {"name": "correctional_officer", "num_bytes": 3504, "num_examples": 6}, {"name": "manager", "num_bytes": 3432, "num_examples": 3}, {"name": "underwriter", "num_bytes": 3504, "num_examples": 6}, {"name": "executive_assistant", "num_bytes": 3456, "num_examples": 4}, {"name": "designer", "num_bytes": 3480, "num_examples": 5}, {"name": "groundskeeper", "num_bytes": 3528, "num_examples": 7}, {"name": "mental_health_counselor", "num_bytes": 3528, "num_examples": 7}, {"name": "aerospace_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "taxi_driver", "num_bytes": 3504, "num_examples": 6}, {"name": "nurse", "num_bytes": 3480, "num_examples": 5}, {"name": "data_entry_keyer", "num_bytes": 3552, "num_examples": 8}, {"name": "musician", "num_bytes": 3528, "num_examples": 7}, {"name": "event_planner", "num_bytes": 3528, "num_examples": 7}, {"name": "writer", "num_bytes": 3504, "num_examples": 6}, {"name": "cook", "num_bytes": 3528, "num_examples": 7}, {"name": "welder", "num_bytes": 3480, "num_examples": 5}, {"name": "producer", "num_bytes": 3528, "num_examples": 7}, {"name": "hairdresser", "num_bytes": 3504, "num_examples": 6}, {"name": "farmer", "num_bytes": 3480, "num_examples": 5}, {"name": "construction_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "air_conditioning_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "electrical_engineer", "num_bytes": 3456, "num_examples": 4}, {"name": "occupational_therapist", "num_bytes": 3480, "num_examples": 5}, {"name": "career_counselor", "num_bytes": 3480, "num_examples": 5}, {"name": "interior_designer", "num_bytes": 3552, "num_examples": 8}, {"name": "jailer", "num_bytes": 3432, "num_examples": 3}, {"name": "office_clerk", "num_bytes": 3480, "num_examples": 5}, {"name": "market_research_analyst", "num_bytes": 3528, "num_examples": 7}, {"name": "laboratory_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "social_assistant", "num_bytes": 3504, "num_examples": 6}, {"name": "medical_records_specialist", "num_bytes": 3504, "num_examples": 6}, {"name": "machinery_mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "police_officer", "num_bytes": 3456, "num_examples": 4}, {"name": "software_developer", "num_bytes": 3456, "num_examples": 4}, {"name": "clerk", "num_bytes": 3456, "num_examples": 4}, {"name": "salesperson", "num_bytes": 3480, "num_examples": 5}, {"name": "social_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "director", "num_bytes": 3456, "num_examples": 4}, {"name": "fast_food_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "singer", "num_bytes": 3576, "num_examples": 9}, {"name": "metal_worker", "num_bytes": 3480, "num_examples": 5}, {"name": "cleaner", "num_bytes": 3528, "num_examples": 7}, {"name": "computer_systems_analyst", "num_bytes": 3528, "num_examples": 7}, {"name": "dental_assistant", "num_bytes": 3456, "num_examples": 4}, {"name": "psychologist", "num_bytes": 3504, "num_examples": 6}, {"name": "machinist", "num_bytes": 3480, "num_examples": 5}, {"name": "therapist", "num_bytes": 3504, "num_examples": 6}, {"name": "veterinarian", "num_bytes": 3456, "num_examples": 4}, {"name": "teacher", "num_bytes": 3504, "num_examples": 6}, {"name": "architect", "num_bytes": 3480, "num_examples": 5}, {"name": "office_worker", "num_bytes": 3480, "num_examples": 5}, {"name": "drywall_installer", "num_bytes": 3504, "num_examples": 6}, {"name": "nutritionist", "num_bytes": 3528, "num_examples": 7}, {"name": "librarian", "num_bytes": 3480, "num_examples": 5}, {"name": "childcare_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "school_bus_driver", "num_bytes": 3480, "num_examples": 5}, {"name": "file_clerk", "num_bytes": 3480, "num_examples": 5}, {"name": "logistician", "num_bytes": 3480, "num_examples": 5}, {"name": "scientist", "num_bytes": 3504, "num_examples": 6}, {"name": "teaching_assistant", "num_bytes": 3456, "num_examples": 4}, {"name": "radiologic_technician", "num_bytes": 3480, "num_examples": 5}, {"name": "manicurist", "num_bytes": 3528, "num_examples": 7}, {"name": "community_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "carpenter", "num_bytes": 3504, "num_examples": 6}, {"name": "claims_appraiser", "num_bytes": 3528, "num_examples": 7}, {"name": "dispatcher", "num_bytes": 3480, "num_examples": 5}, {"name": "cashier", "num_bytes": 3528, "num_examples": 7}, {"name": "roofer", "num_bytes": 3480, "num_examples": 5}, {"name": "photographer", "num_bytes": 3504, "num_examples": 6}, {"name": "detective", "num_bytes": 3480, "num_examples": 5}, {"name": "financial_advisor", "num_bytes": 3480, "num_examples": 5}, {"name": "wholesale_buyer", "num_bytes": 3528, "num_examples": 7}, {"name": "it_specialist", "num_bytes": 3480, "num_examples": 5}, {"name": "pharmacy_technician", "num_bytes": 3528, "num_examples": 7}, {"name": "engineer", "num_bytes": 3432, "num_examples": 3}, {"name": "mover", "num_bytes": 3528, "num_examples": 7}, {"name": "plane_mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "interviewer", "num_bytes": 3504, "num_examples": 6}, {"name": "massage_therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "dishwasher", "num_bytes": 3480, "num_examples": 5}, {"name": "fitness_instructor", "num_bytes": 3552, "num_examples": 8}, {"name": "credit_counselor", "num_bytes": 3504, "num_examples": 6}, {"name": "stocker", "num_bytes": 3552, "num_examples": 8}, {"name": "pharmacist", "num_bytes": 3480, "num_examples": 5}, {"name": "doctor", "num_bytes": 3480, "num_examples": 5}, {"name": "compliance_officer", "num_bytes": 3528, "num_examples": 7}, {"name": "aide", "num_bytes": 3528, "num_examples": 7}, {"name": "bus_driver", "num_bytes": 3480, "num_examples": 5}, {"name": "financial_analyst", "num_bytes": 3480, "num_examples": 5}, {"name": "receptionist", "num_bytes": 3480, "num_examples": 5}, {"name": "janitor", "num_bytes": 3504, "num_examples": 6}, {"name": "plumber", "num_bytes": 3480, "num_examples": 5}, {"name": "physical_therapist", "num_bytes": 3480, "num_examples": 5}, {"name": "inventory_clerk", "num_bytes": 3528, "num_examples": 7}, {"name": "firefighter", "num_bytes": 3432, "num_examples": 3}, {"name": "coach", "num_bytes": 3480, "num_examples": 5}, {"name": "maid", "num_bytes": 3480, "num_examples": 5}, {"name": "pilot", "num_bytes": 3408, "num_examples": 2}, {"name": "repair_worker", "num_bytes": 3504, "num_examples": 6}], "download_size": 863121, "dataset_size": 510384}}
|
2023-06-03T18:10:58+00:00
|
ab8f849f4ab349730eb10b04e7082db39a1f4ded
|
# Dataset Card for "prof_report__dalle-2__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__dalle-2__multi__24
|
[
"region:us"
] |
2023-06-03T18:11:16+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "paralegal", "num_bytes": 3672, "num_examples": 13}, {"name": "bartender", "num_bytes": 3576, "num_examples": 9}, {"name": "facilities_manager", "num_bytes": 3552, "num_examples": 8}, {"name": "accountant", "num_bytes": 3504, "num_examples": 6}, {"name": "graphic_designer", "num_bytes": 3600, "num_examples": 10}, {"name": "network_administrator", "num_bytes": 3624, "num_examples": 11}, {"name": "financial_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "baker", "num_bytes": 3624, "num_examples": 11}, {"name": "security_guard", "num_bytes": 3528, "num_examples": 7}, {"name": "artist", "num_bytes": 3720, "num_examples": 15}, {"name": "author", "num_bytes": 3600, "num_examples": 10}, {"name": "printing_press_operator", "num_bytes": 3552, "num_examples": 8}, {"name": "public_relations_specialist", "num_bytes": 3600, "num_examples": 10}, {"name": "sheet_metal_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "clergy", "num_bytes": 3552, "num_examples": 8}, {"name": "payroll_clerk", "num_bytes": 3648, "num_examples": 12}, {"name": "teller", "num_bytes": 3672, "num_examples": 13}, {"name": "real_estate_broker", "num_bytes": 3528, "num_examples": 7}, {"name": "customer_service_representative", "num_bytes": 3720, "num_examples": 15}, {"name": "painter", "num_bytes": 3648, "num_examples": 12}, {"name": "tractor_operator", "num_bytes": 3504, "num_examples": 6}, {"name": "dental_hygienist", "num_bytes": 3576, "num_examples": 9}, {"name": "industrial_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "electrician", "num_bytes": 3576, "num_examples": 9}, {"name": "head_cook", "num_bytes": 3576, "num_examples": 9}, {"name": "health_technician", "num_bytes": 3648, "num_examples": 12}, {"name": "carpet_installer", "num_bytes": 3576, "num_examples": 9}, {"name": "purchasing_agent", "num_bytes": 3624, "num_examples": 11}, {"name": "supervisor", "num_bytes": 3576, "num_examples": 9}, {"name": "civil_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "lawyer", "num_bytes": 3600, "num_examples": 10}, {"name": "language_pathologist", "num_bytes": 3552, "num_examples": 8}, {"name": "ceo", "num_bytes": 3456, "num_examples": 4}, {"name": "computer_support_specialist", "num_bytes": 3528, "num_examples": 7}, {"name": "postal_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "mechanical_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "nursing_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "dentist", "num_bytes": 3624, "num_examples": 11}, {"name": "tutor", "num_bytes": 3600, "num_examples": 10}, {"name": "butcher", "num_bytes": 3528, "num_examples": 7}, {"name": "insurance_agent", "num_bytes": 3648, "num_examples": 12}, {"name": "courier", "num_bytes": 3552, "num_examples": 8}, {"name": "computer_programmer", "num_bytes": 3552, "num_examples": 8}, {"name": "truck_driver", "num_bytes": 3432, "num_examples": 3}, {"name": "mechanic", "num_bytes": 3528, "num_examples": 7}, {"name": "marketing_manager", "num_bytes": 3504, "num_examples": 6}, {"name": "sales_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "correctional_officer", "num_bytes": 3576, "num_examples": 9}, {"name": "manager", "num_bytes": 3480, "num_examples": 5}, {"name": "underwriter", "num_bytes": 3504, "num_examples": 6}, {"name": "executive_assistant", "num_bytes": 3600, "num_examples": 10}, {"name": "designer", "num_bytes": 3624, "num_examples": 11}, {"name": "groundskeeper", "num_bytes": 3600, "num_examples": 10}, {"name": "mental_health_counselor", "num_bytes": 3648, "num_examples": 12}, {"name": "aerospace_engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "taxi_driver", "num_bytes": 3576, "num_examples": 9}, {"name": "nurse", "num_bytes": 3552, "num_examples": 8}, {"name": "data_entry_keyer", "num_bytes": 3648, "num_examples": 12}, {"name": "musician", "num_bytes": 3672, "num_examples": 13}, {"name": "event_planner", "num_bytes": 3720, "num_examples": 15}, {"name": "writer", "num_bytes": 3600, "num_examples": 10}, {"name": "cook", "num_bytes": 3576, "num_examples": 9}, {"name": "welder", "num_bytes": 3504, "num_examples": 6}, {"name": "producer", "num_bytes": 3576, "num_examples": 9}, {"name": "hairdresser", "num_bytes": 3672, "num_examples": 13}, {"name": "farmer", "num_bytes": 3552, "num_examples": 8}, {"name": "construction_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "air_conditioning_installer", "num_bytes": 3504, "num_examples": 6}, {"name": "electrical_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "occupational_therapist", "num_bytes": 3600, "num_examples": 10}, {"name": "career_counselor", "num_bytes": 3600, "num_examples": 10}, {"name": "interior_designer", "num_bytes": 3648, "num_examples": 12}, {"name": "jailer", "num_bytes": 3552, "num_examples": 8}, {"name": "office_clerk", "num_bytes": 3528, "num_examples": 7}, {"name": "market_research_analyst", "num_bytes": 3648, "num_examples": 12}, {"name": "laboratory_technician", "num_bytes": 3624, "num_examples": 11}, {"name": "social_assistant", "num_bytes": 3648, "num_examples": 12}, {"name": "medical_records_specialist", "num_bytes": 3648, "num_examples": 12}, {"name": "machinery_mechanic", "num_bytes": 3528, "num_examples": 7}, {"name": "police_officer", "num_bytes": 3528, "num_examples": 7}, {"name": "software_developer", "num_bytes": 3528, "num_examples": 7}, {"name": "clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "salesperson", "num_bytes": 3552, "num_examples": 8}, {"name": "social_worker", "num_bytes": 3696, "num_examples": 14}, {"name": "director", "num_bytes": 3552, "num_examples": 8}, {"name": "fast_food_worker", "num_bytes": 3720, "num_examples": 15}, {"name": "singer", "num_bytes": 3768, "num_examples": 17}, {"name": "metal_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "cleaner", "num_bytes": 3720, "num_examples": 15}, {"name": "computer_systems_analyst", "num_bytes": 3648, "num_examples": 12}, {"name": "dental_assistant", "num_bytes": 3600, "num_examples": 10}, {"name": "psychologist", "num_bytes": 3600, "num_examples": 10}, {"name": "machinist", "num_bytes": 3528, "num_examples": 7}, {"name": "therapist", "num_bytes": 3600, "num_examples": 10}, {"name": "veterinarian", "num_bytes": 3552, "num_examples": 8}, {"name": "teacher", "num_bytes": 3552, "num_examples": 8}, {"name": "architect", "num_bytes": 3552, "num_examples": 8}, {"name": "office_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "drywall_installer", "num_bytes": 3504, "num_examples": 6}, {"name": "nutritionist", "num_bytes": 3624, "num_examples": 11}, {"name": "librarian", "num_bytes": 3576, "num_examples": 9}, {"name": "childcare_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "school_bus_driver", "num_bytes": 3528, "num_examples": 7}, {"name": "file_clerk", "num_bytes": 3624, "num_examples": 11}, {"name": "logistician", "num_bytes": 3528, "num_examples": 7}, {"name": "scientist", "num_bytes": 3576, "num_examples": 9}, {"name": "teaching_assistant", "num_bytes": 3528, "num_examples": 7}, {"name": "radiologic_technician", "num_bytes": 3600, "num_examples": 10}, {"name": "manicurist", "num_bytes": 3624, "num_examples": 11}, {"name": "community_manager", "num_bytes": 3600, "num_examples": 10}, {"name": "carpenter", "num_bytes": 3600, "num_examples": 10}, {"name": "claims_appraiser", "num_bytes": 3624, "num_examples": 11}, {"name": "dispatcher", "num_bytes": 3624, "num_examples": 11}, {"name": "cashier", "num_bytes": 3672, "num_examples": 13}, {"name": "roofer", "num_bytes": 3480, "num_examples": 5}, {"name": "photographer", "num_bytes": 3624, "num_examples": 11}, {"name": "detective", "num_bytes": 3528, "num_examples": 7}, {"name": "financial_advisor", "num_bytes": 3480, "num_examples": 5}, {"name": "wholesale_buyer", "num_bytes": 3600, "num_examples": 10}, {"name": "it_specialist", "num_bytes": 3480, "num_examples": 5}, {"name": "pharmacy_technician", "num_bytes": 3576, "num_examples": 9}, {"name": "engineer", "num_bytes": 3456, "num_examples": 4}, {"name": "mover", "num_bytes": 3696, "num_examples": 14}, {"name": "plane_mechanic", "num_bytes": 3552, "num_examples": 8}, {"name": "interviewer", "num_bytes": 3624, "num_examples": 11}, {"name": "massage_therapist", "num_bytes": 3624, "num_examples": 11}, {"name": "dishwasher", "num_bytes": 3672, "num_examples": 13}, {"name": "fitness_instructor", "num_bytes": 3624, "num_examples": 11}, {"name": "credit_counselor", "num_bytes": 3600, "num_examples": 10}, {"name": "stocker", "num_bytes": 3696, "num_examples": 14}, {"name": "pharmacist", "num_bytes": 3600, "num_examples": 10}, {"name": "doctor", "num_bytes": 3552, "num_examples": 8}, {"name": "compliance_officer", "num_bytes": 3648, "num_examples": 12}, {"name": "aide", "num_bytes": 3672, "num_examples": 13}, {"name": "bus_driver", "num_bytes": 3552, "num_examples": 8}, {"name": "financial_analyst", "num_bytes": 3504, "num_examples": 6}, {"name": "receptionist", "num_bytes": 3552, "num_examples": 8}, {"name": "janitor", "num_bytes": 3552, "num_examples": 8}, {"name": "plumber", "num_bytes": 3480, "num_examples": 5}, {"name": "physical_therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "inventory_clerk", "num_bytes": 3648, "num_examples": 12}, {"name": "firefighter", "num_bytes": 3480, "num_examples": 5}, {"name": "coach", "num_bytes": 3528, "num_examples": 7}, {"name": "maid", "num_bytes": 3552, "num_examples": 8}, {"name": "pilot", "num_bytes": 3528, "num_examples": 7}, {"name": "repair_worker", "num_bytes": 3576, "num_examples": 9}], "download_size": 867779, "dataset_size": 522960}}
|
2023-06-03T18:13:06+00:00
|
07f66238b9c1676434461c7e95d254bc5be70144
|
# Dataset Card for "prof_report__dalle-2__sd_21__12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__dalle-2__sd_21__12
|
[
"region:us"
] |
2023-06-03T18:13:24+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "paralegal", "num_bytes": 3528, "num_examples": 7}, {"name": "bartender", "num_bytes": 3504, "num_examples": 6}, {"name": "facilities_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "accountant", "num_bytes": 3456, "num_examples": 4}, {"name": "graphic_designer", "num_bytes": 3528, "num_examples": 7}, {"name": "network_administrator", "num_bytes": 3552, "num_examples": 8}, {"name": "financial_manager", "num_bytes": 3504, "num_examples": 6}, {"name": "baker", "num_bytes": 3576, "num_examples": 9}, {"name": "security_guard", "num_bytes": 3528, "num_examples": 7}, {"name": "artist", "num_bytes": 3576, "num_examples": 9}, {"name": "author", "num_bytes": 3528, "num_examples": 7}, {"name": "printing_press_operator", "num_bytes": 3480, "num_examples": 5}, {"name": "public_relations_specialist", "num_bytes": 3528, "num_examples": 7}, {"name": "sheet_metal_worker", "num_bytes": 3456, "num_examples": 4}, {"name": "clergy", "num_bytes": 3528, "num_examples": 7}, {"name": "payroll_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "teller", "num_bytes": 3552, "num_examples": 8}, {"name": "real_estate_broker", "num_bytes": 3504, "num_examples": 6}, {"name": "customer_service_representative", "num_bytes": 3528, "num_examples": 7}, {"name": "painter", "num_bytes": 3552, "num_examples": 8}, {"name": "tractor_operator", "num_bytes": 3504, "num_examples": 6}, {"name": "dental_hygienist", "num_bytes": 3504, "num_examples": 6}, {"name": "industrial_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "electrician", "num_bytes": 3504, "num_examples": 6}, {"name": "head_cook", "num_bytes": 3528, "num_examples": 7}, {"name": "health_technician", "num_bytes": 3480, "num_examples": 5}, {"name": "carpet_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "purchasing_agent", "num_bytes": 3528, "num_examples": 7}, {"name": "supervisor", "num_bytes": 3528, "num_examples": 7}, {"name": "civil_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "lawyer", "num_bytes": 3552, "num_examples": 8}, {"name": "language_pathologist", "num_bytes": 3480, "num_examples": 5}, {"name": "ceo", "num_bytes": 3456, "num_examples": 4}, {"name": "computer_support_specialist", "num_bytes": 3480, "num_examples": 5}, {"name": "postal_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "mechanical_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "nursing_assistant", "num_bytes": 3504, "num_examples": 6}, {"name": "dentist", "num_bytes": 3528, "num_examples": 7}, {"name": "tutor", "num_bytes": 3504, "num_examples": 6}, {"name": "butcher", "num_bytes": 3528, "num_examples": 7}, {"name": "insurance_agent", "num_bytes": 3528, "num_examples": 7}, {"name": "courier", "num_bytes": 3504, "num_examples": 6}, {"name": "computer_programmer", "num_bytes": 3504, "num_examples": 6}, {"name": "truck_driver", "num_bytes": 3480, "num_examples": 5}, {"name": "mechanic", "num_bytes": 3504, "num_examples": 6}, {"name": "marketing_manager", "num_bytes": 3480, "num_examples": 5}, {"name": "sales_manager", "num_bytes": 3480, "num_examples": 5}, {"name": "correctional_officer", "num_bytes": 3504, "num_examples": 6}, {"name": "manager", "num_bytes": 3456, "num_examples": 4}, {"name": "underwriter", "num_bytes": 3504, "num_examples": 6}, {"name": "executive_assistant", "num_bytes": 3480, "num_examples": 5}, {"name": "designer", "num_bytes": 3528, "num_examples": 7}, {"name": "groundskeeper", "num_bytes": 3576, "num_examples": 9}, {"name": "mental_health_counselor", "num_bytes": 3528, "num_examples": 7}, {"name": "aerospace_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "taxi_driver", "num_bytes": 3504, "num_examples": 6}, {"name": "nurse", "num_bytes": 3480, "num_examples": 5}, {"name": "data_entry_keyer", "num_bytes": 3552, "num_examples": 8}, {"name": "musician", "num_bytes": 3552, "num_examples": 8}, {"name": "event_planner", "num_bytes": 3552, "num_examples": 8}, {"name": "writer", "num_bytes": 3504, "num_examples": 6}, {"name": "cook", "num_bytes": 3600, "num_examples": 10}, {"name": "welder", "num_bytes": 3504, "num_examples": 6}, {"name": "producer", "num_bytes": 3528, "num_examples": 7}, {"name": "hairdresser", "num_bytes": 3480, "num_examples": 5}, {"name": "farmer", "num_bytes": 3504, "num_examples": 6}, {"name": "construction_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "air_conditioning_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "electrical_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "occupational_therapist", "num_bytes": 3504, "num_examples": 6}, {"name": "career_counselor", "num_bytes": 3480, "num_examples": 5}, {"name": "interior_designer", "num_bytes": 3552, "num_examples": 8}, {"name": "jailer", "num_bytes": 3480, "num_examples": 5}, {"name": "office_clerk", "num_bytes": 3480, "num_examples": 5}, {"name": "market_research_analyst", "num_bytes": 3504, "num_examples": 6}, {"name": "laboratory_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "social_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "medical_records_specialist", "num_bytes": 3504, "num_examples": 6}, {"name": "machinery_mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "police_officer", "num_bytes": 3504, "num_examples": 6}, {"name": "software_developer", "num_bytes": 3504, "num_examples": 6}, {"name": "clerk", "num_bytes": 3504, "num_examples": 6}, {"name": "salesperson", "num_bytes": 3552, "num_examples": 8}, {"name": "social_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "director", "num_bytes": 3480, "num_examples": 5}, {"name": "fast_food_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "singer", "num_bytes": 3576, "num_examples": 9}, {"name": "metal_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "cleaner", "num_bytes": 3552, "num_examples": 8}, {"name": "computer_systems_analyst", "num_bytes": 3528, "num_examples": 7}, {"name": "dental_assistant", "num_bytes": 3480, "num_examples": 5}, {"name": "psychologist", "num_bytes": 3480, "num_examples": 5}, {"name": "machinist", "num_bytes": 3480, "num_examples": 5}, {"name": "therapist", "num_bytes": 3480, "num_examples": 5}, {"name": "veterinarian", "num_bytes": 3504, "num_examples": 6}, {"name": "teacher", "num_bytes": 3504, "num_examples": 6}, {"name": "architect", "num_bytes": 3480, "num_examples": 5}, {"name": "office_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "drywall_installer", "num_bytes": 3504, "num_examples": 6}, {"name": "nutritionist", "num_bytes": 3504, "num_examples": 6}, {"name": "librarian", "num_bytes": 3480, "num_examples": 5}, {"name": "childcare_worker", "num_bytes": 3480, "num_examples": 5}, {"name": "school_bus_driver", "num_bytes": 3480, "num_examples": 5}, {"name": "file_clerk", "num_bytes": 3504, "num_examples": 6}, {"name": "logistician", "num_bytes": 3504, "num_examples": 6}, {"name": "scientist", "num_bytes": 3480, "num_examples": 5}, {"name": "teaching_assistant", "num_bytes": 3480, "num_examples": 5}, {"name": "radiologic_technician", "num_bytes": 3480, "num_examples": 5}, {"name": "manicurist", "num_bytes": 3552, "num_examples": 8}, {"name": "community_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "carpenter", "num_bytes": 3504, "num_examples": 6}, {"name": "claims_appraiser", "num_bytes": 3528, "num_examples": 7}, {"name": "dispatcher", "num_bytes": 3528, "num_examples": 7}, {"name": "cashier", "num_bytes": 3528, "num_examples": 7}, {"name": "roofer", "num_bytes": 3528, "num_examples": 7}, {"name": "photographer", "num_bytes": 3504, "num_examples": 6}, {"name": "detective", "num_bytes": 3504, "num_examples": 6}, {"name": "financial_advisor", "num_bytes": 3480, "num_examples": 5}, {"name": "wholesale_buyer", "num_bytes": 3528, "num_examples": 7}, {"name": "it_specialist", "num_bytes": 3480, "num_examples": 5}, {"name": "pharmacy_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "engineer", "num_bytes": 3456, "num_examples": 4}, {"name": "mover", "num_bytes": 3552, "num_examples": 8}, {"name": "plane_mechanic", "num_bytes": 3456, "num_examples": 4}, {"name": "interviewer", "num_bytes": 3528, "num_examples": 7}, {"name": "massage_therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "dishwasher", "num_bytes": 3552, "num_examples": 8}, {"name": "fitness_instructor", "num_bytes": 3528, "num_examples": 7}, {"name": "credit_counselor", "num_bytes": 3504, "num_examples": 6}, {"name": "stocker", "num_bytes": 3576, "num_examples": 9}, {"name": "pharmacist", "num_bytes": 3456, "num_examples": 4}, {"name": "doctor", "num_bytes": 3480, "num_examples": 5}, {"name": "compliance_officer", "num_bytes": 3528, "num_examples": 7}, {"name": "aide", "num_bytes": 3504, "num_examples": 6}, {"name": "bus_driver", "num_bytes": 3528, "num_examples": 7}, {"name": "financial_analyst", "num_bytes": 3480, "num_examples": 5}, {"name": "receptionist", "num_bytes": 3504, "num_examples": 6}, {"name": "janitor", "num_bytes": 3528, "num_examples": 7}, {"name": "plumber", "num_bytes": 3480, "num_examples": 5}, {"name": "physical_therapist", "num_bytes": 3504, "num_examples": 6}, {"name": "inventory_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "firefighter", "num_bytes": 3528, "num_examples": 7}, {"name": "coach", "num_bytes": 3504, "num_examples": 6}, {"name": "maid", "num_bytes": 3480, "num_examples": 5}, {"name": "pilot", "num_bytes": 3480, "num_examples": 5}, {"name": "repair_worker", "num_bytes": 3480, "num_examples": 5}], "download_size": 864405, "dataset_size": 512448}}
|
2023-06-03T18:14:59+00:00
|
30197412710fdf07a61f2fe5505344085e3be718
|
# Dataset Card for "prof_report__dalle-2__sd_21__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__dalle-2__sd_21__24
|
[
"region:us"
] |
2023-06-03T18:15:18+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "paralegal", "num_bytes": 3624, "num_examples": 11}, {"name": "bartender", "num_bytes": 3576, "num_examples": 9}, {"name": "facilities_manager", "num_bytes": 3576, "num_examples": 9}, {"name": "accountant", "num_bytes": 3480, "num_examples": 5}, {"name": "graphic_designer", "num_bytes": 3600, "num_examples": 10}, {"name": "network_administrator", "num_bytes": 3648, "num_examples": 12}, {"name": "financial_manager", "num_bytes": 3504, "num_examples": 6}, {"name": "baker", "num_bytes": 3600, "num_examples": 10}, {"name": "security_guard", "num_bytes": 3528, "num_examples": 7}, {"name": "artist", "num_bytes": 3696, "num_examples": 14}, {"name": "author", "num_bytes": 3648, "num_examples": 12}, {"name": "printing_press_operator", "num_bytes": 3504, "num_examples": 6}, {"name": "public_relations_specialist", "num_bytes": 3552, "num_examples": 8}, {"name": "sheet_metal_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "clergy", "num_bytes": 3600, "num_examples": 10}, {"name": "payroll_clerk", "num_bytes": 3624, "num_examples": 11}, {"name": "teller", "num_bytes": 3600, "num_examples": 10}, {"name": "real_estate_broker", "num_bytes": 3480, "num_examples": 5}, {"name": "customer_service_representative", "num_bytes": 3600, "num_examples": 10}, {"name": "painter", "num_bytes": 3696, "num_examples": 14}, {"name": "tractor_operator", "num_bytes": 3552, "num_examples": 8}, {"name": "dental_hygienist", "num_bytes": 3528, "num_examples": 7}, {"name": "industrial_engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "electrician", "num_bytes": 3576, "num_examples": 9}, {"name": "head_cook", "num_bytes": 3576, "num_examples": 9}, {"name": "health_technician", "num_bytes": 3576, "num_examples": 9}, {"name": "carpet_installer", "num_bytes": 3624, "num_examples": 11}, {"name": "purchasing_agent", "num_bytes": 3552, "num_examples": 8}, {"name": "supervisor", "num_bytes": 3552, "num_examples": 8}, {"name": "civil_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "lawyer", "num_bytes": 3576, "num_examples": 9}, {"name": "language_pathologist", "num_bytes": 3576, "num_examples": 9}, {"name": "ceo", "num_bytes": 3480, "num_examples": 5}, {"name": "computer_support_specialist", "num_bytes": 3480, "num_examples": 5}, {"name": "postal_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "mechanical_engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "nursing_assistant", "num_bytes": 3528, "num_examples": 7}, {"name": "dentist", "num_bytes": 3600, "num_examples": 10}, {"name": "tutor", "num_bytes": 3576, "num_examples": 9}, {"name": "butcher", "num_bytes": 3552, "num_examples": 8}, {"name": "insurance_agent", "num_bytes": 3552, "num_examples": 8}, {"name": "courier", "num_bytes": 3552, "num_examples": 8}, {"name": "computer_programmer", "num_bytes": 3528, "num_examples": 7}, {"name": "truck_driver", "num_bytes": 3480, "num_examples": 5}, {"name": "mechanic", "num_bytes": 3576, "num_examples": 9}, {"name": "marketing_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "sales_manager", "num_bytes": 3480, "num_examples": 5}, {"name": "correctional_officer", "num_bytes": 3528, "num_examples": 7}, {"name": "manager", "num_bytes": 3504, "num_examples": 6}, {"name": "underwriter", "num_bytes": 3528, "num_examples": 7}, {"name": "executive_assistant", "num_bytes": 3528, "num_examples": 7}, {"name": "designer", "num_bytes": 3576, "num_examples": 9}, {"name": "groundskeeper", "num_bytes": 3624, "num_examples": 11}, {"name": "mental_health_counselor", "num_bytes": 3600, "num_examples": 10}, {"name": "aerospace_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "taxi_driver", "num_bytes": 3552, "num_examples": 8}, {"name": "nurse", "num_bytes": 3504, "num_examples": 6}, {"name": "data_entry_keyer", "num_bytes": 3624, "num_examples": 11}, {"name": "musician", "num_bytes": 3624, "num_examples": 11}, {"name": "event_planner", "num_bytes": 3696, "num_examples": 14}, {"name": "writer", "num_bytes": 3576, "num_examples": 9}, {"name": "cook", "num_bytes": 3648, "num_examples": 12}, {"name": "welder", "num_bytes": 3552, "num_examples": 8}, {"name": "producer", "num_bytes": 3648, "num_examples": 12}, {"name": "hairdresser", "num_bytes": 3672, "num_examples": 13}, {"name": "farmer", "num_bytes": 3528, "num_examples": 7}, {"name": "construction_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "air_conditioning_installer", "num_bytes": 3504, "num_examples": 6}, {"name": "electrical_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "occupational_therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "career_counselor", "num_bytes": 3528, "num_examples": 7}, {"name": "interior_designer", "num_bytes": 3648, "num_examples": 12}, {"name": "jailer", "num_bytes": 3528, "num_examples": 7}, {"name": "office_clerk", "num_bytes": 3504, "num_examples": 6}, {"name": "market_research_analyst", "num_bytes": 3576, "num_examples": 9}, {"name": "laboratory_technician", "num_bytes": 3576, "num_examples": 9}, {"name": "social_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "medical_records_specialist", "num_bytes": 3624, "num_examples": 11}, {"name": "machinery_mechanic", "num_bytes": 3504, "num_examples": 6}, {"name": "police_officer", "num_bytes": 3552, "num_examples": 8}, {"name": "software_developer", "num_bytes": 3480, "num_examples": 5}, {"name": "clerk", "num_bytes": 3480, "num_examples": 5}, {"name": "salesperson", "num_bytes": 3552, "num_examples": 8}, {"name": "social_worker", "num_bytes": 3672, "num_examples": 13}, {"name": "director", "num_bytes": 3504, "num_examples": 6}, {"name": "fast_food_worker", "num_bytes": 3648, "num_examples": 12}, {"name": "singer", "num_bytes": 3696, "num_examples": 14}, {"name": "metal_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "cleaner", "num_bytes": 3648, "num_examples": 12}, {"name": "computer_systems_analyst", "num_bytes": 3600, "num_examples": 10}, {"name": "dental_assistant", "num_bytes": 3504, "num_examples": 6}, {"name": "psychologist", "num_bytes": 3576, "num_examples": 9}, {"name": "machinist", "num_bytes": 3504, "num_examples": 6}, {"name": "therapist", "num_bytes": 3504, "num_examples": 6}, {"name": "veterinarian", "num_bytes": 3528, "num_examples": 7}, {"name": "teacher", "num_bytes": 3576, "num_examples": 9}, {"name": "architect", "num_bytes": 3552, "num_examples": 8}, {"name": "office_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "drywall_installer", "num_bytes": 3552, "num_examples": 8}, {"name": "nutritionist", "num_bytes": 3552, "num_examples": 8}, {"name": "librarian", "num_bytes": 3576, "num_examples": 9}, {"name": "childcare_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "school_bus_driver", "num_bytes": 3504, "num_examples": 6}, {"name": "file_clerk", "num_bytes": 3504, "num_examples": 6}, {"name": "logistician", "num_bytes": 3528, "num_examples": 7}, {"name": "scientist", "num_bytes": 3528, "num_examples": 7}, {"name": "teaching_assistant", "num_bytes": 3576, "num_examples": 9}, {"name": "radiologic_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "manicurist", "num_bytes": 3600, "num_examples": 10}, {"name": "community_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "carpenter", "num_bytes": 3600, "num_examples": 10}, {"name": "claims_appraiser", "num_bytes": 3552, "num_examples": 8}, {"name": "dispatcher", "num_bytes": 3576, "num_examples": 9}, {"name": "cashier", "num_bytes": 3600, "num_examples": 10}, {"name": "roofer", "num_bytes": 3504, "num_examples": 6}, {"name": "photographer", "num_bytes": 3600, "num_examples": 10}, {"name": "detective", "num_bytes": 3576, "num_examples": 9}, {"name": "financial_advisor", "num_bytes": 3504, "num_examples": 6}, {"name": "wholesale_buyer", "num_bytes": 3576, "num_examples": 9}, {"name": "it_specialist", "num_bytes": 3456, "num_examples": 4}, {"name": "pharmacy_technician", "num_bytes": 3600, "num_examples": 10}, {"name": "engineer", "num_bytes": 3456, "num_examples": 4}, {"name": "mover", "num_bytes": 3696, "num_examples": 14}, {"name": "plane_mechanic", "num_bytes": 3504, "num_examples": 6}, {"name": "interviewer", "num_bytes": 3552, "num_examples": 8}, {"name": "massage_therapist", "num_bytes": 3624, "num_examples": 11}, {"name": "dishwasher", "num_bytes": 3624, "num_examples": 11}, {"name": "fitness_instructor", "num_bytes": 3576, "num_examples": 9}, {"name": "credit_counselor", "num_bytes": 3552, "num_examples": 8}, {"name": "stocker", "num_bytes": 3672, "num_examples": 13}, {"name": "pharmacist", "num_bytes": 3504, "num_examples": 6}, {"name": "doctor", "num_bytes": 3552, "num_examples": 8}, {"name": "compliance_officer", "num_bytes": 3552, "num_examples": 8}, {"name": "aide", "num_bytes": 3600, "num_examples": 10}, {"name": "bus_driver", "num_bytes": 3552, "num_examples": 8}, {"name": "financial_analyst", "num_bytes": 3504, "num_examples": 6}, {"name": "receptionist", "num_bytes": 3624, "num_examples": 11}, {"name": "janitor", "num_bytes": 3576, "num_examples": 9}, {"name": "plumber", "num_bytes": 3528, "num_examples": 7}, {"name": "physical_therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "inventory_clerk", "num_bytes": 3576, "num_examples": 9}, {"name": "firefighter", "num_bytes": 3528, "num_examples": 7}, {"name": "coach", "num_bytes": 3528, "num_examples": 7}, {"name": "maid", "num_bytes": 3528, "num_examples": 7}, {"name": "pilot", "num_bytes": 3504, "num_examples": 6}, {"name": "repair_worker", "num_bytes": 3576, "num_examples": 9}], "download_size": 867106, "dataset_size": 520104}}
|
2023-06-03T18:16:58+00:00
|
f5749148a5837d2c4f6e53b229b6f99997b5bab4
|
# Dataset Card for "ae-signal_processing_attacks_assembly"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TeamSODA/ae-signal_processing_attacks_assembly_librispeech
|
[
"region:us"
] |
2023-06-03T18:28:42+00:00
|
{"dataset_info": {"features": [{"name": "audio_0", "dtype": "audio"}, {"name": "audio_1", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 220057238.0, "num_examples": 150}], "download_size": 174685031, "dataset_size": 220057238.0}}
|
2023-06-03T18:54:17+00:00
|
c927804d2bd9a5af2d690b2a97c03a5001a7535c
|
kerinin/hackernews
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-03T18:47:09+00:00
|
{"license": "apache-2.0"}
|
2023-06-03T19:04:18+00:00
|
|
21e06c3ffbfc9b2453fbef1b68536fd82dfd541d
|
# Dataset Card for "prof_images_blip__SD_v1.4_random_seeds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_images_blip__SD_v1.4_random_seeds
|
[
"region:us"
] |
2023-06-03T19:15:29+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "paralegal", "num_bytes": 7646841.0, "num_examples": 210}, {"name": "bartender", "num_bytes": 9656403.0, "num_examples": 210}, {"name": "facilities_manager", "num_bytes": 7939596.0, "num_examples": 210}, {"name": "accountant", "num_bytes": 7513737.0, "num_examples": 210}, {"name": "graphic_designer", "num_bytes": 8476206.0, "num_examples": 210}, {"name": "network_administrator", "num_bytes": 8443347.0, "num_examples": 210}, {"name": "financial_manager", "num_bytes": 7227119.0, "num_examples": 210}, {"name": "baker", "num_bytes": 8672857.0, "num_examples": 210}, {"name": "security_guard", "num_bytes": 7942640.0, "num_examples": 210}, {"name": "artist", "num_bytes": 8083290.0, "num_examples": 210}, {"name": "author", "num_bytes": 8873877.0, "num_examples": 210}, {"name": "printing_press_operator", "num_bytes": 10388023.0, "num_examples": 210}, {"name": "public_relations_specialist", "num_bytes": 7199383.0, "num_examples": 210}, {"name": "sheet_metal_worker", "num_bytes": 9353067.0, "num_examples": 210}, {"name": "clergy", "num_bytes": 8002257.0, "num_examples": 210}, {"name": "payroll_clerk", "num_bytes": 7327406.0, "num_examples": 210}, {"name": "teller", "num_bytes": 8138750.0, "num_examples": 210}, {"name": "real_estate_broker", "num_bytes": 7795576.0, "num_examples": 210}, {"name": "customer_service_representative", "num_bytes": 7143626.0, "num_examples": 210}, {"name": "painter", "num_bytes": 9020751.0, "num_examples": 210}, {"name": "tractor_operator", "num_bytes": 12230813.0, "num_examples": 210}, {"name": "dental_hygienist", "num_bytes": 6988033.0, "num_examples": 210}, {"name": "industrial_engineer", "num_bytes": 9066892.0, "num_examples": 210}, {"name": "electrician", "num_bytes": 9641860.0, "num_examples": 210}, {"name": "head_cook", "num_bytes": 8437525.0, "num_examples": 210}, {"name": "health_technician", "num_bytes": 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"num_bytes": 9999259.0, "num_examples": 210}, {"name": "insurance_agent", "num_bytes": 7199147.0, "num_examples": 210}, {"name": "courier", "num_bytes": 8908388.0, "num_examples": 210}, {"name": "computer_programmer", "num_bytes": 7989301.0, "num_examples": 210}, {"name": "truck_driver", "num_bytes": 10286999.0, "num_examples": 210}, {"name": "mechanic", "num_bytes": 9133343.0, "num_examples": 210}, {"name": "marketing_manager", "num_bytes": 7637250.0, "num_examples": 210}, {"name": "sales_manager", "num_bytes": 7243154.0, "num_examples": 210}, {"name": "correctional_officer", "num_bytes": 7942926.0, "num_examples": 210}, {"name": "manager", "num_bytes": 7487408.0, "num_examples": 210}, {"name": "underwriter", "num_bytes": 7621339.0, "num_examples": 210}, {"name": "executive_assistant", "num_bytes": 7137280.0, "num_examples": 210}, {"name": "designer", "num_bytes": 7841206.0, "num_examples": 210}, {"name": "groundskeeper", "num_bytes": 11730261.0, "num_examples": 210}, {"name": "mental_health_counselor", "num_bytes": 7661055.0, "num_examples": 210}, {"name": "aerospace_engineer", "num_bytes": 9256536.0, "num_examples": 210}, {"name": "taxi_driver", "num_bytes": 9294017.0, "num_examples": 210}, {"name": "nurse", "num_bytes": 6942648.0, "num_examples": 210}, {"name": "data_entry_keyer", "num_bytes": 8151562.0, "num_examples": 210}, {"name": "musician", "num_bytes": 8657476.0, "num_examples": 210}, {"name": "event_planner", "num_bytes": 9288583.0, "num_examples": 210}, {"name": "writer", "num_bytes": 9018669.0, "num_examples": 210}, {"name": "cook", "num_bytes": 8648983.0, "num_examples": 210}, {"name": "welder", "num_bytes": 10503130.0, "num_examples": 210}, {"name": "producer", "num_bytes": 8625107.0, "num_examples": 210}, {"name": "hairdresser", "num_bytes": 7737596.0, "num_examples": 210}, {"name": "farmer", "num_bytes": 12081580.0, "num_examples": 210}, {"name": "construction_worker", "num_bytes": 9313129.0, "num_examples": 210}, {"name": "air_conditioning_installer", "num_bytes": 9487400.0, "num_examples": 210}, {"name": "electrical_engineer", "num_bytes": 8923900.0, "num_examples": 210}, {"name": "occupational_therapist", "num_bytes": 8311478.0, "num_examples": 210}, {"name": "career_counselor", "num_bytes": 7998049.0, "num_examples": 210}, {"name": "interior_designer", "num_bytes": 9506542.0, "num_examples": 210}, {"name": "jailer", "num_bytes": 9447296.0, "num_examples": 210}, {"name": "office_clerk", "num_bytes": 7604831.0, "num_examples": 210}, {"name": "market_research_analyst", "num_bytes": 8095959.0, "num_examples": 210}, {"name": "laboratory_technician", "num_bytes": 7612946.0, "num_examples": 210}, {"name": "social_assistant", "num_bytes": 8337646.0, "num_examples": 210}, {"name": "medical_records_specialist", "num_bytes": 7344197.0, "num_examples": 210}, {"name": "machinery_mechanic", "num_bytes": 10418637.0, "num_examples": 210}, {"name": "police_officer", "num_bytes": 7714404.0, "num_examples": 210}, {"name": "software_developer", "num_bytes": 7404422.0, "num_examples": 210}, {"name": "clerk", "num_bytes": 8049553.0, "num_examples": 210}, {"name": "salesperson", "num_bytes": 7342429.0, "num_examples": 210}, {"name": "social_worker", "num_bytes": 8720964.0, "num_examples": 210}, {"name": "director", "num_bytes": 7640512.0, "num_examples": 210}, {"name": "fast_food_worker", "num_bytes": 8453710.0, "num_examples": 210}, {"name": "singer", "num_bytes": 8259292.0, "num_examples": 210}, {"name": "metal_worker", "num_bytes": 10017960.0, "num_examples": 210}, {"name": "cleaner", "num_bytes": 8535334.0, "num_examples": 210}, {"name": "computer_systems_analyst", "num_bytes": 8217200.0, "num_examples": 210}, {"name": "dental_assistant", "num_bytes": 6634326.0, "num_examples": 210}, {"name": "psychologist", "num_bytes": 7503024.0, "num_examples": 210}, {"name": "machinist", "num_bytes": 9438247.0, "num_examples": 210}, {"name": "therapist", "num_bytes": 7341051.0, "num_examples": 210}, {"name": "veterinarian", "num_bytes": 7785661.0, "num_examples": 210}, {"name": "teacher", "num_bytes": 8497608.0, "num_examples": 210}, {"name": "architect", "num_bytes": 8197165.0, "num_examples": 210}, {"name": "office_worker", "num_bytes": 7312206.0, "num_examples": 210}, {"name": "drywall_installer", "num_bytes": 7683345.0, "num_examples": 210}, {"name": "nutritionist", "num_bytes": 8913796.0, "num_examples": 210}, {"name": "librarian", "num_bytes": 10311263.0, "num_examples": 210}, {"name": "childcare_worker", "num_bytes": 8266680.0, "num_examples": 210}, {"name": "school_bus_driver", "num_bytes": 10541264.0, "num_examples": 210}, {"name": "file_clerk", "num_bytes": 9222817.0, "num_examples": 210}, {"name": "logistician", "num_bytes": 9092075.0, "num_examples": 210}, {"name": "scientist", "num_bytes": 7896201.0, "num_examples": 210}, {"name": "teaching_assistant", "num_bytes": 8499137.0, "num_examples": 210}, {"name": "radiologic_technician", "num_bytes": 7081678.0, "num_examples": 210}, {"name": "manicurist", "num_bytes": 7005774.0, "num_examples": 210}, {"name": "community_manager", "num_bytes": 8521851.0, "num_examples": 210}, {"name": "carpenter", "num_bytes": 10007111.0, "num_examples": 210}, {"name": "claims_appraiser", "num_bytes": 8242546.0, "num_examples": 210}, {"name": "dispatcher", "num_bytes": 8085389.0, "num_examples": 210}, {"name": "cashier", "num_bytes": 8962624.0, "num_examples": 210}, {"name": "roofer", "num_bytes": 11218753.0, "num_examples": 210}, {"name": "photographer", "num_bytes": 8526999.0, "num_examples": 210}, {"name": "detective", "num_bytes": 8002048.0, "num_examples": 210}, {"name": "financial_advisor", "num_bytes": 7583126.0, "num_examples": 210}, {"name": "wholesale_buyer", "num_bytes": 10335429.0, "num_examples": 210}, {"name": "it_specialist", "num_bytes": 7860665.0, "num_examples": 210}, {"name": "pharmacy_technician", "num_bytes": 8620337.0, "num_examples": 210}, {"name": "engineer", "num_bytes": 8979311.0, "num_examples": 210}, {"name": "mover", "num_bytes": 8820348.0, "num_examples": 210}, {"name": "plane_mechanic", "num_bytes": 8726367.0, "num_examples": 210}, {"name": "interviewer", "num_bytes": 7299959.0, "num_examples": 210}, {"name": "massage_therapist", "num_bytes": 6975898.0, "num_examples": 210}, {"name": "dishwasher", "num_bytes": 10386508.0, "num_examples": 210}, {"name": "fitness_instructor", "num_bytes": 7931472.0, "num_examples": 210}, {"name": "credit_counselor", "num_bytes": 7800363.0, "num_examples": 210}, {"name": "stocker", "num_bytes": 8874226.0, "num_examples": 210}, {"name": "pharmacist", "num_bytes": 9188954.0, "num_examples": 210}, {"name": "doctor", "num_bytes": 7326990.0, "num_examples": 210}, {"name": "compliance_officer", "num_bytes": 7141629.0, "num_examples": 210}, {"name": "aide", "num_bytes": 7388587.0, "num_examples": 210}, {"name": "bus_driver", "num_bytes": 9396085.0, "num_examples": 210}, {"name": "financial_analyst", "num_bytes": 7264493.0, "num_examples": 210}, {"name": "receptionist", "num_bytes": 6875649.0, "num_examples": 210}, {"name": "janitor", "num_bytes": 8311976.0, "num_examples": 210}, {"name": "plumber", "num_bytes": 9008536.0, "num_examples": 210}, {"name": "physical_therapist", "num_bytes": 7318295.0, "num_examples": 210}, {"name": "inventory_clerk", "num_bytes": 8803050.0, "num_examples": 210}, {"name": "firefighter", "num_bytes": 9575610.0, "num_examples": 210}, {"name": "coach", "num_bytes": 8322409.0, "num_examples": 210}, {"name": "maid", "num_bytes": 7781267.0, "num_examples": 210}, {"name": "pilot", "num_bytes": 8091005.0, "num_examples": 210}, {"name": "repair_worker", "num_bytes": 9253551.0, "num_examples": 210}], "download_size": 1284633786, "dataset_size": 1231689582.0}}
|
2023-06-03T19:20:02+00:00
|
4dc6de316d6159e4f02ef8d9dbe8a946d01e6b85
|
# Dataset Card for "prof_report__SD_v1.4_random_seeds__multi__12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__SD_v1.4_random_seeds__multi__12
|
[
"region:us"
] |
2023-06-03T19:20:39+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "paralegal", "num_bytes": 3504, "num_examples": 6}, {"name": "bartender", "num_bytes": 3480, "num_examples": 5}, {"name": "facilities_manager", "num_bytes": 3552, "num_examples": 8}, {"name": "accountant", "num_bytes": 3504, "num_examples": 6}, {"name": "graphic_designer", "num_bytes": 3600, "num_examples": 10}, {"name": "network_administrator", "num_bytes": 3456, "num_examples": 4}, {"name": "financial_manager", "num_bytes": 3600, "num_examples": 10}, {"name": "baker", "num_bytes": 3600, "num_examples": 10}, {"name": "security_guard", "num_bytes": 3552, "num_examples": 8}, {"name": "artist", "num_bytes": 3624, "num_examples": 11}, {"name": "author", "num_bytes": 3552, "num_examples": 8}, {"name": "printing_press_operator", "num_bytes": 3528, "num_examples": 7}, {"name": "public_relations_specialist", "num_bytes": 3528, "num_examples": 7}, {"name": "sheet_metal_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "clergy", "num_bytes": 3576, "num_examples": 9}, {"name": "payroll_clerk", "num_bytes": 3504, "num_examples": 6}, {"name": "teller", "num_bytes": 3624, "num_examples": 11}, {"name": "real_estate_broker", "num_bytes": 3552, "num_examples": 8}, {"name": "customer_service_representative", "num_bytes": 3504, "num_examples": 6}, {"name": "painter", "num_bytes": 3624, "num_examples": 11}, {"name": "tractor_operator", "num_bytes": 3480, "num_examples": 5}, {"name": "dental_hygienist", "num_bytes": 3456, "num_examples": 4}, {"name": "industrial_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "electrician", "num_bytes": 3480, "num_examples": 5}, {"name": "head_cook", "num_bytes": 3624, "num_examples": 11}, {"name": "health_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "carpet_installer", "num_bytes": 3432, "num_examples": 3}, {"name": "purchasing_agent", "num_bytes": 3504, "num_examples": 6}, {"name": "supervisor", "num_bytes": 3576, "num_examples": 9}, {"name": "civil_engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "lawyer", "num_bytes": 3576, "num_examples": 9}, {"name": "language_pathologist", "num_bytes": 3576, "num_examples": 9}, {"name": "ceo", "num_bytes": 3576, "num_examples": 9}, {"name": "computer_support_specialist", "num_bytes": 3576, "num_examples": 9}, {"name": "postal_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "mechanical_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "nursing_assistant", "num_bytes": 3480, "num_examples": 5}, {"name": "dentist", "num_bytes": 3576, "num_examples": 9}, {"name": "tutor", "num_bytes": 3600, "num_examples": 10}, {"name": "butcher", "num_bytes": 3528, "num_examples": 7}, {"name": "insurance_agent", "num_bytes": 3480, "num_examples": 5}, {"name": "courier", "num_bytes": 3624, "num_examples": 11}, {"name": "computer_programmer", "num_bytes": 3552, "num_examples": 8}, {"name": "truck_driver", "num_bytes": 3480, "num_examples": 5}, {"name": "mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "marketing_manager", "num_bytes": 3504, "num_examples": 6}, {"name": "sales_manager", "num_bytes": 3480, "num_examples": 5}, {"name": "correctional_officer", "num_bytes": 3528, "num_examples": 7}, {"name": "manager", "num_bytes": 3576, "num_examples": 9}, {"name": "underwriter", "num_bytes": 3576, "num_examples": 9}, {"name": "executive_assistant", "num_bytes": 3528, "num_examples": 7}, {"name": "designer", "num_bytes": 3600, "num_examples": 10}, {"name": "groundskeeper", "num_bytes": 3456, "num_examples": 4}, {"name": "mental_health_counselor", "num_bytes": 3552, "num_examples": 8}, {"name": "aerospace_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "taxi_driver", "num_bytes": 3600, "num_examples": 10}, {"name": "nurse", "num_bytes": 3528, "num_examples": 7}, {"name": "data_entry_keyer", "num_bytes": 3504, "num_examples": 6}, {"name": "musician", "num_bytes": 3600, "num_examples": 10}, {"name": "event_planner", "num_bytes": 3528, "num_examples": 7}, {"name": "writer", "num_bytes": 3600, "num_examples": 10}, {"name": "cook", "num_bytes": 3624, "num_examples": 11}, {"name": "welder", "num_bytes": 3528, "num_examples": 7}, {"name": "producer", "num_bytes": 3624, "num_examples": 11}, {"name": "hairdresser", "num_bytes": 3528, "num_examples": 7}, {"name": "farmer", "num_bytes": 3480, "num_examples": 5}, {"name": "construction_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "air_conditioning_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "electrical_engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "occupational_therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "career_counselor", "num_bytes": 3528, "num_examples": 7}, {"name": "interior_designer", "num_bytes": 3528, "num_examples": 7}, {"name": "jailer", "num_bytes": 3600, "num_examples": 10}, {"name": "office_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "market_research_analyst", "num_bytes": 3504, "num_examples": 6}, {"name": "laboratory_technician", "num_bytes": 3528, "num_examples": 7}, {"name": "social_assistant", "num_bytes": 3576, "num_examples": 9}, {"name": "medical_records_specialist", "num_bytes": 3504, "num_examples": 6}, {"name": "machinery_mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "police_officer", "num_bytes": 3528, "num_examples": 7}, {"name": "software_developer", "num_bytes": 3456, "num_examples": 4}, {"name": "clerk", "num_bytes": 3600, "num_examples": 10}, {"name": "salesperson", "num_bytes": 3528, "num_examples": 7}, {"name": "social_worker", "num_bytes": 3624, "num_examples": 11}, {"name": "director", "num_bytes": 3600, "num_examples": 10}, {"name": "fast_food_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "singer", "num_bytes": 3624, "num_examples": 11}, {"name": "metal_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "cleaner", "num_bytes": 3624, "num_examples": 11}, {"name": "computer_systems_analyst", "num_bytes": 3552, "num_examples": 8}, {"name": "dental_assistant", "num_bytes": 3456, "num_examples": 4}, {"name": "psychologist", "num_bytes": 3600, "num_examples": 10}, {"name": "machinist", "num_bytes": 3576, "num_examples": 9}, {"name": "therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "veterinarian", "num_bytes": 3528, "num_examples": 7}, {"name": "teacher", "num_bytes": 3624, "num_examples": 11}, {"name": "architect", "num_bytes": 3552, "num_examples": 8}, {"name": "office_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "drywall_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 3480, "num_examples": 5}, {"name": "librarian", "num_bytes": 3552, "num_examples": 8}, {"name": "childcare_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "school_bus_driver", "num_bytes": 3600, "num_examples": 10}, {"name": "file_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "logistician", "num_bytes": 3528, "num_examples": 7}, {"name": "scientist", "num_bytes": 3552, "num_examples": 8}, {"name": "teaching_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "radiologic_technician", "num_bytes": 3528, "num_examples": 7}, {"name": "manicurist", "num_bytes": 3528, "num_examples": 7}, {"name": "community_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "carpenter", "num_bytes": 3504, "num_examples": 6}, {"name": "claims_appraiser", "num_bytes": 3528, "num_examples": 7}, {"name": "dispatcher", "num_bytes": 3504, "num_examples": 6}, {"name": "cashier", "num_bytes": 3552, "num_examples": 8}, {"name": "roofer", "num_bytes": 3480, "num_examples": 5}, {"name": "photographer", "num_bytes": 3624, "num_examples": 11}, {"name": "detective", "num_bytes": 3576, "num_examples": 9}, {"name": "financial_advisor", "num_bytes": 3528, "num_examples": 7}, {"name": "wholesale_buyer", "num_bytes": 3600, "num_examples": 10}, {"name": "it_specialist", "num_bytes": 3528, "num_examples": 7}, {"name": "pharmacy_technician", "num_bytes": 3456, "num_examples": 4}, {"name": "engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "mover", "num_bytes": 3624, "num_examples": 11}, {"name": "plane_mechanic", "num_bytes": 3504, "num_examples": 6}, {"name": "interviewer", "num_bytes": 3624, "num_examples": 11}, {"name": "massage_therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "dishwasher", "num_bytes": 3552, "num_examples": 8}, {"name": "fitness_instructor", "num_bytes": 3528, "num_examples": 7}, {"name": "credit_counselor", "num_bytes": 3552, "num_examples": 8}, {"name": "stocker", "num_bytes": 3624, "num_examples": 11}, {"name": "pharmacist", "num_bytes": 3600, "num_examples": 10}, {"name": "doctor", "num_bytes": 3600, "num_examples": 10}, {"name": "compliance_officer", "num_bytes": 3528, "num_examples": 7}, {"name": "aide", "num_bytes": 3600, "num_examples": 10}, {"name": "bus_driver", "num_bytes": 3600, "num_examples": 10}, {"name": "financial_analyst", "num_bytes": 3576, "num_examples": 9}, {"name": "receptionist", "num_bytes": 3432, "num_examples": 3}, {"name": "janitor", "num_bytes": 3576, "num_examples": 9}, {"name": "plumber", "num_bytes": 3480, "num_examples": 5}, {"name": "physical_therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "inventory_clerk", "num_bytes": 3528, "num_examples": 7}, {"name": "firefighter", "num_bytes": 3552, "num_examples": 8}, {"name": "coach", "num_bytes": 3600, "num_examples": 10}, {"name": "maid", "num_bytes": 3528, "num_examples": 7}, {"name": "pilot", "num_bytes": 3600, "num_examples": 10}, {"name": "repair_worker", "num_bytes": 3576, "num_examples": 9}], "download_size": 867336, "dataset_size": 517776}}
|
2023-06-03T19:22:21+00:00
|
5de1ee2dc50b36e383c1200e974affa04e3fe5a8
|
# Dataset Card for "prof_report__SD_v1.4_random_seeds__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__SD_v1.4_random_seeds__multi__24
|
[
"region:us"
] |
2023-06-03T19:22:39+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "paralegal", "num_bytes": 3552, "num_examples": 8}, {"name": "bartender", "num_bytes": 3528, "num_examples": 7}, {"name": "facilities_manager", "num_bytes": 3648, "num_examples": 12}, {"name": "accountant", "num_bytes": 3624, "num_examples": 11}, {"name": "graphic_designer", "num_bytes": 3696, "num_examples": 14}, {"name": "network_administrator", "num_bytes": 3456, "num_examples": 4}, {"name": "financial_manager", "num_bytes": 3648, "num_examples": 12}, {"name": "baker", "num_bytes": 3768, "num_examples": 17}, {"name": "security_guard", "num_bytes": 3600, "num_examples": 10}, {"name": "artist", "num_bytes": 3864, "num_examples": 21}, {"name": "author", "num_bytes": 3648, "num_examples": 12}, {"name": "printing_press_operator", "num_bytes": 3600, "num_examples": 10}, {"name": "public_relations_specialist", "num_bytes": 3672, "num_examples": 13}, {"name": "sheet_metal_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "clergy", "num_bytes": 3672, "num_examples": 13}, {"name": "payroll_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "teller", "num_bytes": 3840, "num_examples": 20}, {"name": "real_estate_broker", "num_bytes": 3600, "num_examples": 10}, {"name": "customer_service_representative", "num_bytes": 3672, "num_examples": 13}, {"name": "painter", "num_bytes": 3864, "num_examples": 21}, {"name": "tractor_operator", "num_bytes": 3480, "num_examples": 5}, {"name": "dental_hygienist", "num_bytes": 3504, "num_examples": 6}, {"name": "industrial_engineer", "num_bytes": 3600, "num_examples": 10}, {"name": "electrician", "num_bytes": 3504, "num_examples": 6}, {"name": "head_cook", "num_bytes": 3768, "num_examples": 17}, {"name": "health_technician", "num_bytes": 3624, "num_examples": 11}, {"name": "carpet_installer", "num_bytes": 3504, "num_examples": 6}, {"name": "purchasing_agent", "num_bytes": 3672, "num_examples": 13}, {"name": "supervisor", "num_bytes": 3696, "num_examples": 14}, {"name": "civil_engineer", "num_bytes": 3720, "num_examples": 15}, {"name": "lawyer", "num_bytes": 3720, "num_examples": 15}, {"name": "language_pathologist", "num_bytes": 3648, "num_examples": 12}, {"name": "ceo", "num_bytes": 3648, "num_examples": 12}, {"name": "computer_support_specialist", "num_bytes": 3696, "num_examples": 14}, {"name": "postal_worker", "num_bytes": 3744, "num_examples": 16}, {"name": "mechanical_engineer", "num_bytes": 3648, "num_examples": 12}, {"name": "nursing_assistant", "num_bytes": 3600, "num_examples": 10}, {"name": "dentist", "num_bytes": 3720, "num_examples": 15}, {"name": "tutor", "num_bytes": 3768, "num_examples": 17}, {"name": "butcher", "num_bytes": 3624, "num_examples": 11}, {"name": "insurance_agent", "num_bytes": 3552, "num_examples": 8}, {"name": "courier", "num_bytes": 3744, "num_examples": 16}, {"name": "computer_programmer", "num_bytes": 3672, "num_examples": 13}, {"name": "truck_driver", "num_bytes": 3504, "num_examples": 6}, {"name": "mechanic", "num_bytes": 3528, "num_examples": 7}, {"name": "marketing_manager", "num_bytes": 3648, "num_examples": 12}, {"name": "sales_manager", "num_bytes": 3552, "num_examples": 8}, {"name": "correctional_officer", "num_bytes": 3600, "num_examples": 10}, {"name": "manager", "num_bytes": 3672, "num_examples": 13}, {"name": "underwriter", "num_bytes": 3696, "num_examples": 14}, {"name": "executive_assistant", "num_bytes": 3624, "num_examples": 11}, {"name": "designer", "num_bytes": 3744, "num_examples": 16}, {"name": "groundskeeper", "num_bytes": 3528, "num_examples": 7}, {"name": "mental_health_counselor", "num_bytes": 3696, "num_examples": 14}, {"name": "aerospace_engineer", "num_bytes": 3720, "num_examples": 15}, {"name": "taxi_driver", "num_bytes": 3744, "num_examples": 16}, {"name": "nurse", "num_bytes": 3600, "num_examples": 10}, {"name": "data_entry_keyer", "num_bytes": 3648, "num_examples": 12}, {"name": "musician", "num_bytes": 3720, "num_examples": 15}, {"name": "event_planner", "num_bytes": 3576, "num_examples": 9}, {"name": "writer", "num_bytes": 3696, "num_examples": 14}, {"name": "cook", "num_bytes": 3768, "num_examples": 17}, {"name": "welder", "num_bytes": 3528, "num_examples": 7}, {"name": "producer", "num_bytes": 3792, "num_examples": 18}, {"name": "hairdresser", "num_bytes": 3672, "num_examples": 13}, {"name": "farmer", "num_bytes": 3552, "num_examples": 8}, {"name": "construction_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "air_conditioning_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "electrical_engineer", "num_bytes": 3744, "num_examples": 16}, {"name": "occupational_therapist", "num_bytes": 3648, "num_examples": 12}, {"name": "career_counselor", "num_bytes": 3648, "num_examples": 12}, {"name": "interior_designer", "num_bytes": 3696, "num_examples": 14}, {"name": "jailer", "num_bytes": 3744, "num_examples": 16}, {"name": "office_clerk", "num_bytes": 3624, "num_examples": 11}, {"name": "market_research_analyst", "num_bytes": 3576, "num_examples": 9}, {"name": "laboratory_technician", "num_bytes": 3696, "num_examples": 14}, {"name": "social_assistant", "num_bytes": 3744, "num_examples": 16}, {"name": "medical_records_specialist", "num_bytes": 3624, "num_examples": 11}, {"name": "machinery_mechanic", "num_bytes": 3552, "num_examples": 8}, {"name": "police_officer", "num_bytes": 3648, "num_examples": 12}, {"name": "software_developer", "num_bytes": 3528, "num_examples": 7}, {"name": "clerk", "num_bytes": 3768, "num_examples": 17}, {"name": "salesperson", "num_bytes": 3672, "num_examples": 13}, {"name": "social_worker", "num_bytes": 3792, "num_examples": 18}, {"name": "director", "num_bytes": 3720, "num_examples": 15}, {"name": "fast_food_worker", "num_bytes": 3696, "num_examples": 14}, {"name": "singer", "num_bytes": 3792, "num_examples": 18}, {"name": "metal_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "cleaner", "num_bytes": 3888, "num_examples": 22}, {"name": "computer_systems_analyst", "num_bytes": 3696, "num_examples": 14}, {"name": "dental_assistant", "num_bytes": 3504, "num_examples": 6}, {"name": "psychologist", "num_bytes": 3696, "num_examples": 14}, {"name": "machinist", "num_bytes": 3672, "num_examples": 13}, {"name": "therapist", "num_bytes": 3696, "num_examples": 14}, {"name": "veterinarian", "num_bytes": 3600, "num_examples": 10}, {"name": "teacher", "num_bytes": 3768, "num_examples": 17}, {"name": "architect", "num_bytes": 3720, "num_examples": 15}, {"name": "office_worker", "num_bytes": 3720, "num_examples": 15}, {"name": "drywall_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "nutritionist", "num_bytes": 3504, "num_examples": 6}, {"name": "librarian", "num_bytes": 3672, "num_examples": 13}, {"name": "childcare_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "school_bus_driver", "num_bytes": 3768, "num_examples": 17}, {"name": "file_clerk", "num_bytes": 3672, "num_examples": 13}, {"name": "logistician", "num_bytes": 3648, "num_examples": 12}, {"name": "scientist", "num_bytes": 3744, "num_examples": 16}, {"name": "teaching_assistant", "num_bytes": 3696, "num_examples": 14}, {"name": "radiologic_technician", "num_bytes": 3648, "num_examples": 12}, {"name": "manicurist", "num_bytes": 3624, "num_examples": 11}, {"name": "community_manager", "num_bytes": 3624, "num_examples": 11}, {"name": "carpenter", "num_bytes": 3552, "num_examples": 8}, {"name": "claims_appraiser", "num_bytes": 3600, "num_examples": 10}, {"name": "dispatcher", "num_bytes": 3552, "num_examples": 8}, {"name": "cashier", "num_bytes": 3648, "num_examples": 12}, {"name": "roofer", "num_bytes": 3480, "num_examples": 5}, {"name": "photographer", "num_bytes": 3840, "num_examples": 20}, {"name": "detective", "num_bytes": 3720, "num_examples": 15}, {"name": "financial_advisor", "num_bytes": 3600, "num_examples": 10}, {"name": "wholesale_buyer", "num_bytes": 3768, "num_examples": 17}, {"name": "it_specialist", "num_bytes": 3552, "num_examples": 8}, {"name": "pharmacy_technician", "num_bytes": 3552, "num_examples": 8}, {"name": "engineer", "num_bytes": 3672, "num_examples": 13}, {"name": "mover", "num_bytes": 3816, "num_examples": 19}, {"name": "plane_mechanic", "num_bytes": 3624, "num_examples": 11}, {"name": "interviewer", "num_bytes": 3768, "num_examples": 17}, {"name": "massage_therapist", "num_bytes": 3600, "num_examples": 10}, {"name": "dishwasher", "num_bytes": 3720, "num_examples": 15}, {"name": "fitness_instructor", "num_bytes": 3528, "num_examples": 7}, {"name": "credit_counselor", "num_bytes": 3648, "num_examples": 12}, {"name": "stocker", "num_bytes": 3768, "num_examples": 17}, {"name": "pharmacist", "num_bytes": 3720, "num_examples": 15}, {"name": "doctor", "num_bytes": 3720, "num_examples": 15}, {"name": "compliance_officer", "num_bytes": 3624, "num_examples": 11}, {"name": "aide", "num_bytes": 3768, "num_examples": 17}, {"name": "bus_driver", "num_bytes": 3744, "num_examples": 16}, {"name": "financial_analyst", "num_bytes": 3672, "num_examples": 13}, {"name": "receptionist", "num_bytes": 3480, "num_examples": 5}, {"name": "janitor", "num_bytes": 3672, "num_examples": 13}, {"name": "plumber", "num_bytes": 3528, "num_examples": 7}, {"name": "physical_therapist", "num_bytes": 3648, "num_examples": 12}, {"name": "inventory_clerk", "num_bytes": 3648, "num_examples": 12}, {"name": "firefighter", "num_bytes": 3552, "num_examples": 8}, {"name": "coach", "num_bytes": 3744, "num_examples": 16}, {"name": "maid", "num_bytes": 3720, "num_examples": 15}, {"name": "pilot", "num_bytes": 3744, "num_examples": 16}, {"name": "repair_worker", "num_bytes": 3672, "num_examples": 13}], "download_size": 873221, "dataset_size": 533520}}
|
2023-06-03T19:24:20+00:00
|
b6ad0ce31355e8962bd8912254098be54c0e8c3e
|
# Dataset Card for "prof_report__SD_v1.4_random_seeds__sd_21__12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__SD_v1.4_random_seeds__sd_21__12
|
[
"region:us"
] |
2023-06-03T19:24:38+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "paralegal", "num_bytes": 3528, "num_examples": 7}, {"name": "bartender", "num_bytes": 3480, "num_examples": 5}, {"name": "facilities_manager", "num_bytes": 3552, "num_examples": 8}, {"name": "accountant", "num_bytes": 3552, "num_examples": 8}, {"name": "graphic_designer", "num_bytes": 3576, "num_examples": 9}, {"name": "network_administrator", "num_bytes": 3456, "num_examples": 4}, {"name": "financial_manager", "num_bytes": 3576, "num_examples": 9}, {"name": "baker", "num_bytes": 3624, "num_examples": 11}, {"name": "security_guard", "num_bytes": 3576, "num_examples": 9}, {"name": "artist", "num_bytes": 3600, "num_examples": 10}, {"name": "author", "num_bytes": 3552, "num_examples": 8}, {"name": "printing_press_operator", "num_bytes": 3528, "num_examples": 7}, {"name": "public_relations_specialist", "num_bytes": 3528, "num_examples": 7}, {"name": "sheet_metal_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "clergy", "num_bytes": 3600, "num_examples": 10}, {"name": "payroll_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "teller", "num_bytes": 3624, "num_examples": 11}, {"name": "real_estate_broker", "num_bytes": 3528, "num_examples": 7}, {"name": "customer_service_representative", "num_bytes": 3528, "num_examples": 7}, {"name": "painter", "num_bytes": 3624, "num_examples": 11}, {"name": "tractor_operator", "num_bytes": 3456, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 3480, "num_examples": 5}, {"name": "industrial_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "electrician", "num_bytes": 3408, "num_examples": 2}, {"name": "head_cook", "num_bytes": 3600, "num_examples": 10}, {"name": "health_technician", "num_bytes": 3528, "num_examples": 7}, {"name": "carpet_installer", "num_bytes": 3432, "num_examples": 3}, {"name": "purchasing_agent", "num_bytes": 3552, "num_examples": 8}, {"name": "supervisor", "num_bytes": 3600, "num_examples": 10}, {"name": "civil_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "lawyer", "num_bytes": 3576, "num_examples": 9}, {"name": "language_pathologist", "num_bytes": 3528, "num_examples": 7}, {"name": "ceo", "num_bytes": 3576, "num_examples": 9}, {"name": "computer_support_specialist", "num_bytes": 3576, "num_examples": 9}, {"name": "postal_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "mechanical_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "nursing_assistant", "num_bytes": 3504, "num_examples": 6}, {"name": "dentist", "num_bytes": 3552, "num_examples": 8}, {"name": "tutor", "num_bytes": 3600, "num_examples": 10}, {"name": "butcher", "num_bytes": 3528, "num_examples": 7}, {"name": "insurance_agent", "num_bytes": 3480, "num_examples": 5}, {"name": "courier", "num_bytes": 3600, "num_examples": 10}, {"name": "computer_programmer", "num_bytes": 3552, "num_examples": 8}, {"name": "truck_driver", "num_bytes": 3504, "num_examples": 6}, {"name": "mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "marketing_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "sales_manager", "num_bytes": 3504, "num_examples": 6}, {"name": "correctional_officer", "num_bytes": 3576, "num_examples": 9}, {"name": "manager", "num_bytes": 3552, "num_examples": 8}, {"name": "underwriter", "num_bytes": 3576, "num_examples": 9}, {"name": "executive_assistant", "num_bytes": 3576, "num_examples": 9}, {"name": "designer", "num_bytes": 3600, "num_examples": 10}, {"name": "groundskeeper", "num_bytes": 3456, "num_examples": 4}, {"name": "mental_health_counselor", "num_bytes": 3552, "num_examples": 8}, {"name": "aerospace_engineer", "num_bytes": 3552, "num_examples": 8}, {"name": "taxi_driver", "num_bytes": 3600, "num_examples": 10}, {"name": "nurse", "num_bytes": 3528, "num_examples": 7}, {"name": "data_entry_keyer", "num_bytes": 3552, "num_examples": 8}, {"name": "musician", "num_bytes": 3576, "num_examples": 9}, {"name": "event_planner", "num_bytes": 3552, "num_examples": 8}, {"name": "writer", "num_bytes": 3576, "num_examples": 9}, {"name": "cook", "num_bytes": 3624, "num_examples": 11}, {"name": "welder", "num_bytes": 3528, "num_examples": 7}, {"name": "producer", "num_bytes": 3600, "num_examples": 10}, {"name": "hairdresser", "num_bytes": 3528, "num_examples": 7}, {"name": "farmer", "num_bytes": 3480, "num_examples": 5}, {"name": "construction_worker", "num_bytes": 3480, "num_examples": 5}, {"name": "air_conditioning_installer", "num_bytes": 3408, "num_examples": 2}, {"name": "electrical_engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "occupational_therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "career_counselor", "num_bytes": 3528, "num_examples": 7}, {"name": "interior_designer", "num_bytes": 3576, "num_examples": 9}, {"name": "jailer", "num_bytes": 3600, "num_examples": 10}, {"name": "office_clerk", "num_bytes": 3528, "num_examples": 7}, {"name": "market_research_analyst", "num_bytes": 3528, "num_examples": 7}, {"name": "laboratory_technician", "num_bytes": 3552, "num_examples": 8}, {"name": "social_assistant", "num_bytes": 3624, "num_examples": 11}, {"name": "medical_records_specialist", "num_bytes": 3504, "num_examples": 6}, {"name": "machinery_mechanic", "num_bytes": 3504, "num_examples": 6}, {"name": "police_officer", "num_bytes": 3576, "num_examples": 9}, {"name": "software_developer", "num_bytes": 3456, "num_examples": 4}, {"name": "clerk", "num_bytes": 3624, "num_examples": 11}, {"name": "salesperson", "num_bytes": 3552, "num_examples": 8}, {"name": "social_worker", "num_bytes": 3624, "num_examples": 11}, {"name": "director", "num_bytes": 3624, "num_examples": 11}, {"name": "fast_food_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "singer", "num_bytes": 3600, "num_examples": 10}, {"name": "metal_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "cleaner", "num_bytes": 3624, "num_examples": 11}, {"name": "computer_systems_analyst", "num_bytes": 3552, "num_examples": 8}, {"name": "dental_assistant", "num_bytes": 3480, "num_examples": 5}, {"name": "psychologist", "num_bytes": 3576, "num_examples": 9}, {"name": "machinist", "num_bytes": 3552, "num_examples": 8}, {"name": "therapist", "num_bytes": 3600, "num_examples": 10}, {"name": "veterinarian", "num_bytes": 3528, "num_examples": 7}, {"name": "teacher", "num_bytes": 3576, "num_examples": 9}, {"name": "architect", "num_bytes": 3552, "num_examples": 8}, {"name": "office_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "drywall_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 3480, "num_examples": 5}, {"name": "librarian", "num_bytes": 3552, "num_examples": 8}, {"name": "childcare_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "school_bus_driver", "num_bytes": 3576, "num_examples": 9}, {"name": "file_clerk", "num_bytes": 3528, "num_examples": 7}, {"name": "logistician", "num_bytes": 3528, "num_examples": 7}, {"name": "scientist", "num_bytes": 3528, "num_examples": 7}, {"name": "teaching_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "radiologic_technician", "num_bytes": 3552, "num_examples": 8}, {"name": "manicurist", "num_bytes": 3528, "num_examples": 7}, {"name": "community_manager", "num_bytes": 3552, "num_examples": 8}, {"name": "carpenter", "num_bytes": 3456, "num_examples": 4}, {"name": "claims_appraiser", "num_bytes": 3480, "num_examples": 5}, {"name": "dispatcher", "num_bytes": 3504, "num_examples": 6}, {"name": "cashier", "num_bytes": 3528, "num_examples": 7}, {"name": "roofer", "num_bytes": 3456, "num_examples": 4}, {"name": "photographer", "num_bytes": 3624, "num_examples": 11}, {"name": "detective", "num_bytes": 3552, "num_examples": 8}, {"name": "financial_advisor", "num_bytes": 3504, "num_examples": 6}, {"name": "wholesale_buyer", "num_bytes": 3552, "num_examples": 8}, {"name": "it_specialist", "num_bytes": 3552, "num_examples": 8}, {"name": "pharmacy_technician", "num_bytes": 3480, "num_examples": 5}, {"name": "engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "mover", "num_bytes": 3600, "num_examples": 10}, {"name": "plane_mechanic", "num_bytes": 3576, "num_examples": 9}, {"name": "interviewer", "num_bytes": 3600, "num_examples": 10}, {"name": "massage_therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "dishwasher", "num_bytes": 3576, "num_examples": 9}, {"name": "fitness_instructor", "num_bytes": 3528, "num_examples": 7}, {"name": "credit_counselor", "num_bytes": 3552, "num_examples": 8}, {"name": "stocker", "num_bytes": 3600, "num_examples": 10}, {"name": "pharmacist", "num_bytes": 3600, "num_examples": 10}, {"name": "doctor", "num_bytes": 3600, "num_examples": 10}, {"name": "compliance_officer", "num_bytes": 3576, "num_examples": 9}, {"name": "aide", "num_bytes": 3576, "num_examples": 9}, {"name": "bus_driver", "num_bytes": 3552, "num_examples": 8}, {"name": "financial_analyst", "num_bytes": 3552, "num_examples": 8}, {"name": "receptionist", "num_bytes": 3480, "num_examples": 5}, {"name": "janitor", "num_bytes": 3552, "num_examples": 8}, {"name": "plumber", "num_bytes": 3504, "num_examples": 6}, {"name": "physical_therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "inventory_clerk", "num_bytes": 3504, "num_examples": 6}, {"name": "firefighter", "num_bytes": 3552, "num_examples": 8}, {"name": "coach", "num_bytes": 3576, "num_examples": 9}, {"name": "maid", "num_bytes": 3624, "num_examples": 11}, {"name": "pilot", "num_bytes": 3600, "num_examples": 10}, {"name": "repair_worker", "num_bytes": 3576, "num_examples": 9}], "download_size": 866892, "dataset_size": 517872}}
|
2023-06-03T19:27:21+00:00
|
ce749e5a6b09ee76cb12535bacb208ae2f4291fa
|
# Dataset Card for "prof_report__SD_v1.4_random_seeds__sd_21__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__SD_v1.4_random_seeds__sd_21__24
|
[
"region:us"
] |
2023-06-03T19:48:05+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "paralegal", "num_bytes": 3600, "num_examples": 10}, {"name": "bartender", "num_bytes": 3504, "num_examples": 6}, {"name": "facilities_manager", "num_bytes": 3600, "num_examples": 10}, {"name": "accountant", "num_bytes": 3600, "num_examples": 10}, {"name": "graphic_designer", "num_bytes": 3672, "num_examples": 13}, {"name": "network_administrator", "num_bytes": 3408, "num_examples": 2}, {"name": "financial_manager", "num_bytes": 3624, "num_examples": 11}, {"name": "baker", "num_bytes": 3720, "num_examples": 15}, {"name": "security_guard", "num_bytes": 3648, "num_examples": 12}, {"name": "artist", "num_bytes": 3840, "num_examples": 20}, {"name": "author", "num_bytes": 3600, "num_examples": 10}, {"name": "printing_press_operator", "num_bytes": 3552, "num_examples": 8}, {"name": "public_relations_specialist", "num_bytes": 3648, "num_examples": 12}, {"name": "sheet_metal_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "clergy", "num_bytes": 3648, "num_examples": 12}, {"name": "payroll_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "teller", "num_bytes": 3816, "num_examples": 19}, {"name": "real_estate_broker", "num_bytes": 3552, "num_examples": 8}, {"name": "customer_service_representative", "num_bytes": 3600, "num_examples": 10}, {"name": "painter", "num_bytes": 3768, "num_examples": 17}, {"name": "tractor_operator", "num_bytes": 3480, "num_examples": 5}, {"name": "dental_hygienist", "num_bytes": 3504, "num_examples": 6}, {"name": "industrial_engineer", "num_bytes": 3576, "num_examples": 9}, {"name": "electrician", "num_bytes": 3480, "num_examples": 5}, {"name": "head_cook", "num_bytes": 3744, "num_examples": 16}, {"name": "health_technician", "num_bytes": 3600, "num_examples": 10}, {"name": "carpet_installer", 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"num_examples": 15}, {"name": "computer_programmer", "num_bytes": 3624, "num_examples": 11}, {"name": "truck_driver", "num_bytes": 3504, "num_examples": 6}, {"name": "mechanic", "num_bytes": 3528, "num_examples": 7}, {"name": "marketing_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "sales_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "correctional_officer", "num_bytes": 3696, "num_examples": 14}, {"name": "manager", "num_bytes": 3648, "num_examples": 12}, {"name": "underwriter", "num_bytes": 3672, "num_examples": 13}, {"name": "executive_assistant", "num_bytes": 3600, "num_examples": 10}, {"name": "designer", "num_bytes": 3648, "num_examples": 12}, {"name": "groundskeeper", "num_bytes": 3480, "num_examples": 5}, {"name": "mental_health_counselor", "num_bytes": 3672, "num_examples": 13}, {"name": "aerospace_engineer", "num_bytes": 3648, "num_examples": 12}, {"name": "taxi_driver", "num_bytes": 3696, "num_examples": 14}, {"name": "nurse", "num_bytes": 3576, "num_examples": 9}, {"name": "data_entry_keyer", "num_bytes": 3624, "num_examples": 11}, {"name": "musician", "num_bytes": 3696, "num_examples": 14}, {"name": "event_planner", "num_bytes": 3552, "num_examples": 8}, {"name": "writer", "num_bytes": 3672, "num_examples": 13}, {"name": "cook", "num_bytes": 3792, "num_examples": 18}, {"name": "welder", "num_bytes": 3624, "num_examples": 11}, {"name": "producer", "num_bytes": 3744, "num_examples": 16}, {"name": "hairdresser", "num_bytes": 3600, "num_examples": 10}, {"name": "farmer", "num_bytes": 3528, "num_examples": 7}, {"name": "construction_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "air_conditioning_installer", "num_bytes": 3432, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 3648, "num_examples": 12}, {"name": "occupational_therapist", "num_bytes": 3624, "num_examples": 11}, {"name": "career_counselor", "num_bytes": 3600, "num_examples": 10}, {"name": "interior_designer", "num_bytes": 3624, "num_examples": 11}, {"name": "jailer", "num_bytes": 3744, "num_examples": 16}, {"name": "office_clerk", "num_bytes": 3624, "num_examples": 11}, {"name": "market_research_analyst", "num_bytes": 3576, "num_examples": 9}, {"name": "laboratory_technician", "num_bytes": 3624, "num_examples": 11}, {"name": "social_assistant", "num_bytes": 3744, "num_examples": 16}, {"name": "medical_records_specialist", "num_bytes": 3576, "num_examples": 9}, {"name": "machinery_mechanic", "num_bytes": 3552, "num_examples": 8}, {"name": "police_officer", "num_bytes": 3672, "num_examples": 13}, {"name": "software_developer", "num_bytes": 3528, "num_examples": 7}, {"name": "clerk", "num_bytes": 3720, "num_examples": 15}, {"name": "salesperson", "num_bytes": 3648, "num_examples": 12}, {"name": "social_worker", "num_bytes": 3744, "num_examples": 16}, {"name": "director", "num_bytes": 3720, "num_examples": 15}, {"name": "fast_food_worker", "num_bytes": 3696, "num_examples": 14}, {"name": "singer", "num_bytes": 3792, "num_examples": 18}, {"name": "metal_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "cleaner", "num_bytes": 3792, "num_examples": 18}, {"name": "computer_systems_analyst", "num_bytes": 3600, "num_examples": 10}, {"name": "dental_assistant", "num_bytes": 3504, "num_examples": 6}, {"name": "psychologist", "num_bytes": 3696, "num_examples": 14}, {"name": "machinist", "num_bytes": 3648, "num_examples": 12}, {"name": "therapist", "num_bytes": 3648, "num_examples": 12}, {"name": "veterinarian", "num_bytes": 3576, "num_examples": 9}, {"name": "teacher", "num_bytes": 3720, "num_examples": 15}, {"name": "architect", "num_bytes": 3720, "num_examples": 15}, {"name": "office_worker", "num_bytes": 3672, "num_examples": 13}, {"name": "drywall_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "nutritionist", "num_bytes": 3480, "num_examples": 5}, {"name": "librarian", "num_bytes": 3672, "num_examples": 13}, {"name": "childcare_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "school_bus_driver", "num_bytes": 3696, "num_examples": 14}, {"name": "file_clerk", "num_bytes": 3600, "num_examples": 10}, {"name": "logistician", "num_bytes": 3576, "num_examples": 9}, {"name": "scientist", "num_bytes": 3648, "num_examples": 12}, {"name": "teaching_assistant", "num_bytes": 3672, "num_examples": 13}, {"name": "radiologic_technician", "num_bytes": 3600, "num_examples": 10}, {"name": "manicurist", "num_bytes": 3576, "num_examples": 9}, {"name": "community_manager", "num_bytes": 3576, "num_examples": 9}, {"name": "carpenter", "num_bytes": 3480, "num_examples": 5}, {"name": "claims_appraiser", "num_bytes": 3576, "num_examples": 9}, {"name": "dispatcher", "num_bytes": 3528, "num_examples": 7}, {"name": "cashier", "num_bytes": 3600, "num_examples": 10}, {"name": "roofer", "num_bytes": 3504, "num_examples": 6}, {"name": "photographer", "num_bytes": 3792, "num_examples": 18}, {"name": "detective", "num_bytes": 3648, "num_examples": 12}, {"name": "financial_advisor", "num_bytes": 3576, "num_examples": 9}, {"name": "wholesale_buyer", "num_bytes": 3672, "num_examples": 13}, {"name": "it_specialist", "num_bytes": 3552, "num_examples": 8}, {"name": "pharmacy_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "engineer", "num_bytes": 3648, "num_examples": 12}, {"name": "mover", "num_bytes": 3768, "num_examples": 17}, {"name": "plane_mechanic", "num_bytes": 3624, "num_examples": 11}, {"name": "interviewer", "num_bytes": 3672, "num_examples": 13}, {"name": "massage_therapist", "num_bytes": 3624, "num_examples": 11}, {"name": "dishwasher", "num_bytes": 3672, "num_examples": 13}, {"name": "fitness_instructor", "num_bytes": 3600, "num_examples": 10}, {"name": "credit_counselor", "num_bytes": 3624, "num_examples": 11}, {"name": "stocker", "num_bytes": 3816, "num_examples": 19}, {"name": "pharmacist", "num_bytes": 3672, "num_examples": 13}, {"name": "doctor", "num_bytes": 3672, "num_examples": 13}, {"name": "compliance_officer", "num_bytes": 3648, "num_examples": 12}, {"name": "aide", "num_bytes": 3768, "num_examples": 17}, {"name": "bus_driver", "num_bytes": 3672, "num_examples": 13}, {"name": "financial_analyst", "num_bytes": 3624, "num_examples": 11}, {"name": "receptionist", "num_bytes": 3504, "num_examples": 6}, {"name": "janitor", "num_bytes": 3672, "num_examples": 13}, {"name": "plumber", "num_bytes": 3504, "num_examples": 6}, {"name": "physical_therapist", "num_bytes": 3600, "num_examples": 10}, {"name": "inventory_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "firefighter", "num_bytes": 3600, "num_examples": 10}, {"name": "coach", "num_bytes": 3696, "num_examples": 14}, {"name": "maid", "num_bytes": 3648, "num_examples": 12}, {"name": "pilot", "num_bytes": 3696, "num_examples": 14}, {"name": "repair_worker", "num_bytes": 3624, "num_examples": 11}], "download_size": 871516, "dataset_size": 529248}}
|
2023-06-03T19:50:15+00:00
|
1fc0b5c5235178d36eac57474c9edeb853b77f62
|
# Dataset Card for "ag_newskeywords_lem"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/ag_newskeywords_lem
|
[
"region:us"
] |
2023-06-03T19:49:47+00:00
|
{"dataset_info": {"features": [{"name": "keyword", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "keyword_lem", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 50741, "num_examples": 1830}], "download_size": 47664, "dataset_size": 50741}}
|
2023-06-03T19:49:48+00:00
|
75f1e820d63a4c894dd20b5ff541f7d8c79ac4d2
|
# Dataset Card for "conversation-starters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Langame/conversation-starters
|
[
"region:us"
] |
2023-06-03T20:08:55+00:00
|
{"dataset_info": {"features": [{"name": "topics", "sequence": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2079285, "num_examples": 17470}], "download_size": 966258, "dataset_size": 2079285}}
|
2023-06-03T20:09:20+00:00
|
c5199a3ef2fc994bc898342da2fd6b51880ad552
|
# Dataset Card for "prof_images_blip__SD_v2_random_seeds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_images_blip__SD_v2_random_seeds
|
[
"region:us"
] |
2023-06-03T20:19:43+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "paralegal", "num_bytes": 7318486.0, "num_examples": 210}, {"name": "bartender", "num_bytes": 9962460.0, "num_examples": 210}, {"name": "facilities_manager", "num_bytes": 7289204.0, "num_examples": 210}, {"name": "accountant", "num_bytes": 6909069.0, "num_examples": 210}, {"name": "graphic_designer", "num_bytes": 7583565.0, "num_examples": 210}, {"name": "network_administrator", "num_bytes": 7987215.0, "num_examples": 210}, {"name": "financial_manager", "num_bytes": 6723858.0, "num_examples": 210}, {"name": "baker", "num_bytes": 7612344.0, "num_examples": 210}, {"name": "security_guard", "num_bytes": 7064225.0, "num_examples": 210}, {"name": "artist", "num_bytes": 7371224.0, "num_examples": 210}, {"name": "author", "num_bytes": 7756269.0, "num_examples": 210}, {"name": "printing_press_operator", "num_bytes": 9471204.0, "num_examples": 210}, {"name": 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{"name": "manicurist", "num_bytes": 6894697.0, "num_examples": 210}, {"name": "community_manager", "num_bytes": 7589020.0, "num_examples": 210}, {"name": "carpenter", "num_bytes": 8417470.0, "num_examples": 210}, {"name": "claims_appraiser", "num_bytes": 7057174.0, "num_examples": 210}, {"name": "dispatcher", "num_bytes": 7111905.0, "num_examples": 210}, {"name": "cashier", "num_bytes": 8422908.0, "num_examples": 210}, {"name": "roofer", "num_bytes": 8910783.0, "num_examples": 210}, {"name": "photographer", "num_bytes": 7508323.0, "num_examples": 210}, {"name": "detective", "num_bytes": 7606742.0, "num_examples": 210}, {"name": "financial_advisor", "num_bytes": 6605338.0, "num_examples": 210}, {"name": "wholesale_buyer", "num_bytes": 9320426.0, "num_examples": 210}, {"name": "it_specialist", "num_bytes": 7201798.0, "num_examples": 210}, {"name": "pharmacy_technician", "num_bytes": 8173939.0, "num_examples": 210}, {"name": "engineer", "num_bytes": 7485900.0, "num_examples": 210}, {"name": "mover", "num_bytes": 7409428.0, "num_examples": 210}, {"name": "plane_mechanic", "num_bytes": 8697598.0, "num_examples": 210}, {"name": "interviewer", "num_bytes": 6421369.0, "num_examples": 210}, {"name": "massage_therapist", "num_bytes": 6439125.0, "num_examples": 210}, {"name": "dishwasher", "num_bytes": 9661619.0, "num_examples": 210}, {"name": "fitness_instructor", "num_bytes": 6832101.0, "num_examples": 210}, {"name": "credit_counselor", "num_bytes": 6907573.0, "num_examples": 210}, {"name": "stocker", "num_bytes": 9484149.0, "num_examples": 210}, {"name": "pharmacist", "num_bytes": 8414409.0, "num_examples": 210}, {"name": "doctor", "num_bytes": 6669475.0, "num_examples": 210}, {"name": "compliance_officer", "num_bytes": 6578437.0, "num_examples": 210}, {"name": "aide", "num_bytes": 6765586.0, "num_examples": 210}, {"name": "bus_driver", "num_bytes": 8894973.0, "num_examples": 210}, {"name": "financial_analyst", "num_bytes": 6659678.0, "num_examples": 210}, {"name": "receptionist", "num_bytes": 6410167.0, "num_examples": 210}, {"name": "janitor", "num_bytes": 7148774.0, "num_examples": 210}, {"name": "plumber", "num_bytes": 7828285.0, "num_examples": 210}, {"name": "physical_therapist", "num_bytes": 6675681.0, "num_examples": 210}, {"name": "inventory_clerk", "num_bytes": 8559201.0, "num_examples": 210}, {"name": "firefighter", "num_bytes": 8438408.0, "num_examples": 210}, {"name": "coach", "num_bytes": 7342173.0, "num_examples": 210}, {"name": "maid", "num_bytes": 6733909.0, "num_examples": 210}, {"name": "pilot", "num_bytes": 7879490.0, "num_examples": 210}, {"name": "repair_worker", "num_bytes": 7972885.0, "num_examples": 210}], "download_size": 1160823534, "dataset_size": 1107977251.0}}
|
2023-06-03T20:24:16+00:00
|
da0e39253c45d3f04658b871c5069f2efdf16ff3
|
henrywintif/nyc-events-emails
|
[
"license:gpl-2.0",
"region:us"
] |
2023-06-03T20:23:42+00:00
|
{"license": "gpl-2.0"}
|
2023-06-03T20:23:42+00:00
|
|
1a28d63dcb40867c926dcb9e264494c3c4df45d8
|
# Dataset Card for "ag_news_embed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
xwjzds/ag_news_embed
|
[
"region:us"
] |
2023-06-03T20:50:57+00:00
|
{"dataset_info": {"features": [{"name": "train", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 77000000, "num_examples": 50000}], "download_size": 5355833, "dataset_size": 77000000}}
|
2023-06-03T20:51:00+00:00
|
83d3ac8899ac70373888f9c7207ebd446714a6c9
|
# Dataset Card for "ag_news_embed_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
xwjzds/ag_news_embed_train
|
[
"region:us"
] |
2023-06-03T21:01:49+00:00
|
{"dataset_info": {"features": [{"name": "train", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 184800000, "num_examples": 120000}], "download_size": 259201020, "dataset_size": 184800000}}
|
2023-06-03T21:02:03+00:00
|
30ab54dd1b0dc2d8c114328561907a9f3193c852
|
# Dataset Card for "ag_news_embed_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
xwjzds/ag_news_embed_test
|
[
"region:us"
] |
2023-06-03T21:02:03+00:00
|
{"dataset_info": {"features": [{"name": "test", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 11704000, "num_examples": 7600}], "download_size": 16563075, "dataset_size": 11704000}}
|
2023-06-03T21:02:06+00:00
|
7c0606470421fb47c6690b055af9d43414f7862e
|
# Massive Multitask Language Understanding (MMLU) in German
This dataset is to be used for the evaluation of LLM German language understanding.
It is based on the hendrycksTest dataset ([here](https://huggingface.co/datasets/cais/mmlu) and [here](https://huggingface.co/datasets/tasksource/mmlu)) and was created
by using the GPT-3.5 API to translate the entire test set and a few examples of the validation set. To make sure the answer options follow the intended sentence structure
and are always of the correct format, GPT was prompted to output in a JSON format. This came with some complications that were later manually fixed.
The prompt used to translate a single example was the following:
```
insert prompt here @TODO
```
This translation cost a total of ~13€ including iterating on the prompt and fixing broken examples.
|
LeoLM/MMLU_de
|
[
"license:mit",
"region:us"
] |
2023-06-03T21:07:16+00:00
|
{"license": "mit"}
|
2024-01-03T01:11:10+00:00
|
36cbffcf908a0f387f6a27ff118e50b8cc4a9210
|
---
# Dataset Card for "Serbian Wiki Dataset"
---
> **Dataset contain text from Wikipedia articles in Serbian (obtained in early 2020) totaling in 477473 articles, as well as some of the WikiSource.**
- Dataset is constituted of TXT files.
- [Fixed and used from: **JeRTeh/SrpWiki**](https://huggingface.co/datasets/JeRTeh/SrpWiki)
|
datatab/SrpWikiDataset
|
[
"task_categories:text-generation",
"language:sr",
"license:apache-2.0",
"region:us"
] |
2023-06-03T22:20:59+00:00
|
{"language": ["sr"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "Serbian Wiki Dataset", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 468569155, "num_examples": 3796604}], "download_size": 257869459, "dataset_size": 468569155}}
|
2023-06-03T22:56:04+00:00
|
f5d8548db28fd1bb0886cf52c011e122c4475580
|
# AutoTrain Dataset for project: ta-winda-ota-sentiment-analysis
## Dataset Description
This dataset has been automatically processed by AutoTrain for project ta-winda-ota-sentiment-analysis.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_reviewId": "11e13237-0fe6-40ae-b035-e6d6d0287a80",
"feat_userName": "Sulaiman",
"feat_userImage": "https://play-lh.googleusercontent.com/a-/AD_cMMQbSKYMfa0BWeV5LYPf0kZ1MV3PKx_VgYzByqUb5Q",
"text": "ok",
"target": 4,
"feat_thumbsUpCount": 0,
"feat_reviewCreatedVersion": "3.77.1",
"feat_at": "2023-05-27 01:49:05",
"feat_replyContent": "Hi, we are so grateful to get a lot of support from you. Hope you continue to enjoy our offers. If you have any feedback or suggestions, let us know on https://www.traveloka.com/contactus, our customer service would love to serve you in 24 hours. Thank you!",
"feat_repliedAt": "2023-05-27 02:12:14",
"feat_appVersion": "3.77.1",
"feat_sortOrder": "newest",
"feat_appId": "com.traveloka.android"
},
{
"feat_reviewId": "671f8bed-8371-490f-bc33-51034fc798f3",
"feat_userName": "Feri Yadi",
"feat_userImage": "https://play-lh.googleusercontent.com/a-/AD_cMMT7JhwvdqMkI84xvo_4HZ-2xV04Pvsn75E_SD3GoQ",
"text": "ok",
"target": 0,
"feat_thumbsUpCount": 0,
"feat_reviewCreatedVersion": "10.37.0",
"feat_at": "2023-05-08 02:38:38",
"feat_replyContent": "We apologize for any inconvenience this has caused you. Your experience is important to us. If there is something more we can help you with,\n\nplease write an email to [email protected] and include your phone number if you would prefer to be contacted by phone.\n\nOur team will review the information and contact you back as soon as possible.",
"feat_repliedAt": "2023-05-08 05:14:09",
"feat_appVersion": "10.37.0",
"feat_sortOrder": "newest",
"feat_appId": "com.agoda.mobile.consumer"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_reviewId": "Value(dtype='string', id=None)",
"feat_userName": "Value(dtype='string', id=None)",
"feat_userImage": "Value(dtype='string', id=None)",
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['1', '2', '3', '4', '5'], id=None)",
"feat_thumbsUpCount": "Value(dtype='int64', id=None)",
"feat_reviewCreatedVersion": "Value(dtype='string', id=None)",
"feat_at": "Value(dtype='string', id=None)",
"feat_replyContent": "Value(dtype='string', id=None)",
"feat_repliedAt": "Value(dtype='string', id=None)",
"feat_appVersion": "Value(dtype='string', id=None)",
"feat_sortOrder": "Value(dtype='string', id=None)",
"feat_appId": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2826 |
| valid | 709 |
|
windaan/autotrain-data-ta-winda-ota-sentiment-analysis
|
[
"task_categories:text-classification",
"region:us"
] |
2023-06-03T23:02:09+00:00
|
{"task_categories": ["text-classification"]}
|
2023-06-04T02:33:13+00:00
|
208ea6bc22d863bbc1cbaef1829b3e646600dfb6
|
HSSD: Habitat Synthetic Scenes Dataset
==================================
The [Habitat Synthetic Scenes Dataset (HSSD)](https://3dlg-hcvc.github.io/hssd/) is a human-authored 3D scene dataset that more closely mirrors real scenes than prior datasets.
Our dataset represents real interiors and contains a diverse set of 211 scenes and more than 18000 models of real-world objects.
<img src="https://i.imgur.com/XEkLxNs.png" width=50%>
|
hssd/hssd-models
|
[
"language:en",
"license:cc-by-nc-4.0",
"3D scenes",
"Embodied AI",
"region:us"
] |
2023-06-04T00:00:01+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "pretty_name": "HSSD", "tags": ["3D scenes", "Embodied AI"], "extra_gated_heading": "Acknowledge license to accept the repository", "extra_gated_prompt": "You agree to use this dataset under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/) terms"}
|
2023-06-15T02:32:23+00:00
|
1ee971187046f5acf2356aeee12e4d8ccfd68a58
|
# Dataset Card for "brighten-all-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dqymaggie/brighten-all-dataset
|
[
"region:us"
] |
2023-06-04T00:56:10+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input_image", "dtype": "image"}, {"name": "ground_truth_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 32678092378.25, "num_examples": 5250}], "download_size": 31111022320, "dataset_size": 32678092378.25}}
|
2023-06-04T15:39:04+00:00
|
3f6cd6b3cc55ce853475c50621022ca66fe915a0
|
## Dataset
The dataset is composed of messages labeled by ham or spam, merged from three data sources:
- SMS Spam Collection https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
- Telegram Spam Ham https://huggingface.co/datasets/thehamkercat/telegram-spam-ham/tree/main
- Enron Spam: https://huggingface.co/datasets/SetFit/enron_spam/tree/main (only used message column and labels)
The prepare script for enron is available at https://github.com/mshenoda/roberta-spam/tree/main/data/enron.
The data is split 80% train 10% validation, and 10% test sets; the scripts used to split and merge of the three data sources are available at: https://github.com/mshenoda/roberta-spam/tree/main/data/utils.
### Dataset Class Distribution
Training 80% | Validation 10% | Testing 10%
:-------------------------:|:-------------------------:|:-------------------------:
 Class Distribution |  Class Distribution |  Class Distribution
|
mshenoda/spam-messages
|
[
"license:mit",
"region:us"
] |
2023-06-04T01:36:32+00:00
|
{"license": "mit"}
|
2023-06-08T00:29:46+00:00
|
d8c7652244bb04dcc2a337804debbb8ed695bec0
|
Astrale0031/hardware_prices
|
[
"license:other",
"region:us"
] |
2023-06-04T02:15:39+00:00
|
{"license": "other"}
|
2023-06-04T02:17:09+00:00
|
|
ac848081a0d2fbc6bfb67711de6739d9b47e7f3a
|
FAISS Vector Database generated in [FAISS Chat Project](https://huggingface.co/spaces/shaocongma/faiss_chat).
|
shaocongma/shared-faiss-vdb
|
[
"license:mit",
"region:us"
] |
2023-06-04T02:23:39+00:00
|
{"license": "mit"}
|
2023-12-19T08:18:48+00:00
|
42c8e1f8804e0e92c8444a3767257a0754cee342
|
ThendCN/faiss
|
[
"license:unknown",
"region:us"
] |
2023-06-04T02:27:12+00:00
|
{"license": "unknown"}
|
2023-06-04T03:17:17+00:00
|
|
bfef2b8200c3883106bfe0e368f6a2b217a45214
|
swaption2009/cyber-threat-intelligence-custom-data
|
[
"task_categories:text-generation",
"task_categories:table-question-answering",
"language:en",
"region:us"
] |
2023-06-04T06:31:03+00:00
|
{"language": ["en"], "task_categories": ["text-generation", "table-question-answering"]}
|
2023-06-04T06:35:25+00:00
|
|
a5240955e851c4e555ff13c5296c5f553091e8d6
|
<a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/dataset-whu3d-green"></a> <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a>
# Installation
In order to use the pywhu3d tool, you need to install the pwhu3d library for your interpreter. We recommend you use python=3.7 to follow this tutorial.
```zsh
# this will install the latest version of pywhu3d
pip install pywhu3d
```
# Usage
## Initialization
Create a WHU3D object:
```python
from pywhu3d.tool import WHU3D
data_root = '/data/datasets/whu'
scenes = ['0404', '0940']
# whu3d = WHU3D(data_root=data_root, data_type='mls', format='txt')
whu3d = WHU3D(data_root=data_root, data_type='mls', format='h5', scenes=scenes)
```
Parameters:
- **data_root**: [data root folder]
- **data_type**: `als`, `mls`, `pc`, `img`
- **format**: `txt`, `ply`, `npy`, `h5`, `pickle`
- **[optional] scenes**: a list of scenes, if not specified, will be represented by all of the files
The structure of the data folder should be like this:
```
data_root
├── images
├── als
│ ├── h5
│ │ ├── [scene_1].h5
│ │ ├── [scene_2].h5
│ │ └── [scene_*].h5
│ └── [optional] pkl/npy/pth
└── mls
├── h5
│ ├── [scene_1].h5
│ ├── [scene_2].h5
│ └── [scene_*].h5
└── [optional] pkl/npy/pth
```
It is also recommended to use default split scenes to create a whu3d object, by using `whu3d.train_split`.
```python
# print(whu3d.split.val)
whu3d = WHU3D(data_root=data_root, data_type='mls', format='txt', scenes=whu3d.val_split)
```
Then some of the attributes could be directly accessed, including data_root, data_type, scenes, download_link
```python
# e.g., you could print current scenes
print(whu3d.scenes)
```
## Attributes
The attributes of whu3d may differ depending on your operations (e.g., after applying the `compute_normals` function, the attributes may include `normals` that may not exist before). Nonetheless, you could always use the `list_attributes` function to see the current attributes that you could currently access.
```python
# this command will show you a table with all the attributes
# that you could currently use.
whu3d.list_attributes()
```
You could simply get a specific attribute of all scenes by using `get_attribute` function.
```python
# this function will return a list of the attributes
attr = whu3d.get_attribute('coords')
```
### Data
You could access the data of a specific scene by using `whu3d.data[scene][attribute]`.
```python
xyz = whu3d.data['0414']['coords']
```
### Labels
Labels could also be directly accessed.
```python
semantics = whu3d.labels['0414']['semantics']
instances = whu3d.labels['0414']['instances']
```
If you have interpreted the labels by using `interprete_labels` function, you could also get interpreted labels.
```python
semantics = whu3d.interpreted_labels['0414']['semantics']
instances = whu3d.interpreted_labels['0414']['instances']
```
## Visualization
### Point cloud
You can visualize a specific scene or a list of scenes using the `vis` function. By default, this function will show both the point cloud and image frames, and the points are randomly sampled with sample_ratio = 0.01 for faster visualization. It will show color according to the height of the point if `color` is not specified, or you could choose a specific color, including intensity, normals, semantics, instances, and other features (some features should be computed first via whu3d functions if they do not exist, and you could use `whu3d.list_attributes()` to see the current attributes first).
```python
# This will show sampled points and images
whu3d.vis(scene='0414', type='pc', color='intensity')
# Show all the points
whu3d.vis(scene='0414', sample_ratio=1.0, type='pc', color='intensity')
# if you want to show normals, please set 'show_normals' to True
whu3d.vis(scene='0414', type='pc', color='normals', show_normals=True)
```
or you can use a remote visualization function that allows you to visualize the scene on your local machine if the script is run on a remote server.
```python
# This function should be used if you want to visualize points
# and the script is run on a remote machine.
whu3d.remote_vis(scene='0424', type='pc', color='intensity')
```
Before running the `remove_vis` function on your remote machine, you should start another ssh connection to your remote machine, and launch open3d on your local machine.
### Images
Similarly, you could use the `vis` function to see a series of images of a specific scene.
```python
whu3d.vis(scene='0414', type='img')
```
### BEV
[Will be available soon.]
### Renderings
[Will be available soon.]
### Labels
If you want to visualize the labels of semantics or instances, you must run the `interprete_labels` function first (please refer to the 'labels interpretation' section).
```python
# you should run this function first to interpret the labels
info, labels = whu3d.interprete_labels()
# you could visualize semantics with specified colors
whu3d.vis(scene='0414', type='pc', color='semantics')
# or you could visualize instances with random colors
whu3d.vis(scene='0414', type='pc', color='instances')
```
## Export
Note that all the `export` functions will export data to `self.data_path` by default and you should better not change it if you want to load it later via pywhu3d.
### Export data
You could export other formats of whu3d, including las, ply, numpy, pickle, h5py, image, et al, by just using the `export_[type]` function.
```python
scenes = ['0404', '0940']
whu3d.export_h5(output='.')
whu3d.export_images(output='.', scenes=scenes)
# this will export las to the '[self.data_path]/las' folder if
# output is not specified, you can also specify 'scenes'
whu3d.export_las()
```
If `scenes` is not specified, it will export all the scenes by default.
### Export labels
`export_labels` function could export raw labels or interpreted labels.
```python
# this will export '[scene].labels' files to your 'output' folder
whu3d.export_labels(output='./labels', scenes=scenes)
# whu3d.export_labels()
```
### Export statistics
You could also export detailed statistics of the data and label to excel by using the `export_statistics` function.
```python
whu3d.export_statistics(output='./whu3d_statistics.xlsx')
```
For the export of metrics, you could refer to the 'Evaluation' part.
### Custom export
You could use the `export` function to export a specified type of data.
```python
whu3d.export(output='', attribute='interpreted_labels')
```
## Labels interpretation
You could use the `interprete_labels` function to merge similar categories and remap the labels to consecutive numbers like 0, 1, 2, ...
```python
# this will interpret the labels and create the 'gt' attribute
whu3d.interprete_labels()
```
After applying this function, you could access the interpreted labels by using `whu3d.gt`. For more information, you could use the `get_label_map` function to see the interpretation table.
```python
# this will output a table showing the detailed information
# this only shows you the information of semantics
whu3d.get_label_map()
```
### Block division
If you want to divide the whole scene into rectangle blocks along XY plane, you could use `save_divided_blocks` function. This function will directly save the divided blocks into `.h5` file.
```python
# this will divide the scene into 10m * 10m blocks with 5m overlap$
whu3d.save_divided_blocks(out_dir='', num_points=4096, size=(10, 10), stride=5, threshold=100, show_points=False)
```
### Custom interpretation
If you could use your own file to interpret the labels, you should follow the steps:
Step1: Create `label_interpretion.json`. This file should include
```json
{
"sem_no_list_ins": "2, 3, 7",
"sem_label_mapping": [
{"175": "2"},
{"18": "5"}
]
}
```
`sem_no_list_ins` exclude the categories which should be not interpreted as instances;
`sem_label_mapping` specifies the mapping rules of semantic labels.
Step 2: Put the JSON file into the data root folder.
Step 3: Perform the `interprete_labels` function.
## Evaluation
The interpretation of predicted results should be consistent with that of the interpreted labels.
### Semantic segmentation evaluation
Or you could use the evaluation tool as in the 'instance segmentation evaluation' section, just by replacing the instance results with semantics.
### Instance segmentation evaluation
For instance segmentation evaluation, you should use our `evaluation.Evaluator` tool.
```python
# define an evaluator for evaluation
# preds is a list with num_scenes items:
# [scene_1_gt_arr, ..., scene_k_gt_arr]. Each item is a 2D
# array with shape (num_points, 2), of which the first column
# is semantic prediction and the second is instance prediction
# there are two ways to create an evaluator
# first way
evaluator = whu3d.create_evaluator(preds)
# second way
from pywhu3d.evluation import Evaluator
evaluator = Evaluator(whu3d, preds)
# then you could use evaluator functions
evaluator.compute_metrics()
```
You could get metrics, including:
- instance metrics: MUCov, MWCov, Pre, Rec, F1-score
- semantic metrics: oAcc, mAcc, mIoU
```python
print(evaluator.info)
print(evaluator.eval_list)
print(evaluator.eval_table)
```
You could also export evaluation results.
```python
# this will export an Excel file with detailed metrics
evaluator.export(output_dir='./')
```
### Custom evaluation
If you want to define a different list of ground truth labels instead of using the default labels, you could use `set_gt` function to set the ground truth labels
```python
from pywhu3d.evluation import Evaluator
evaluator = Evaluator(whu3d, preds)
# use this script to define your custom labels
# truths: a list of scenes [scene_1_gt_arr, ..., scene_k_gt_arr]
# gt_arr is a numpy array with shape (num_points, 2)
eval.set_gt(truths)
# then you could use evaluator functions
evaluator.compute_metrics()
```
# Custom dataset
You can also use the whu3d tool to customize your own dataset for all pywhu3d features simply by using the `format` function.
```python
data_root = '/data/datasets/you_custom_dataset'
scenes = ['scene1', 'scene2']
whu3d = WHU3D(data_root=data_root, data_type='mls', format='txt', scenes=scenes)
# this will format your data as whu3d format
# 'attributes' should be consistent with your input data
in_attributes = ['coords', 'semantics', 'instances', 'intensities']
whu3d.format(attributes=in_attributes)
```
After applying the `format` function, you could use all the features the whu3d tool provides just as the whu3d-dataset.
## Demo
This is a demo for preprocessing MLS dataset.
```python
from pywhu3d.tool import WHU3D
data_root = 'data/whu-dataset'
mls_scenes = ['0404', '0940']
# als_scenes = ['5033', '3922']
# whu3d = WHU3D(data_root=data_root, data_type='mls', format='txt')
whu3d = WHU3D(data_root=data_root, data_type='mls', format='h5', scenes=mls_scenes)
whu3d.norm_coords()
# self.compute_normals()
whu3d.interprete_labels()
whu3d.compute_normals(radius=0.8)
whu3d.save_divided_blocks(out_dir='', num_points=60000, size=(20, 20), stride=10, threshold=100, show_points=False)
```
# More
`pywhu3d` is a tool to manage the whu3d dataset, with limited ability to process the dataset (e.g., segmentation). But if you need more features for processing the outdoor scene dataset, you could refer to [well soon be available]. For more details about our dataset, please refer to our website.
|
astroy/WHU-Urban-3D
|
[
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-06-04T07:51:07+00:00
|
{"license": "cc-by-nc-sa-4.0"}
|
2023-07-31T02:48:59+00:00
|
c0bf9915d7720e50d1502f618dd89db4a1652161
|
# Dataset Card for Dataset Name
### Dataset Summary
This dataset given clearly voice lines that you can using on AI like DDSP-SVC to train your voice model.
### Notice
I will not be responsible for any problems caused by this database to you.
### Languages
English
### Licensing Information
CC0-1.0
|
NoProblem20230518/ADJVoice
|
[
"language:en",
"license:cc0-1.0",
"valve",
"portal",
"voice",
"Aperture",
"Aperture Desk Job",
"clearly",
"region:us"
] |
2023-06-04T08:36:57+00:00
|
{"language": ["en"], "license": "cc0-1.0", "pretty_name": "Aperture Desk Job Voice Lines Clearly Version", "tags": ["valve", "portal", "voice", "Aperture", "Aperture Desk Job", "clearly"]}
|
2023-06-27T08:24:17+00:00
|
1f6951d2fa14a2a055c5aedbe44e9aa291267855
|
# Dataset Description
This dataset contains the first paragraph of cleaned Wikipedia articles in English.
It was obtained by transorming the [Wikipedia](https://huggingface.co/datasets/wikipedia) "20220301.en" dataset as follows:
```python
from datasets import load_dataset
dataset = load_dataset("wikipedia", "20220301.en")["train"]
def get_first_paragraph(example):
example["text"] = example['text'].split('\n\n')[0]
return example
dataset = dataset.map(get_first_paragraph)
```
# Why use this dataset?
The size of the original English Wikipedia dataset is over 20GB. It takes 20min to load it on a Google Colab notebook and running computations on that dataset can be costly.
If you want to create a use case that mostly needs the information in the first paragraph of a Wikipedia article (which is the paragraph with the most important information), this 'wikipedia-first-paragraph' dataset is for you.
Its size is 1.39GB and it takes 5 min to load it on a Google colab notebook.
# How to load dataset
You can load it by runnning:
```python
from datasets import load_dataset
load_dataset("abokbot/wikipedia-first-paragraph")
```
# Dataset Structure
An example looks as follows:
```
{
'id': '12',
'url': 'https://en.wikipedia.org/wiki/Anarchism',
'title': 'Anarchism',
'text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects \
all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, \
which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, \
placed on the farthest left of the political spectrum, it is usually described alongside communalism \
and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement, and \
has a strong historical association with anti-capitalism and socialism.'
}
```
|
abokbot/wikipedia-first-paragraph
|
[
"language:en",
"wikipedia",
"region:us"
] |
2023-06-04T09:06:17+00:00
|
{"language": ["en"], "tags": ["wikipedia"]}
|
2023-06-04T09:58:32+00:00
|
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