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d5ddb776323307fdf4081cf5f5c2bb36106b3348
|
# Dataset Card for "chat_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sam-mosaic/chat-v2
|
[
"language:en",
"region:us"
] |
2023-06-04T09:09:03+00:00
|
{"language": "en", "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1053541716.4621352, "num_examples": 306305}, {"name": "test", "num_bytes": 20265459.694286585, "num_examples": 5339}], "download_size": 505718158, "dataset_size": 1073807176.1564217}}
|
2023-07-17T23:23:25+00:00
|
40b19fdc832d434f9a9453b64b94641698c08d29
|
# Korean Visual Instruct 150K Dataset Card
🌋[LLaVA](https://llava-vl.github.io/)의 Instruction-following Dataset을 한국어로 번역한 데이터셋입니다. (feat. DeepL)
### 1. Conversation
- 이미지에 대해 질문하는 사람과 이에 답하는 Assistant 사이의 대화 형식으로 디자인합니다. 대답은 Assistant가 이미지를 보고 질문에 대답하는 것과 같은 어조이며, 이미지의 시각적인 정보(객체의 유형, 수, 행동, 위치, 객체간의 상대적인 위치 등)에 대해 다양한 질문을 합니다. 또한 명확하게 답변이 있는 질문만 고려합니다.
### 2. Detailed description
- 이미지에 대한 풍부하고 포괄적인 설명을 내포하게 디자인 했습니다. 이러한 자세한 설명을 요구하는 여러 promt 리스트를 만든 뒤 그중 하나를 샘플해 답을 생성합니다.
### 3. Complex reasoning
- 위의 두 가지 유형은 시각적 content 자체에 중점을 두는데요. Complex reasoning에서는 이를 기반으로 심층 추론 질문을 추가로 생성합니다. 답변은 타당한 논리를 갖춘 단계별 추론 프로세스를 요구합니다.
## Done
- Detail_23k
- Conversation_58k
- Complex_resoning_77k
- ko_llava_instruct_150k
## Project Repo
- Github Repo : [tabtoyou/KoLLaVA](https://github.com/tabtoyou/KoLLaVA)
### License
- Attribution-NonCommercial 4.0 International | OpenAI [policy](https://openai.com/policies/terms-of-use) 준수
|
tabtoyou/KoLLaVA-Instruct-150k
|
[
"task_categories:visual-question-answering",
"task_categories:question-answering",
"language:ko",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-06-04T09:24:11+00:00
|
{"language": ["ko"], "license": "cc-by-nc-4.0", "task_categories": ["visual-question-answering", "question-answering"], "pretty_name": "Korean Visual Instruct"}
|
2023-11-30T12:45:44+00:00
|
160183342ccb242505d4a00964314a384c346c35
|
# Dataset Card for "FineTune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Wilsonlab/FineTune
|
[
"region:us"
] |
2023-06-04T10:20:08+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 129504780.0, "num_examples": 433}], "download_size": 0, "dataset_size": 129504780.0}}
|
2023-06-04T10:55:28+00:00
|
bfa57d4fd2287aa4fb9008686c05102a43579d50
|
heyal/carbon_data
|
[
"license:openrail",
"region:us"
] |
2023-06-04T10:38:55+00:00
|
{"license": "openrail"}
|
2023-06-04T10:40:01+00:00
|
|
ebe1f6075006c15e2af11a6ae317964d0c1c988d
|
# Dataset Card for "turhishReviews-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MertCey/turhishReviews-ds-mini
|
[
"region:us"
] |
2023-06-04T10:39:43+00:00
|
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1260260.3526904634, "num_examples": 3378}, {"name": "validation", "num_bytes": 140277.6473095365, "num_examples": 376}], "download_size": 907452, "dataset_size": 1400538.0}}
|
2023-06-04T10:39:46+00:00
|
4275f5a35fe63cc4eb225fcf80e9cfd1b1e8d397
|
deepghs/anime_ai_check
|
[
"license:mit",
"region:us"
] |
2023-06-04T10:55:54+00:00
|
{"license": "mit"}
|
2023-06-10T13:32:25+00:00
|
|
22d71e5d1e50c95c538e021596db6e5e6a57e301
|
# Dataset Card for "nyaya-acts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sukantan/nyaya-acts
|
[
"region:us"
] |
2023-06-04T10:58:07+00:00
|
{"dataset_info": {"features": [{"name": "section_href", "dtype": "string"}, {"name": "act_enactment_date", "dtype": "timestamp[s]"}, {"name": "act_short_title", "dtype": "string"}, {"name": "act_long_title", "dtype": "string"}, {"name": "act_id", "dtype": "string"}, {"name": "ministry", "dtype": "string"}, {"name": "section_number", "dtype": "string"}, {"name": "section_title", "dtype": "string"}, {"name": "section_content", "dtype": "string"}, {"name": "section_part_no", "dtype": "string"}, {"name": "section_part", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 648190303, "num_examples": 53788}], "download_size": 35939204, "dataset_size": 648190303}}
|
2023-06-08T05:56:32+00:00
|
b61313ba0f4673c2813cba213015762481ff888e
|
ggg
|
Eugenezzz/patients
|
[
"region:us"
] |
2023-06-04T10:58:16+00:00
|
{}
|
2023-06-04T11:01:01+00:00
|
080ffc2e7e4f8325eed0d1e7b90584bc1303a377
|
# Dataset Card for "medication_chat_new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
stoddur/medication_chat_new
|
[
"region:us"
] |
2023-06-04T10:58:51+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 340308408.0, "num_examples": 220407}], "download_size": 11802692, "dataset_size": 340308408.0}}
|
2023-06-04T20:11:28+00:00
|
059984362b18c79681bc4c1818773529d0d55d57
|
# Dataset Card for "turhishReviews-ds-mini2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MertCey/turhishReviews-ds-mini2
|
[
"region:us"
] |
2023-06-04T11:01:29+00:00
|
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1251306.9429941396, "num_examples": 3378}, {"name": "validation", "num_bytes": 139281.0570058604, "num_examples": 376}], "download_size": 897865, "dataset_size": 1390588.0}}
|
2023-06-04T11:13:53+00:00
|
0aa901b33ff15691ac5e94c6893abb1cb2b8b11b
|
# Dataset Card for "adgen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HasturOfficial/adgen
|
[
"region:us"
] |
2023-06-04T11:06:23+00:00
|
{"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51127446, "num_examples": 114599}, {"name": "validation", "num_bytes": 473784, "num_examples": 1070}], "download_size": 27853861, "dataset_size": 51601230}}
|
2023-06-04T11:06:50+00:00
|
0cc03634649d1412319bd12ed270d5ef9e329cdb
|
# Dataset Card for "turhishReviews-ds-mini3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MertCey/turhishReviews-ds-mini3
|
[
"region:us"
] |
2023-06-04T11:15:30+00:00
|
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1251560, "num_examples": 3378}, {"name": "validation", "num_bytes": 139028, "num_examples": 376}], "download_size": 897865, "dataset_size": 1390588}}
|
2023-06-04T11:15:33+00:00
|
95064c4b295f6a880b59ad6393a7861623ae577d
|
**Datasets URL**：[https://drive.google.com/drive/folders/13r-l_OEUt63A8K-ol6jQiaKNuGdseZ7j?usp=sharing](https://drive.google.com/drive/folders/13r-l_OEUt63A8K-ol6jQiaKNuGdseZ7j?usp=sharing)
**Datasets Paper**: Chen Y, Tang Y, Hao H, et al. AMFF-YOLOX: Towards an Attention Mechanism and Multiple Feature Fusion Based on YOLOX for Industrial Defect Detection[J]. *Electronics*, 2023, 12(7): 1662.
Dataset Original Repository: [MCnet](https://github.com/zdfcvsn/MCnet)
Dataset Original Paper: Zhang D, Song K, Xu J, et al. MCnet: Multiple context information segmentation network of no-service rail surface defects[J]. *IEEE Transactions on Instrumentation and Measurement*, 2020, 70: 1-9.
If you want to cite this.
```
@Article{electronics12071662,
author = {Chen, Yu and Tang, Yongwei and Hao, Huijuan and Zhou, Jun and Yuan, Huimiao and Zhang, Yu and Zhao, Yuanyuan},
title = {AMFF-YOLOX: Towards an Attention Mechanism and Multiple Feature Fusion Based on YOLOX for Industrial Defect Detection},
journal = {Electronics},
volumn = {12},
year = {2023},
number = {7},
article-number = {1662},
url = {https://www.mdpi.com/2079-9292/12/7/1662},
issn = {2079-9292},
doi = {10.3390/electronics12071662}
}
```
and
```
@Article{9285332,
author = {Zhang, Defu and Song, Kechen and Xu, Jing and He, Yu and Niu, Menghui and Yan, Yunhui},
journal = {IEEE Transactions on Instrumentation and Measurement},
title = {MCnet: Multiple Context Information Segmentation Network of No-Service Rail Surface Defects},
year = {2021},
volume = {70},
number = {},
pages = {1-9},
doi = {10.1109/TIM.2020.3040890}}
```
|
chairc/NRSD-MN-relabel
|
[
"task_categories:object-detection",
"language:en",
"license:apache-2.0",
"doi:10.57967/hf/0724",
"region:us"
] |
2023-06-04T11:24:12+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["object-detection"], "paper": "AMFF-YOLOX: Towards an Attention Mechanism and Multiple Feature Fusion Based on YOLOX for Industrial Defect Detection", "paper doi": "https://doi.org/10.3390/electronics12071662"}
|
2023-07-18T08:48:17+00:00
|
b47d342e7d7f3ce5796ae828878a5df30ab44c81
|
# Dataset Card for "augmented_prompts_images_77_tokens"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rbeauchamp/augmented_prompts_images_77_tokens
|
[
"region:us"
] |
2023-06-04T12:02:02+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1396948970.61, "num_examples": 1397}], "download_size": 1398661041, "dataset_size": 1396948970.61}}
|
2023-06-04T12:02:39+00:00
|
056ea8d672f9b263ebfc7d3911ff3f35ee0a4080
|
FahedShadid/tshirt-captions
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-06-04T12:44:16+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-06-04T12:44:16+00:00
|
|
c749efd7c4277fb7073cc702eca8ee9b1d40011f
|
bhuwanupadhyay/wikisql-reduced-data
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-04T12:50:06+00:00
|
{"license": "apache-2.0"}
|
2023-06-04T13:08:29+00:00
|
|
3fc939795a77c4cbfeb44b1620a1cd91aa5a6feb
|
# Dataset Card for "SerbianOscarDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
datatab/SerbianOscarDataset
|
[
"region:us"
] |
2023-06-04T13:21:08+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 374855299.3164062, "num_examples": 3037283}, {"name": "test", "num_bytes": 46856989.550781436, "num_examples": 379661}, {"name": "valid", "num_bytes": 46856866.13281237, "num_examples": 379660}], "download_size": 328089963, "dataset_size": 468569155.0}}
|
2023-06-04T13:34:49+00:00
|
c0f696b148adfff797294a54df3ac395b1a87af9
|
# Dataset Card for "mn_wiki"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
khulegu/mn_wiki
|
[
"region:us"
] |
2023-06-04T13:27:18+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 88813927, "num_examples": 23385}], "download_size": 40026785, "dataset_size": 88813927}}
|
2023-06-04T13:27:20+00:00
|
b7b78d3204f829d950645af992f7eff3641ae95a
|
happylkx/CodeInstruct
|
[
"license:mit",
"region:us"
] |
2023-06-04T13:53:17+00:00
|
{"license": "mit"}
|
2023-06-04T13:53:17+00:00
|
|
83ea22b92e4f98e60268167d6ae90ce0012af93a
|
Prot10/CrossValidated
|
[
"task_categories:text-generation",
"language:en",
"math",
"stats",
"prob",
"ml",
"sl",
"region:us"
] |
2023-06-04T14:06:58+00:00
|
{"language": ["en"], "task_categories": ["text-generation"], "pretty_name": "statsDF", "tags": ["math", "stats", "prob", "ml", "sl"]}
|
2023-06-04T14:38:30+00:00
|
|
ce3f7d68aa01b82a5bee1f5e210f142c783bc690
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
subsevenphp/csv-name
|
[
"region:us"
] |
2023-06-04T14:08:39+00:00
|
{}
|
2023-06-04T14:24:31+00:00
|
3085cbd8efc7b379c4fcefecf3250fc945e0cdfe
|
# Dataset Card for "wiki_articles_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lprat/wiki_articles_data
|
[
"region:us"
] |
2023-06-04T14:33:54+00:00
|
{"dataset_info": {"features": [{"name": "texts", "dtype": "string"}, {"name": "questions", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 42832878, "num_examples": 10001}], "download_size": 4511591, "dataset_size": 42832878}}
|
2023-06-04T14:33:56+00:00
|
f06794556f1167ef1d9665bd6eb97b86f9d5ad68
|
# Dataset Card for "diffusiondb_2m_random_5k_blur_61KS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
wtcherr/diffusiondb_2m_random_5k_blur_61KS
|
[
"region:us"
] |
2023-06-04T15:44:42+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "guide", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4779852215.0, "num_examples": 5000}], "download_size": 4247346716, "dataset_size": 4779852215.0}}
|
2023-06-04T15:48:30+00:00
|
4e47500fa3a39252562453941947a5f8cde415ee
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-04T16:06:36+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 215348, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 228928, "num_examples": 500}], "download_size": 57706, "dataset_size": 444276}}
|
2023-06-14T02:33:48+00:00
|
85e49c510237c66d40cd54dba0a165cf4e927cb0
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_T_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_T_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-04T16:09:00+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 290330, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 309562, "num_examples": 500}], "download_size": 98393, "dataset_size": 599892}}
|
2023-06-14T02:40:14+00:00
|
d5f4eb3030c1fc3a49721d7d604a2b9a92fe1383
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_A_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_A_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-04T16:11:22+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 436166, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 452042, "num_examples": 500}], "download_size": 140571, "dataset_size": 888208}}
|
2023-06-14T02:46:46+00:00
|
a7c3a9d2f9a93a3fdfd24da2b2eed679775e2b2a
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_C_HM_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_C_HM_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-04T16:13:43+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 263159, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 279535, "num_examples": 500}], "download_size": 76743, "dataset_size": 542694}}
|
2023-06-14T02:53:10+00:00
|
65512accefad986125e3f5ed54cefacac4b36c51
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_T_A_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_T_A_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-04T16:16:07+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 512736, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 536400, "num_examples": 500}], "download_size": 191315, "dataset_size": 1049136}}
|
2023-06-14T02:59:45+00:00
|
838069f124f4a5058fecb35402128bf67c48d854
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_A_T_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_A_T_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-04T16:18:33+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 512736, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 536436, "num_examples": 500}], "download_size": 190999, "dataset_size": 1049172}}
|
2023-06-20T23:51:32+00:00
|
49404ca08a01d545d0d3affe2d7bfd6780403126
|
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_C_HM_OCR_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_C_HM_OCR_rices_ns_1000
|
[
"region:us"
] |
2023-06-04T16:35:22+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 528784, "num_examples": 1000}, {"name": "fewshot_0", "num_bytes": 565019, "num_examples": 1000}], "download_size": 137263, "dataset_size": 1093803}}
|
2023-06-20T21:17:32+00:00
|
116ed45b3bcc9d9935ddebaa5826c0cc7cf865bb
|
# HowTo100M-subtitles-small
The subtitles from a subset of the HowTo100M dataset.
|
diyarhamedi/HowTo100M-subtitles-small
|
[
"region:us"
] |
2023-06-04T17:09:58+00:00
|
{"dataset_info": {"features": [{"name": "video_id", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "subcategory", "dtype": "string"}, {"name": "rank", "dtype": "int64"}, {"name": "task_id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 71867294, "num_examples": 16015}], "download_size": 39671033, "dataset_size": 71867294}}
|
2023-06-05T04:43:47+00:00
|
da4940951f42e5530a20fa118c1aacfed598f2eb
|
# Dataset Card for "prof_report__SD_v2_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_v2_random_seeds__multi__12
|
[
"region:us"
] |
2023-06-04T17:11:49+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": 3480, "num_examples": 5}, {"name": "bartender", "num_bytes": 3504, "num_examples": 6}, {"name": "facilities_manager", "num_bytes": 3552, "num_examples": 8}, {"name": "accountant", "num_bytes": 3528, "num_examples": 7}, {"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": 3600, "num_examples": 10}, {"name": "security_guard", "num_bytes": 3480, "num_examples": 5}, {"name": "artist", "num_bytes": 3552, "num_examples": 8}, {"name": "author", "num_bytes": 3456, "num_examples": 4}, {"name": "printing_press_operator", "num_bytes": 3600, "num_examples": 10}, {"name": "public_relations_specialist", "num_bytes": 3504, "num_examples": 6}, {"name": "sheet_metal_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "clergy", "num_bytes": 3552, "num_examples": 8}, {"name": "payroll_clerk", "num_bytes": 3528, "num_examples": 7}, {"name": "teller", "num_bytes": 3576, "num_examples": 9}, {"name": "real_estate_broker", "num_bytes": 3480, "num_examples": 5}, {"name": "customer_service_representative", "num_bytes": 3504, "num_examples": 6}, {"name": "painter", "num_bytes": 3600, "num_examples": 10}, {"name": "tractor_operator", "num_bytes": 3480, "num_examples": 5}, {"name": "dental_hygienist", "num_bytes": 3480, "num_examples": 5}, {"name": "industrial_engineer", "num_bytes": 3456, "num_examples": 4}, {"name": "electrician", "num_bytes": 3456, "num_examples": 4}, {"name": "head_cook", "num_bytes": 3528, "num_examples": 7}, {"name": "health_technician", "num_bytes": 3552, "num_examples": 8}, {"name": "carpet_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "purchasing_agent", "num_bytes": 3480, "num_examples": 5}, {"name": "supervisor", "num_bytes": 3528, "num_examples": 7}, {"name": "civil_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "lawyer", "num_bytes": 3528, "num_examples": 7}, {"name": "language_pathologist", "num_bytes": 3600, "num_examples": 10}, {"name": "ceo", "num_bytes": 3528, "num_examples": 7}, {"name": "computer_support_specialist", "num_bytes": 3552, "num_examples": 8}, {"name": "postal_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "mechanical_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "nursing_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "dentist", "num_bytes": 3504, "num_examples": 6}, {"name": "tutor", "num_bytes": 3600, "num_examples": 10}, {"name": "butcher", "num_bytes": 3480, "num_examples": 5}, {"name": "insurance_agent", "num_bytes": 3504, "num_examples": 6}, {"name": "courier", "num_bytes": 3504, "num_examples": 6}, {"name": "computer_programmer", "num_bytes": 3480, "num_examples": 5}, {"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": 3504, "num_examples": 6}, {"name": "manager", "num_bytes": 3504, "num_examples": 6}, {"name": "underwriter", "num_bytes": 3528, "num_examples": 7}, {"name": "executive_assistant", "num_bytes": 3480, "num_examples": 5}, {"name": "designer", "num_bytes": 3504, "num_examples": 6}, {"name": "groundskeeper", "num_bytes": 3528, "num_examples": 7}, {"name": "mental_health_counselor", "num_bytes": 3528, "num_examples": 7}, {"name": "aerospace_engineer", "num_bytes": 3456, "num_examples": 4}, {"name": "taxi_driver", "num_bytes": 3528, "num_examples": 7}, {"name": "nurse", "num_bytes": 3528, "num_examples": 7}, {"name": "data_entry_keyer", "num_bytes": 3456, "num_examples": 4}, {"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": 3576, "num_examples": 9}, {"name": "welder", "num_bytes": 3528, "num_examples": 7}, {"name": "producer", "num_bytes": 3528, "num_examples": 7}, {"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": 3432, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "occupational_therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "career_counselor", "num_bytes": 3552, "num_examples": 8}, {"name": "interior_designer", "num_bytes": 3504, "num_examples": 6}, {"name": "jailer", "num_bytes": 3528, "num_examples": 7}, {"name": "office_clerk", "num_bytes": 3480, "num_examples": 5}, {"name": "market_research_analyst", "num_bytes": 3528, "num_examples": 7}, {"name": "laboratory_technician", "num_bytes": 3552, "num_examples": 8}, {"name": "social_assistant", "num_bytes": 3528, "num_examples": 7}, {"name": "medical_records_specialist", "num_bytes": 3504, "num_examples": 6}, {"name": "machinery_mechanic", "num_bytes": 3456, "num_examples": 4}, {"name": "police_officer", "num_bytes": 3528, "num_examples": 7}, {"name": "software_developer", "num_bytes": 3456, "num_examples": 4}, {"name": "clerk", "num_bytes": 3576, "num_examples": 9}, {"name": "salesperson", "num_bytes": 3528, "num_examples": 7}, {"name": "social_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "director", "num_bytes": 3528, "num_examples": 7}, {"name": "fast_food_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "singer", "num_bytes": 3576, "num_examples": 9}, {"name": "metal_worker", "num_bytes": 3480, "num_examples": 5}, {"name": "cleaner", "num_bytes": 3552, "num_examples": 8}, {"name": "computer_systems_analyst", "num_bytes": 3552, "num_examples": 8}, {"name": "dental_assistant", "num_bytes": 3456, "num_examples": 4}, {"name": "psychologist", "num_bytes": 3528, "num_examples": 7}, {"name": "machinist", "num_bytes": 3432, "num_examples": 3}, {"name": "therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "veterinarian", "num_bytes": 3456, "num_examples": 4}, {"name": "teacher", "num_bytes": 3576, "num_examples": 9}, {"name": "architect", "num_bytes": 3480, "num_examples": 5}, {"name": "office_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "drywall_installer", "num_bytes": 3432, "num_examples": 3}, {"name": "nutritionist", "num_bytes": 3480, "num_examples": 5}, {"name": "librarian", "num_bytes": 3504, "num_examples": 6}, {"name": "childcare_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "school_bus_driver", "num_bytes": 3600, "num_examples": 10}, {"name": "file_clerk", "num_bytes": 3576, "num_examples": 9}, {"name": "logistician", "num_bytes": 3504, "num_examples": 6}, {"name": "scientist", "num_bytes": 3528, "num_examples": 7}, {"name": "teaching_assistant", "num_bytes": 3504, "num_examples": 6}, {"name": "radiologic_technician", "num_bytes": 3528, "num_examples": 7}, {"name": "manicurist", "num_bytes": 3504, "num_examples": 6}, {"name": "community_manager", "num_bytes": 3528, "num_examples": 7}, {"name": "carpenter", "num_bytes": 3456, "num_examples": 4}, {"name": "claims_appraiser", "num_bytes": 3504, "num_examples": 6}, {"name": "dispatcher", "num_bytes": 3552, "num_examples": 8}, {"name": "cashier", "num_bytes": 3600, "num_examples": 10}, {"name": "roofer", "num_bytes": 3432, "num_examples": 3}, {"name": "photographer", "num_bytes": 3504, "num_examples": 6}, {"name": "detective", "num_bytes": 3528, "num_examples": 7}, {"name": "financial_advisor", "num_bytes": 3528, "num_examples": 7}, {"name": "wholesale_buyer", "num_bytes": 3552, "num_examples": 8}, {"name": "it_specialist", "num_bytes": 3504, "num_examples": 6}, {"name": "pharmacy_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "mover", "num_bytes": 3552, "num_examples": 8}, {"name": "plane_mechanic", "num_bytes": 3456, "num_examples": 4}, {"name": "interviewer", "num_bytes": 3552, "num_examples": 8}, {"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": 3528, "num_examples": 7}, {"name": "stocker", "num_bytes": 3504, "num_examples": 6}, {"name": "pharmacist", "num_bytes": 3528, "num_examples": 7}, {"name": "doctor", "num_bytes": 3552, "num_examples": 8}, {"name": "compliance_officer", "num_bytes": 3552, "num_examples": 8}, {"name": "aide", "num_bytes": 3552, "num_examples": 8}, {"name": "bus_driver", "num_bytes": 3552, "num_examples": 8}, {"name": "financial_analyst", "num_bytes": 3528, "num_examples": 7}, {"name": "receptionist", "num_bytes": 3504, "num_examples": 6}, {"name": "janitor", "num_bytes": 3528, "num_examples": 7}, {"name": "plumber", "num_bytes": 3432, "num_examples": 3}, {"name": "physical_therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "inventory_clerk", "num_bytes": 3504, "num_examples": 6}, {"name": "firefighter", "num_bytes": 3456, "num_examples": 4}, {"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": 3552, "num_examples": 8}], "download_size": 402970, "dataset_size": 513432}}
|
2023-06-04T17:18:35+00:00
|
2dfb0ce6b7afa1ed8d3018d5d31282adc092b056
|
# Dataset Card for "prof_report__SD_v2_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_v2_random_seeds__multi__24
|
[
"region:us"
] |
2023-06-04T17:18:54+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": 3576, "num_examples": 9}, {"name": "facilities_manager", "num_bytes": 3696, "num_examples": 14}, {"name": "accountant", "num_bytes": 3648, "num_examples": 12}, {"name": "graphic_designer", "num_bytes": 3576, "num_examples": 9}, {"name": "network_administrator", "num_bytes": 3600, "num_examples": 10}, {"name": "financial_manager", "num_bytes": 3576, "num_examples": 9}, {"name": "baker", "num_bytes": 3720, "num_examples": 15}, {"name": "security_guard", "num_bytes": 3528, "num_examples": 7}, {"name": "artist", "num_bytes": 3720, "num_examples": 15}, {"name": "author", "num_bytes": 3576, "num_examples": 9}, {"name": "printing_press_operator", "num_bytes": 3744, "num_examples": 16}, {"name": "public_relations_specialist", "num_bytes": 3624, "num_examples": 11}, {"name": "sheet_metal_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "clergy", "num_bytes": 3600, "num_examples": 10}, {"name": "payroll_clerk", "num_bytes": 3648, "num_examples": 12}, {"name": "teller", "num_bytes": 3768, "num_examples": 17}, {"name": "real_estate_broker", "num_bytes": 3528, "num_examples": 7}, {"name": "customer_service_representative", "num_bytes": 3624, "num_examples": 11}, {"name": "painter", "num_bytes": 3696, "num_examples": 14}, {"name": "tractor_operator", "num_bytes": 3480, "num_examples": 5}, {"name": "dental_hygienist", "num_bytes": 3480, "num_examples": 5}, {"name": "industrial_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "electrician", "num_bytes": 3456, "num_examples": 4}, {"name": "head_cook", "num_bytes": 3624, "num_examples": 11}, {"name": "health_technician", "num_bytes": 3672, "num_examples": 13}, {"name": "carpet_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "purchasing_agent", "num_bytes": 3576, "num_examples": 9}, {"name": "supervisor", "num_bytes": 3672, "num_examples": 13}, {"name": "civil_engineer", "num_bytes": 3528, "num_examples": 7}, {"name": "lawyer", "num_bytes": 3648, "num_examples": 12}, {"name": "language_pathologist", "num_bytes": 3744, "num_examples": 16}, {"name": "ceo", "num_bytes": 3576, "num_examples": 9}, {"name": "computer_support_specialist", "num_bytes": 3672, "num_examples": 13}, {"name": "postal_worker", "num_bytes": 3696, "num_examples": 14}, {"name": "mechanical_engineer", "num_bytes": 3528, "num_examples": 7}, {"name": "nursing_assistant", "num_bytes": 3600, "num_examples": 10}, {"name": "dentist", "num_bytes": 3576, "num_examples": 9}, {"name": "tutor", "num_bytes": 3696, "num_examples": 14}, {"name": "butcher", "num_bytes": 3528, "num_examples": 7}, {"name": "insurance_agent", "num_bytes": 3600, "num_examples": 10}, {"name": "courier", "num_bytes": 3600, "num_examples": 10}, {"name": "computer_programmer", "num_bytes": 3504, "num_examples": 6}, {"name": "truck_driver", "num_bytes": 3552, "num_examples": 8}, {"name": "mechanic", "num_bytes": 3528, "num_examples": 7}, {"name": "marketing_manager", "num_bytes": 3576, "num_examples": 9}, {"name": "sales_manager", "num_bytes": 3600, "num_examples": 10}, {"name": "correctional_officer", "num_bytes": 3600, "num_examples": 10}, {"name": "manager", "num_bytes": 3600, "num_examples": 10}, {"name": "underwriter", "num_bytes": 3600, "num_examples": 10}, {"name": "executive_assistant", "num_bytes": 3576, "num_examples": 9}, {"name": "designer", "num_bytes": 3576, "num_examples": 9}, {"name": "groundskeeper", "num_bytes": 3528, "num_examples": 7}, {"name": "mental_health_counselor", "num_bytes": 3600, "num_examples": 10}, {"name": "aerospace_engineer", "num_bytes": 3528, "num_examples": 7}, {"name": "taxi_driver", "num_bytes": 3576, "num_examples": 9}, {"name": "nurse", "num_bytes": 3648, "num_examples": 12}, {"name": "data_entry_keyer", "num_bytes": 3504, "num_examples": 6}, {"name": "musician", "num_bytes": 3744, "num_examples": 16}, {"name": "event_planner", "num_bytes": 3696, "num_examples": 14}, {"name": "writer", "num_bytes": 3648, "num_examples": 12}, {"name": "cook", "num_bytes": 3648, "num_examples": 12}, {"name": "welder", "num_bytes": 3528, "num_examples": 7}, {"name": "producer", "num_bytes": 3552, "num_examples": 8}, {"name": "hairdresser", "num_bytes": 3672, "num_examples": 13}, {"name": "farmer", "num_bytes": 3504, "num_examples": 6}, {"name": "construction_worker", "num_bytes": 3480, "num_examples": 5}, {"name": "air_conditioning_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "electrical_engineer", "num_bytes": 3528, "num_examples": 7}, {"name": "occupational_therapist", "num_bytes": 3648, "num_examples": 12}, {"name": "career_counselor", "num_bytes": 3648, "num_examples": 12}, {"name": "interior_designer", "num_bytes": 3600, "num_examples": 10}, {"name": "jailer", "num_bytes": 3672, "num_examples": 13}, {"name": "office_clerk", "num_bytes": 3600, "num_examples": 10}, {"name": "market_research_analyst", "num_bytes": 3648, "num_examples": 12}, {"name": "laboratory_technician", "num_bytes": 3696, "num_examples": 14}, {"name": "social_assistant", "num_bytes": 3696, "num_examples": 14}, {"name": "medical_records_specialist", "num_bytes": 3576, "num_examples": 9}, {"name": "machinery_mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "police_officer", "num_bytes": 3648, "num_examples": 12}, {"name": "software_developer", "num_bytes": 3552, "num_examples": 8}, {"name": "clerk", "num_bytes": 3696, "num_examples": 14}, {"name": "salesperson", "num_bytes": 3648, "num_examples": 12}, {"name": "social_worker", "num_bytes": 3624, "num_examples": 11}, {"name": "director", "num_bytes": 3600, "num_examples": 10}, {"name": "fast_food_worker", "num_bytes": 3696, "num_examples": 14}, {"name": "singer", "num_bytes": 3720, "num_examples": 15}, {"name": "metal_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "cleaner", "num_bytes": 3744, "num_examples": 16}, {"name": "computer_systems_analyst", "num_bytes": 3696, "num_examples": 14}, {"name": "dental_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "psychologist", "num_bytes": 3624, "num_examples": 11}, {"name": "machinist", "num_bytes": 3456, "num_examples": 4}, {"name": "therapist", "num_bytes": 3672, "num_examples": 13}, {"name": "veterinarian", "num_bytes": 3576, "num_examples": 9}, {"name": "teacher", "num_bytes": 3744, "num_examples": 16}, {"name": "architect", "num_bytes": 3552, "num_examples": 8}, {"name": "office_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "drywall_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 3600, "num_examples": 10}, {"name": "librarian", "num_bytes": 3552, "num_examples": 8}, {"name": "childcare_worker", "num_bytes": 3648, "num_examples": 12}, {"name": "school_bus_driver", "num_bytes": 3768, "num_examples": 17}, {"name": "file_clerk", "num_bytes": 3672, "num_examples": 13}, {"name": "logistician", "num_bytes": 3528, "num_examples": 7}, {"name": "scientist", "num_bytes": 3624, "num_examples": 11}, {"name": "teaching_assistant", "num_bytes": 3600, "num_examples": 10}, {"name": "radiologic_technician", "num_bytes": 3624, "num_examples": 11}, {"name": "manicurist", "num_bytes": 3600, "num_examples": 10}, {"name": "community_manager", "num_bytes": 3672, "num_examples": 13}, {"name": "carpenter", "num_bytes": 3456, "num_examples": 4}, {"name": "claims_appraiser", "num_bytes": 3504, "num_examples": 6}, {"name": "dispatcher", "num_bytes": 3696, "num_examples": 14}, {"name": "cashier", "num_bytes": 3672, "num_examples": 13}, {"name": "roofer", "num_bytes": 3456, "num_examples": 4}, {"name": "photographer", "num_bytes": 3648, "num_examples": 12}, {"name": "detective", "num_bytes": 3576, "num_examples": 9}, {"name": "financial_advisor", "num_bytes": 3576, "num_examples": 9}, {"name": "wholesale_buyer", "num_bytes": 3672, "num_examples": 13}, {"name": "it_specialist", "num_bytes": 3576, "num_examples": 9}, {"name": "pharmacy_technician", "num_bytes": 3624, "num_examples": 11}, {"name": "engineer", "num_bytes": 3528, "num_examples": 7}, {"name": "mover", "num_bytes": 3648, "num_examples": 12}, {"name": "plane_mechanic", "num_bytes": 3504, "num_examples": 6}, {"name": "interviewer", "num_bytes": 3720, "num_examples": 15}, {"name": "massage_therapist", "num_bytes": 3648, "num_examples": 12}, {"name": "dishwasher", "num_bytes": 3648, "num_examples": 12}, {"name": "fitness_instructor", "num_bytes": 3624, "num_examples": 11}, {"name": "credit_counselor", "num_bytes": 3600, "num_examples": 10}, {"name": "stocker", "num_bytes": 3648, "num_examples": 12}, {"name": "pharmacist", "num_bytes": 3648, "num_examples": 12}, {"name": "doctor", "num_bytes": 3696, "num_examples": 14}, {"name": "compliance_officer", "num_bytes": 3624, "num_examples": 11}, {"name": "aide", "num_bytes": 3672, "num_examples": 13}, {"name": "bus_driver", "num_bytes": 3672, "num_examples": 13}, {"name": "financial_analyst", "num_bytes": 3600, "num_examples": 10}, {"name": "receptionist", "num_bytes": 3600, "num_examples": 10}, {"name": "janitor", "num_bytes": 3600, "num_examples": 10}, {"name": "plumber", "num_bytes": 3456, "num_examples": 4}, {"name": "physical_therapist", "num_bytes": 3600, "num_examples": 10}, {"name": "inventory_clerk", "num_bytes": 3624, "num_examples": 11}, {"name": "firefighter", "num_bytes": 3504, "num_examples": 6}, {"name": "coach", "num_bytes": 3600, "num_examples": 10}, {"name": "maid", "num_bytes": 3720, "num_examples": 15}, {"name": "pilot", "num_bytes": 3576, "num_examples": 9}, {"name": "repair_worker", "num_bytes": 3600, "num_examples": 10}], "download_size": 870345, "dataset_size": 526416}}
|
2023-06-04T17:21:01+00:00
|
b583125b8a20567689bbf716eefc5bfe55c3d1d2
|
# Dataset Card for "prof_report__SD_v2_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_v2_random_seeds__sd_21__12
|
[
"region:us"
] |
2023-06-04T17:21:19+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": 3528, "num_examples": 7}, {"name": "graphic_designer", "num_bytes": 3552, "num_examples": 8}, {"name": "network_administrator", "num_bytes": 3528, "num_examples": 7}, {"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": 3576, "num_examples": 9}, {"name": "author", "num_bytes": 3504, "num_examples": 6}, {"name": "printing_press_operator", "num_bytes": 3600, "num_examples": 10}, {"name": "public_relations_specialist", "num_bytes": 3552, "num_examples": 8}, {"name": "sheet_metal_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "clergy", "num_bytes": 3552, "num_examples": 8}, {"name": "payroll_clerk", "num_bytes": 3504, "num_examples": 6}, {"name": "teller", "num_bytes": 3600, "num_examples": 10}, {"name": "real_estate_broker", "num_bytes": 3456, "num_examples": 4}, {"name": "customer_service_representative", "num_bytes": 3528, "num_examples": 7}, {"name": "painter", "num_bytes": 3576, "num_examples": 9}, {"name": "tractor_operator", "num_bytes": 3456, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 3456, "num_examples": 4}, {"name": "industrial_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "electrician", "num_bytes": 3480, "num_examples": 5}, {"name": "head_cook", "num_bytes": 3576, "num_examples": 9}, {"name": "health_technician", "num_bytes": 3528, "num_examples": 7}, {"name": "carpet_installer", "num_bytes": 3432, "num_examples": 3}, {"name": "purchasing_agent", "num_bytes": 3504, "num_examples": 6}, {"name": "supervisor", "num_bytes": 3552, "num_examples": 8}, {"name": "civil_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "lawyer", "num_bytes": 3528, "num_examples": 7}, {"name": "language_pathologist", "num_bytes": 3600, "num_examples": 10}, {"name": "ceo", "num_bytes": 3528, "num_examples": 7}, {"name": "computer_support_specialist", "num_bytes": 3528, "num_examples": 7}, {"name": "postal_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "mechanical_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "nursing_assistant", "num_bytes": 3528, "num_examples": 7}, {"name": "dentist", "num_bytes": 3480, "num_examples": 5}, {"name": "tutor", "num_bytes": 3552, "num_examples": 8}, {"name": "butcher", "num_bytes": 3480, "num_examples": 5}, {"name": "insurance_agent", "num_bytes": 3480, "num_examples": 5}, {"name": "courier", "num_bytes": 3576, "num_examples": 9}, {"name": "computer_programmer", "num_bytes": 3480, "num_examples": 5}, {"name": "truck_driver", "num_bytes": 3504, "num_examples": 6}, {"name": "mechanic", "num_bytes": 3456, "num_examples": 4}, {"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": 3504, "num_examples": 6}, {"name": "underwriter", "num_bytes": 3528, "num_examples": 7}, {"name": "executive_assistant", "num_bytes": 3480, "num_examples": 5}, {"name": "designer", "num_bytes": 3504, "num_examples": 6}, {"name": "groundskeeper", "num_bytes": 3480, "num_examples": 5}, {"name": "mental_health_counselor", "num_bytes": 3600, "num_examples": 10}, {"name": "aerospace_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "taxi_driver", "num_bytes": 3528, "num_examples": 7}, {"name": "nurse", "num_bytes": 3528, "num_examples": 7}, {"name": "data_entry_keyer", "num_bytes": 3480, "num_examples": 5}, {"name": "musician", "num_bytes": 3552, "num_examples": 8}, {"name": "event_planner", "num_bytes": 3552, "num_examples": 8}, {"name": "writer", "num_bytes": 3576, "num_examples": 9}, {"name": "cook", "num_bytes": 3576, "num_examples": 9}, {"name": "welder", "num_bytes": 3528, "num_examples": 7}, {"name": "producer", "num_bytes": 3552, "num_examples": 8}, {"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": 3432, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "occupational_therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "career_counselor", "num_bytes": 3528, "num_examples": 7}, {"name": "interior_designer", "num_bytes": 3504, "num_examples": 6}, {"name": "jailer", "num_bytes": 3576, "num_examples": 9}, {"name": "office_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "market_research_analyst", "num_bytes": 3552, "num_examples": 8}, {"name": "laboratory_technician", "num_bytes": 3552, "num_examples": 8}, {"name": "social_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "medical_records_specialist", "num_bytes": 3528, "num_examples": 7}, {"name": "machinery_mechanic", "num_bytes": 3456, "num_examples": 4}, {"name": "police_officer", "num_bytes": 3552, "num_examples": 8}, {"name": "software_developer", "num_bytes": 3480, "num_examples": 5}, {"name": "clerk", "num_bytes": 3576, "num_examples": 9}, {"name": "salesperson", "num_bytes": 3528, "num_examples": 7}, {"name": "social_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "director", "num_bytes": 3528, "num_examples": 7}, {"name": "fast_food_worker", "num_bytes": 3552, "num_examples": 8}, {"name": "singer", "num_bytes": 3576, "num_examples": 9}, {"name": "metal_worker", "num_bytes": 3456, "num_examples": 4}, {"name": "cleaner", "num_bytes": 3576, "num_examples": 9}, {"name": "computer_systems_analyst", "num_bytes": 3552, "num_examples": 8}, {"name": "dental_assistant", "num_bytes": 3432, "num_examples": 3}, {"name": "psychologist", "num_bytes": 3528, "num_examples": 7}, {"name": "machinist", "num_bytes": 3456, "num_examples": 4}, {"name": "therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "veterinarian", "num_bytes": 3504, "num_examples": 6}, {"name": "teacher", "num_bytes": 3576, "num_examples": 9}, {"name": "architect", "num_bytes": 3480, "num_examples": 5}, {"name": "office_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "drywall_installer", "num_bytes": 3480, "num_examples": 5}, {"name": "nutritionist", "num_bytes": 3528, "num_examples": 7}, {"name": "librarian", "num_bytes": 3528, "num_examples": 7}, {"name": "childcare_worker", "num_bytes": 3576, "num_examples": 9}, {"name": "school_bus_driver", "num_bytes": 3600, "num_examples": 10}, {"name": "file_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "logistician", "num_bytes": 3480, "num_examples": 5}, {"name": "scientist", "num_bytes": 3552, "num_examples": 8}, {"name": "teaching_assistant", "num_bytes": 3528, "num_examples": 7}, {"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": 3552, "num_examples": 8}, {"name": "cashier", "num_bytes": 3576, "num_examples": 9}, {"name": "roofer", "num_bytes": 3480, "num_examples": 5}, {"name": "photographer", "num_bytes": 3552, "num_examples": 8}, {"name": "detective", "num_bytes": 3528, "num_examples": 7}, {"name": "financial_advisor", "num_bytes": 3504, "num_examples": 6}, {"name": "wholesale_buyer", "num_bytes": 3552, "num_examples": 8}, {"name": "it_specialist", "num_bytes": 3528, "num_examples": 7}, {"name": "pharmacy_technician", "num_bytes": 3504, "num_examples": 6}, {"name": "engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "mover", "num_bytes": 3528, "num_examples": 7}, {"name": "plane_mechanic", "num_bytes": 3480, "num_examples": 5}, {"name": "interviewer", "num_bytes": 3528, "num_examples": 7}, {"name": "massage_therapist", "num_bytes": 3528, "num_examples": 7}, {"name": "dishwasher", "num_bytes": 3528, "num_examples": 7}, {"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": 3528, "num_examples": 7}, {"name": "doctor", "num_bytes": 3528, "num_examples": 7}, {"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": 3480, "num_examples": 5}, {"name": "receptionist", "num_bytes": 3504, "num_examples": 6}, {"name": "janitor", "num_bytes": 3504, "num_examples": 6}, {"name": "plumber", "num_bytes": 3432, "num_examples": 3}, {"name": "physical_therapist", "num_bytes": 3552, "num_examples": 8}, {"name": "inventory_clerk", "num_bytes": 3528, "num_examples": 7}, {"name": "firefighter", "num_bytes": 3528, "num_examples": 7}, {"name": "coach", "num_bytes": 3528, "num_examples": 7}, {"name": "maid", "num_bytes": 3576, "num_examples": 9}, {"name": "pilot", "num_bytes": 3528, "num_examples": 7}, {"name": "repair_worker", "num_bytes": 3552, "num_examples": 8}], "download_size": 865199, "dataset_size": 514632}}
|
2023-06-04T17:23:37+00:00
|
b2ec8bb9379c4d99384a9580a3d28aabb1d505f6
|
# Dataset Card for "prof_report__SD_v2_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_v2_random_seeds__sd_21__24
|
[
"region:us"
] |
2023-06-04T17:23:55+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": 3480, "num_examples": 5}, {"name": "facilities_manager", "num_bytes": 3624, "num_examples": 11}, {"name": "accountant", "num_bytes": 3600, "num_examples": 10}, {"name": "graphic_designer", "num_bytes": 3504, "num_examples": 6}, {"name": "network_administrator", "num_bytes": 3552, "num_examples": 8}, {"name": "financial_manager", "num_bytes": 3576, "num_examples": 9}, {"name": "baker", "num_bytes": 3672, "num_examples": 13}, {"name": "security_guard", "num_bytes": 3528, "num_examples": 7}, {"name": "artist", "num_bytes": 3696, "num_examples": 14}, {"name": "author", "num_bytes": 3504, "num_examples": 6}, {"name": "printing_press_operator", "num_bytes": 3768, "num_examples": 17}, {"name": "public_relations_specialist", "num_bytes": 3576, "num_examples": 9}, {"name": "sheet_metal_worker", "num_bytes": 3624, "num_examples": 11}, {"name": "clergy", "num_bytes": 3624, "num_examples": 11}, {"name": "payroll_clerk", "num_bytes": 3552, "num_examples": 8}, {"name": "teller", "num_bytes": 3672, "num_examples": 13}, {"name": "real_estate_broker", "num_bytes": 3456, "num_examples": 4}, {"name": "customer_service_representative", "num_bytes": 3576, "num_examples": 9}, {"name": "painter", "num_bytes": 3696, "num_examples": 14}, {"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": 3456, "num_examples": 4}, {"name": "head_cook", "num_bytes": 3648, "num_examples": 12}, {"name": "health_technician", "num_bytes": 3624, "num_examples": 11}, {"name": "carpet_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "purchasing_agent", "num_bytes": 3528, "num_examples": 7}, {"name": "supervisor", "num_bytes": 3624, "num_examples": 11}, {"name": "civil_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "lawyer", "num_bytes": 3600, "num_examples": 10}, {"name": "language_pathologist", "num_bytes": 3720, "num_examples": 15}, {"name": "ceo", "num_bytes": 3528, "num_examples": 7}, {"name": "computer_support_specialist", "num_bytes": 3600, "num_examples": 10}, {"name": "postal_worker", "num_bytes": 3720, "num_examples": 15}, {"name": "mechanical_engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "nursing_assistant", "num_bytes": 3600, "num_examples": 10}, {"name": "dentist", "num_bytes": 3480, "num_examples": 5}, {"name": "tutor", "num_bytes": 3696, "num_examples": 14}, {"name": "butcher", "num_bytes": 3576, "num_examples": 9}, {"name": "insurance_agent", "num_bytes": 3528, "num_examples": 7}, {"name": "courier", "num_bytes": 3600, "num_examples": 10}, {"name": "computer_programmer", "num_bytes": 3480, "num_examples": 5}, {"name": "truck_driver", "num_bytes": 3528, "num_examples": 7}, {"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": 3648, "num_examples": 12}, {"name": "manager", "num_bytes": 3528, "num_examples": 7}, {"name": "underwriter", "num_bytes": 3600, "num_examples": 10}, {"name": "executive_assistant", "num_bytes": 3528, "num_examples": 7}, {"name": "designer", "num_bytes": 3504, "num_examples": 6}, {"name": "groundskeeper", "num_bytes": 3504, "num_examples": 6}, {"name": "mental_health_counselor", "num_bytes": 3672, "num_examples": 13}, {"name": "aerospace_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "taxi_driver", "num_bytes": 3528, "num_examples": 7}, {"name": "nurse", "num_bytes": 3528, "num_examples": 7}, {"name": "data_entry_keyer", "num_bytes": 3480, "num_examples": 5}, {"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": 3624, "num_examples": 11}, {"name": "producer", "num_bytes": 3528, "num_examples": 7}, {"name": "hairdresser", "num_bytes": 3600, "num_examples": 10}, {"name": "farmer", "num_bytes": 3456, "num_examples": 4}, {"name": "construction_worker", "num_bytes": 3480, "num_examples": 5}, {"name": "air_conditioning_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "electrical_engineer", "num_bytes": 3504, "num_examples": 6}, {"name": "occupational_therapist", "num_bytes": 3600, "num_examples": 10}, {"name": "career_counselor", "num_bytes": 3576, "num_examples": 9}, {"name": "interior_designer", "num_bytes": 3552, "num_examples": 8}, {"name": "jailer", "num_bytes": 3648, "num_examples": 12}, {"name": "office_clerk", "num_bytes": 3576, "num_examples": 9}, {"name": "market_research_analyst", "num_bytes": 3624, "num_examples": 11}, {"name": "laboratory_technician", "num_bytes": 3648, "num_examples": 12}, {"name": "social_assistant", "num_bytes": 3576, "num_examples": 9}, {"name": "medical_records_specialist", "num_bytes": 3576, "num_examples": 9}, {"name": "machinery_mechanic", "num_bytes": 3456, "num_examples": 4}, {"name": "police_officer", "num_bytes": 3648, "num_examples": 12}, {"name": "software_developer", "num_bytes": 3504, "num_examples": 6}, {"name": "clerk", "num_bytes": 3696, "num_examples": 14}, {"name": "salesperson", "num_bytes": 3624, "num_examples": 11}, {"name": "social_worker", "num_bytes": 3648, "num_examples": 12}, {"name": "director", "num_bytes": 3576, "num_examples": 9}, {"name": "fast_food_worker", "num_bytes": 3648, "num_examples": 12}, {"name": "singer", "num_bytes": 3720, "num_examples": 15}, {"name": "metal_worker", "num_bytes": 3528, "num_examples": 7}, {"name": "cleaner", "num_bytes": 3696, "num_examples": 14}, {"name": "computer_systems_analyst", "num_bytes": 3576, "num_examples": 9}, {"name": "dental_assistant", "num_bytes": 3504, "num_examples": 6}, {"name": "psychologist", "num_bytes": 3576, "num_examples": 9}, {"name": "machinist", "num_bytes": 3456, "num_examples": 4}, {"name": "therapist", "num_bytes": 3600, "num_examples": 10}, {"name": "veterinarian", "num_bytes": 3528, "num_examples": 7}, {"name": "teacher", "num_bytes": 3672, "num_examples": 13}, {"name": "architect", "num_bytes": 3528, "num_examples": 7}, {"name": "office_worker", "num_bytes": 3504, "num_examples": 6}, {"name": "drywall_installer", "num_bytes": 3456, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 3552, "num_examples": 8}, {"name": "librarian", "num_bytes": 3600, "num_examples": 10}, {"name": "childcare_worker", "num_bytes": 3600, "num_examples": 10}, {"name": "school_bus_driver", "num_bytes": 3744, "num_examples": 16}, {"name": "file_clerk", "num_bytes": 3648, "num_examples": 12}, {"name": "logistician", "num_bytes": 3504, "num_examples": 6}, {"name": "scientist", "num_bytes": 3600, "num_examples": 10}, {"name": "teaching_assistant", "num_bytes": 3552, "num_examples": 8}, {"name": "radiologic_technician", "num_bytes": 3600, "num_examples": 10}, {"name": "manicurist", "num_bytes": 3624, "num_examples": 11}, {"name": "community_manager", "num_bytes": 3552, "num_examples": 8}, {"name": "carpenter", "num_bytes": 3456, "num_examples": 4}, {"name": "claims_appraiser", "num_bytes": 3528, "num_examples": 7}, {"name": "dispatcher", "num_bytes": 3624, "num_examples": 11}, {"name": "cashier", "num_bytes": 3672, "num_examples": 13}, {"name": "roofer", "num_bytes": 3456, "num_examples": 4}, {"name": "photographer", "num_bytes": 3624, "num_examples": 11}, {"name": "detective", "num_bytes": 3600, "num_examples": 10}, {"name": "financial_advisor", "num_bytes": 3552, "num_examples": 8}, {"name": "wholesale_buyer", "num_bytes": 3672, "num_examples": 13}, {"name": "it_specialist", "num_bytes": 3576, "num_examples": 9}, {"name": "pharmacy_technician", "num_bytes": 3552, "num_examples": 8}, {"name": "engineer", "num_bytes": 3480, "num_examples": 5}, {"name": "mover", "num_bytes": 3600, "num_examples": 10}, {"name": "plane_mechanic", "num_bytes": 3504, "num_examples": 6}, {"name": "interviewer", "num_bytes": 3648, "num_examples": 12}, {"name": "massage_therapist", "num_bytes": 3624, "num_examples": 11}, {"name": "dishwasher", "num_bytes": 3600, "num_examples": 10}, {"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": 3576, "num_examples": 9}, {"name": "doctor", "num_bytes": 3600, "num_examples": 10}, {"name": "compliance_officer", "num_bytes": 3648, "num_examples": 12}, {"name": "aide", "num_bytes": 3672, "num_examples": 13}, {"name": "bus_driver", "num_bytes": 3600, "num_examples": 10}, {"name": "financial_analyst", "num_bytes": 3528, "num_examples": 7}, {"name": "receptionist", "num_bytes": 3552, "num_examples": 8}, {"name": "janitor", "num_bytes": 3576, "num_examples": 9}, {"name": "plumber", "num_bytes": 3408, "num_examples": 2}, {"name": "physical_therapist", "num_bytes": 3624, "num_examples": 11}, {"name": "inventory_clerk", "num_bytes": 3528, "num_examples": 7}, {"name": "firefighter", "num_bytes": 3600, "num_examples": 10}, {"name": "coach", "num_bytes": 3600, "num_examples": 10}, {"name": "maid", "num_bytes": 3672, "num_examples": 13}, {"name": "pilot", "num_bytes": 3576, "num_examples": 9}, {"name": "repair_worker", "num_bytes": 3576, "num_examples": 9}], "download_size": 868129, "dataset_size": 522024}}
|
2023-06-04T17:28:19+00:00
|
dafec656dbce4f703669e9a0b997407b7c9f8c96
|
# Dataset Card for "synthetic-role-play-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/synthetic-role-play-chatml
|
[
"region:us"
] |
2023-06-04T17:34:58+00:00
|
{"dataset_info": {"features": [{"name": "conversation", "list": [{"name": "content", "dtype": "string"}, {"name": "do_train", "dtype": "bool"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 14418109, "num_examples": 6192}], "download_size": 8477725, "dataset_size": 14418109}}
|
2023-06-07T18:39:18+00:00
|
2189983d00dbd2ce71b7f2d8a25fd2b9a487ff1a
|
All 2.3 million papers in the Arxiv, embedded via abstract with the InstructorXL model.
No claims are made about the copyright or license of contained materials. We assume no responsibilty for and are not liable under any circumstances for damages. Use at your own risk.
Good luck, have fun.
|
macrocosm/arxiv_abstracts
|
[
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"region:us"
] |
2023-06-04T17:47:38+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"]}
|
2023-06-04T18:09:46+00:00
|
067afd673cca57d43015cdebaf42d2ab08ff90d2
|
UjjwalKraft/covid
|
[
"license:other",
"region:us"
] |
2023-06-04T17:52:19+00:00
|
{"license": "other"}
|
2023-06-04T17:53:24+00:00
|
|
9ec06b0d25cba85b6ee422b02e52e14ec4acafc7
|
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%>
This repository provides a Habitat consumption-ready compressed version of HSSD.
See [this repository](https://huggingface.co/datasets/hssd/hssd-models) for corresponding uncompressed assets.
## Dataset Structure
```
├── objects
│ ├── */*.glb
│ ├── */*.collider.glb
│ ├── */*.filteredSupportSurface(.ply|.glb)
│ ├── */*.object_config.json
├── stages
│ ├── *.glb
│ ├── *.stage_config.json
├── scenes
│ ├── *.scene_instance.json
├── scenes_uncluttered
│ ├── *.scene_instance.json
├── scene_filter_files
│ ├── *.rec_filter.json
└── hssd-hab.scene_dataset_config.json
└── hssd-hab-uncluttered.scene_dataset_config.json
```
- `hssd-hab.scene_dataset_config.json`: This SceneDataset config file aggregates the assets and metadata necessary to fully describe the set of stages, objects, and scenes constituting the dataset.
- `objects`: 3D models representing distinct objects that are used to compose scenes. Contains configuration files, render assets, collider assets, and Receptacle mesh assets.
- `stages`: A stage in Habitat is the set of static mesh components which make up the backdrop of a scene (e.g. floor, walls, stairs, etc.).
- `scenes`: A scene is a single 3D world composed of a static stage and a variable number of objects.
### Rearrange-ready assets:
Supporting Habitat 3.0 embodied rearrangement tasks with updated colliders, adjusted and de-cluttered scene contents, receptacle meshes, and receptacle filter files. See [aihabitat.org/habitat3/](aihabitat.org/habitat3/) for more details.
- `hssd-hab-uncluttered.scene_dataset_config.json`: This SceneDataset config file aggregates adds the adjusted and uncluttered scenes for rearrangement tasks.
- `scenes_uncluttered`: Contains the adjusted scene instance configuration files.
- `scene_filter_files`: A scene filter file organizes available Receptacle instances in a scene into active and inactive groups based on simualtion heuristics and manual edits. It is consumed by the RearrangeEpisodeGenerator to construct valid RearrangeEpisodeDatasets.
## Getting Started
To load HSSD scenes into the Habitat simulator, you can start by installing [habitat-sim](https://github.com/facebookresearch/habitat-sim) using instructions specified [here](https://github.com/facebookresearch/habitat-sim#installation).
Once installed, you can run the interactive Habitat viewer to load a scene:
```
habitat-viewer --dataset /path/to/hssd-hab/hssd-hab.scene_dataset_config.json -- 102344280
# or ./build/viewer if compiling from source
```
You can find more information about using the interactive viewer [here](https://github.com/facebookresearch/habitat-sim#testing:~:text=path/to/data/-,Interactive%20testing,-%3A%20Use%20the%20interactive).
Habitat-Sim is typically used with [Habitat-Lab](https://github.com/facebookresearch/habitat-lab), a modular high-level library for end-to-end experiments in embodied AI.
To define embodied AI tasks (e.g. navigation, instruction following, question answering), train agents, and benchmark their performance using standard metrics, you can download habitat-lab using the instructions provided [here](https://github.com/facebookresearch/habitat-lab#installation).
## Changelog
- `v0.2.5`: **Rearrange-ready HSSD**
- Note: this is a checkpoint. Known issues exist and continued polish is ongoing.
- Adds Receptacle meshes describing support surfaces for small objects (e.g. table or shelf surfaces).
- Adds collider meshes (.collider.glb) for assets with Receptacle meshes to support simulation.
- Adds new scenes 'scenes_uncluttered' and new SceneDataset 'hssd-hab-uncluttered' containing adjusted and de-cluttered versions of the scenes for use in embodied rearrangement tasks.
- Adds 'scene_filter_files' which sort Receptacles in each scene into active and inactive groups for RearrangeEpisode generation.
- `v0.2.4`:
- Recompresses several object GLBs to preserve PBR material status.
- Adds CSV with object metadata and semantic lexicon files for Habitat.
- Adds train/val scene splits file.
- `v0.2.3`: First release.
|
hssd/hssd-hab
|
[
"language:en",
"license:cc-by-nc-4.0",
"3D scenes",
"Embodied AI",
"region:us"
] |
2023-06-04T17:59:50+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", "viewer": false}
|
2023-11-10T18:17:32+00:00
|
03a4d69c69ff5656703770886306c9f427d051eb
|
All 2.3 million papers in the Arxiv, embedded via title with the InstructorXL model.
No claims are made about the copyright or license of contained materials. We assume no responsibilty for and are not liable under any circumstances for damages. Use at your own risk.
Good luck, have fun.
|
macrocosm/arxiv_titles
|
[
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"region:us"
] |
2023-06-04T18:12:34+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"]}
|
2023-06-04T18:17:18+00:00
|
dbb5932d2411a6800bd099f9c69a021523bd8458
|
All of the data together is around 41GB. It's the last hidden states of 131,072 samples from refinedweb padded/truncated to 512 tokens on the left, fed through [google/flan-t5-small](https://hf.co/google/flan-t5-small).
Structure:
```
{
"encoding": List, shaped (512, 512) aka (tokens, d_model),
"text": String, the original text that was encoded,
"attention_mask": List, binary mask to pass to your model with encoding to not attend to pad tokens
}
```
just a tip, you cannot load this with the RAM in the free ver of google colab, not even a single file, streaming won't work either. I have 80gb RAM and it was barely enough to work with streaming.
|
crumb/flan-t5-small-embed-refinedweb
|
[
"task_categories:feature-extraction",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"t5",
"flan",
"region:us"
] |
2023-06-04T18:18:05+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction"], "tags": ["t5", "flan"]}
|
2023-06-07T14:42:41+00:00
|
f8acde3ae5fd81bdbcd887650bce8e1b0ae84d63
|
All of the data together is around 61GB. It's the last hidden states of 131,072 samples from refinedweb padded/truncated to 512 tokens on the left, fed through [google/flan-t5-base](https://hf.co/google/flan-t5-base).
Structure:
```
{
"encoding": List, shaped (512, 768) aka (tokens, d_model),
"text": String, the original text that was encoded,
"attention_mask": List, binary mask to pass to your model with encoding to not attend to pad tokens
}
```
|
crumb/flan-t5-base-embed-refinedweb
|
[
"task_categories:feature-extraction",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"t5",
"flan",
"region:us"
] |
2023-06-04T18:18:21+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction"], "tags": ["t5", "flan"]}
|
2023-06-05T16:01:06+00:00
|
08726511c892b90f467bdc351116e004bc116231
|
All of the data together is around 81.3GB. It's the last hidden states of 131,072 samples from refinedweb padded/truncated to 512 tokens on the left, fed through [google/flan-t5-base](https://hf.co/google/flan-t5-base).
Structure:
```
{
"encoding": List, shaped (512, 1024) aka (tokens, d_model),
"text": String, the original text that was encoded,
"attention_mask": List, binary mask to pass to your model with encoding to not attend to pad tokens
}
```
|
crumb/flan-t5-large-embed-refinedweb
|
[
"task_categories:feature-extraction",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"t5",
"flan",
"region:us"
] |
2023-06-04T18:18:28+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["feature-extraction"], "tags": ["t5", "flan"]}
|
2023-06-06T10:40:34+00:00
|
d061eac000bb0f8621d5f2858b37fc2af6e27068
|
# Dataset Card for "scale_prompts_098236"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HuggingFaceH4/scale_prompts_098236
|
[
"region:us"
] |
2023-06-04T18:18:34+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "annotator", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[s]"}, {"name": "rating", "dtype": "string"}, {"name": "tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1529368, "num_examples": 1278}], "download_size": 562895, "dataset_size": 1529368}}
|
2023-06-04T18:18:41+00:00
|
7766187af22f4033ad2f7a4c46d063604423e69e
|
**Lilia Milcrabe** from **Viper F-40**
- *Trained with anime (full-final-pruned) model.*
- *3 versions; 5, 8, and 10 epochs.*
- *Recommended LoRA weigh blocks: MIDD, OUTD, and OUTALL. (ALL is a bit messy, but you can still use it under your own risk.)*
- *Works best with 0.7+ weights, but use 0.8-1.0 weights to get the character as accurate as possible, specially if using OUTD and OUTALL LoRA weight blocks.*
- *Recommended weighting the activation tag lilia milcrabe (preferably with 1:1 or 1:2) if you didn't get the character right first.*
|
Cheetor1996/Lilia_Milcrabe
|
[
"language:en",
"license:cc-by-2.0",
"art",
"region:us"
] |
2023-06-04T18:26:52+00:00
|
{"language": ["en"], "license": "cc-by-2.0", "tags": ["art"]}
|
2023-06-04T18:45:49+00:00
|
9de6e24251c802289bde26eaac9225064615b981
|
# Dataset Card for "test-ine"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ShoukanLabs/ine-dataset
|
[
"region:us"
] |
2023-06-04T18:42:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23487126222.19, "num_examples": 36890}], "download_size": 23624410753, "dataset_size": 23487126222.19}}
|
2023-06-04T18:52:04+00:00
|
e4ec66a428dbd2adba6b817478b53f0a02ed8a4b
|
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xl_mode_C_HM_T_A_OCR_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xl_mode_C_HM_T_A_OCR_rices_ns_1000
|
[
"region:us"
] |
2023-06-04T19:09:56+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 1189337, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full__text", "num_bytes": 1172002, "num_examples": 1000}, {"name": "fewshot_0", "num_bytes": 1161039, "num_examples": 1000}], "download_size": 605126, "dataset_size": 3522378}}
|
2023-06-14T19:57:18+00:00
|
50f0ea8d54eba919c14c988b387fd05e34b1eda0
|
# Dataset Card for "sharegpt_prompts_annotated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lewtun/sharegpt_prompts_annotated
|
[
"region:us"
] |
2023-06-04T19:10:16+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "annotator", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[ns]"}, {"name": "rating", "dtype": "string"}, {"name": "tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 669141, "num_examples": 1096}, {"name": "no_code", "num_bytes": 669141, "num_examples": 1096}], "download_size": 415342, "dataset_size": 1338282}}
|
2023-06-04T19:12:37+00:00
|
ac925ccb2a0a7a3ba9297228f48a474ab248a2bf
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xl_mode_C_HM_T_A_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xl_mode_C_HM_T_A_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-04T20:39:33+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 600963, "num_examples": 500}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full__text", "num_bytes": 583968, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 585423, "num_examples": 500}], "download_size": 331241, "dataset_size": 1770354}}
|
2023-06-14T20:08:21+00:00
|
1fdd6743360eaf9ae468ea5fd866d32e6026f7b9
|
# Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlexRog228/dreambooth-hackathon-images
|
[
"region:us"
] |
2023-06-04T20:40:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 4326507.0, "num_examples": 20}], "download_size": 4310996, "dataset_size": 4326507.0}}
|
2023-06-04T20:40:16+00:00
|
889cb5d0904f486a01c2f35dd54a87a73d5894ae
|
nesticot/exit_df
|
[
"license:afl-3.0",
"region:us"
] |
2023-06-04T20:48:38+00:00
|
{"license": "afl-3.0"}
|
2023-06-04T20:53:12+00:00
|
|
8dd0ad8841b3dd9cd418e4999c8b263337f9ad29
|
Birchlabs/openai-prm800k-stepwise-best
|
[
"license:mit",
"region:us"
] |
2023-06-04T20:54:07+00:00
|
{"license": "mit"}
|
2023-06-04T20:55:19+00:00
|
|
b20e1ec42412431717943d71035807ca515b8616
|
Birchlabs/openai-prm800k-solutions-only
|
[
"license:mit",
"region:us"
] |
2023-06-04T20:55:50+00:00
|
{"license": "mit"}
|
2023-06-04T20:56:21+00:00
|
|
e914966e7c091fc8c28943e6dc0f5f32f882dcba
|
# Dataset Card for "OK-VQA_test_google_flan_t5_xl_mode_CM_D_PNP_GENERIC_Q_rices_ns_5046"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xl_mode_CM_D_PNP_GENERIC_Q_rices_ns_5046
|
[
"region:us"
] |
2023-06-04T21:06:35+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0___DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__", "num_bytes": 57168479, "num_examples": 5046}], "download_size": 10079801, "dataset_size": 57168479}}
|
2023-06-04T21:06:41+00:00
|
d3940a970659a18a671c4aebdb417a79ec425a65
|
# Dataset Card for "aircraft_bbcrop"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/aircraft_bbcrop
|
[
"region:us"
] |
2023-06-04T21:22:30+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 253644684.5, "num_examples": 3500}, {"name": "validation", "num_bytes": 70984494.0, "num_examples": 1000}, {"name": "test", "num_bytes": 80183818.5, "num_examples": 1100}], "download_size": 404802117, "dataset_size": 404812997.0}}
|
2023-06-04T21:22:54+00:00
|
4f418dac0299941abffd368b4726735737ea20e1
|
# Dataset Card for "prof_report__dreamlike-art-dreamlike-photoreal-2.0__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__dreamlike-art-dreamlike-photoreal-2.0__multi__24
|
[
"region:us"
] |
2023-06-04T21:57:50+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": "accountant", "num_bytes": 1672, "num_examples": 3}, {"name": "aerospace_engineer", "num_bytes": 1864, "num_examples": 11}, {"name": "aide", "num_bytes": 1768, "num_examples": 7}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1696, "num_examples": 4}, {"name": "artist", "num_bytes": 1936, "num_examples": 14}, {"name": "author", "num_bytes": 1720, "num_examples": 5}, {"name": "baker", "num_bytes": 1672, "num_examples": 3}, {"name": "bartender", "num_bytes": 1672, "num_examples": 3}, {"name": "bus_driver", "num_bytes": 1864, "num_examples": 11}, {"name": "butcher", "num_bytes": 1744, "num_examples": 6}, {"name": "career_counselor", "num_bytes": 1696, "num_examples": 4}, {"name": "carpenter", "num_bytes": 1696, "num_examples": 4}, {"name": "carpet_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "cashier", "num_bytes": 1792, "num_examples": 8}, {"name": "ceo", "num_bytes": 1672, "num_examples": 3}, {"name": "childcare_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "civil_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "claims_appraiser", "num_bytes": 1696, "num_examples": 4}, {"name": "cleaner", "num_bytes": 1888, "num_examples": 12}, {"name": "clergy", "num_bytes": 1720, "num_examples": 5}, {"name": "clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "coach", "num_bytes": 1648, "num_examples": 2}, {"name": "community_manager", "num_bytes": 1768, "num_examples": 7}, {"name": "compliance_officer", "num_bytes": 1720, "num_examples": 5}, {"name": "computer_programmer", "num_bytes": 1720, "num_examples": 5}, {"name": "computer_support_specialist", "num_bytes": 1816, "num_examples": 9}, {"name": "computer_systems_analyst", "num_bytes": 1888, "num_examples": 12}, {"name": "construction_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "cook", "num_bytes": 1720, "num_examples": 5}, {"name": "correctional_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "courier", "num_bytes": 1720, "num_examples": 5}, {"name": "credit_counselor", "num_bytes": 1720, "num_examples": 5}, {"name": "customer_service_representative", "num_bytes": 1744, "num_examples": 6}, {"name": "data_entry_keyer", "num_bytes": 1624, "num_examples": 1}, {"name": "dental_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 1672, "num_examples": 3}, {"name": "dentist", "num_bytes": 1672, "num_examples": 3}, {"name": "designer", "num_bytes": 1744, "num_examples": 6}, {"name": "detective", "num_bytes": 1672, "num_examples": 3}, {"name": "director", "num_bytes": 1672, "num_examples": 3}, {"name": "dishwasher", "num_bytes": 1768, "num_examples": 7}, {"name": "dispatcher", "num_bytes": 1672, "num_examples": 3}, {"name": "doctor", "num_bytes": 1672, "num_examples": 3}, {"name": "drywall_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "electrical_engineer", "num_bytes": 1816, "num_examples": 9}, {"name": "electrician", "num_bytes": 1696, "num_examples": 4}, {"name": "engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "event_planner", "num_bytes": 1672, "num_examples": 3}, {"name": "executive_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "facilities_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "farmer", "num_bytes": 1648, "num_examples": 2}, {"name": "fast_food_worker", "num_bytes": 1912, "num_examples": 13}, {"name": "file_clerk", "num_bytes": 1816, "num_examples": 9}, {"name": "financial_advisor", "num_bytes": 1624, "num_examples": 1}, {"name": "financial_analyst", "num_bytes": 1672, "num_examples": 3}, {"name": "financial_manager", "num_bytes": 1672, "num_examples": 3}, {"name": "firefighter", "num_bytes": 1696, "num_examples": 4}, {"name": "fitness_instructor", "num_bytes": 1744, "num_examples": 6}, {"name": "graphic_designer", "num_bytes": 1792, "num_examples": 8}, {"name": "groundskeeper", "num_bytes": 1720, "num_examples": 5}, {"name": "hairdresser", "num_bytes": 1768, "num_examples": 7}, {"name": "head_cook", "num_bytes": 1720, "num_examples": 5}, {"name": "health_technician", "num_bytes": 1816, "num_examples": 9}, {"name": "industrial_engineer", "num_bytes": 1720, "num_examples": 5}, {"name": "insurance_agent", "num_bytes": 1672, "num_examples": 3}, {"name": "interior_designer", "num_bytes": 1792, "num_examples": 8}, {"name": "interviewer", "num_bytes": 1744, "num_examples": 6}, {"name": "inventory_clerk", "num_bytes": 1816, "num_examples": 9}, {"name": "it_specialist", "num_bytes": 1648, "num_examples": 2}, {"name": "jailer", "num_bytes": 1696, "num_examples": 4}, {"name": "janitor", "num_bytes": 1768, "num_examples": 7}, {"name": "laboratory_technician", "num_bytes": 1840, "num_examples": 10}, {"name": "language_pathologist", "num_bytes": 1720, "num_examples": 5}, {"name": "lawyer", "num_bytes": 1696, "num_examples": 4}, {"name": "librarian", "num_bytes": 1696, "num_examples": 4}, {"name": "logistician", "num_bytes": 1720, "num_examples": 5}, {"name": "machinery_mechanic", "num_bytes": 1720, "num_examples": 5}, {"name": "machinist", "num_bytes": 1648, "num_examples": 2}, {"name": "maid", "num_bytes": 1744, "num_examples": 6}, {"name": "manager", "num_bytes": 1696, "num_examples": 4}, {"name": "manicurist", "num_bytes": 1720, "num_examples": 5}, {"name": "market_research_analyst", "num_bytes": 1696, "num_examples": 4}, {"name": "marketing_manager", "num_bytes": 1696, "num_examples": 4}, {"name": "massage_therapist", "num_bytes": 1792, "num_examples": 8}, {"name": "mechanic", "num_bytes": 1744, "num_examples": 6}, {"name": "mechanical_engineer", "num_bytes": 1720, "num_examples": 5}, {"name": "medical_records_specialist", "num_bytes": 1744, "num_examples": 6}, {"name": "mental_health_counselor", "num_bytes": 1840, "num_examples": 10}, {"name": "metal_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "mover", "num_bytes": 1864, "num_examples": 11}, {"name": "musician", "num_bytes": 1744, "num_examples": 6}, {"name": "network_administrator", "num_bytes": 1624, "num_examples": 1}, {"name": "nurse", "num_bytes": 1648, "num_examples": 2}, {"name": "nursing_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 1648, "num_examples": 2}, {"name": "occupational_therapist", "num_bytes": 1696, "num_examples": 4}, {"name": "office_clerk", "num_bytes": 1744, "num_examples": 6}, {"name": "office_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "painter", "num_bytes": 1696, "num_examples": 4}, {"name": "paralegal", "num_bytes": 1768, "num_examples": 7}, {"name": "payroll_clerk", "num_bytes": 1720, "num_examples": 5}, {"name": "pharmacist", "num_bytes": 1768, "num_examples": 7}, {"name": "pharmacy_technician", "num_bytes": 1792, "num_examples": 8}, {"name": "photographer", "num_bytes": 1792, "num_examples": 8}, {"name": "physical_therapist", "num_bytes": 1672, "num_examples": 3}, {"name": "pilot", "num_bytes": 1744, "num_examples": 6}, {"name": "plane_mechanic", "num_bytes": 1768, "num_examples": 7}, {"name": "plumber", "num_bytes": 1696, "num_examples": 4}, {"name": "police_officer", "num_bytes": 1744, "num_examples": 6}, {"name": "postal_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "printing_press_operator", "num_bytes": 1816, "num_examples": 9}, {"name": "producer", "num_bytes": 1696, "num_examples": 4}, {"name": "psychologist", "num_bytes": 1720, "num_examples": 5}, {"name": "public_relations_specialist", "num_bytes": 1672, "num_examples": 3}, {"name": "purchasing_agent", "num_bytes": 1720, "num_examples": 5}, {"name": "radiologic_technician", "num_bytes": 1816, "num_examples": 9}, {"name": "real_estate_broker", "num_bytes": 1696, "num_examples": 4}, {"name": "receptionist", "num_bytes": 1672, "num_examples": 3}, {"name": "repair_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1624, "num_examples": 1}, {"name": "salesperson", "num_bytes": 1672, "num_examples": 3}, {"name": "school_bus_driver", "num_bytes": 1864, "num_examples": 11}, {"name": "scientist", "num_bytes": 1744, "num_examples": 6}, {"name": "security_guard", "num_bytes": 1672, "num_examples": 3}, {"name": "sheet_metal_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "singer", "num_bytes": 1768, "num_examples": 7}, {"name": "social_assistant", "num_bytes": 1816, "num_examples": 9}, {"name": "social_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "software_developer", "num_bytes": 1648, "num_examples": 2}, {"name": "stocker", "num_bytes": 1792, "num_examples": 8}, {"name": "supervisor", "num_bytes": 1744, "num_examples": 6}, {"name": "taxi_driver", "num_bytes": 1720, "num_examples": 5}, {"name": "teacher", "num_bytes": 1744, "num_examples": 6}, {"name": "teaching_assistant", "num_bytes": 1744, "num_examples": 6}, {"name": "teller", "num_bytes": 1792, "num_examples": 8}, {"name": "therapist", "num_bytes": 1792, "num_examples": 8}, {"name": "tractor_operator", "num_bytes": 1672, "num_examples": 3}, {"name": "truck_driver", "num_bytes": 1672, "num_examples": 3}, {"name": "tutor", "num_bytes": 1816, "num_examples": 9}, {"name": "underwriter", "num_bytes": 1720, "num_examples": 5}, {"name": "veterinarian", "num_bytes": 1648, "num_examples": 2}, {"name": "welder", "num_bytes": 1744, "num_examples": 6}, {"name": "wholesale_buyer", "num_bytes": 1768, "num_examples": 7}, {"name": "writer", "num_bytes": 1768, "num_examples": 7}], "download_size": 632511, "dataset_size": 253040}}
|
2023-06-04T21:59:12+00:00
|
b1a503190abe7e702815a9873a8a159380e519f5
|
# Dataset Card for "prof_report__prompthero-openjourney-v4__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__prompthero-openjourney-v4__multi__24
|
[
"region:us"
] |
2023-06-04T22:03:05+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": "accountant", "num_bytes": 1696, "num_examples": 4}, {"name": "aerospace_engineer", "num_bytes": 1864, "num_examples": 11}, {"name": "aide", "num_bytes": 1792, "num_examples": 8}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1720, "num_examples": 5}, {"name": "artist", "num_bytes": 1864, "num_examples": 11}, {"name": "author", "num_bytes": 1792, "num_examples": 8}, {"name": "baker", "num_bytes": 1768, "num_examples": 7}, {"name": "bartender", "num_bytes": 1672, "num_examples": 3}, {"name": "bus_driver", "num_bytes": 1840, "num_examples": 10}, {"name": "butcher", "num_bytes": 1744, "num_examples": 6}, {"name": "career_counselor", "num_bytes": 1720, "num_examples": 5}, {"name": "carpenter", "num_bytes": 1720, "num_examples": 5}, {"name": "carpet_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "cashier", "num_bytes": 1744, "num_examples": 6}, {"name": "ceo", "num_bytes": 1672, "num_examples": 3}, {"name": "childcare_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "civil_engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "claims_appraiser", "num_bytes": 1792, "num_examples": 8}, {"name": "cleaner", "num_bytes": 1888, "num_examples": 12}, {"name": "clergy", "num_bytes": 1768, "num_examples": 7}, {"name": "clerk", "num_bytes": 1888, "num_examples": 12}, {"name": "coach", "num_bytes": 1672, "num_examples": 3}, {"name": "community_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "compliance_officer", "num_bytes": 1744, "num_examples": 6}, {"name": "computer_programmer", "num_bytes": 1696, "num_examples": 4}, {"name": "computer_support_specialist", "num_bytes": 1816, "num_examples": 9}, {"name": "computer_systems_analyst", "num_bytes": 1792, "num_examples": 8}, {"name": "construction_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "cook", "num_bytes": 1768, "num_examples": 7}, {"name": "correctional_officer", "num_bytes": 1768, "num_examples": 7}, {"name": "courier", "num_bytes": 1816, "num_examples": 9}, {"name": "credit_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "customer_service_representative", "num_bytes": 1744, "num_examples": 6}, {"name": "data_entry_keyer", "num_bytes": 1768, "num_examples": 7}, {"name": "dental_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 1696, "num_examples": 4}, {"name": "dentist", "num_bytes": 1720, "num_examples": 5}, {"name": "designer", "num_bytes": 1768, "num_examples": 7}, {"name": "detective", "num_bytes": 1744, "num_examples": 6}, {"name": "director", "num_bytes": 1696, "num_examples": 4}, {"name": "dishwasher", "num_bytes": 1816, "num_examples": 9}, {"name": "dispatcher", "num_bytes": 1696, "num_examples": 4}, {"name": "doctor", "num_bytes": 1744, "num_examples": 6}, {"name": "drywall_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "electrician", "num_bytes": 1672, "num_examples": 3}, {"name": "engineer", "num_bytes": 1720, "num_examples": 5}, {"name": "event_planner", "num_bytes": 1696, "num_examples": 4}, {"name": "executive_assistant", "num_bytes": 1648, "num_examples": 2}, {"name": "facilities_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "farmer", "num_bytes": 1672, "num_examples": 3}, {"name": "fast_food_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "file_clerk", "num_bytes": 1816, "num_examples": 9}, {"name": "financial_advisor", "num_bytes": 1672, "num_examples": 3}, {"name": "financial_analyst", "num_bytes": 1696, "num_examples": 4}, {"name": "financial_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "firefighter", "num_bytes": 1672, "num_examples": 3}, {"name": "fitness_instructor", "num_bytes": 1744, "num_examples": 6}, {"name": "graphic_designer", "num_bytes": 1792, "num_examples": 8}, {"name": "groundskeeper", "num_bytes": 1720, "num_examples": 5}, {"name": "hairdresser", "num_bytes": 1792, "num_examples": 8}, {"name": "head_cook", "num_bytes": 1840, "num_examples": 10}, {"name": "health_technician", "num_bytes": 1696, "num_examples": 4}, {"name": "industrial_engineer", "num_bytes": 1672, "num_examples": 3}, {"name": "insurance_agent", "num_bytes": 1744, "num_examples": 6}, {"name": "interior_designer", "num_bytes": 1816, "num_examples": 9}, {"name": "interviewer", "num_bytes": 1816, "num_examples": 9}, {"name": "inventory_clerk", "num_bytes": 1816, "num_examples": 9}, {"name": "it_specialist", "num_bytes": 1672, "num_examples": 3}, {"name": "jailer", "num_bytes": 1720, "num_examples": 5}, {"name": "janitor", "num_bytes": 1792, "num_examples": 8}, {"name": "laboratory_technician", "num_bytes": 1864, "num_examples": 11}, {"name": "language_pathologist", "num_bytes": 1840, "num_examples": 10}, {"name": "lawyer", "num_bytes": 1696, "num_examples": 4}, {"name": "librarian", "num_bytes": 1720, "num_examples": 5}, {"name": "logistician", "num_bytes": 1768, "num_examples": 7}, {"name": "machinery_mechanic", "num_bytes": 1696, "num_examples": 4}, {"name": "machinist", "num_bytes": 1696, "num_examples": 4}, {"name": "maid", "num_bytes": 1816, "num_examples": 9}, {"name": "manager", "num_bytes": 1624, "num_examples": 1}, {"name": "manicurist", "num_bytes": 1792, "num_examples": 8}, {"name": "market_research_analyst", "num_bytes": 1744, "num_examples": 6}, {"name": "marketing_manager", "num_bytes": 1696, "num_examples": 4}, {"name": "massage_therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "mechanic", "num_bytes": 1720, "num_examples": 5}, {"name": "mechanical_engineer", "num_bytes": 1720, "num_examples": 5}, {"name": "medical_records_specialist", "num_bytes": 1840, "num_examples": 10}, {"name": "mental_health_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "metal_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "mover", "num_bytes": 1816, "num_examples": 9}, {"name": "musician", "num_bytes": 1768, "num_examples": 7}, {"name": "network_administrator", "num_bytes": 1624, "num_examples": 1}, {"name": "nurse", "num_bytes": 1696, "num_examples": 4}, {"name": "nursing_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 1672, "num_examples": 3}, {"name": "occupational_therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "office_clerk", "num_bytes": 1816, "num_examples": 9}, {"name": "office_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "painter", "num_bytes": 1816, "num_examples": 9}, {"name": "paralegal", "num_bytes": 1720, "num_examples": 5}, {"name": "payroll_clerk", "num_bytes": 1744, "num_examples": 6}, {"name": "pharmacist", "num_bytes": 1840, "num_examples": 10}, {"name": "pharmacy_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "photographer", "num_bytes": 1864, "num_examples": 11}, {"name": "physical_therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "pilot", "num_bytes": 1744, "num_examples": 6}, {"name": "plane_mechanic", "num_bytes": 1720, "num_examples": 5}, {"name": "plumber", "num_bytes": 1720, "num_examples": 5}, {"name": "police_officer", "num_bytes": 1696, "num_examples": 4}, {"name": "postal_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "printing_press_operator", "num_bytes": 1792, "num_examples": 8}, {"name": "producer", "num_bytes": 1768, "num_examples": 7}, {"name": "psychologist", "num_bytes": 1696, "num_examples": 4}, {"name": "public_relations_specialist", "num_bytes": 1696, "num_examples": 4}, {"name": "purchasing_agent", "num_bytes": 1816, "num_examples": 9}, {"name": "radiologic_technician", "num_bytes": 1768, "num_examples": 7}, {"name": "real_estate_broker", "num_bytes": 1696, "num_examples": 4}, {"name": "receptionist", "num_bytes": 1696, "num_examples": 4}, {"name": "repair_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1648, "num_examples": 2}, {"name": "salesperson", "num_bytes": 1696, "num_examples": 4}, {"name": "school_bus_driver", "num_bytes": 1888, "num_examples": 12}, {"name": "scientist", "num_bytes": 1840, "num_examples": 10}, {"name": "security_guard", "num_bytes": 1720, "num_examples": 5}, {"name": "sheet_metal_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "singer", "num_bytes": 1888, "num_examples": 12}, {"name": "social_assistant", "num_bytes": 1792, "num_examples": 8}, {"name": "social_worker", "num_bytes": 1864, "num_examples": 11}, {"name": "software_developer", "num_bytes": 1672, "num_examples": 3}, {"name": "stocker", "num_bytes": 1840, "num_examples": 10}, {"name": "supervisor", "num_bytes": 1744, "num_examples": 6}, {"name": "taxi_driver", "num_bytes": 1720, "num_examples": 5}, {"name": "teacher", "num_bytes": 1840, "num_examples": 10}, {"name": "teaching_assistant", "num_bytes": 1816, "num_examples": 9}, {"name": "teller", "num_bytes": 1912, "num_examples": 13}, {"name": "therapist", "num_bytes": 1768, "num_examples": 7}, {"name": "tractor_operator", "num_bytes": 1720, "num_examples": 5}, {"name": "truck_driver", "num_bytes": 1720, "num_examples": 5}, {"name": "tutor", "num_bytes": 1936, "num_examples": 14}, {"name": "underwriter", "num_bytes": 1720, "num_examples": 5}, {"name": "veterinarian", "num_bytes": 1672, "num_examples": 3}, {"name": "welder", "num_bytes": 1720, "num_examples": 5}, {"name": "wholesale_buyer", "num_bytes": 1768, "num_examples": 7}, {"name": "writer", "num_bytes": 1768, "num_examples": 7}], "download_size": 633664, "dataset_size": 255656}}
|
2023-06-04T22:05:05+00:00
|
1b62cc2b010f2e7d81b0201bd9c93d7ff058d75a
|
# Dataset Card for "prof_report__22h-vintedois-diffusion-v0-1__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__22h-vintedois-diffusion-v0-1__multi__24
|
[
"region:us"
] |
2023-06-04T22:09:14+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": "accountant", "num_bytes": 1672, "num_examples": 3}, {"name": "aerospace_engineer", "num_bytes": 1864, "num_examples": 11}, {"name": "aide", "num_bytes": 1768, "num_examples": 7}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1816, "num_examples": 9}, {"name": "artist", "num_bytes": 1936, "num_examples": 14}, {"name": "author", "num_bytes": 1792, "num_examples": 8}, {"name": "baker", "num_bytes": 1768, "num_examples": 7}, {"name": "bartender", "num_bytes": 1648, "num_examples": 2}, {"name": "bus_driver", "num_bytes": 1816, "num_examples": 9}, {"name": "butcher", "num_bytes": 1744, "num_examples": 6}, {"name": "career_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "carpenter", "num_bytes": 1672, "num_examples": 3}, {"name": "carpet_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "cashier", "num_bytes": 1816, "num_examples": 9}, {"name": "ceo", "num_bytes": 1696, "num_examples": 4}, {"name": "childcare_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "civil_engineer", "num_bytes": 1816, "num_examples": 9}, {"name": "claims_appraiser", "num_bytes": 1744, "num_examples": 6}, {"name": "cleaner", "num_bytes": 1864, "num_examples": 11}, {"name": "clergy", "num_bytes": 1720, "num_examples": 5}, {"name": "clerk", "num_bytes": 1936, "num_examples": 14}, {"name": "coach", "num_bytes": 1672, "num_examples": 3}, {"name": "community_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "compliance_officer", "num_bytes": 1720, "num_examples": 5}, {"name": "computer_programmer", "num_bytes": 1744, "num_examples": 6}, {"name": "computer_support_specialist", "num_bytes": 1720, "num_examples": 5}, {"name": "computer_systems_analyst", "num_bytes": 1840, "num_examples": 10}, {"name": "construction_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "cook", "num_bytes": 1792, "num_examples": 8}, {"name": "correctional_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "courier", "num_bytes": 1912, "num_examples": 13}, {"name": "credit_counselor", "num_bytes": 1720, "num_examples": 5}, {"name": "customer_service_representative", "num_bytes": 1720, "num_examples": 5}, {"name": "data_entry_keyer", "num_bytes": 1768, "num_examples": 7}, {"name": "dental_assistant", "num_bytes": 1672, "num_examples": 3}, {"name": "dental_hygienist", "num_bytes": 1648, "num_examples": 2}, {"name": "dentist", "num_bytes": 1672, "num_examples": 3}, {"name": "designer", "num_bytes": 1840, "num_examples": 10}, {"name": "detective", "num_bytes": 1720, "num_examples": 5}, {"name": "director", "num_bytes": 1720, "num_examples": 5}, {"name": "dishwasher", "num_bytes": 1816, "num_examples": 9}, {"name": "dispatcher", "num_bytes": 1720, "num_examples": 5}, {"name": "doctor", "num_bytes": 1696, "num_examples": 4}, {"name": "drywall_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 1840, "num_examples": 10}, {"name": "electrician", "num_bytes": 1672, "num_examples": 3}, {"name": "engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "event_planner", "num_bytes": 1720, "num_examples": 5}, {"name": "executive_assistant", "num_bytes": 1672, "num_examples": 3}, {"name": "facilities_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "farmer", "num_bytes": 1648, "num_examples": 2}, {"name": "fast_food_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "file_clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "financial_advisor", "num_bytes": 1648, "num_examples": 2}, {"name": "financial_analyst", "num_bytes": 1696, "num_examples": 4}, {"name": "financial_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "firefighter", "num_bytes": 1624, "num_examples": 1}, {"name": "fitness_instructor", "num_bytes": 1768, "num_examples": 7}, {"name": "graphic_designer", "num_bytes": 1792, "num_examples": 8}, {"name": "groundskeeper", "num_bytes": 1696, "num_examples": 4}, {"name": "hairdresser", "num_bytes": 1816, "num_examples": 9}, {"name": "head_cook", "num_bytes": 1720, "num_examples": 5}, {"name": "health_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "industrial_engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "insurance_agent", "num_bytes": 1696, "num_examples": 4}, {"name": "interior_designer", "num_bytes": 1720, "num_examples": 5}, {"name": "interviewer", "num_bytes": 1816, "num_examples": 9}, {"name": "inventory_clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "it_specialist", "num_bytes": 1672, "num_examples": 3}, {"name": "jailer", "num_bytes": 1744, "num_examples": 6}, {"name": "janitor", "num_bytes": 1792, "num_examples": 8}, {"name": "laboratory_technician", "num_bytes": 1792, "num_examples": 8}, {"name": "language_pathologist", "num_bytes": 1768, "num_examples": 7}, {"name": "lawyer", "num_bytes": 1792, "num_examples": 8}, {"name": "librarian", "num_bytes": 1696, "num_examples": 4}, {"name": "logistician", "num_bytes": 1792, "num_examples": 8}, {"name": "machinery_mechanic", "num_bytes": 1648, "num_examples": 2}, {"name": "machinist", "num_bytes": 1768, "num_examples": 7}, {"name": "maid", "num_bytes": 1792, "num_examples": 8}, {"name": "manager", "num_bytes": 1744, "num_examples": 6}, {"name": "manicurist", "num_bytes": 1768, "num_examples": 7}, {"name": "market_research_analyst", "num_bytes": 1768, "num_examples": 7}, {"name": "marketing_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "massage_therapist", "num_bytes": 1792, "num_examples": 8}, {"name": "mechanic", "num_bytes": 1696, "num_examples": 4}, {"name": "mechanical_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "medical_records_specialist", "num_bytes": 1792, "num_examples": 8}, {"name": "mental_health_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "metal_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "mover", "num_bytes": 1816, "num_examples": 9}, {"name": "musician", "num_bytes": 1816, "num_examples": 9}, {"name": "network_administrator", "num_bytes": 1624, "num_examples": 1}, {"name": "nurse", "num_bytes": 1672, "num_examples": 3}, {"name": "nursing_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 1672, "num_examples": 3}, {"name": "occupational_therapist", "num_bytes": 1696, "num_examples": 4}, {"name": "office_clerk", "num_bytes": 1768, "num_examples": 7}, {"name": "office_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "painter", "num_bytes": 1888, "num_examples": 12}, {"name": "paralegal", "num_bytes": 1744, "num_examples": 6}, {"name": "payroll_clerk", "num_bytes": 1744, "num_examples": 6}, {"name": "pharmacist", "num_bytes": 1768, "num_examples": 7}, {"name": "pharmacy_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "photographer", "num_bytes": 1864, "num_examples": 11}, {"name": "physical_therapist", "num_bytes": 1720, "num_examples": 5}, {"name": "pilot", "num_bytes": 1768, "num_examples": 7}, {"name": "plane_mechanic", "num_bytes": 1744, "num_examples": 6}, {"name": "plumber", "num_bytes": 1696, "num_examples": 4}, {"name": "police_officer", "num_bytes": 1744, "num_examples": 6}, {"name": "postal_worker", "num_bytes": 1864, "num_examples": 11}, {"name": "printing_press_operator", "num_bytes": 1744, "num_examples": 6}, {"name": "producer", "num_bytes": 1840, "num_examples": 10}, {"name": "psychologist", "num_bytes": 1768, "num_examples": 7}, {"name": "public_relations_specialist", "num_bytes": 1672, "num_examples": 3}, {"name": "purchasing_agent", "num_bytes": 1840, "num_examples": 10}, {"name": "radiologic_technician", "num_bytes": 1744, "num_examples": 6}, {"name": "real_estate_broker", "num_bytes": 1696, "num_examples": 4}, {"name": "receptionist", "num_bytes": 1672, "num_examples": 3}, {"name": "repair_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1672, "num_examples": 3}, {"name": "salesperson", "num_bytes": 1672, "num_examples": 3}, {"name": "school_bus_driver", "num_bytes": 1864, "num_examples": 11}, {"name": "scientist", "num_bytes": 1792, "num_examples": 8}, {"name": "security_guard", "num_bytes": 1720, "num_examples": 5}, {"name": "sheet_metal_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "singer", "num_bytes": 1888, "num_examples": 12}, {"name": "social_assistant", "num_bytes": 1768, "num_examples": 7}, {"name": "social_worker", "num_bytes": 1936, "num_examples": 14}, {"name": "software_developer", "num_bytes": 1720, "num_examples": 5}, {"name": "stocker", "num_bytes": 1672, "num_examples": 3}, {"name": "supervisor", "num_bytes": 1672, "num_examples": 3}, {"name": "taxi_driver", "num_bytes": 1840, "num_examples": 10}, {"name": "teacher", "num_bytes": 1864, "num_examples": 11}, {"name": "teaching_assistant", "num_bytes": 1768, "num_examples": 7}, {"name": "teller", "num_bytes": 1936, "num_examples": 14}, {"name": "therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "tractor_operator", "num_bytes": 1672, "num_examples": 3}, {"name": "truck_driver", "num_bytes": 1648, "num_examples": 2}, {"name": "tutor", "num_bytes": 1840, "num_examples": 10}, {"name": "underwriter", "num_bytes": 1792, "num_examples": 8}, {"name": "veterinarian", "num_bytes": 1720, "num_examples": 5}, {"name": "welder", "num_bytes": 1744, "num_examples": 6}, {"name": "wholesale_buyer", "num_bytes": 1792, "num_examples": 8}, {"name": "writer", "num_bytes": 1792, "num_examples": 8}], "download_size": 633706, "dataset_size": 255800}}
|
2023-06-04T22:10:43+00:00
|
32f0fdcacbe24d7a3f216f45d8a92ff6235e6162
|
# Dataset Card for "prof_report__plasmo-vox2__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__plasmo-vox2__multi__24
|
[
"region:us"
] |
2023-06-04T22:14:57+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": "accountant", "num_bytes": 1744, "num_examples": 6}, {"name": "aerospace_engineer", "num_bytes": 1936, "num_examples": 14}, {"name": "aide", "num_bytes": 1912, "num_examples": 13}, {"name": "air_conditioning_installer", "num_bytes": 1720, "num_examples": 5}, {"name": "architect", "num_bytes": 1816, "num_examples": 9}, {"name": "artist", "num_bytes": 1936, "num_examples": 14}, {"name": "author", "num_bytes": 1768, "num_examples": 7}, {"name": "baker", "num_bytes": 1960, "num_examples": 15}, {"name": "bartender", "num_bytes": 1696, "num_examples": 4}, {"name": "bus_driver", "num_bytes": 1816, "num_examples": 9}, {"name": "butcher", "num_bytes": 1768, "num_examples": 7}, {"name": "career_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "carpenter", "num_bytes": 1768, "num_examples": 7}, {"name": "carpet_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "cashier", "num_bytes": 1888, "num_examples": 12}, {"name": "ceo", "num_bytes": 1792, "num_examples": 8}, {"name": "childcare_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "civil_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "claims_appraiser", "num_bytes": 1744, "num_examples": 6}, {"name": "cleaner", "num_bytes": 1888, "num_examples": 12}, {"name": "clergy", "num_bytes": 1816, "num_examples": 9}, {"name": "clerk", "num_bytes": 1984, "num_examples": 16}, {"name": "coach", "num_bytes": 1768, "num_examples": 7}, {"name": "community_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "compliance_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "computer_programmer", "num_bytes": 1720, "num_examples": 5}, {"name": "computer_support_specialist", "num_bytes": 1792, "num_examples": 8}, {"name": "computer_systems_analyst", "num_bytes": 1744, "num_examples": 6}, {"name": "construction_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "cook", "num_bytes": 1864, "num_examples": 11}, {"name": "correctional_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "courier", "num_bytes": 1984, "num_examples": 16}, {"name": "credit_counselor", "num_bytes": 1864, "num_examples": 11}, {"name": "customer_service_representative", "num_bytes": 1792, "num_examples": 8}, {"name": "data_entry_keyer", "num_bytes": 1840, "num_examples": 10}, {"name": "dental_assistant", "num_bytes": 1744, "num_examples": 6}, {"name": "dental_hygienist", "num_bytes": 1696, "num_examples": 4}, {"name": "dentist", "num_bytes": 1744, "num_examples": 6}, {"name": "designer", "num_bytes": 1864, "num_examples": 11}, {"name": "detective", "num_bytes": 1744, "num_examples": 6}, {"name": "director", "num_bytes": 1816, "num_examples": 9}, {"name": "dishwasher", "num_bytes": 1864, "num_examples": 11}, {"name": "dispatcher", "num_bytes": 1768, "num_examples": 7}, {"name": "doctor", "num_bytes": 1816, "num_examples": 9}, {"name": "drywall_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "electrical_engineer", "num_bytes": 1816, "num_examples": 9}, {"name": "electrician", "num_bytes": 1696, "num_examples": 4}, {"name": "engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "event_planner", "num_bytes": 1720, "num_examples": 5}, {"name": "executive_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "facilities_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "farmer", "num_bytes": 1696, "num_examples": 4}, {"name": "fast_food_worker", "num_bytes": 1912, "num_examples": 13}, {"name": "file_clerk", "num_bytes": 1864, "num_examples": 11}, {"name": "financial_advisor", "num_bytes": 1672, "num_examples": 3}, {"name": "financial_analyst", "num_bytes": 1768, "num_examples": 7}, {"name": "financial_manager", "num_bytes": 1768, "num_examples": 7}, {"name": "firefighter", "num_bytes": 1696, "num_examples": 4}, {"name": "fitness_instructor", "num_bytes": 1720, "num_examples": 5}, {"name": "graphic_designer", "num_bytes": 1840, "num_examples": 10}, {"name": "groundskeeper", "num_bytes": 1720, "num_examples": 5}, {"name": "hairdresser", "num_bytes": 1792, "num_examples": 8}, {"name": "head_cook", "num_bytes": 1840, "num_examples": 10}, {"name": "health_technician", "num_bytes": 1840, "num_examples": 10}, {"name": "industrial_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "insurance_agent", "num_bytes": 1768, "num_examples": 7}, {"name": "interior_designer", "num_bytes": 1840, "num_examples": 10}, {"name": "interviewer", "num_bytes": 1840, "num_examples": 10}, {"name": "inventory_clerk", "num_bytes": 1888, "num_examples": 12}, {"name": "it_specialist", "num_bytes": 1720, "num_examples": 5}, {"name": "jailer", "num_bytes": 1768, "num_examples": 7}, {"name": "janitor", "num_bytes": 1840, "num_examples": 10}, {"name": "laboratory_technician", "num_bytes": 1816, "num_examples": 9}, {"name": "language_pathologist", "num_bytes": 1864, "num_examples": 11}, {"name": "lawyer", "num_bytes": 1768, "num_examples": 7}, {"name": "librarian", "num_bytes": 1840, "num_examples": 10}, {"name": "logistician", "num_bytes": 1816, "num_examples": 9}, {"name": "machinery_mechanic", "num_bytes": 1744, "num_examples": 6}, {"name": "machinist", "num_bytes": 1768, "num_examples": 7}, {"name": "maid", "num_bytes": 1768, "num_examples": 7}, {"name": "manager", "num_bytes": 1768, "num_examples": 7}, {"name": "manicurist", "num_bytes": 1792, "num_examples": 8}, {"name": "market_research_analyst", "num_bytes": 1840, "num_examples": 10}, {"name": "marketing_manager", "num_bytes": 1768, "num_examples": 7}, {"name": "massage_therapist", "num_bytes": 1768, "num_examples": 7}, {"name": "mechanic", "num_bytes": 1720, "num_examples": 5}, {"name": "mechanical_engineer", "num_bytes": 1816, "num_examples": 9}, {"name": "medical_records_specialist", "num_bytes": 1792, "num_examples": 8}, {"name": "mental_health_counselor", "num_bytes": 1840, "num_examples": 10}, {"name": "metal_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "mover", "num_bytes": 1888, "num_examples": 12}, {"name": "musician", "num_bytes": 1840, "num_examples": 10}, {"name": "network_administrator", "num_bytes": 1648, "num_examples": 2}, {"name": "nurse", "num_bytes": 1744, "num_examples": 6}, {"name": "nursing_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 1768, "num_examples": 7}, {"name": "occupational_therapist", "num_bytes": 1840, "num_examples": 10}, {"name": "office_clerk", "num_bytes": 1840, "num_examples": 10}, {"name": "office_worker", "num_bytes": 1864, "num_examples": 11}, {"name": "painter", "num_bytes": 1864, "num_examples": 11}, {"name": "paralegal", "num_bytes": 1792, "num_examples": 8}, {"name": "payroll_clerk", "num_bytes": 1768, "num_examples": 7}, {"name": "pharmacist", "num_bytes": 1864, "num_examples": 11}, {"name": "pharmacy_technician", "num_bytes": 1744, "num_examples": 6}, {"name": "photographer", "num_bytes": 1864, "num_examples": 11}, {"name": "physical_therapist", "num_bytes": 1840, "num_examples": 10}, {"name": "pilot", "num_bytes": 1816, "num_examples": 9}, {"name": "plane_mechanic", "num_bytes": 1888, "num_examples": 12}, {"name": "plumber", "num_bytes": 1720, "num_examples": 5}, {"name": "police_officer", "num_bytes": 1768, "num_examples": 7}, {"name": "postal_worker", "num_bytes": 1912, "num_examples": 13}, {"name": "printing_press_operator", "num_bytes": 1816, "num_examples": 9}, {"name": "producer", "num_bytes": 1888, "num_examples": 12}, {"name": "psychologist", "num_bytes": 1768, "num_examples": 7}, {"name": "public_relations_specialist", "num_bytes": 1720, "num_examples": 5}, {"name": "purchasing_agent", "num_bytes": 1816, "num_examples": 9}, {"name": "radiologic_technician", "num_bytes": 1768, "num_examples": 7}, {"name": "real_estate_broker", "num_bytes": 1696, "num_examples": 4}, {"name": "receptionist", "num_bytes": 1696, "num_examples": 4}, {"name": "repair_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1624, "num_examples": 1}, {"name": "salesperson", "num_bytes": 1696, "num_examples": 4}, {"name": "school_bus_driver", "num_bytes": 1840, "num_examples": 10}, {"name": "scientist", "num_bytes": 1888, "num_examples": 12}, {"name": "security_guard", "num_bytes": 1744, "num_examples": 6}, {"name": "sheet_metal_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "singer", "num_bytes": 1960, "num_examples": 15}, {"name": "social_assistant", "num_bytes": 1864, "num_examples": 11}, {"name": "social_worker", "num_bytes": 1936, "num_examples": 14}, {"name": "software_developer", "num_bytes": 1672, "num_examples": 3}, {"name": "stocker", "num_bytes": 1864, "num_examples": 11}, {"name": "supervisor", "num_bytes": 1864, "num_examples": 11}, {"name": "taxi_driver", "num_bytes": 1864, "num_examples": 11}, {"name": "teacher", "num_bytes": 1912, "num_examples": 13}, {"name": "teaching_assistant", "num_bytes": 1816, "num_examples": 9}, {"name": "teller", "num_bytes": 1984, "num_examples": 16}, {"name": "therapist", "num_bytes": 1840, "num_examples": 10}, {"name": "tractor_operator", "num_bytes": 1696, "num_examples": 4}, {"name": "truck_driver", "num_bytes": 1696, "num_examples": 4}, {"name": "tutor", "num_bytes": 1984, "num_examples": 16}, {"name": "underwriter", "num_bytes": 1744, "num_examples": 6}, {"name": "veterinarian", "num_bytes": 1744, "num_examples": 6}, {"name": "welder", "num_bytes": 1768, "num_examples": 7}, {"name": "wholesale_buyer", "num_bytes": 1792, "num_examples": 8}, {"name": "writer", "num_bytes": 1816, "num_examples": 9}], "download_size": 635999, "dataset_size": 262520}}
|
2023-06-04T22:31:30+00:00
|
0369c07e7c8d8442fab946fd2e39a65f69dfcfcc
|
# Dataset Card for "hackernews-stories"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kerinin/hackernews-stories
|
[
"region:us"
] |
2023-06-04T22:34:15+00:00
|
{"dataset_info": {"features": [{"name": "labels", "dtype": "float32"}, {"name": "Title", "dtype": "string"}, {"name": "Text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2855338468.84556, "num_examples": 313317}, {"name": "validation", "num_bytes": 713841452.15444, "num_examples": 78330}], "download_size": 2306170034, "dataset_size": 3569179921.0}}
|
2023-06-04T23:30:29+00:00
|
476a2e81997f9c94150c685d5e842a13b75c1d72
|
# Dataset Card for "prof_report__SG161222-Realistic_Vision_V1.4__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__SG161222-Realistic_Vision_V1.4__multi__24
|
[
"region:us"
] |
2023-06-04T22:35:17+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": "accountant", "num_bytes": 1696, "num_examples": 4}, {"name": "aerospace_engineer", "num_bytes": 1816, "num_examples": 9}, {"name": "aide", "num_bytes": 1744, "num_examples": 6}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1744, "num_examples": 6}, {"name": "artist", "num_bytes": 1864, "num_examples": 11}, {"name": "author", "num_bytes": 1768, "num_examples": 7}, {"name": "baker", "num_bytes": 1816, "num_examples": 9}, {"name": "bartender", "num_bytes": 1696, "num_examples": 4}, {"name": "bus_driver", "num_bytes": 1888, "num_examples": 12}, {"name": "butcher", "num_bytes": 1744, "num_examples": 6}, {"name": "career_counselor", "num_bytes": 1696, "num_examples": 4}, {"name": "carpenter", "num_bytes": 1696, "num_examples": 4}, {"name": "carpet_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "cashier", "num_bytes": 1696, "num_examples": 4}, {"name": "ceo", "num_bytes": 1840, "num_examples": 10}, {"name": "childcare_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "civil_engineer", "num_bytes": 1720, "num_examples": 5}, {"name": "claims_appraiser", "num_bytes": 1720, "num_examples": 5}, {"name": "cleaner", "num_bytes": 1816, "num_examples": 9}, {"name": "clergy", "num_bytes": 1768, "num_examples": 7}, {"name": "clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "coach", "num_bytes": 1696, "num_examples": 4}, {"name": "community_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "compliance_officer", "num_bytes": 1720, "num_examples": 5}, {"name": "computer_programmer", "num_bytes": 1768, "num_examples": 7}, {"name": "computer_support_specialist", "num_bytes": 1840, "num_examples": 10}, {"name": "computer_systems_analyst", "num_bytes": 1792, "num_examples": 8}, {"name": "construction_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "cook", "num_bytes": 1744, "num_examples": 6}, {"name": "correctional_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "courier", "num_bytes": 1768, "num_examples": 7}, {"name": "credit_counselor", "num_bytes": 1696, "num_examples": 4}, {"name": "customer_service_representative", "num_bytes": 1696, "num_examples": 4}, {"name": "data_entry_keyer", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 1648, "num_examples": 2}, {"name": "dentist", "num_bytes": 1744, "num_examples": 6}, {"name": "designer", "num_bytes": 1816, "num_examples": 9}, {"name": "detective", "num_bytes": 1744, "num_examples": 6}, {"name": "director", "num_bytes": 1696, "num_examples": 4}, {"name": "dishwasher", "num_bytes": 1744, "num_examples": 6}, {"name": "dispatcher", "num_bytes": 1672, "num_examples": 3}, {"name": "doctor", "num_bytes": 1744, "num_examples": 6}, {"name": "drywall_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "electrical_engineer", "num_bytes": 1840, "num_examples": 10}, {"name": "electrician", "num_bytes": 1696, "num_examples": 4}, {"name": "engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "event_planner", "num_bytes": 1672, "num_examples": 3}, {"name": "executive_assistant", "num_bytes": 1648, "num_examples": 2}, {"name": "facilities_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "farmer", "num_bytes": 1624, "num_examples": 1}, {"name": "fast_food_worker", "num_bytes": 1888, "num_examples": 12}, {"name": "file_clerk", "num_bytes": 1768, "num_examples": 7}, {"name": "financial_advisor", "num_bytes": 1648, "num_examples": 2}, {"name": "financial_analyst", "num_bytes": 1672, "num_examples": 3}, {"name": "financial_manager", "num_bytes": 1696, "num_examples": 4}, {"name": "firefighter", "num_bytes": 1672, "num_examples": 3}, {"name": "fitness_instructor", "num_bytes": 1672, "num_examples": 3}, {"name": "graphic_designer", "num_bytes": 1768, "num_examples": 7}, {"name": "groundskeeper", "num_bytes": 1720, "num_examples": 5}, {"name": "hairdresser", "num_bytes": 1768, "num_examples": 7}, {"name": "head_cook", "num_bytes": 1696, "num_examples": 4}, {"name": "health_technician", "num_bytes": 1696, "num_examples": 4}, {"name": "industrial_engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "insurance_agent", "num_bytes": 1672, "num_examples": 3}, {"name": "interior_designer", "num_bytes": 1672, "num_examples": 3}, {"name": "interviewer", "num_bytes": 1744, "num_examples": 6}, {"name": "inventory_clerk", "num_bytes": 1768, "num_examples": 7}, {"name": "it_specialist", "num_bytes": 1672, "num_examples": 3}, {"name": "jailer", "num_bytes": 1696, "num_examples": 4}, {"name": "janitor", "num_bytes": 1744, "num_examples": 6}, {"name": "laboratory_technician", "num_bytes": 1840, "num_examples": 10}, {"name": "language_pathologist", "num_bytes": 1696, "num_examples": 4}, {"name": "lawyer", "num_bytes": 1720, "num_examples": 5}, {"name": "librarian", "num_bytes": 1672, "num_examples": 3}, {"name": "logistician", "num_bytes": 1744, "num_examples": 6}, {"name": "machinery_mechanic", "num_bytes": 1720, "num_examples": 5}, {"name": "machinist", "num_bytes": 1696, "num_examples": 4}, {"name": "maid", "num_bytes": 1768, "num_examples": 7}, {"name": "manager", "num_bytes": 1696, "num_examples": 4}, {"name": "manicurist", "num_bytes": 1768, "num_examples": 7}, {"name": "market_research_analyst", "num_bytes": 1744, "num_examples": 6}, {"name": "marketing_manager", "num_bytes": 1672, "num_examples": 3}, {"name": "massage_therapist", "num_bytes": 1696, "num_examples": 4}, {"name": "mechanic", "num_bytes": 1672, "num_examples": 3}, {"name": "mechanical_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "medical_records_specialist", "num_bytes": 1696, "num_examples": 4}, {"name": "mental_health_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "metal_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "mover", "num_bytes": 1744, "num_examples": 6}, {"name": "musician", "num_bytes": 1720, "num_examples": 5}, {"name": "network_administrator", "num_bytes": 1624, "num_examples": 1}, {"name": "nurse", "num_bytes": 1672, "num_examples": 3}, {"name": "nursing_assistant", "num_bytes": 1672, "num_examples": 3}, {"name": "nutritionist", "num_bytes": 1672, "num_examples": 3}, {"name": "occupational_therapist", "num_bytes": 1696, "num_examples": 4}, {"name": "office_clerk", "num_bytes": 1648, "num_examples": 2}, {"name": "office_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "painter", "num_bytes": 1936, "num_examples": 14}, {"name": "paralegal", "num_bytes": 1672, "num_examples": 3}, {"name": "payroll_clerk", "num_bytes": 1696, "num_examples": 4}, {"name": "pharmacist", "num_bytes": 1720, "num_examples": 5}, {"name": "pharmacy_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "photographer", "num_bytes": 1816, "num_examples": 9}, {"name": "physical_therapist", "num_bytes": 1720, "num_examples": 5}, {"name": "pilot", "num_bytes": 1720, "num_examples": 5}, {"name": "plane_mechanic", "num_bytes": 1816, "num_examples": 9}, {"name": "plumber", "num_bytes": 1696, "num_examples": 4}, {"name": "police_officer", "num_bytes": 1792, "num_examples": 8}, {"name": "postal_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "printing_press_operator", "num_bytes": 1792, "num_examples": 8}, {"name": "producer", "num_bytes": 1816, "num_examples": 9}, {"name": "psychologist", "num_bytes": 1744, "num_examples": 6}, {"name": "public_relations_specialist", "num_bytes": 1672, "num_examples": 3}, {"name": "purchasing_agent", "num_bytes": 1720, "num_examples": 5}, {"name": "radiologic_technician", "num_bytes": 1792, "num_examples": 8}, {"name": "real_estate_broker", "num_bytes": 1672, "num_examples": 3}, {"name": "receptionist", "num_bytes": 1648, "num_examples": 2}, {"name": "repair_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1672, "num_examples": 3}, {"name": "salesperson", "num_bytes": 1696, "num_examples": 4}, {"name": "school_bus_driver", "num_bytes": 1888, "num_examples": 12}, {"name": "scientist", "num_bytes": 1744, "num_examples": 6}, {"name": "security_guard", "num_bytes": 1696, "num_examples": 4}, {"name": "sheet_metal_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "singer", "num_bytes": 1816, "num_examples": 9}, {"name": "social_assistant", "num_bytes": 1792, "num_examples": 8}, {"name": "social_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "software_developer", "num_bytes": 1648, "num_examples": 2}, {"name": "stocker", "num_bytes": 1792, "num_examples": 8}, {"name": "supervisor", "num_bytes": 1792, "num_examples": 8}, {"name": "taxi_driver", "num_bytes": 1720, "num_examples": 5}, {"name": "teacher", "num_bytes": 1744, "num_examples": 6}, {"name": "teaching_assistant", "num_bytes": 1744, "num_examples": 6}, {"name": "teller", "num_bytes": 1792, "num_examples": 8}, {"name": "therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "tractor_operator", "num_bytes": 1720, "num_examples": 5}, {"name": "truck_driver", "num_bytes": 1720, "num_examples": 5}, {"name": "tutor", "num_bytes": 1768, "num_examples": 7}, {"name": "underwriter", "num_bytes": 1696, "num_examples": 4}, {"name": "veterinarian", "num_bytes": 1648, "num_examples": 2}, {"name": "welder", "num_bytes": 1696, "num_examples": 4}, {"name": "wholesale_buyer", "num_bytes": 1840, "num_examples": 10}, {"name": "writer", "num_bytes": 1792, "num_examples": 8}], "download_size": 632814, "dataset_size": 253040}}
|
2023-06-04T22:36:42+00:00
|
59d5cd3182662967c57d62fd25b35ad319ccddcb
|
# Dataset Card for "prof_report__Lykon-DreamShaper__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__Lykon-DreamShaper__multi__24
|
[
"region:us"
] |
2023-06-04T22:40:37+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": "accountant", "num_bytes": 1768, "num_examples": 7}, {"name": "aerospace_engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "aide", "num_bytes": 1696, "num_examples": 4}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1792, "num_examples": 8}, {"name": "artist", "num_bytes": 1840, "num_examples": 10}, {"name": "author", "num_bytes": 1744, "num_examples": 6}, {"name": "baker", "num_bytes": 1864, "num_examples": 11}, {"name": "bartender", "num_bytes": 1744, "num_examples": 6}, {"name": "bus_driver", "num_bytes": 1768, "num_examples": 7}, {"name": "butcher", "num_bytes": 1720, "num_examples": 5}, {"name": "career_counselor", "num_bytes": 1696, "num_examples": 4}, {"name": "carpenter", "num_bytes": 1672, "num_examples": 3}, {"name": "carpet_installer", "num_bytes": 1720, "num_examples": 5}, {"name": "cashier", "num_bytes": 1696, "num_examples": 4}, {"name": "ceo", "num_bytes": 1792, "num_examples": 8}, {"name": "childcare_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "civil_engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "claims_appraiser", "num_bytes": 1744, "num_examples": 6}, {"name": "cleaner", "num_bytes": 1768, "num_examples": 7}, {"name": "clergy", "num_bytes": 1720, "num_examples": 5}, {"name": "clerk", "num_bytes": 1720, "num_examples": 5}, {"name": "coach", "num_bytes": 1696, "num_examples": 4}, {"name": "community_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "compliance_officer", "num_bytes": 1672, "num_examples": 3}, {"name": "computer_programmer", "num_bytes": 1768, "num_examples": 7}, {"name": "computer_support_specialist", "num_bytes": 1744, "num_examples": 6}, {"name": "computer_systems_analyst", "num_bytes": 1720, "num_examples": 5}, {"name": "construction_worker", "num_bytes": 1648, "num_examples": 2}, {"name": "cook", "num_bytes": 1720, "num_examples": 5}, {"name": "correctional_officer", "num_bytes": 1768, "num_examples": 7}, {"name": "courier", "num_bytes": 1816, "num_examples": 9}, {"name": "credit_counselor", "num_bytes": 1672, "num_examples": 3}, {"name": "customer_service_representative", "num_bytes": 1672, "num_examples": 3}, {"name": "data_entry_keyer", "num_bytes": 1744, "num_examples": 6}, {"name": "dental_assistant", "num_bytes": 1672, "num_examples": 3}, {"name": "dental_hygienist", "num_bytes": 1672, "num_examples": 3}, {"name": "dentist", "num_bytes": 1816, "num_examples": 9}, {"name": "designer", "num_bytes": 1792, "num_examples": 8}, {"name": "detective", "num_bytes": 1744, "num_examples": 6}, {"name": "director", "num_bytes": 1840, "num_examples": 10}, {"name": "dishwasher", "num_bytes": 1792, "num_examples": 8}, {"name": "dispatcher", "num_bytes": 1672, "num_examples": 3}, {"name": "doctor", "num_bytes": 1816, "num_examples": 9}, {"name": "drywall_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 1840, "num_examples": 10}, {"name": "electrician", "num_bytes": 1672, "num_examples": 3}, {"name": "engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "event_planner", "num_bytes": 1672, "num_examples": 3}, {"name": "executive_assistant", "num_bytes": 1672, "num_examples": 3}, {"name": "facilities_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "farmer", "num_bytes": 1648, "num_examples": 2}, {"name": "fast_food_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "file_clerk", "num_bytes": 1720, "num_examples": 5}, {"name": "financial_advisor", "num_bytes": 1672, "num_examples": 3}, {"name": "financial_analyst", "num_bytes": 1696, "num_examples": 4}, {"name": "financial_manager", "num_bytes": 1696, "num_examples": 4}, {"name": "firefighter", "num_bytes": 1648, "num_examples": 2}, {"name": "fitness_instructor", "num_bytes": 1672, "num_examples": 3}, {"name": "graphic_designer", "num_bytes": 1768, "num_examples": 7}, {"name": "groundskeeper", "num_bytes": 1696, "num_examples": 4}, {"name": "hairdresser", "num_bytes": 1792, "num_examples": 8}, {"name": "head_cook", "num_bytes": 1696, "num_examples": 4}, {"name": "health_technician", "num_bytes": 1744, "num_examples": 6}, {"name": "industrial_engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "insurance_agent", "num_bytes": 1696, "num_examples": 4}, {"name": "interior_designer", "num_bytes": 1672, "num_examples": 3}, {"name": "interviewer", "num_bytes": 1696, "num_examples": 4}, {"name": "inventory_clerk", "num_bytes": 1816, "num_examples": 9}, {"name": "it_specialist", "num_bytes": 1672, "num_examples": 3}, {"name": "jailer", "num_bytes": 1744, "num_examples": 6}, {"name": "janitor", "num_bytes": 1720, "num_examples": 5}, {"name": "laboratory_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "language_pathologist", "num_bytes": 1696, "num_examples": 4}, {"name": "lawyer", "num_bytes": 1720, "num_examples": 5}, {"name": "librarian", "num_bytes": 1672, "num_examples": 3}, {"name": "logistician", "num_bytes": 1744, "num_examples": 6}, {"name": "machinery_mechanic", "num_bytes": 1720, "num_examples": 5}, {"name": "machinist", "num_bytes": 1744, "num_examples": 6}, {"name": "maid", "num_bytes": 1768, "num_examples": 7}, {"name": "manager", "num_bytes": 1720, "num_examples": 5}, {"name": "manicurist", "num_bytes": 1768, "num_examples": 7}, {"name": "market_research_analyst", "num_bytes": 1720, "num_examples": 5}, {"name": "marketing_manager", "num_bytes": 1696, "num_examples": 4}, {"name": "massage_therapist", "num_bytes": 1672, "num_examples": 3}, {"name": "mechanic", "num_bytes": 1648, "num_examples": 2}, {"name": "mechanical_engineer", "num_bytes": 1816, "num_examples": 9}, {"name": "medical_records_specialist", "num_bytes": 1720, "num_examples": 5}, {"name": "mental_health_counselor", "num_bytes": 1744, "num_examples": 6}, {"name": "metal_worker", "num_bytes": 1648, "num_examples": 2}, {"name": "mover", "num_bytes": 1768, "num_examples": 7}, {"name": "musician", "num_bytes": 1720, "num_examples": 5}, {"name": "network_administrator", "num_bytes": 1624, "num_examples": 1}, {"name": "nurse", "num_bytes": 1720, "num_examples": 5}, {"name": "nursing_assistant", "num_bytes": 1672, "num_examples": 3}, {"name": "nutritionist", "num_bytes": 1672, "num_examples": 3}, {"name": "occupational_therapist", "num_bytes": 1696, "num_examples": 4}, {"name": "office_clerk", "num_bytes": 1696, "num_examples": 4}, {"name": "office_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "painter", "num_bytes": 1816, "num_examples": 9}, {"name": "paralegal", "num_bytes": 1648, "num_examples": 2}, {"name": "payroll_clerk", "num_bytes": 1648, "num_examples": 2}, {"name": "pharmacist", "num_bytes": 1696, "num_examples": 4}, {"name": "pharmacy_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "photographer", "num_bytes": 1792, "num_examples": 8}, {"name": "physical_therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "pilot", "num_bytes": 1744, "num_examples": 6}, {"name": "plane_mechanic", "num_bytes": 1768, "num_examples": 7}, {"name": "plumber", "num_bytes": 1648, "num_examples": 2}, {"name": "police_officer", "num_bytes": 1768, "num_examples": 7}, {"name": "postal_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "printing_press_operator", "num_bytes": 1720, "num_examples": 5}, {"name": "producer", "num_bytes": 1816, "num_examples": 9}, {"name": "psychologist", "num_bytes": 1768, "num_examples": 7}, {"name": "public_relations_specialist", "num_bytes": 1648, "num_examples": 2}, {"name": "purchasing_agent", "num_bytes": 1696, "num_examples": 4}, {"name": "radiologic_technician", "num_bytes": 1840, "num_examples": 10}, {"name": "real_estate_broker", "num_bytes": 1696, "num_examples": 4}, {"name": "receptionist", "num_bytes": 1672, "num_examples": 3}, {"name": "repair_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "salesperson", "num_bytes": 1720, "num_examples": 5}, {"name": "school_bus_driver", "num_bytes": 1864, "num_examples": 11}, {"name": "scientist", "num_bytes": 1696, "num_examples": 4}, {"name": "security_guard", "num_bytes": 1720, "num_examples": 5}, {"name": "sheet_metal_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "singer", "num_bytes": 1768, "num_examples": 7}, {"name": "social_assistant", "num_bytes": 1720, "num_examples": 5}, {"name": "social_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "software_developer", "num_bytes": 1648, "num_examples": 2}, {"name": "stocker", "num_bytes": 1888, "num_examples": 12}, {"name": "supervisor", "num_bytes": 1816, "num_examples": 9}, {"name": "taxi_driver", "num_bytes": 1744, "num_examples": 6}, {"name": "teacher", "num_bytes": 1720, "num_examples": 5}, {"name": "teaching_assistant", "num_bytes": 1720, "num_examples": 5}, {"name": "teller", "num_bytes": 1792, "num_examples": 8}, {"name": "therapist", "num_bytes": 1672, "num_examples": 3}, {"name": "tractor_operator", "num_bytes": 1744, "num_examples": 6}, {"name": "truck_driver", "num_bytes": 1696, "num_examples": 4}, {"name": "tutor", "num_bytes": 1672, "num_examples": 3}, {"name": "underwriter", "num_bytes": 1696, "num_examples": 4}, {"name": "veterinarian", "num_bytes": 1648, "num_examples": 2}, {"name": "welder", "num_bytes": 1648, "num_examples": 2}, {"name": "wholesale_buyer", "num_bytes": 1864, "num_examples": 11}, {"name": "writer", "num_bytes": 1768, "num_examples": 7}], "download_size": 631776, "dataset_size": 252200}}
|
2023-06-04T22:42:00+00:00
|
83038cd0cfec03fee21a696d732aed1bcbee6b11
|
ddgpath/rcc
|
[
"license:bigscience-openrail-m",
"region:us"
] |
2023-06-04T22:44:42+00:00
|
{"license": "bigscience-openrail-m"}
|
2023-06-04T22:44:42+00:00
|
|
27da9e188abe8f9c053dea473b646727316b532f
|
# Dataset Card for "prof_report__wavymulder-Analog-Diffusion__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__wavymulder-Analog-Diffusion__multi__24
|
[
"region:us"
] |
2023-06-04T22:45:46+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": "accountant", "num_bytes": 1864, "num_examples": 11}, {"name": "aerospace_engineer", "num_bytes": 1888, "num_examples": 12}, {"name": "aide", "num_bytes": 2008, "num_examples": 17}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1864, "num_examples": 11}, {"name": "artist", "num_bytes": 1840, "num_examples": 10}, {"name": "author", "num_bytes": 1792, "num_examples": 8}, {"name": "baker", "num_bytes": 1888, "num_examples": 12}, {"name": "bartender", "num_bytes": 1888, "num_examples": 12}, {"name": "bus_driver", "num_bytes": 1912, "num_examples": 13}, {"name": "butcher", "num_bytes": 1792, "num_examples": 8}, {"name": "career_counselor", "num_bytes": 1816, "num_examples": 9}, {"name": "carpenter", "num_bytes": 1720, "num_examples": 5}, {"name": "carpet_installer", "num_bytes": 1720, "num_examples": 5}, {"name": "cashier", "num_bytes": 1792, "num_examples": 8}, {"name": "ceo", "num_bytes": 1888, "num_examples": 12}, {"name": "childcare_worker", "num_bytes": 1864, "num_examples": 11}, {"name": "civil_engineer", "num_bytes": 1840, "num_examples": 10}, {"name": "claims_appraiser", "num_bytes": 1720, "num_examples": 5}, {"name": "cleaner", "num_bytes": 1864, "num_examples": 11}, {"name": "clergy", "num_bytes": 1936, "num_examples": 14}, {"name": "clerk", "num_bytes": 2104, "num_examples": 21}, {"name": "coach", "num_bytes": 1840, "num_examples": 10}, {"name": "community_manager", "num_bytes": 1840, "num_examples": 10}, {"name": "compliance_officer", "num_bytes": 1912, "num_examples": 13}, {"name": "computer_programmer", "num_bytes": 1840, "num_examples": 10}, {"name": "computer_support_specialist", "num_bytes": 1888, "num_examples": 12}, {"name": "computer_systems_analyst", "num_bytes": 1840, "num_examples": 10}, {"name": "construction_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "cook", "num_bytes": 1864, "num_examples": 11}, {"name": "correctional_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "courier", "num_bytes": 1960, "num_examples": 15}, {"name": "credit_counselor", "num_bytes": 1816, "num_examples": 9}, {"name": "customer_service_representative", "num_bytes": 1768, "num_examples": 7}, {"name": "data_entry_keyer", "num_bytes": 1840, "num_examples": 10}, {"name": "dental_assistant", "num_bytes": 1720, "num_examples": 5}, {"name": "dental_hygienist", "num_bytes": 1768, "num_examples": 7}, {"name": "dentist", "num_bytes": 1864, "num_examples": 11}, {"name": "designer", "num_bytes": 1840, "num_examples": 10}, {"name": "detective", "num_bytes": 1912, "num_examples": 13}, {"name": "director", "num_bytes": 1864, "num_examples": 11}, {"name": "dishwasher", "num_bytes": 1936, "num_examples": 14}, {"name": "dispatcher", "num_bytes": 1864, "num_examples": 11}, {"name": "doctor", "num_bytes": 1912, "num_examples": 13}, {"name": "drywall_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "electrical_engineer", "num_bytes": 1888, "num_examples": 12}, {"name": "electrician", "num_bytes": 1768, "num_examples": 7}, {"name": "engineer", "num_bytes": 1840, "num_examples": 10}, {"name": "event_planner", "num_bytes": 1720, "num_examples": 5}, {"name": "executive_assistant", "num_bytes": 1792, "num_examples": 8}, {"name": "facilities_manager", "num_bytes": 1840, "num_examples": 10}, {"name": "farmer", "num_bytes": 1792, "num_examples": 8}, {"name": "fast_food_worker", "num_bytes": 1912, "num_examples": 13}, {"name": "file_clerk", "num_bytes": 1912, "num_examples": 13}, {"name": "financial_advisor", "num_bytes": 1720, "num_examples": 5}, {"name": "financial_analyst", "num_bytes": 1840, "num_examples": 10}, {"name": "financial_manager", "num_bytes": 1864, "num_examples": 11}, {"name": "firefighter", "num_bytes": 1720, "num_examples": 5}, {"name": "fitness_instructor", "num_bytes": 1792, "num_examples": 8}, {"name": "graphic_designer", "num_bytes": 1840, "num_examples": 10}, {"name": "groundskeeper", "num_bytes": 1720, "num_examples": 5}, {"name": "hairdresser", "num_bytes": 1864, "num_examples": 11}, {"name": "head_cook", "num_bytes": 1816, "num_examples": 9}, {"name": "health_technician", "num_bytes": 1888, "num_examples": 12}, {"name": "industrial_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "insurance_agent", "num_bytes": 1912, "num_examples": 13}, {"name": "interior_designer", "num_bytes": 1792, "num_examples": 8}, {"name": "interviewer", "num_bytes": 1888, "num_examples": 12}, {"name": "inventory_clerk", "num_bytes": 1936, "num_examples": 14}, {"name": "it_specialist", "num_bytes": 1720, "num_examples": 5}, {"name": "jailer", "num_bytes": 1912, "num_examples": 13}, {"name": "janitor", "num_bytes": 1912, "num_examples": 13}, {"name": "laboratory_technician", "num_bytes": 1936, "num_examples": 14}, {"name": "language_pathologist", "num_bytes": 1888, "num_examples": 12}, {"name": "lawyer", "num_bytes": 1912, "num_examples": 13}, {"name": "librarian", "num_bytes": 1792, "num_examples": 8}, {"name": "logistician", "num_bytes": 1912, "num_examples": 13}, {"name": "machinery_mechanic", "num_bytes": 1720, "num_examples": 5}, {"name": "machinist", "num_bytes": 1816, "num_examples": 9}, {"name": "maid", "num_bytes": 1912, "num_examples": 13}, {"name": "manager", "num_bytes": 1888, "num_examples": 12}, {"name": "manicurist", "num_bytes": 1840, "num_examples": 10}, {"name": "market_research_analyst", "num_bytes": 1816, "num_examples": 9}, {"name": "marketing_manager", "num_bytes": 1816, "num_examples": 9}, {"name": "massage_therapist", "num_bytes": 1816, "num_examples": 9}, {"name": "mechanic", "num_bytes": 1816, "num_examples": 9}, {"name": "mechanical_engineer", "num_bytes": 1840, "num_examples": 10}, {"name": "medical_records_specialist", "num_bytes": 1840, "num_examples": 10}, {"name": "mental_health_counselor", "num_bytes": 1816, "num_examples": 9}, {"name": "metal_worker", "num_bytes": 1792, "num_examples": 8}, {"name": "mover", "num_bytes": 1936, "num_examples": 14}, {"name": "musician", "num_bytes": 1960, "num_examples": 15}, {"name": "network_administrator", "num_bytes": 1696, "num_examples": 4}, {"name": "nurse", "num_bytes": 1840, "num_examples": 10}, {"name": "nursing_assistant", "num_bytes": 1768, "num_examples": 7}, {"name": "nutritionist", "num_bytes": 1720, "num_examples": 5}, {"name": "occupational_therapist", "num_bytes": 1840, "num_examples": 10}, {"name": "office_clerk", "num_bytes": 1888, "num_examples": 12}, {"name": "office_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "painter", "num_bytes": 1888, "num_examples": 12}, {"name": "paralegal", "num_bytes": 1936, "num_examples": 14}, {"name": "payroll_clerk", "num_bytes": 1864, "num_examples": 11}, {"name": "pharmacist", "num_bytes": 1864, "num_examples": 11}, {"name": "pharmacy_technician", "num_bytes": 1744, "num_examples": 6}, {"name": "photographer", "num_bytes": 1936, "num_examples": 14}, {"name": "physical_therapist", "num_bytes": 1840, "num_examples": 10}, {"name": "pilot", "num_bytes": 1960, "num_examples": 15}, {"name": "plane_mechanic", "num_bytes": 1840, "num_examples": 10}, {"name": "plumber", "num_bytes": 1768, "num_examples": 7}, {"name": "police_officer", "num_bytes": 1792, "num_examples": 8}, {"name": "postal_worker", "num_bytes": 1936, "num_examples": 14}, {"name": "printing_press_operator", "num_bytes": 1888, "num_examples": 12}, {"name": "producer", "num_bytes": 1888, "num_examples": 12}, {"name": "psychologist", "num_bytes": 1864, "num_examples": 11}, {"name": "public_relations_specialist", "num_bytes": 1792, "num_examples": 8}, {"name": "purchasing_agent", "num_bytes": 1936, "num_examples": 14}, {"name": "radiologic_technician", "num_bytes": 1888, "num_examples": 12}, {"name": "real_estate_broker", "num_bytes": 1744, "num_examples": 6}, {"name": "receptionist", "num_bytes": 1720, "num_examples": 5}, {"name": "repair_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "roofer", "num_bytes": 1744, "num_examples": 6}, {"name": "sales_manager", "num_bytes": 1768, "num_examples": 7}, {"name": "salesperson", "num_bytes": 1840, "num_examples": 10}, {"name": "school_bus_driver", "num_bytes": 1984, "num_examples": 16}, {"name": "scientist", "num_bytes": 1912, "num_examples": 13}, {"name": "security_guard", "num_bytes": 1720, "num_examples": 5}, {"name": "sheet_metal_worker", "num_bytes": 1792, "num_examples": 8}, {"name": "singer", "num_bytes": 1912, "num_examples": 13}, {"name": "social_assistant", "num_bytes": 2008, "num_examples": 17}, {"name": "social_worker", "num_bytes": 1912, "num_examples": 13}, {"name": "software_developer", "num_bytes": 1768, "num_examples": 7}, {"name": "stocker", "num_bytes": 1912, "num_examples": 13}, {"name": "supervisor", "num_bytes": 1936, "num_examples": 14}, {"name": "taxi_driver", "num_bytes": 1864, "num_examples": 11}, {"name": "teacher", "num_bytes": 2032, "num_examples": 18}, {"name": "teaching_assistant", "num_bytes": 1840, "num_examples": 10}, {"name": "teller", "num_bytes": 1960, "num_examples": 15}, {"name": "therapist", "num_bytes": 1816, "num_examples": 9}, {"name": "tractor_operator", "num_bytes": 1744, "num_examples": 6}, {"name": "truck_driver", "num_bytes": 1792, "num_examples": 8}, {"name": "tutor", "num_bytes": 1936, "num_examples": 14}, {"name": "underwriter", "num_bytes": 1840, "num_examples": 10}, {"name": "veterinarian", "num_bytes": 1792, "num_examples": 8}, {"name": "welder", "num_bytes": 1816, "num_examples": 9}, {"name": "wholesale_buyer", "num_bytes": 1840, "num_examples": 10}, {"name": "writer", "num_bytes": 1888, "num_examples": 12}], "download_size": 638852, "dataset_size": 269360}}
|
2023-06-04T22:47:14+00:00
|
766426e4fa2759c51adacce843171c6b552f2a7a
|
# Dataset Card for "prof_report__andite-pastel-mix__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__andite-pastel-mix__multi__24
|
[
"region:us"
] |
2023-06-04T22:51:00+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": "accountant", "num_bytes": 1744, "num_examples": 6}, {"name": "aerospace_engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "aide", "num_bytes": 1720, "num_examples": 5}, {"name": "air_conditioning_installer", "num_bytes": 1768, "num_examples": 7}, {"name": "architect", "num_bytes": 1768, "num_examples": 7}, {"name": "artist", "num_bytes": 1792, "num_examples": 8}, {"name": "author", "num_bytes": 1744, "num_examples": 6}, {"name": "baker", "num_bytes": 1720, "num_examples": 5}, {"name": "bartender", "num_bytes": 1696, "num_examples": 4}, {"name": "bus_driver", "num_bytes": 1720, "num_examples": 5}, {"name": "butcher", "num_bytes": 1888, "num_examples": 12}, {"name": "career_counselor", "num_bytes": 1768, "num_examples": 7}, {"name": "carpenter", "num_bytes": 1816, "num_examples": 9}, {"name": "carpet_installer", "num_bytes": 1816, "num_examples": 9}, {"name": "cashier", "num_bytes": 1744, "num_examples": 6}, {"name": "ceo", "num_bytes": 1696, "num_examples": 4}, {"name": "childcare_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "civil_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "claims_appraiser", "num_bytes": 1792, "num_examples": 8}, {"name": "cleaner", "num_bytes": 1792, "num_examples": 8}, {"name": "clergy", "num_bytes": 1720, "num_examples": 5}, {"name": "clerk", "num_bytes": 1744, "num_examples": 6}, {"name": "coach", "num_bytes": 1744, "num_examples": 6}, {"name": "community_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "compliance_officer", "num_bytes": 1744, "num_examples": 6}, {"name": "computer_programmer", "num_bytes": 1744, "num_examples": 6}, {"name": "computer_support_specialist", "num_bytes": 1792, "num_examples": 8}, {"name": "computer_systems_analyst", "num_bytes": 1696, "num_examples": 4}, {"name": "construction_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "cook", "num_bytes": 1720, "num_examples": 5}, {"name": "correctional_officer", "num_bytes": 1768, "num_examples": 7}, {"name": "courier", "num_bytes": 1720, "num_examples": 5}, {"name": "credit_counselor", "num_bytes": 1536, "num_examples": 4}, {"name": "customer_service_representative", "num_bytes": 1744, "num_examples": 6}, {"name": "data_entry_keyer", "num_bytes": 1744, "num_examples": 6}, {"name": "dental_assistant", "num_bytes": 1768, "num_examples": 7}, {"name": "dental_hygienist", "num_bytes": 1696, "num_examples": 4}, {"name": "dentist", "num_bytes": 1744, "num_examples": 6}, {"name": "designer", "num_bytes": 1744, "num_examples": 6}, {"name": "detective", "num_bytes": 1768, "num_examples": 7}, {"name": "director", "num_bytes": 1720, "num_examples": 5}, {"name": "dishwasher", "num_bytes": 1768, "num_examples": 7}, {"name": "dispatcher", "num_bytes": 1720, "num_examples": 5}, {"name": "doctor", "num_bytes": 1720, "num_examples": 5}, {"name": "drywall_installer", "num_bytes": 1720, "num_examples": 5}, {"name": "electrical_engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "electrician", "num_bytes": 1768, "num_examples": 7}, {"name": "engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "event_planner", "num_bytes": 1720, "num_examples": 5}, {"name": "executive_assistant", "num_bytes": 1720, "num_examples": 5}, {"name": "facilities_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "farmer", "num_bytes": 1696, "num_examples": 4}, {"name": "fast_food_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "file_clerk", "num_bytes": 1720, "num_examples": 5}, {"name": "financial_advisor", "num_bytes": 1792, "num_examples": 8}, {"name": "financial_analyst", "num_bytes": 1768, "num_examples": 7}, {"name": "financial_manager", "num_bytes": 1696, "num_examples": 4}, {"name": "firefighter", "num_bytes": 1720, "num_examples": 5}, {"name": "fitness_instructor", "num_bytes": 1744, "num_examples": 6}, {"name": "graphic_designer", "num_bytes": 1816, "num_examples": 9}, {"name": "groundskeeper", "num_bytes": 1768, "num_examples": 7}, {"name": "hairdresser", "num_bytes": 1816, "num_examples": 9}, {"name": "head_cook", "num_bytes": 1720, "num_examples": 5}, {"name": "health_technician", "num_bytes": 1744, "num_examples": 6}, {"name": "industrial_engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "insurance_agent", "num_bytes": 1744, "num_examples": 6}, {"name": "interior_designer", "num_bytes": 1744, "num_examples": 6}, {"name": "interviewer", "num_bytes": 1696, "num_examples": 4}, {"name": "inventory_clerk", "num_bytes": 1744, "num_examples": 6}, {"name": "it_specialist", "num_bytes": 1792, "num_examples": 8}, {"name": "jailer", "num_bytes": 1768, "num_examples": 7}, {"name": "janitor", "num_bytes": 1768, "num_examples": 7}, {"name": "laboratory_technician", "num_bytes": 1744, "num_examples": 6}, {"name": "language_pathologist", "num_bytes": 1696, "num_examples": 4}, {"name": "lawyer", "num_bytes": 1744, "num_examples": 6}, {"name": "librarian", "num_bytes": 1696, "num_examples": 4}, {"name": "logistician", "num_bytes": 1768, "num_examples": 7}, {"name": "machinery_mechanic", "num_bytes": 1816, "num_examples": 9}, {"name": "machinist", "num_bytes": 1720, "num_examples": 5}, {"name": "maid", "num_bytes": 1720, "num_examples": 5}, {"name": "manager", "num_bytes": 1744, "num_examples": 6}, {"name": "manicurist", "num_bytes": 1696, "num_examples": 4}, {"name": "market_research_analyst", "num_bytes": 1672, "num_examples": 3}, {"name": "marketing_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "massage_therapist", "num_bytes": 1768, "num_examples": 7}, {"name": "mechanic", "num_bytes": 1888, "num_examples": 12}, {"name": "mechanical_engineer", "num_bytes": 1840, "num_examples": 10}, {"name": "medical_records_specialist", "num_bytes": 1768, "num_examples": 7}, {"name": "mental_health_counselor", "num_bytes": 1744, "num_examples": 6}, {"name": "metal_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "mover", "num_bytes": 1840, "num_examples": 10}, {"name": "musician", "num_bytes": 1768, "num_examples": 7}, {"name": "network_administrator", "num_bytes": 1768, "num_examples": 7}, {"name": "nurse", "num_bytes": 1696, "num_examples": 4}, {"name": "nursing_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 1696, "num_examples": 4}, {"name": "occupational_therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "office_clerk", "num_bytes": 1720, "num_examples": 5}, {"name": "office_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "painter", "num_bytes": 1768, "num_examples": 7}, {"name": "paralegal", "num_bytes": 1720, "num_examples": 5}, {"name": "payroll_clerk", "num_bytes": 1744, "num_examples": 6}, {"name": "pharmacist", "num_bytes": 1720, "num_examples": 5}, {"name": "pharmacy_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "photographer", "num_bytes": 1768, "num_examples": 7}, {"name": "physical_therapist", "num_bytes": 1792, "num_examples": 8}, {"name": "pilot", "num_bytes": 1696, "num_examples": 4}, {"name": "plane_mechanic", "num_bytes": 1816, "num_examples": 9}, {"name": "plumber", "num_bytes": 1768, "num_examples": 7}, {"name": "police_officer", "num_bytes": 1792, "num_examples": 8}, {"name": "postal_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "printing_press_operator", "num_bytes": 1840, "num_examples": 10}, {"name": "producer", "num_bytes": 1720, "num_examples": 5}, {"name": "psychologist", "num_bytes": 1648, "num_examples": 2}, {"name": "public_relations_specialist", "num_bytes": 1696, "num_examples": 4}, {"name": "purchasing_agent", "num_bytes": 1696, "num_examples": 4}, {"name": "radiologic_technician", "num_bytes": 1696, "num_examples": 4}, {"name": "real_estate_broker", "num_bytes": 1792, "num_examples": 8}, {"name": "receptionist", "num_bytes": 1792, "num_examples": 8}, {"name": "repair_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "roofer", "num_bytes": 1744, "num_examples": 6}, {"name": "sales_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "salesperson", "num_bytes": 1744, "num_examples": 6}, {"name": "school_bus_driver", "num_bytes": 1768, "num_examples": 7}, {"name": "scientist", "num_bytes": 1696, "num_examples": 4}, {"name": "security_guard", "num_bytes": 1792, "num_examples": 8}, {"name": "sheet_metal_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "singer", "num_bytes": 1744, "num_examples": 6}, {"name": "social_assistant", "num_bytes": 1720, "num_examples": 5}, {"name": "social_worker", "num_bytes": 1672, "num_examples": 3}, {"name": "software_developer", "num_bytes": 1768, "num_examples": 7}, {"name": "stocker", "num_bytes": 1696, "num_examples": 4}, {"name": "supervisor", "num_bytes": 1768, "num_examples": 7}, {"name": "taxi_driver", "num_bytes": 1680, "num_examples": 10}, {"name": "teacher", "num_bytes": 1696, "num_examples": 4}, {"name": "teaching_assistant", "num_bytes": 1672, "num_examples": 3}, {"name": "teller", "num_bytes": 1696, "num_examples": 4}, {"name": "therapist", "num_bytes": 1696, "num_examples": 4}, {"name": "tractor_operator", "num_bytes": 1840, "num_examples": 10}, {"name": "truck_driver", "num_bytes": 1816, "num_examples": 9}, {"name": "tutor", "num_bytes": 1720, "num_examples": 5}, {"name": "underwriter", "num_bytes": 1744, "num_examples": 6}, {"name": "veterinarian", "num_bytes": 1768, "num_examples": 7}, {"name": "welder", "num_bytes": 1816, "num_examples": 9}, {"name": "wholesale_buyer", "num_bytes": 1672, "num_examples": 3}, {"name": "writer", "num_bytes": 1768, "num_examples": 7}], "download_size": 633389, "dataset_size": 254856}}
|
2023-06-04T22:52:20+00:00
|
9b9430fa383972abd2c7aa8c039d7e5dc5f004ad
|
# Dataset Card for "MedQA-USMLE-4-options-hf-DBPedia-context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GBaker/MedQA-USMLE-4-options-hf-DBPedia-context
|
[
"region:us"
] |
2023-06-04T22:53:05+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sent1", "dtype": "string"}, {"name": "sent2", "dtype": "string"}, {"name": "ending0", "dtype": "string"}, {"name": "ending1", "dtype": "string"}, {"name": "ending2", "dtype": "string"}, {"name": "ending3", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 3472206, "num_examples": 1273}], "download_size": 1928988, "dataset_size": 3472206}}
|
2023-06-04T22:53:05+00:00
|
b699add3f83d85063f0cb35dafa5d96816aa7fa6
|
# Dataset Card for "prof_report__andite-anything-v4.0__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__andite-anything-v4.0__multi__24
|
[
"region:us"
] |
2023-06-04T22:54:05+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": "accountant", "num_bytes": 1744, "num_examples": 6}, {"name": "aerospace_engineer", "num_bytes": 1672, "num_examples": 3}, {"name": "aide", "num_bytes": 1720, "num_examples": 5}, {"name": "air_conditioning_installer", "num_bytes": 1792, "num_examples": 8}, {"name": "architect", "num_bytes": 1720, "num_examples": 5}, {"name": "artist", "num_bytes": 1720, "num_examples": 5}, {"name": "author", "num_bytes": 1720, "num_examples": 5}, {"name": "baker", "num_bytes": 1792, "num_examples": 8}, {"name": "bartender", "num_bytes": 1744, "num_examples": 6}, {"name": "bus_driver", "num_bytes": 1720, "num_examples": 5}, {"name": "butcher", "num_bytes": 1768, "num_examples": 7}, {"name": "career_counselor", "num_bytes": 1696, "num_examples": 4}, {"name": "carpenter", "num_bytes": 1792, "num_examples": 8}, {"name": "carpet_installer", "num_bytes": 1816, "num_examples": 9}, {"name": "cashier", "num_bytes": 1696, "num_examples": 4}, {"name": "ceo", "num_bytes": 1720, "num_examples": 5}, {"name": "childcare_worker", "num_bytes": 1648, "num_examples": 2}, {"name": "civil_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "claims_appraiser", "num_bytes": 1816, "num_examples": 9}, {"name": "cleaner", "num_bytes": 1672, "num_examples": 3}, {"name": "clergy", "num_bytes": 1720, "num_examples": 5}, {"name": "clerk", "num_bytes": 1744, "num_examples": 6}, {"name": "coach", "num_bytes": 1768, "num_examples": 7}, {"name": "community_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "compliance_officer", "num_bytes": 1696, "num_examples": 4}, {"name": "computer_programmer", "num_bytes": 1792, "num_examples": 8}, {"name": "computer_support_specialist", "num_bytes": 1744, "num_examples": 6}, {"name": "computer_systems_analyst", "num_bytes": 1744, "num_examples": 6}, {"name": "construction_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "cook", "num_bytes": 1768, "num_examples": 7}, {"name": "correctional_officer", "num_bytes": 1696, "num_examples": 4}, {"name": "courier", "num_bytes": 1696, "num_examples": 4}, {"name": "credit_counselor", "num_bytes": 1720, "num_examples": 5}, {"name": "customer_service_representative", "num_bytes": 1744, "num_examples": 6}, {"name": "data_entry_keyer", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 1720, "num_examples": 5}, {"name": "dentist", "num_bytes": 1720, "num_examples": 5}, {"name": "designer", "num_bytes": 1768, "num_examples": 7}, {"name": "detective", "num_bytes": 1720, "num_examples": 5}, {"name": "director", "num_bytes": 1744, "num_examples": 6}, {"name": "dishwasher", "num_bytes": 1720, "num_examples": 5}, {"name": "dispatcher", "num_bytes": 1720, "num_examples": 5}, {"name": "doctor", "num_bytes": 1720, "num_examples": 5}, {"name": "drywall_installer", "num_bytes": 1768, "num_examples": 7}, {"name": "electrical_engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "electrician", "num_bytes": 1720, "num_examples": 5}, {"name": "engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "event_planner", "num_bytes": 1768, "num_examples": 7}, {"name": "executive_assistant", "num_bytes": 1744, "num_examples": 6}, {"name": "facilities_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "farmer", "num_bytes": 1792, "num_examples": 8}, {"name": "fast_food_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "file_clerk", "num_bytes": 1696, "num_examples": 4}, {"name": "financial_advisor", "num_bytes": 1792, "num_examples": 8}, {"name": "financial_analyst", "num_bytes": 1768, "num_examples": 7}, {"name": "financial_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "firefighter", "num_bytes": 1792, "num_examples": 8}, {"name": "fitness_instructor", "num_bytes": 1768, "num_examples": 7}, {"name": "graphic_designer", "num_bytes": 1792, "num_examples": 8}, {"name": "groundskeeper", "num_bytes": 1840, "num_examples": 10}, {"name": "hairdresser", "num_bytes": 1768, "num_examples": 7}, {"name": "head_cook", "num_bytes": 1840, "num_examples": 10}, {"name": "health_technician", "num_bytes": 1696, "num_examples": 4}, {"name": "industrial_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "insurance_agent", "num_bytes": 1696, "num_examples": 4}, {"name": "interior_designer", "num_bytes": 1792, "num_examples": 8}, {"name": "interviewer", "num_bytes": 1744, "num_examples": 6}, {"name": "inventory_clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "it_specialist", "num_bytes": 1696, "num_examples": 4}, {"name": "jailer", "num_bytes": 1696, "num_examples": 4}, {"name": "janitor", "num_bytes": 1720, "num_examples": 5}, {"name": "laboratory_technician", "num_bytes": 1696, "num_examples": 4}, {"name": "language_pathologist", "num_bytes": 1032, "num_examples": 3}], "download_size": 307031, "dataset_size": 128344}}
|
2023-06-04T22:54:47+00:00
|
313a619be59479d3297779e4c53ec9e4c6433600
|
# Dataset Card for "cubirds200_bbcrop"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/cubirds200_bbcrop
|
[
"region:us"
] |
2023-06-04T22:54:48+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 669852245.5, "num_examples": 8204}, {"name": "validation", "num_bytes": 141492702.625, "num_examples": 1771}, {"name": "test", "num_bytes": 146984302.75, "num_examples": 1770}], "download_size": 958308742, "dataset_size": 958329250.875}}
|
2023-06-04T22:55:39+00:00
|
003a9c033cb85c6b28f512854dcda7e08602616f
|
marcelarosalesj/inria-person
|
[
"region:us"
] |
2023-06-04T22:56:03+00:00
|
{}
|
2023-06-04T22:57:50+00:00
|
|
621e69b3c0f3bd97449783f1c90b51b07f8084dc
|
# Dataset Card for "describable_textures"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/describable_textures
|
[
"region:us"
] |
2023-06-04T22:57:38+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 350355304.0, "num_examples": 3960}, {"name": "validation", "num_bytes": 72331220.0, "num_examples": 840}, {"name": "test", "num_bytes": 73428430.0, "num_examples": 840}], "download_size": 0, "dataset_size": 496114954.0}}
|
2023-06-05T16:13:02+00:00
|
9b90db5bf125f3f8cd84f377da3b1503df922406
|
# Dataset Card for "prof_report__CompVis-stable-diffusion-v1-4__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__CompVis-stable-diffusion-v1-4__multi__24
|
[
"region:us"
] |
2023-06-04T23:00:05+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": "accountant", "num_bytes": 1768, "num_examples": 7}, {"name": "aerospace_engineer", "num_bytes": 1840, "num_examples": 10}, {"name": "aide", "num_bytes": 1792, "num_examples": 8}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1792, "num_examples": 8}, {"name": "artist", "num_bytes": 1960, "num_examples": 15}, {"name": "author", "num_bytes": 1792, "num_examples": 8}, {"name": "baker", "num_bytes": 1656, "num_examples": 9}, {"name": "bartender", "num_bytes": 1720, "num_examples": 5}, {"name": "bus_driver", "num_bytes": 1912, "num_examples": 13}, {"name": "butcher", "num_bytes": 1768, "num_examples": 7}, {"name": "career_counselor", "num_bytes": 1768, "num_examples": 7}, {"name": "carpenter", "num_bytes": 1744, "num_examples": 6}, {"name": "carpet_installer", "num_bytes": 1720, "num_examples": 5}, {"name": "cashier", "num_bytes": 1744, "num_examples": 6}, {"name": "ceo", "num_bytes": 1680, "num_examples": 10}, {"name": "childcare_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "civil_engineer", "num_bytes": 1720, "num_examples": 5}, {"name": "claims_appraiser", "num_bytes": 1744, "num_examples": 6}, {"name": "cleaner", "num_bytes": 1912, "num_examples": 13}, {"name": "clergy", "num_bytes": 1792, "num_examples": 8}, {"name": "clerk", "num_bytes": 1912, "num_examples": 13}, {"name": "coach", "num_bytes": 1840, "num_examples": 10}, {"name": "community_manager", "num_bytes": 1768, "num_examples": 7}, {"name": "compliance_officer", "num_bytes": 1792, "num_examples": 8}, {"name": "computer_programmer", "num_bytes": 1864, "num_examples": 11}, {"name": "computer_support_specialist", "num_bytes": 1744, "num_examples": 6}, {"name": "computer_systems_analyst", "num_bytes": 1888, "num_examples": 12}, {"name": "construction_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "cook", "num_bytes": 1840, "num_examples": 10}, {"name": "correctional_officer", "num_bytes": 1864, "num_examples": 11}, {"name": "courier", "num_bytes": 1912, "num_examples": 13}, {"name": "credit_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "customer_service_representative", "num_bytes": 1792, "num_examples": 8}, {"name": "data_entry_keyer", "num_bytes": 1768, "num_examples": 7}, {"name": "dental_assistant", "num_bytes": 1720, "num_examples": 5}, {"name": "dental_hygienist", "num_bytes": 1696, "num_examples": 4}, {"name": "dentist", "num_bytes": 1840, "num_examples": 10}, {"name": "designer", "num_bytes": 1888, "num_examples": 12}, {"name": "detective", "num_bytes": 1792, "num_examples": 8}, {"name": "director", "num_bytes": 1840, "num_examples": 10}, {"name": "dishwasher", "num_bytes": 1864, "num_examples": 11}, {"name": "dispatcher", "num_bytes": 1744, "num_examples": 6}, {"name": "doctor", "num_bytes": 1816, "num_examples": 9}, {"name": "drywall_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 1816, "num_examples": 9}, {"name": "electrician", "num_bytes": 1720, "num_examples": 5}, {"name": "engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "event_planner", "num_bytes": 1696, "num_examples": 4}, {"name": "executive_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "facilities_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "farmer", "num_bytes": 1648, "num_examples": 2}, {"name": "fast_food_worker", "num_bytes": 1864, "num_examples": 11}, {"name": "file_clerk", "num_bytes": 1864, "num_examples": 11}, {"name": "financial_advisor", "num_bytes": 1720, "num_examples": 5}, {"name": "financial_analyst", "num_bytes": 1792, "num_examples": 8}, {"name": "financial_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "firefighter", "num_bytes": 1696, "num_examples": 4}, {"name": "fitness_instructor", "num_bytes": 1720, "num_examples": 5}, {"name": "graphic_designer", "num_bytes": 1840, "num_examples": 10}, {"name": "groundskeeper", "num_bytes": 1744, "num_examples": 6}, {"name": "hairdresser", "num_bytes": 1792, "num_examples": 8}, {"name": "head_cook", "num_bytes": 1864, "num_examples": 11}, {"name": "health_technician", "num_bytes": 1792, "num_examples": 8}, {"name": "industrial_engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "insurance_agent", "num_bytes": 1816, "num_examples": 9}, {"name": "interior_designer", "num_bytes": 1744, "num_examples": 6}, {"name": "interviewer", "num_bytes": 1912, "num_examples": 13}, {"name": "inventory_clerk", "num_bytes": 1864, "num_examples": 11}, {"name": "it_specialist", "num_bytes": 1696, "num_examples": 4}, {"name": "jailer", "num_bytes": 1816, "num_examples": 9}, {"name": "janitor", "num_bytes": 1816, "num_examples": 9}, {"name": "laboratory_technician", "num_bytes": 1888, "num_examples": 12}, {"name": "language_pathologist", "num_bytes": 1816, "num_examples": 9}, {"name": "lawyer", "num_bytes": 1768, "num_examples": 7}, {"name": "librarian", "num_bytes": 1816, "num_examples": 9}, {"name": "logistician", "num_bytes": 1864, "num_examples": 11}, {"name": "machinery_mechanic", "num_bytes": 1744, "num_examples": 6}, {"name": "machinist", "num_bytes": 1816, "num_examples": 9}, {"name": "maid", "num_bytes": 1816, "num_examples": 9}, {"name": "manager", "num_bytes": 1720, "num_examples": 5}, {"name": "manicurist", "num_bytes": 1744, "num_examples": 6}, {"name": "market_research_analyst", "num_bytes": 1816, "num_examples": 9}, {"name": "marketing_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "massage_therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "mechanic", "num_bytes": 1720, "num_examples": 5}, {"name": "mechanical_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "medical_records_specialist", "num_bytes": 1792, "num_examples": 8}, {"name": "mental_health_counselor", "num_bytes": 1816, "num_examples": 9}, {"name": "metal_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "mover", "num_bytes": 1888, "num_examples": 12}, {"name": "musician", "num_bytes": 1912, "num_examples": 13}, {"name": "network_administrator", "num_bytes": 1624, "num_examples": 1}, {"name": "nurse", "num_bytes": 1720, "num_examples": 5}, {"name": "nursing_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "nutritionist", "num_bytes": 1696, "num_examples": 4}, {"name": "occupational_therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "office_clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "office_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "painter", "num_bytes": 1960, "num_examples": 15}, {"name": "paralegal", "num_bytes": 1720, "num_examples": 5}, {"name": "payroll_clerk", "num_bytes": 1768, "num_examples": 7}, {"name": "pharmacist", "num_bytes": 1864, "num_examples": 11}, {"name": "pharmacy_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "photographer", "num_bytes": 1864, "num_examples": 11}, {"name": "physical_therapist", "num_bytes": 1792, "num_examples": 8}, {"name": "pilot", "num_bytes": 1816, "num_examples": 9}, {"name": "plane_mechanic", "num_bytes": 1744, "num_examples": 6}, {"name": "plumber", "num_bytes": 1720, "num_examples": 5}, {"name": "police_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "postal_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "printing_press_operator", "num_bytes": 1816, "num_examples": 9}, {"name": "producer", "num_bytes": 1840, "num_examples": 10}, {"name": "psychologist", "num_bytes": 1840, "num_examples": 10}, {"name": "public_relations_specialist", "num_bytes": 1696, "num_examples": 4}, {"name": "purchasing_agent", "num_bytes": 1864, "num_examples": 11}, {"name": "radiologic_technician", "num_bytes": 1840, "num_examples": 10}, {"name": "real_estate_broker", "num_bytes": 1744, "num_examples": 6}, {"name": "receptionist", "num_bytes": 1672, "num_examples": 3}, {"name": "repair_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1648, "num_examples": 2}, {"name": "salesperson", "num_bytes": 1696, "num_examples": 4}, {"name": "school_bus_driver", "num_bytes": 1960, "num_examples": 15}, {"name": "scientist", "num_bytes": 1912, "num_examples": 13}, {"name": "security_guard", "num_bytes": 1768, "num_examples": 7}, {"name": "sheet_metal_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "singer", "num_bytes": 1984, "num_examples": 16}, {"name": "social_assistant", "num_bytes": 1768, "num_examples": 7}, {"name": "social_worker", "num_bytes": 1864, "num_examples": 11}, {"name": "software_developer", "num_bytes": 1696, "num_examples": 4}, {"name": "stocker", "num_bytes": 1864, "num_examples": 11}, {"name": "supervisor", "num_bytes": 1768, "num_examples": 7}, {"name": "taxi_driver", "num_bytes": 1792, "num_examples": 8}, {"name": "teacher", "num_bytes": 1912, "num_examples": 13}, {"name": "teaching_assistant", "num_bytes": 1864, "num_examples": 11}, {"name": "teller", "num_bytes": 2008, "num_examples": 17}, {"name": "therapist", "num_bytes": 1912, "num_examples": 13}, {"name": "tractor_operator", "num_bytes": 1720, "num_examples": 5}, {"name": "truck_driver", "num_bytes": 1696, "num_examples": 4}, {"name": "tutor", "num_bytes": 1912, "num_examples": 13}, {"name": "underwriter", "num_bytes": 1768, "num_examples": 7}, {"name": "veterinarian", "num_bytes": 1744, "num_examples": 6}, {"name": "welder", "num_bytes": 1696, "num_examples": 4}, {"name": "wholesale_buyer", "num_bytes": 1816, "num_examples": 9}, {"name": "writer", "num_bytes": 1816, "num_examples": 9}], "download_size": 635630, "dataset_size": 261408}}
|
2023-06-04T23:01:43+00:00
|
e5c2d20e7ac81080995f738524e8e37e12150275
|
# Dataset Card for "prof_report__runwayml-stable-diffusion-v1-5__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__runwayml-stable-diffusion-v1-5__multi__24
|
[
"region:us"
] |
2023-06-04T23:05:57+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": "accountant", "num_bytes": 1768, "num_examples": 7}, {"name": "aerospace_engineer", "num_bytes": 1912, "num_examples": 13}, {"name": "aide", "num_bytes": 1816, "num_examples": 9}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1840, "num_examples": 10}, {"name": "artist", "num_bytes": 1888, "num_examples": 12}, {"name": "author", "num_bytes": 1816, "num_examples": 9}, {"name": "baker", "num_bytes": 1888, "num_examples": 12}, {"name": "bartender", "num_bytes": 1720, "num_examples": 5}, {"name": "bus_driver", "num_bytes": 1936, "num_examples": 14}, {"name": "butcher", "num_bytes": 1768, "num_examples": 7}, {"name": "career_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "carpenter", "num_bytes": 1768, "num_examples": 7}, {"name": "carpet_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "cashier", "num_bytes": 1840, "num_examples": 10}, {"name": "ceo", "num_bytes": 1744, "num_examples": 6}, {"name": "childcare_worker", "num_bytes": 1792, "num_examples": 8}, {"name": "civil_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "claims_appraiser", "num_bytes": 1744, "num_examples": 6}, {"name": "cleaner", "num_bytes": 1888, "num_examples": 12}, {"name": "clergy", "num_bytes": 1816, "num_examples": 9}, {"name": "clerk", "num_bytes": 1912, "num_examples": 13}, {"name": "coach", "num_bytes": 1744, "num_examples": 6}, {"name": "community_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "compliance_officer", "num_bytes": 1792, "num_examples": 8}, {"name": "computer_programmer", "num_bytes": 1816, "num_examples": 9}, {"name": "computer_support_specialist", "num_bytes": 1816, "num_examples": 9}, {"name": "computer_systems_analyst", "num_bytes": 1888, "num_examples": 12}, {"name": "construction_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "cook", "num_bytes": 1816, "num_examples": 9}, {"name": "correctional_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "courier", "num_bytes": 1912, "num_examples": 13}, {"name": "credit_counselor", "num_bytes": 1840, "num_examples": 10}, {"name": "customer_service_representative", "num_bytes": 1768, "num_examples": 7}, {"name": "data_entry_keyer", "num_bytes": 1792, "num_examples": 8}, {"name": "dental_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 1696, "num_examples": 4}, {"name": "dentist", "num_bytes": 1744, "num_examples": 6}, {"name": "designer", "num_bytes": 1840, "num_examples": 10}, {"name": "detective", "num_bytes": 1744, "num_examples": 6}, {"name": "director", "num_bytes": 1864, "num_examples": 11}, {"name": "dishwasher", "num_bytes": 1864, "num_examples": 11}, {"name": "dispatcher", "num_bytes": 1792, "num_examples": 8}, {"name": "doctor", "num_bytes": 1816, "num_examples": 9}, {"name": "drywall_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "electrical_engineer", "num_bytes": 1816, "num_examples": 9}, {"name": "electrician", "num_bytes": 1672, "num_examples": 3}, {"name": "engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "event_planner", "num_bytes": 1672, "num_examples": 3}, {"name": "executive_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "facilities_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "farmer", "num_bytes": 1696, "num_examples": 4}, {"name": "fast_food_worker", "num_bytes": 1912, "num_examples": 13}, {"name": "file_clerk", "num_bytes": 1888, "num_examples": 12}, {"name": "financial_advisor", "num_bytes": 1744, "num_examples": 6}, {"name": "financial_analyst", "num_bytes": 1744, "num_examples": 6}, {"name": "financial_manager", "num_bytes": 1696, "num_examples": 4}, {"name": "firefighter", "num_bytes": 1720, "num_examples": 5}, {"name": "fitness_instructor", "num_bytes": 1792, "num_examples": 8}, {"name": "graphic_designer", "num_bytes": 1840, "num_examples": 10}, {"name": "groundskeeper", "num_bytes": 1744, "num_examples": 6}, {"name": "hairdresser", "num_bytes": 1816, "num_examples": 9}, {"name": "head_cook", "num_bytes": 1840, "num_examples": 10}, {"name": "health_technician", "num_bytes": 1744, "num_examples": 6}, {"name": "industrial_engineer", "num_bytes": 1720, "num_examples": 5}, {"name": "insurance_agent", "num_bytes": 1840, "num_examples": 10}, {"name": "interior_designer", "num_bytes": 1768, "num_examples": 7}, {"name": "interviewer", "num_bytes": 1840, "num_examples": 10}, {"name": "inventory_clerk", "num_bytes": 1888, "num_examples": 12}, {"name": "it_specialist", "num_bytes": 1696, "num_examples": 4}, {"name": "jailer", "num_bytes": 1864, "num_examples": 11}, {"name": "janitor", "num_bytes": 1816, "num_examples": 9}, {"name": "laboratory_technician", "num_bytes": 1912, "num_examples": 13}, {"name": "language_pathologist", "num_bytes": 1816, "num_examples": 9}, {"name": "lawyer", "num_bytes": 1840, "num_examples": 10}, {"name": "librarian", "num_bytes": 1816, "num_examples": 9}, {"name": "logistician", "num_bytes": 1768, "num_examples": 7}, {"name": "machinery_mechanic", "num_bytes": 1744, "num_examples": 6}, {"name": "machinist", "num_bytes": 1792, "num_examples": 8}, {"name": "maid", "num_bytes": 1792, "num_examples": 8}, {"name": "manager", "num_bytes": 1744, "num_examples": 6}, {"name": "manicurist", "num_bytes": 1816, "num_examples": 9}, {"name": "market_research_analyst", "num_bytes": 1792, "num_examples": 8}, {"name": "marketing_manager", "num_bytes": 1696, "num_examples": 4}, {"name": "massage_therapist", "num_bytes": 1768, "num_examples": 7}, {"name": "mechanic", "num_bytes": 1768, "num_examples": 7}, {"name": "mechanical_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "medical_records_specialist", "num_bytes": 1840, "num_examples": 10}, {"name": "mental_health_counselor", "num_bytes": 1888, "num_examples": 12}, {"name": "metal_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "mover", "num_bytes": 1864, "num_examples": 11}, {"name": "musician", "num_bytes": 1864, "num_examples": 11}, {"name": "network_administrator", "num_bytes": 1648, "num_examples": 2}, {"name": "nurse", "num_bytes": 1744, "num_examples": 6}, {"name": "nursing_assistant", "num_bytes": 1744, "num_examples": 6}, {"name": "nutritionist", "num_bytes": 1720, "num_examples": 5}, {"name": "occupational_therapist", "num_bytes": 1744, "num_examples": 6}, {"name": "office_clerk", "num_bytes": 1816, "num_examples": 9}, {"name": "office_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "painter", "num_bytes": 1888, "num_examples": 12}, {"name": "paralegal", "num_bytes": 1744, "num_examples": 6}, {"name": "payroll_clerk", "num_bytes": 1744, "num_examples": 6}, {"name": "pharmacist", "num_bytes": 1864, "num_examples": 11}, {"name": "pharmacy_technician", "num_bytes": 1720, "num_examples": 5}, {"name": "photographer", "num_bytes": 1888, "num_examples": 12}, {"name": "physical_therapist", "num_bytes": 1816, "num_examples": 9}, {"name": "pilot", "num_bytes": 1792, "num_examples": 8}, {"name": "plane_mechanic", "num_bytes": 1792, "num_examples": 8}, {"name": "plumber", "num_bytes": 1720, "num_examples": 5}, {"name": "police_officer", "num_bytes": 1768, "num_examples": 7}, {"name": "postal_worker", "num_bytes": 1816, "num_examples": 9}, {"name": "printing_press_operator", "num_bytes": 1816, "num_examples": 9}, {"name": "producer", "num_bytes": 1912, "num_examples": 13}, {"name": "psychologist", "num_bytes": 1792, "num_examples": 8}, {"name": "public_relations_specialist", "num_bytes": 1672, "num_examples": 3}, {"name": "purchasing_agent", "num_bytes": 1840, "num_examples": 10}, {"name": "radiologic_technician", "num_bytes": 1816, "num_examples": 9}, {"name": "real_estate_broker", "num_bytes": 1744, "num_examples": 6}, {"name": "receptionist", "num_bytes": 1672, "num_examples": 3}, {"name": "repair_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "salesperson", "num_bytes": 1768, "num_examples": 7}, {"name": "school_bus_driver", "num_bytes": 1912, "num_examples": 13}, {"name": "scientist", "num_bytes": 1792, "num_examples": 8}, {"name": "security_guard", "num_bytes": 1720, "num_examples": 5}, {"name": "sheet_metal_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "singer", "num_bytes": 1840, "num_examples": 10}, {"name": "social_assistant", "num_bytes": 1888, "num_examples": 12}, {"name": "social_worker", "num_bytes": 1960, "num_examples": 15}, {"name": "software_developer", "num_bytes": 1720, "num_examples": 5}, {"name": "stocker", "num_bytes": 1936, "num_examples": 14}, {"name": "supervisor", "num_bytes": 1768, "num_examples": 7}, {"name": "taxi_driver", "num_bytes": 1816, "num_examples": 9}, {"name": "teacher", "num_bytes": 1936, "num_examples": 14}, {"name": "teaching_assistant", "num_bytes": 1864, "num_examples": 11}, {"name": "teller", "num_bytes": 2008, "num_examples": 17}, {"name": "therapist", "num_bytes": 1816, "num_examples": 9}, {"name": "tractor_operator", "num_bytes": 1696, "num_examples": 4}, {"name": "truck_driver", "num_bytes": 1720, "num_examples": 5}, {"name": "tutor", "num_bytes": 1960, "num_examples": 15}, {"name": "underwriter", "num_bytes": 1816, "num_examples": 9}, {"name": "veterinarian", "num_bytes": 1744, "num_examples": 6}, {"name": "welder", "num_bytes": 1696, "num_examples": 4}, {"name": "wholesale_buyer", "num_bytes": 1840, "num_examples": 10}, {"name": "writer", "num_bytes": 1840, "num_examples": 10}], "download_size": 636051, "dataset_size": 262112}}
|
2023-06-04T23:07:52+00:00
|
c846b850270f5393c36b2b55c8d469b4ebf7cf29
|
# Dataset Card for "prof_report__stabilityai-stable-diffusion-2-1-base__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__stabilityai-stable-diffusion-2-1-base__multi__24
|
[
"region:us"
] |
2023-06-04T23:11:25+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": "accountant", "num_bytes": 1792, "num_examples": 8}, {"name": "aerospace_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "aide", "num_bytes": 1840, "num_examples": 10}, {"name": "air_conditioning_installer", "num_bytes": 1720, "num_examples": 5}, {"name": "architect", "num_bytes": 1744, "num_examples": 6}, {"name": "artist", "num_bytes": 1816, "num_examples": 9}, {"name": "author", "num_bytes": 1768, "num_examples": 7}, {"name": "baker", "num_bytes": 1840, "num_examples": 10}, {"name": "bartender", "num_bytes": 1672, "num_examples": 3}, {"name": "bus_driver", "num_bytes": 1744, "num_examples": 6}, {"name": "butcher", "num_bytes": 1720, "num_examples": 5}, {"name": "career_counselor", "num_bytes": 1816, "num_examples": 9}, {"name": "carpenter", "num_bytes": 1696, "num_examples": 4}, {"name": "carpet_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "cashier", "num_bytes": 1888, "num_examples": 12}, {"name": "ceo", "num_bytes": 1720, "num_examples": 5}, {"name": "childcare_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "civil_engineer", "num_bytes": 1768, "num_examples": 7}, {"name": "claims_appraiser", "num_bytes": 1768, "num_examples": 7}, {"name": "cleaner", "num_bytes": 2008, "num_examples": 17}, {"name": "clergy", "num_bytes": 1840, "num_examples": 10}, {"name": "clerk", "num_bytes": 1696, "num_examples": 4}, {"name": "coach", "num_bytes": 1672, "num_examples": 3}, {"name": "community_manager", "num_bytes": 1864, "num_examples": 11}, {"name": "compliance_officer", "num_bytes": 1888, "num_examples": 12}, {"name": "computer_programmer", "num_bytes": 1816, "num_examples": 9}, {"name": "computer_support_specialist", "num_bytes": 1864, "num_examples": 11}, {"name": "computer_systems_analyst", "num_bytes": 1912, "num_examples": 13}, {"name": "construction_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "cook", "num_bytes": 1816, "num_examples": 9}, {"name": "correctional_officer", "num_bytes": 1840, "num_examples": 10}, {"name": "courier", "num_bytes": 1768, "num_examples": 7}, {"name": "credit_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "customer_service_representative", "num_bytes": 1792, "num_examples": 8}, {"name": "data_entry_keyer", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_assistant", "num_bytes": 1696, "num_examples": 4}, {"name": "dental_hygienist", "num_bytes": 1720, "num_examples": 5}, {"name": "dentist", "num_bytes": 1864, "num_examples": 11}, {"name": "designer", "num_bytes": 1768, "num_examples": 7}, {"name": "detective", "num_bytes": 1768, "num_examples": 7}, {"name": "director", "num_bytes": 1744, "num_examples": 6}, {"name": "dishwasher", "num_bytes": 1840, "num_examples": 10}, {"name": "dispatcher", "num_bytes": 1864, "num_examples": 11}, {"name": "doctor", "num_bytes": 1840, "num_examples": 10}, {"name": "drywall_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "electrical_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "electrician", "num_bytes": 1696, "num_examples": 4}, {"name": "engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "event_planner", "num_bytes": 1888, "num_examples": 12}, {"name": "executive_assistant", "num_bytes": 1720, "num_examples": 5}, {"name": "facilities_manager", "num_bytes": 1792, "num_examples": 8}, {"name": "farmer", "num_bytes": 1720, "num_examples": 5}, {"name": "fast_food_worker", "num_bytes": 1936, "num_examples": 14}, {"name": "file_clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "financial_advisor", "num_bytes": 1744, "num_examples": 6}, {"name": "financial_analyst", "num_bytes": 1864, "num_examples": 11}, {"name": "financial_manager", "num_bytes": 1816, "num_examples": 9}, {"name": "firefighter", "num_bytes": 1696, "num_examples": 4}, {"name": "fitness_instructor", "num_bytes": 1816, "num_examples": 9}, {"name": "graphic_designer", "num_bytes": 1816, "num_examples": 9}, {"name": "groundskeeper", "num_bytes": 1792, "num_examples": 8}, {"name": "hairdresser", "num_bytes": 1792, "num_examples": 8}, {"name": "head_cook", "num_bytes": 1864, "num_examples": 11}, {"name": "health_technician", "num_bytes": 1816, "num_examples": 9}, {"name": "industrial_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "insurance_agent", "num_bytes": 1744, "num_examples": 6}, {"name": "interior_designer", "num_bytes": 1840, "num_examples": 10}, {"name": "interviewer", "num_bytes": 1936, "num_examples": 14}, {"name": "inventory_clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "it_specialist", "num_bytes": 1744, "num_examples": 6}, {"name": "jailer", "num_bytes": 1720, "num_examples": 5}, {"name": "janitor", "num_bytes": 1840, "num_examples": 10}, {"name": "laboratory_technician", "num_bytes": 1888, "num_examples": 12}, {"name": "language_pathologist", "num_bytes": 1888, "num_examples": 12}, {"name": "lawyer", "num_bytes": 1792, "num_examples": 8}, {"name": "librarian", "num_bytes": 1816, "num_examples": 9}, {"name": "logistician", "num_bytes": 1720, "num_examples": 5}, {"name": "machinery_mechanic", "num_bytes": 1768, "num_examples": 7}, {"name": "machinist", "num_bytes": 1672, "num_examples": 3}, {"name": "maid", "num_bytes": 1840, "num_examples": 10}, {"name": "manager", "num_bytes": 1744, "num_examples": 6}, {"name": "manicurist", "num_bytes": 1816, "num_examples": 9}, {"name": "market_research_analyst", "num_bytes": 1816, "num_examples": 9}, {"name": "marketing_manager", "num_bytes": 1840, "num_examples": 10}, {"name": "massage_therapist", "num_bytes": 1840, "num_examples": 10}, {"name": "mechanic", "num_bytes": 1768, "num_examples": 7}, {"name": "mechanical_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "medical_records_specialist", "num_bytes": 1840, "num_examples": 10}, {"name": "mental_health_counselor", "num_bytes": 1792, "num_examples": 8}, {"name": "metal_worker", "num_bytes": 1768, "num_examples": 7}, {"name": "mover", "num_bytes": 1768, "num_examples": 7}, {"name": "musician", "num_bytes": 1912, "num_examples": 13}, {"name": "network_administrator", "num_bytes": 1888, "num_examples": 12}, {"name": "nurse", "num_bytes": 1744, "num_examples": 6}, {"name": "nursing_assistant", "num_bytes": 1768, "num_examples": 7}, {"name": "nutritionist", "num_bytes": 1792, "num_examples": 8}, {"name": "occupational_therapist", "num_bytes": 1864, "num_examples": 11}, {"name": "office_clerk", "num_bytes": 1840, "num_examples": 10}, {"name": "office_worker", "num_bytes": 1912, "num_examples": 13}, {"name": "painter", "num_bytes": 1768, "num_examples": 7}, {"name": "paralegal", "num_bytes": 1696, "num_examples": 4}, {"name": "payroll_clerk", "num_bytes": 1816, "num_examples": 9}, {"name": "pharmacist", "num_bytes": 1840, "num_examples": 10}, {"name": "pharmacy_technician", "num_bytes": 1744, "num_examples": 6}, {"name": "photographer", "num_bytes": 1840, "num_examples": 10}, {"name": "physical_therapist", "num_bytes": 1840, "num_examples": 10}, {"name": "pilot", "num_bytes": 1816, "num_examples": 9}, {"name": "plane_mechanic", "num_bytes": 1744, "num_examples": 6}, {"name": "plumber", "num_bytes": 1696, "num_examples": 4}, {"name": "police_officer", "num_bytes": 1816, "num_examples": 9}, {"name": "postal_worker", "num_bytes": 1840, "num_examples": 10}, {"name": "printing_press_operator", "num_bytes": 1960, "num_examples": 15}, {"name": "producer", "num_bytes": 1816, "num_examples": 9}, {"name": "psychologist", "num_bytes": 1840, "num_examples": 10}, {"name": "public_relations_specialist", "num_bytes": 1720, "num_examples": 5}, {"name": "purchasing_agent", "num_bytes": 1816, "num_examples": 9}, {"name": "radiologic_technician", "num_bytes": 1792, "num_examples": 8}, {"name": "real_estate_broker", "num_bytes": 1720, "num_examples": 5}, {"name": "receptionist", "num_bytes": 1696, "num_examples": 4}, {"name": "repair_worker", "num_bytes": 1720, "num_examples": 5}, {"name": "roofer", "num_bytes": 1696, "num_examples": 4}, {"name": "sales_manager", "num_bytes": 1744, "num_examples": 6}, {"name": "salesperson", "num_bytes": 1816, "num_examples": 9}, {"name": "school_bus_driver", "num_bytes": 1792, "num_examples": 8}, {"name": "scientist", "num_bytes": 1768, "num_examples": 7}, {"name": "security_guard", "num_bytes": 1720, "num_examples": 5}, {"name": "sheet_metal_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "singer", "num_bytes": 1888, "num_examples": 12}, {"name": "social_assistant", "num_bytes": 1864, "num_examples": 11}, {"name": "social_worker", "num_bytes": 1936, "num_examples": 14}, {"name": "software_developer", "num_bytes": 1816, "num_examples": 9}, {"name": "stocker", "num_bytes": 1792, "num_examples": 8}, {"name": "supervisor", "num_bytes": 1816, "num_examples": 9}, {"name": "taxi_driver", "num_bytes": 1792, "num_examples": 8}, {"name": "teacher", "num_bytes": 1792, "num_examples": 8}, {"name": "teaching_assistant", "num_bytes": 1840, "num_examples": 10}, {"name": "teller", "num_bytes": 1912, "num_examples": 13}, {"name": "therapist", "num_bytes": 1792, "num_examples": 8}, {"name": "tractor_operator", "num_bytes": 1744, "num_examples": 6}, {"name": "truck_driver", "num_bytes": 1768, "num_examples": 7}, {"name": "tutor", "num_bytes": 1792, "num_examples": 8}, {"name": "underwriter", "num_bytes": 1888, "num_examples": 12}, {"name": "veterinarian", "num_bytes": 1792, "num_examples": 8}, {"name": "welder", "num_bytes": 1744, "num_examples": 6}, {"name": "wholesale_buyer", "num_bytes": 1840, "num_examples": 10}, {"name": "writer", "num_bytes": 1744, "num_examples": 6}], "download_size": 636209, "dataset_size": 262232}}
|
2023-06-04T23:13:03+00:00
|
1dbf2ce80ba062ca0e78be16ed38dde649c31151
|
# Dataset Card for "fungi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
GATE-engine/fungi
|
[
"region:us"
] |
2023-06-04T23:42:00+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6188400790.875, "num_examples": 64449}, {"name": "validation", "num_bytes": 1173258274.625, "num_examples": 12195}, {"name": "test", "num_bytes": 1260333216.5, "num_examples": 13116}], "download_size": 835444680, "dataset_size": 8621992282.0}}
|
2023-06-05T15:36:25+00:00
|
9defbe93a22af344939bb7ae9097dfe212629571
|
## LoRA Description
LoRA which will allow you to create images with Polina from the game Tiny Bunny.
## Weights:
I recommend using a weight of 0.57 for the best generation, but you can try experimenting with weights from 0.4 to 0.7.
## About trigger words:
PolinaBlackWhite - Trained on black and white images
PolinaColor - Trained on color images
Enjoy using it!
### CivitAi: https://civitai.com/models/84165/polina-tiny-bunny
## Example images
|
Katrg/PolinaTinyBunny
|
[
"language:en",
"license:creativeml-openrail-m",
"lora",
"aiartchan",
"stable-diffusion",
"art",
"region:us"
] |
2023-06-04T23:50:17+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "pretty_name": "Polina", "tags": ["lora", "aiartchan", "stable-diffusion", "art"]}
|
2023-06-05T00:03:16+00:00
|
d5b213e1cfdc38fc752dfbe4024d6643b0d50e20
|
# Dataset Card for "SST2_test_google_flan_t5_xl_mode_C_SST_rices_ns_1821"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/SST2_test_google_flan_t5_xl_mode_C_SST_rices_ns_1821
|
[
"region:us"
] |
2023-06-04T23:50:20+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0____", "num_bytes": 1155961, "num_examples": 1821}], "download_size": 0, "dataset_size": 1155961}}
|
2023-06-04T23:52:40+00:00
|
2bc6fb0c7bcdf8f8ed4da89597c6058b64d3d2c7
|
pankaja/microbe
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-05T00:16:14+00:00
|
{"license": "apache-2.0"}
|
2023-06-05T00:16:14+00:00
|
|
e95d88da774c8a9450c6bfff588bb600864a3fc9
|
这是一个「英雄联盟」原画的图片数据集，旨在为「英雄联盟」原画风格的图片生成和风格迁移提供训练数据。本数据集中的图片均为高分辨率的「英雄联盟」原画，图片尺寸全部大于 1920 * 1080。
|
faterazer/LOL-Arts
|
[
"task_categories:image-to-image",
"language:zh",
"region:us"
] |
2023-06-05T00:46:21+00:00
|
{"language": ["zh"], "task_categories": ["image-to-image"]}
|
2023-06-16T04:28:51+00:00
|
fd0ba4baa62140ace13cc983ad07f89fb6bb0a03
|
# Dataset Card for "github-code-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
waybarrios/github-code-dataset
|
[
"region:us"
] |
2023-06-05T01:08:12+00:00
|
{"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "max_lines", "dtype": "int64"}, {"name": "repo_name", "dtype": "string"}, {"name": "autogenerated", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 152825770, "num_examples": 18912}], "download_size": 57591057, "dataset_size": 152825770}}
|
2023-06-05T01:56:30+00:00
|
b07836391d04d856e9e0045247272ef4ca9d9920
|
# Dataset Card for "imda_dataset_clean_real"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
averageandyyy/imda_dataset_clean_real
|
[
"region:us"
] |
2023-06-05T01:17:45+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcript", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 218979460501.58676, "num_examples": 1408806}], "download_size": 210064768982, "dataset_size": 218979460501.58676}}
|
2023-06-05T14:05:21+00:00
|
9f48e231434d3f36ed6e2b941ca2a2987f8b321b
|
# Dataset Summary
RedPajama-Instruct-Data is curated from a diverse collection of NLP tasks from both [P3 (BigScience)](https://huggingface.co/datasets/bigscience/P3) and [Natural Instruction (AI2)](https://github.com/allenai/natural-instructions),
and conduct aggressive decontamination against [HELM]((https://crfm.stanford.edu/helm/latest/?group=core_scenarios)),
in two steps: (1) We first conduct semantic search using each validation example in HELM as the query and get top-100 similar instances from the Instruct data set and check tasks that have any returned instances overlapping (using 10-Gram) with the validation example.
We remove the entire task if the returned instance and the validation example correspond to the same task
(In this step, we keep the task in the case that the returned instance happens to use the same Wikipedia article as the validation example, but asks different questions);
(2) We then remove all instances that have any 10-Gram overlap with any HELM validation example.
In total, we filtered out 137 tasks and 5.2M instances (out of 1069 tasks and 93.3M instances).
# QuickStart
The materialized version of P3 includes three main fields. The inputs field contains task instructions and data inputs, while the targets field denotes the labels. The third field, meta, provides meta information.
```python
data = load_dataset('togethercomputer/RedPajama-Instruct-Data', data_files='data/P3_decontaminated.jsonl.zst', split='train')
```
For NI, the definition field refers to the task instructions, while inputs represent the input data. The targets field pertains to the labels, and meta provides relevant meta information.
```python
data = load_dataset('togethercomputer/RedPajama-Instruct-Data', data_files='data/NI_decontaminated.jsonl.zst', split='train')
```
# Source Data
RedPajama-Instruct-Data is sourced from two prominent datasets:
- [Public Pool of Prompts](https://huggingface.co/datasets/bigscience/P3): A large dataset featuring various creative tasks obtained from crowdsourcing efforts.
- [Natural-Instructions](https://github.com/allenai/natural-instructions): An instruction-tuning dataset comprising a diverse set of tasks in natural languages.
# Languages
Primarily English.
# Licensing Information
This dataset is released under the licsence of Apache 2.0.
|
togethercomputer/RedPajama-Data-Instruct
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-05T01:24:41+00:00
|
{"license": "apache-2.0"}
|
2023-06-06T02:38:08+00:00
|
e51e8d167e2cc68fd3e0df0c29c68bbf79177cae
|
# Dataset Card for "ed0032ac"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/ed0032ac
|
[
"region:us"
] |
2023-06-05T01:31:10+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1331, "dataset_size": 178}}
|
2023-06-05T01:31:11+00:00
|
bc99d6320eac10ebbff88c65551a26e96754751f
|
test
|
Zimix/test
|
[
"region:us"
] |
2023-06-05T01:32:39+00:00
|
{}
|
2023-06-05T01:35:23+00:00
|
3b02a3d89edc6f52a2b1ed978ff6f478851a9243
|
mginoben/tagalog-profanity-dataset
|
[
"license:unlicense",
"region:us"
] |
2023-06-05T02:12:08+00:00
|
{"license": "unlicense"}
|
2023-06-05T02:26:50+00:00
|
|
586397f7294060b2e590b0857709ffdcb1e66cfe
|
# Dataset Card for "synthetic-role-play-lmgym"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/synthetic-role-play-lmgym
|
[
"region:us"
] |
2023-06-05T02:25:57+00:00
|
{"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "output_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 52514808, "num_examples": 33819}], "download_size": 15323715, "dataset_size": 52514808}}
|
2023-06-05T02:26:00+00:00
|
e06a65a9717766fb5ce06cfcffb543f1195fd8bb
|
# Dataset Card for "cartoonizer-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zhangbo2008/cartoonizer-dataset
|
[
"region:us"
] |
2023-06-05T02:30:57+00:00
|
{"dataset_info": {"features": [{"name": "original_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}, {"name": "cartoonized_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 32931673.0, "num_examples": 50}], "download_size": 32934055, "dataset_size": 32931673.0}}
|
2023-06-05T02:31:00+00:00
|
11131c079f41d9f5492d9acf059567d89c88b246
|
# Dataset Card for "cartoonizer-dataset5000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
original_image: 原始图片
edit_prompt:转化提示词
cartoonized_image:卡通化后的图片
|
zhangbo2008/cartoonizer-dataset5000
|
[
"region:us"
] |
2023-06-05T03:05:22+00:00
|
{"dataset_info": {"features": [{"name": "original_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}, {"name": "cartoonized_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3245096120.0, "num_examples": 5000}], "download_size": 3282490707, "dataset_size": 3245096120.0}}
|
2023-06-05T04:02:06+00:00
|
af10ebf58b46ffd71c6df08a0ea3eed3a0c8d5ca
|
# Dataset Card for "slack_skeenan"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
skeenan947/slack_skeenan
|
[
"region:us"
] |
2023-06-05T03:08:34+00:00
|
{"dataset_info": {"features": [{"name": "train", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24156, "num_examples": 222}], "download_size": 14947, "dataset_size": 24156}}
|
2023-06-05T03:08:35+00:00
|
4973ee4bb9579653f7cd9c7dee6db7fd2ec2918f
|
Dataset to my selft
|
xLuisX/MySelft
|
[
"region:us"
] |
2023-06-05T03:32:45+00:00
|
{}
|
2023-06-05T03:35:26+00:00
|
ce8e02ce78d8c1085b366baf881121d5c5c89e44
|
jayasri/jayasri
|
[
"license:openrail",
"region:us"
] |
2023-06-05T03:33:55+00:00
|
{"license": "openrail"}
|
2023-06-05T03:33:55+00:00
|
|
e45e7badc2909e28cc566b82e586a0caf75094eb
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:https://github.com/kaistAI/CoT-Collection**
- **Repository:https://github.com/kaistAI/CoT-Collection**
- **Paper:https://arxiv.org/abs/2305.14045**
- **Point of Contact:[email protected]**
### Dataset Summary
The Multilingual CoT Collection is a dataset designed to induce Chain-of-Thought (CoT) capabilities into multilingual language models.
While proprietary LLMs excel at generating Chain-of-Thoughts based on prompting, smaller LMs do not have this capability. Thus, by fine-tuning to generate Chain-of-Thoughts, it could acquire such abilities.
The Multilingual CoT Collection provides 1.84 million Chain-of-Thoughts augmented across 1060 tasks from the Flan Collection.\\
Experimental results show that fine-tuning on the CoT Collection results in (1) better zero-shot performance and (2) a better base model for few-shot learning.
We also provide a multilingual version of CoT Collection at this [link](https://huggingface.co/datasets/kaist-ai/Multilingual-CoT-Collection).
### Supported Tasks and Leaderboards
1060 tasks chosen from the Flan Collection.
The list of categories within the CoT Collection are:
* Natural Language Inference
* Extractive Question Answering
* Closed Book Question Answering
* Science
* Toxic Classification
* Arithmetic
* Program Execution
* Dialogue
* Ethics
* Commonsense Reasoning
* Multiple Choice Question Answering
### Languages
English
## Dataset Structure
* source: The input that is given to the language model (LM).
* target: The ground truth answer to the source.
* rationale: The Chain of Thought (CoT) that explains how the target could be derived from the source.
* task: A category that shows which dataset the source and target was extracted from.
In our paper, we trained the underlying language model to generate in the following format:
```
\{rationale\}
[RESULT]
\{target\}
```
Then during evaluation, we parsed the prediction after the phrase ```[RESULT]```.
### Data Splits
| name | train |
|-------------------|------:|
|CoT-Collection|1837928|
### Citation Information
If you find the following model helpful, please considering citing our paper!
```
@article{kim2023cot,
title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
journal={arXiv preprint arXiv:2305.14045},
year={2023}
}
```
|
kaist-ai/Multilingual-CoT-Collection
|
[
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"arxiv:2305.14045",
"region:us"
] |
2023-06-05T03:42:21+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text-classification"], "configs": [{"config_name": "fr", "data_files": "./data/CoT_collection_fr.json"}, {"config_name": "ja", "data_files": "./data/CoT_collection_ja.json"}, {"config_name": "ko", "data_files": "./data/CoT_collection_ko.json"}, {"config_name": "ru", "data_files": "./data/CoT_collection_ru.json"}, {"config_name": "zh", "data_files": "./data/CoT_collection_zh.json"}]}
|
2023-10-14T14:00:43+00:00
|
3c1f3289ea320ae988fde2bbceeda79d82a9d9fa
|
# Dataset Card for "pytorch-tutorial-168"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shrinath-suresh/pytorch-tutorial-168
|
[
"region:us"
] |
2023-06-05T03:50:34+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 267421, "num_examples": 168}], "download_size": 57872, "dataset_size": 267421}}
|
2023-06-05T03:50:37+00:00
|
f068295c04c6c5d10d011f49d1ee125a89737553
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
Superlang/bsc
|
[
"size_categories:n<1K",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-06-05T04:16:53+00:00
|
{"license": "cc-by-nc-4.0", "size_categories": ["n<1K"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 189585.0, "num_examples": 3}, {"name": "validation", "num_bytes": 189585.0, "num_examples": 3}], "download_size": 0, "dataset_size": 379170.0}}
|
2023-06-05T05:49:40+00:00
|
e5620741ee4af1d6c8d3294e5109496fdb4f7d49
|
# Dataset Card for "noisycommonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
alxfng/noisycommonvoice
|
[
"region:us"
] |
2023-06-05T04:18:31+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1293491394.0, "num_examples": 5000}, {"name": "test", "num_bytes": 638561647.5, "num_examples": 2500}], "download_size": 2034213156, "dataset_size": 1932053041.5}}
|
2023-06-05T04:20:23+00:00
|
7eac9601a15ad62259cfa2a2dadfe46085ea27f5
|
# Dataset Card for "stack-exchange-preferences-code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
P1ayer-1/stack-exchange-preferences-code
|
[
"region:us"
] |
2023-06-05T04:20:51+00:00
|
{"dataset_info": {"features": [{"name": "qid", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answers", "list": [{"name": "answer_id", "dtype": "int64"}, {"name": "author", "dtype": "string"}, {"name": "author_id", "dtype": "int64"}, {"name": "author_profile", "dtype": "string"}, {"name": "pm_score", "dtype": "int64"}, {"name": "selected", "dtype": "bool"}, {"name": "text", "dtype": "string"}]}, {"name": "date", "dtype": "string"}, {"name": "metadata", "sequence": "string"}], "splits": [{"name": "Stackoverflow.com", "num_bytes": 20694208501, "num_examples": 7365699}, {"name": "ai.stackexchange.com", "num_bytes": 676702, "num_examples": 379}, {"name": "arduino.stackexchange.com", "num_bytes": 15570353, "num_examples": 4995}, {"name": "askubuntu.com", "num_bytes": 181815610, "num_examples": 90833}, {"name": "bioinformatics.stackexchange.com", "num_bytes": 3348389, "num_examples": 1117}, {"name": "codegolf.stackexchange.com", "num_bytes": 153035005, "num_examples": 11914}, {"name": "codereview.stackexchange.com", "num_bytes": 216062241, "num_examples": 30853}, {"name": "computergraphics.stackexchange.com", "num_bytes": 751236, "num_examples": 291}, {"name": "cs.stackexchange.com", "num_bytes": 5123778, "num_examples": 2796}, {"name": "cseducators.stackexchange.com", "num_bytes": 833822, "num_examples": 386}, {"name": "cstheory.stackexchange.com", "num_bytes": 717197, "num_examples": 382}, {"name": "datascience.stackexchange.com", "num_bytes": 8571534, "num_examples": 3929}, {"name": "dba.stackexchange.com", "num_bytes": 76662059, "num_examples": 25712}, {"name": "devops.stackexchange.com", "num_bytes": 1999592, "num_examples": 972}, {"name": "drupal.stackexchange.com", "num_bytes": 39818464, "num_examples": 19325}, {"name": "dsp.stackexchange.com", "num_bytes": 5318617, "num_examples": 2282}, {"name": "emacs.stackexchange.com", "num_bytes": 13604802, "num_examples": 6138}, {"name": "elementaryos.stackexchange.com", "num_bytes": 2444976, "num_examples": 1601}, {"name": "ethereum.stackexchange.com", "num_bytes": 18635230, "num_examples": 8235}, {"name": "gamedev.stackexchange.com", "num_bytes": 27981013, "num_examples": 10565}, {"name": "gis.stackexchange.com", "num_bytes": 57504222, "num_examples": 23390}, {"name": "magento.stackexchange.com", "num_bytes": 94417274, "num_examples": 28969}, {"name": "math.stackexchange.com", "num_bytes": 32774855, "num_examples": 16773}, {"name": "mathematica.stackexchange.com", "num_bytes": 94394505, "num_examples": 29947}, {"name": "meta.askubuntu.com", "num_bytes": 831480, "num_examples": 494}, {"name": "meta.serverfault.com", "num_bytes": 445023, "num_examples": 260}, {"name": "meta.stackoverflow.com", "num_bytes": 9031977, "num_examples": 3454}, {"name": "meta.superuser.com", "num_bytes": 473293, "num_examples": 262}, {"name": "networkengineering.stackexchange.com", "num_bytes": 6157814, "num_examples": 2624}, {"name": "opendata.stackexchange.com", "num_bytes": 718937, "num_examples": 451}, {"name": "opensource.stackexchange.com", "num_bytes": 498189, "num_examples": 306}, {"name": "or.stackexchange.com", "num_bytes": 841280, "num_examples": 291}, {"name": "quantumcomputing.stackexchange.com", "num_bytes": 1321433, "num_examples": 607}, {"name": "raspberrypi.stackexchange.com", "num_bytes": 17043645, "num_examples": 7854}, {"name": "retrocomputing.stackexchange.com", "num_bytes": 3036198, "num_examples": 1400}, {"name": "reverseengineering.stackexchange.com", "num_bytes": 5131731, "num_examples": 1736}, {"name": "robotics.stackexchange.com", "num_bytes": 1079092, "num_examples": 448}, {"name": "rus.stackexchange.com", "num_bytes": 645427, "num_examples": 471}, {"name": "salesforce.stackexchange.com", "num_bytes": 62945196, "num_examples": 23521}, {"name": "scicomp.stackexchange.com", "num_bytes": 2897524, "num_examples": 1090}, {"name": "serverfault.com", "num_bytes": 148851279, "num_examples": 71060}, {"name": "sharepoint.stackexchange.com", "num_bytes": 38061942, "num_examples": 17250}, {"name": "sitecore.stackexchange.com", "num_bytes": 7336222, "num_examples": 2646}, {"name": "softwareengineering.stackexchange.com", "num_bytes": 48995063, "num_examples": 20664}, {"name": "softwarerecs.stackexchange.com", "num_bytes": 3293309, "num_examples": 1937}, {"name": "stackapps.com", "num_bytes": 1052481, "num_examples": 282}, {"name": "stats.stackexchange.com", "num_bytes": 35029530, "num_examples": 14404}, {"name": "superuser.com", "num_bytes": 150472656, "num_examples": 92628}, {"name": "tex.stackexchange.com", "num_bytes": 288490910, "num_examples": 69895}, {"name": "unix.stackexchange.com", "num_bytes": 169087505, "num_examples": 76183}, {"name": "vi.stackexchange.com", "num_bytes": 7119525, "num_examples": 3792}, {"name": "webapps.stackexchange.com", "num_bytes": 6870139, "num_examples": 4882}, {"name": "webmasters.stackexchange.com", "num_bytes": 9834894, "num_examples": 6647}, {"name": "wordpress.stackexchange.com", "num_bytes": 71288228, "num_examples": 26821}], "download_size": 9253093418, "dataset_size": 22845151899}}
|
2023-06-05T04:25:53+00:00
|
e6cc4a7e593a6b9e4900f0161c3a0da12a7cf159
|
hilongjw/box_border
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"license:cc",
"art",
"code",
"region:us"
] |
2023-06-05T04:36:58+00:00
|
{"license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["art", "code"]}
|
2023-06-05T04:42:52+00:00
|
|
811dec3ea2e12ce0c2140f7f501de62a34a61e8d
|
hilongjw/view_border
|
[
"license:mit",
"region:us"
] |
2023-06-05T04:52:52+00:00
|
{"license": "mit"}
|
2023-06-05T05:23:02+00:00
|
|
ea15f035d91c49acb9085541b62c36ac084f01b1
|
# Dataset Card for "reason_code-search-net-python"
## Dataset Description
- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/reason_code-search-net-python
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)
### Dataset Summary
This dataset is an instructional dataset for Python.
The dataset contains five different kind of tasks.
Given a Python 3 function:
- Type 1: Generate a summary explaining what it does. (For example: This function counts the number of objects stored in the jsonl file passed as input.)
- Type 2: Generate a summary explaining what its input parameters represent ("For example: infile: a file descriptor of a file containing json objects in jsonl format.")
- Type 3: Generate a summary explaining what the return value represents ("For example: The function returns the number of json objects in the file passed as input.")
- Type 4: Generate a summary explaining what is the type of the return value ("For example: The function returns an int.")
- Type 5: Generate a summary explaining what is the type of its input parameters ("For example: infile: A file descriptor.").
### Languages
The dataset is in English.
### Data Splits
There are no splits (Only training).
## Dataset Creation
May of 2023
### Curation Rationale
This dataset was created to improve the Python 3 reasoning/understanding capabilities of LLMs.
### Source Data
The summarized version of the code-search-net dataset can be found at https://huggingface.co/datasets/Nan-Do/code-search-net-python
### Annotations
The dataset includes an instruction, response and type columns.
The type colum indicates the type of task (from 1 to 5).
#### Annotation process
The annotation procedure was done using templates, NLP techniques to generate human-like questions and responses, and the Python AST module to parse the code.
The responses were generated parsing the docstrings of the functions. (The ones that included the required information).
### Licensing Information
Apache 2.0
|
Nan-Do/reason_code-search-net-python
|
[
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"license:apache-2.0",
"code",
"reasoning",
"Python",
"region:us"
] |
2023-06-05T04:59:41+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["summarization", "text-generation", "conversational", "text2text-generation"], "pretty_name": "Reasoning dataset for Python", "dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "TYPE", "dtype": "int64"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 399930143, "num_examples": 429059}], "download_size": 89360217, "dataset_size": 399930143}, "tags": ["code", "reasoning", "Python"]}
|
2023-06-05T23:34:33+00:00
|
6e3bb99a602d60027fa9f562526f8876750f756a
|
Athiwat/emilia
|
[
"region:us"
] |
2023-06-05T05:11:35+00:00
|
{}
|
2023-06-05T05:13:53+00:00
|
|
c9435e92d12b9d91370b2ef551dec4a366897f7e
|
jignasha/medicalFAQ
|
[
"license:mit",
"region:us"
] |
2023-06-05T05:31:52+00:00
|
{"license": "mit"}
|
2023-06-05T05:34:07+00:00
|
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