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8f05e5548657b057150a2556baf7c90ea6cc95f0
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-05T00:19:41+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 44840, "num_examples": 100}], "download_size": 12275, "dataset_size": 44840}}
|
2023-04-05T00:42:31+00:00
|
87bd8236c3ece479aed80eb1ca0dd24799fa9038
|
# V1
The images are from [SketchyCOCO](https://github.com/sysu-imsl/SketchyCOCO). 🤗
Things to improve:
- Better prompts
- More variety
- More sheeps
|
GreeneryScenery/SheepsNet
|
[
"art",
"SketchyCOCO",
"region:us"
] |
2023-04-05T00:22:46+00:00
|
{"tags": ["art", "SketchyCOCO"]}
|
2023-04-07T00:51:01+00:00
|
37cb317784d4733cbbe6bb4b0ab5fe4ea381c449
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_3333
|
[
"region:us"
] |
2023-04-05T00:37:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1492908, "num_examples": 3333}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2893373, "num_examples": 3333}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5702031, "num_examples": 3333}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 1484173, "num_examples": 3333}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2772155, "num_examples": 3333}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 2876794, "num_examples": 3333}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5457699, "num_examples": 3333}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1422853, "num_examples": 3333}], "download_size": 3475675, "dataset_size": 24101986}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T04:04:29+00:00
|
198e3bb0311c3e207e1501e3d3a5357146665213
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3333
|
[
"region:us"
] |
2023-04-05T01:03:49+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1486108, "num_examples": 3333}, {"name": "fewshot_1_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2879731, "num_examples": 3333}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1492678, "num_examples": 3333}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2892999, "num_examples": 3333}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5701653, "num_examples": 3333}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1428942, "num_examples": 3333}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2771870, "num_examples": 3333}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5457391, "num_examples": 3333}], "download_size": 3463840, "dataset_size": 24111372}}
|
2023-05-05T23:29:55+00:00
|
1f5d1808bfead2c202c7cbdf7ed159f467acfb5b
|
DaydreamAtNight/gtp4forallcleaned
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-05T01:08:33+00:00
|
{"license": "apache-2.0"}
|
2023-04-05T01:13:04+00:00
|
|
70caee435a023f82ffff6abced046c636158388f
|
Safetensors model showing the results of my data augmentation Python script.
https://github.com/DevArqSangoi/das-dataAugmentation
|
devarqsangoi/jenna-ortega_from_das-dataAugmentation
|
[
"language:en",
"license:mit",
"region:us"
] |
2023-04-05T01:10:26+00:00
|
{"language": ["en"], "license": "mit", "pretty_name": "das-dataAugmentation"}
|
2023-04-05T01:17:46+00:00
|
6854d6fc31db517d41cc2f81a188e8d1e886ce78
|
RyokoAI/Sensei
|
[
"license:cc0-1.0",
"region:us"
] |
2023-04-05T01:36:32+00:00
|
{"license": "cc0-1.0"}
|
2023-04-05T01:36:32+00:00
|
|
977c32adba2425217ad75751f5a3eff05744827c
|
KevinGeng/testdataset
|
[
"license:bsd-2-clause",
"region:us"
] |
2023-04-05T01:39:41+00:00
|
{"license": "bsd-2-clause", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18526857.0, "num_examples": 160}], "download_size": 18528461, "dataset_size": 18526857.0}}
|
2023-04-05T02:26:41+00:00
|
|
226c592461da9b0a4c14fb9af6c73b234dd6c493
|
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xl_mode_T_A_ns_3333
|
[
"region:us"
] |
2023-04-05T01:53:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_classes_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1925739, "num_examples": 3333}], "download_size": 337848, "dataset_size": 1925739}}
|
2023-04-05T01:53:13+00:00
|
01b633e2f47512d51515da6d8ba3cc5c4ccc3b5f
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_1880
|
[
"region:us"
] |
2023-04-05T01:56:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 830705, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 834516, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1624134, "num_examples": 1880}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 3204157, "num_examples": 1880}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 811447, "num_examples": 1880}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1578034, "num_examples": 1880}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 3113327, "num_examples": 1880}], "download_size": 2393167, "dataset_size": 11996320}}
|
2023-05-05T22:55:31+00:00
|
0c82f4e7ed5bb540afd2e9d5be6a86df0de531b3
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_3669
|
[
"region:us"
] |
2023-04-05T02:18:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1535357, "num_examples": 3669}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1535404, "num_examples": 3669}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5797400, "num_examples": 3669}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 1457941, "num_examples": 3669}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2808418, "num_examples": 3669}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5508030, "num_examples": 3669}], "download_size": 2261474, "dataset_size": 18642550}}
|
2023-05-05T21:46:36+00:00
|
294ccceb4b5e44887b7d2e03de4319918ddd758b
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-05T02:26:03+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 46399, "num_examples": 100}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 89414, "num_examples": 100}], "download_size": 17317, "dataset_size": 135813}}
|
2023-04-05T14:03:19+00:00
|
17848768a0b66bdb791c671a6152b8c56888224a
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100
|
[
"region:us"
] |
2023-04-05T02:28:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 17698, "num_examples": 100}], "download_size": 3986, "dataset_size": 17698}}
|
2023-04-05T02:28:47+00:00
|
8ad794c4c092baa4f54d948ad3b1022ab443ece6
|
# Dataset Card for "pokemon-and-fakemon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
RoryCochrane/pokemon-and-fakemon
|
[
"region:us"
] |
2023-04-05T02:34:04+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 480609633.745, "num_examples": 4763}], "download_size": 391516344, "dataset_size": 480609633.745}}
|
2023-05-19T09:20:04+00:00
|
9bb9f8c103c0cd341cc75831b79f72c227c8dfc3
|
# Dataset Card for "ask2democracy-cfqa-pension"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jorge-henao/ask2democracy-cfqa-pension
|
[
"region:us"
] |
2023-04-05T02:40:14+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "topics", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2464607, "num_examples": 1069}], "download_size": 237794, "dataset_size": 2464607}}
|
2023-04-05T02:40:19+00:00
|
13a257821b1e97a71bd29d17e1b7e3238b240752
|
# Dataset Card for "ask2democracy-cfqa-salud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jorge-henao/ask2democracy-cfqa-salud
|
[
"region:us"
] |
2023-04-05T02:46:26+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "topics", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2592190, "num_examples": 1356}], "download_size": 309725, "dataset_size": 2592190}}
|
2023-04-05T02:46:32+00:00
|
f0c87618a40a51a859a28759ae417cf4b8c806b0
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6149"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6149
|
[
"region:us"
] |
2023-04-05T02:53:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2744420, "num_examples": 6149}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5301849, "num_examples": 6149}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 10411971, "num_examples": 6149}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 2707863, "num_examples": 6149}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5047853, "num_examples": 6149}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 5240644, "num_examples": 6149}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 9920183, "num_examples": 6149}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2610935, "num_examples": 6149}], "download_size": 4051257, "dataset_size": 43985718}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T04:21:36+00:00
|
fba35db7041c88628449c7fc4b2cfb0cdc391270
|
# Prompt-Reply Objects from Origin to March 2023 with top 5 Comments
## Source Data
The data for this project was compiled from three main sources:
Pushshift Reddit submissions dataset, which includes the following fields: "title, post_id, over_18, subreddit, link_flair_text, self_text"
BigQuery, where the Pushshift data was uploaded and queried for submissions from the r/math subreddit
A web scraper (https://github.com/P1ayer-1/Reddit-Convo-Tree-Builder) used to extract updated post content, including any replies to the original posts
In cases where the updated content was not available (e.g., because it was deleted), the title or self_text from the original Pushshift submissions was used to create a more comprehensive dataset.
## Output Data
The output data is in JSON Lines format and includes prompt-reply objects for posts from the r/math subreddit, along with the top 5 comments for each post.
---
license: cc-by-4.0
---
|
P1ayer-1/reddit-math
|
[
"region:us"
] |
2023-04-05T03:18:05+00:00
|
{}
|
2023-04-05T06:21:21+00:00
|
9c2b66d2fabaa8a4936df294f5c96667e3e806e6
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_100
|
[
"region:us"
] |
2023-04-05T03:19:59+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 14844, "num_examples": 100}], "download_size": 3874, "dataset_size": 14844}}
|
2023-04-05T03:20:02+00:00
|
9a406ce8665b8c518c5a4429e33bf762d49fd48f
|
# Dataset Card for "oscar_tamil_2201"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnanthZeke/oscar_tamil_2201
|
[
"region:us"
] |
2023-04-05T03:21:27+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sent_token", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 18576297122, "num_examples": 556772}], "download_size": 6242500521, "dataset_size": 18576297122}}
|
2023-04-05T14:40:53+00:00
|
a9eb2daaabbee962a6510c5adb588e9192e1a732
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-05T03:23:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 41886, "num_examples": 100}], "download_size": 11704, "dataset_size": 41886}}
|
2023-04-05T03:23:12+00:00
|
29a5efe4439b881548376200d988c3c059aa602d
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_A_ns_100
|
[
"region:us"
] |
2023-04-05T03:25:11+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 40109, "num_examples": 100}], "download_size": 11527, "dataset_size": 40109}}
|
2023-04-05T03:25:13+00:00
|
02a5421c898a077894e61e64b3eac0402e8e6a6b
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_ns_300
|
[
"region:us"
] |
2023-04-05T03:27:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 44656, "num_examples": 300}], "download_size": 5342, "dataset_size": 44656}}
|
2023-04-05T03:27:11+00:00
|
971d02ba393bbdf17dda383a4df4a29d5f614bd8
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_300
|
[
"region:us"
] |
2023-04-05T03:29:20+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 126783, "num_examples": 300}], "download_size": 24456, "dataset_size": 126783}}
|
2023-04-05T03:29:22+00:00
|
c7f4630efb651656f35b5f2eeb1edd02fbc7d07e
|
# Dataset Card for "VALUE_stsb_dey_it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_dey_it
|
[
"region:us"
] |
2023-04-05T03:41:30+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 13139, "num_examples": 69}, {"name": "test", "num_bytes": 6243, "num_examples": 48}, {"name": "train", "num_bytes": 7725, "num_examples": 40}], "download_size": 27352, "dataset_size": 27107}}
|
2023-04-05T03:41:35+00:00
|
e159e41acdfbedb225a4a768a3b3c94d2b9e8efb
|
# Dataset Card for "VALUE_stsb_been_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_been_done
|
[
"region:us"
] |
2023-04-05T03:41:33+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 30345, "num_examples": 130}, {"name": "test", "num_bytes": 27542, "num_examples": 109}, {"name": "train", "num_bytes": 111144, "num_examples": 442}], "download_size": 118500, "dataset_size": 169031}}
|
2023-04-05T03:41:37+00:00
|
c21e237be2250e2fe68c0ae896d330aa69f7cc0b
|
# Dataset Card for "VALUE_stsb_got"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_got
|
[
"region:us"
] |
2023-04-05T03:41:54+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 8486, "num_examples": 44}, {"name": "test", "num_bytes": 4738, "num_examples": 34}, {"name": "train", "num_bytes": 10468, "num_examples": 68}], "download_size": 24423, "dataset_size": 23692}}
|
2023-04-05T03:41:59+00:00
|
dc5312e938fa4cf2a95bd12271f6f6301d6a8a19
|
# Dataset Card for "VALUE_stsb_negative_inversion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_negative_inversion
|
[
"region:us"
] |
2023-04-05T03:42:02+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 246, "num_examples": 1}, {"name": "train", "num_bytes": 281, "num_examples": 2}], "download_size": 6194, "dataset_size": 527}}
|
2023-04-05T03:42:05+00:00
|
3bf43d068261818b5584d9d678a66ee4bda02f70
|
# Dataset Card for "VALUE_stsb_negative_concord"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_negative_concord
|
[
"region:us"
] |
2023-04-05T03:42:03+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 15153, "num_examples": 73}, {"name": "test", "num_bytes": 7809, "num_examples": 55}, {"name": "train", "num_bytes": 21792, "num_examples": 133}], "download_size": 38287, "dataset_size": 44754}}
|
2023-04-05T03:42:07+00:00
|
ef368a1148a8c8b7e67d85faae36af827c512a8e
|
# Dataset Card for "VALUE_stsb_drop_aux"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_drop_aux
|
[
"region:us"
] |
2023-04-05T03:42:05+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 30406, "num_examples": 157}, {"name": "test", "num_bytes": 10799, "num_examples": 70}, {"name": "train", "num_bytes": 39673, "num_examples": 251}], "download_size": 62964, "dataset_size": 80878}}
|
2023-04-05T03:42:09+00:00
|
2395420c2d64615262d87063dd7dd342974970eb
|
# Dataset Card for "VALUE_stsb_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_null_relcl
|
[
"region:us"
] |
2023-04-05T03:42:07+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 16568, "num_examples": 78}, {"name": "test", "num_bytes": 8365, "num_examples": 38}, {"name": "train", "num_bytes": 37183, "num_examples": 151}], "download_size": 53793, "dataset_size": 62116}}
|
2023-04-05T03:42:11+00:00
|
ce32cc142267d0bf5ea270eba48959953c930cc8
|
# Dataset Card for "VALUE_stsb_uninflect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_uninflect
|
[
"region:us"
] |
2023-04-05T03:42:19+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 75957, "num_examples": 465}, {"name": "test", "num_bytes": 55377, "num_examples": 378}, {"name": "train", "num_bytes": 309673, "num_examples": 2043}], "download_size": 284725, "dataset_size": 441007}}
|
2023-04-05T03:42:23+00:00
|
8432d32c6fdeb8072f58bb953bc0443401240554
|
# Dataset Card for "VALUE_stsb_null_genetive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_null_genetive
|
[
"region:us"
] |
2023-04-05T03:42:21+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 28614, "num_examples": 141}, {"name": "test", "num_bytes": 21904, "num_examples": 104}, {"name": "train", "num_bytes": 125384, "num_examples": 658}], "download_size": 124757, "dataset_size": 175902}}
|
2023-04-05T03:42:25+00:00
|
0acf79b5d6e57277e102abd5155ba77081b1992b
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6084"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_6084
|
[
"region:us"
] |
2023-04-05T03:45:04+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2521067, "num_examples": 6084}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4868124, "num_examples": 6084}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 9570212, "num_examples": 6084}, {"name": "fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 2503329, "num_examples": 6084}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 4649211, "num_examples": 6084}, {"name": "fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices", "num_bytes": 4833725, "num_examples": 6084}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 9130589, "num_examples": 6084}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2415416, "num_examples": 6084}], "download_size": 5652574, "dataset_size": 40491673}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T04:50:32+00:00
|
72797ed5d11b5e1ce9038d49df59a3c8f8b5f453
|
# Dataset Card for "VALUE_stsb_lexical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/VALUE_stsb_lexical
|
[
"region:us"
] |
2023-04-05T03:53:55+00:00
|
{"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 183294, "num_examples": 1078}, {"name": "test", "num_bytes": 137798, "num_examples": 951}, {"name": "train", "num_bytes": 526834, "num_examples": 3263}], "download_size": 542023, "dataset_size": 847926}}
|
2023-04-05T03:54:00+00:00
|
338647c6c76f52427959271a5c6c28831f146219
|
Aria2c called the catbox links fat so I uploaded these here.
|
feelinrealcute/OMORILoras
|
[
"region:us"
] |
2023-04-05T04:20:09+00:00
|
{}
|
2023-04-05T04:20:29+00:00
|
2ff90a7c177e4b31e2e207d72ff25a8ef266c3eb
|
# Dataset Card for "oscar_tamil_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnanthZeke/oscar_tamil_clean
|
[
"region:us"
] |
2023-04-05T04:20:30+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sent_token", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 19533337624, "num_examples": 1263180}], "download_size": 6504957774, "dataset_size": 19533337624}}
|
2023-04-05T08:20:37+00:00
|
78476f1d40d9b0782cc8161e8f03432e7665fdbe
|
MeshLabs/bj2mint_large
|
[
"license:afl-3.0",
"region:us"
] |
2023-04-05T04:22:11+00:00
|
{"license": "afl-3.0"}
|
2023-04-05T04:34:57+00:00
|
|
c8b62de56d0c8fdbb3cd03eb171094d7c3b8a86b
|
limcheekin/flutter-website-3.7
|
[
"license:cc-by-3.0",
"region:us"
] |
2023-04-05T04:53:55+00:00
|
{"license": "cc-by-3.0"}
|
2023-04-05T07:59:12+00:00
|
|
3060fc30c16a105d527a901f5536e42e71d15488
|
# Dataset Card for "makoto-shinkai-picture"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Fung804/makoto-shinkai-picture
|
[
"region:us"
] |
2023-04-05T04:56:38+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1012000102.307, "num_examples": 1347}], "download_size": 1044953186, "dataset_size": 1012000102.307}}
|
2023-04-06T00:31:52+00:00
|
96aa651d1d079217d7efdee12573e3e0ab6eef0d
|
# Dataset Card for "Spanish_MLM_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_1
|
[
"region:us"
] |
2023-04-05T05:33:21+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3504255, "num_examples": 25000}], "download_size": 1949854, "dataset_size": 3504255}}
|
2023-04-05T05:33:24+00:00
|
8fad6d06ed073a01553e6b43ef908ed2708a8737
|
# Dataset Card for "Spanish_MLM_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_2
|
[
"region:us"
] |
2023-04-05T05:33:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3904324, "num_examples": 25000}], "download_size": 2433847, "dataset_size": 3904324}}
|
2023-04-05T05:33:56+00:00
|
92e9a8216bb3c23d699050d2af2764a901905999
|
# Dataset Card for "Spanish_MLM_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_3
|
[
"region:us"
] |
2023-04-05T05:34:18+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3451474, "num_examples": 25000}], "download_size": 1919406, "dataset_size": 3451474}}
|
2023-04-05T05:34:21+00:00
|
7187f3a64ef23cf72812aefd21c082e71bc7a847
|
# Dataset Card for "Spanish_MLM_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_4
|
[
"region:us"
] |
2023-04-05T05:34:26+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3448922, "num_examples": 25000}], "download_size": 1925871, "dataset_size": 3448922}}
|
2023-04-05T05:34:30+00:00
|
06b635d323c6272459fc5b84e1cf1cd4bb10918c
|
# Dataset Card for "Spanish_MLM_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_5
|
[
"region:us"
] |
2023-04-05T05:34:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3567673, "num_examples": 25000}], "download_size": 1978049, "dataset_size": 3567673}}
|
2023-04-05T05:34:56+00:00
|
303eded3cf8379f6d9398501db2e9f6238f0b7ab
|
# Dataset Card for "cmudict-0.7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bookbot/cmudict-0.7b
|
[
"region:us"
] |
2023-04-05T05:35:21+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3233546, "num_examples": 108132}, {"name": "test", "num_bytes": 385494, "num_examples": 12855}, {"name": "validation", "num_bytes": 163320, "num_examples": 5447}], "download_size": 2020319, "dataset_size": 3782360}}
|
2023-04-05T05:35:48+00:00
|
8a2816d85884ab48ebd4e0128adaf29f93fba2bc
|
# About Dataset
This is an extension of the HC3 Dataset. We added around 25k new ChatGPT responses, which roughly equates to around 25k new rows of data as compared to HC3.
The main dataset is in HC3_With_Scraped_Data.csv
The other files consists of other features such as GLTR scores, perplexity scores etc.
|
GPTGone/hc3_v2
|
[
"license:mit",
"region:us"
] |
2023-04-05T05:37:35+00:00
|
{"license": "mit"}
|
2023-04-16T13:08:07+00:00
|
920db22ebd0e5579d359c9038c81045d5f2980db
|
# Dataset Card for "StanfordCars_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_8041"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/StanfordCars_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_8041
|
[
"region:us"
] |
2023-04-05T05:37:50+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4049742, "num_examples": 8041}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 15120375, "num_examples": 8041}, {"name": "fewshot_0__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4323240, "num_examples": 8041}, {"name": "fewshot_1__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 8285003, "num_examples": 8041}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 8232541, "num_examples": 8041}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 16110353, "num_examples": 8041}, {"name": "fewshot_3__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 16213718, "num_examples": 8041}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 4321120, "num_examples": 8041}], "download_size": 13641398, "dataset_size": 76656092}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "path": "data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*"}]}]}
|
2024-01-30T05:58:59+00:00
|
d8d156e301646dce2cd4082cb5deef6e3151ac7c
|
# Dataset Card for "Spanish_MLM_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ashwathjadhav23/Spanish_MLM_6
|
[
"region:us"
] |
2023-04-05T05:40:36+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2305167, "num_examples": 24999}], "download_size": 1495017, "dataset_size": 2305167}}
|
2023-04-05T05:40:40+00:00
|
864935f56dacd91f8235a21cf038e7eacaf8b556
|
# Dataset Card for "crypto-news-headlines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
flowfree/crypto-news-headlines
|
[
"region:us"
] |
2023-04-05T05:54:10+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "neutral", "2": "positive"}}}}], "splits": [{"name": "train", "num_bytes": 112117, "num_examples": 518}, {"name": "validation", "num_bytes": 55863, "num_examples": 260}, {"name": "test", "num_bytes": 55964, "num_examples": 257}], "download_size": 146743, "dataset_size": 223944}}
|
2023-04-05T05:54:43+00:00
|
312128ce8739671820af35e8bc8afc6629566494
|
ydydyy/sgaat
|
[
"license:other",
"region:us"
] |
2023-04-05T05:54:29+00:00
|
{"license": "other"}
|
2023-04-05T05:54:29+00:00
|
|
d56756ac7f98efe1b743ff0fd820ec94a5786f55
|
soumyanjan/testDataSet
|
[
"license:openrail",
"region:us"
] |
2023-04-05T05:57:25+00:00
|
{"license": "openrail"}
|
2023-04-05T05:57:25+00:00
|
|
2c422f5a1029d2b7eefa286fb805b8a680602d7d
|
# Dataset Card for "Food101_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_10000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_10000
|
[
"region:us"
] |
2023-04-05T06:04:30+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4288864, "num_examples": 10000}], "download_size": 509945, "dataset_size": 4288864}}
|
2023-04-05T06:04:33+00:00
|
ac0f6728bc6e24895abbe432a3ba04a97d43d1f6
|
# OWCA - Optimized and Well-Translated Customization of Alpaca
The OWCA dataset is a Polish-translated dataset of instructions for fine-tuning the Alpaca model made by Stanford.
## OWCA Dataset
The OWCA dataset is a customized and well-translated dataset of instructions for fine-tuning the Alpaca model made by Stanford. The Alpaca model is a state-of-the-art natural language processing (NLP) model that can be fine-tuned on various tasks such as sentiment analysis, text classification, and question answering.
## Purpose of the Dataset
The OWCA dataset was created to provide a high-quality Polish-translated version of instructions for fine-tuning the Alpaca model. It aims to help researchers and data scientists who are interested in utilizing the Alpaca model for NLP tasks in the Polish language.
## Data Source
The OWCA dataset was created by translating the original instructions for fine-tuning the Alpaca model into Polish. The original cleaned instructions were made by cleaning the original Stanford instructions and can be found [here](https://github.com/gururise/AlpacaDataCleaned). The translation was done algorithmically generated from various sources. It is ongoing proofreading is taking place by a team of experienced translators and NLP experts to ensure the accuracy and quality of the dataset.
#TODO proofread
## Contents of the Dataset
The dataset is provided in a text format and can be easily integrated into NLP projects that require fine-tuning of the Alpaca model for Polish language tasks.
Optimized - Dataset is being transformed into more relevant for polish use : law, metrics etc.
Well-Translated - translated and ongoing proofreading is taking place
Customization - output differs from original Alpaca and often contains deeper and broader explanations of output, especially code
## Potential Uses of the Dataset
The OWCA dataset can be used by researchers and data scientists who are working on NLP tasks in the Polish language. It can be particularly useful for those who are interested in utilizing the Alpaca model, which is a state-of-the-art NLP model that has shown impressive performance in various tasks. The dataset can also serve as a valuable resource for those who are interested in studying the process of fine-tuning NLP models.
|
emplocity/owca
|
[
"region:us"
] |
2023-04-05T06:32:10+00:00
|
{}
|
2023-05-17T06:21:38+00:00
|
4b2ddc9b85d27afb6ce3b20fe48c3e42a908eda5
|
# Dataset Card for "flores200_eng_output_scaffolding_cotr_mt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hlillemark/flores200_eng_output_scaffolding_cotr_mt5
|
[
"region:us"
] |
2023-04-05T06:48:36+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10398083512, "num_examples": 10240000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 4655425908, "dataset_size": 10409581548}}
|
2023-04-05T06:56:19+00:00
|
d0604ec092e341455d038ca1169d53c292e11bb9
|
# Dataset Card for "common_voice_10_1_th_clean_split_3_augment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DylanonWic/common_voice_10_1_th_clean_split_3_augment_old
|
[
"region:us"
] |
2023-04-05T06:54:32+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "input_values", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 12080523613, "num_examples": 50530}], "download_size": 12068838971, "dataset_size": 12080523613}}
|
2023-04-05T07:05:59+00:00
|
1c1d248dad7d289b7947656051ce5ff50db86d06
|
# Dataset Card for "hpqa_ret"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/hpqa_ret
|
[
"region:us"
] |
2023-04-05T07:21:09+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2155444816, "num_examples": 836741}, {"name": "validation", "num_bytes": 162097376, "num_examples": 62926}, {"name": "test", "num_bytes": 189851200, "num_examples": 73700}], "download_size": 269845048, "dataset_size": 2507393392}}
|
2023-04-05T09:07:45+00:00
|
ab7d9ad4db0fdbc9f08e68faa7b1deba8bce27a0
|
WilliamWen/NI_chemical_formula
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-05T07:23:23+00:00
|
{"license": "apache-2.0"}
|
2023-04-05T09:05:04+00:00
|
|
bc8ad5c898b235f8314da31e0d03fba10c063975
|
gustproof/sd-data
|
[
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-04-05T07:47:31+00:00
|
{"license": "cc-by-nc-sa-4.0"}
|
2023-10-20T06:59:08+00:00
|
|
fd3ad3877e0e3f4772d1d46dd399f418c40d7c0a
|
# AutoTrain Dataset for project: test-qa
## Dataset Description
This dataset has been automatically processed by AutoTrain for project test-qa.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"context": "['scriptures,which lay down the discipline.If you adhere strictly to the path of virtue and stick to the yearning, you can become an ascetic of the highestorder, a realised sage, though you may now be a novice or even a non-believer!Sometimes, by just seizing a chance, you can elevate yourselves steadily. Someone comes to Me to get hisstomach-ache cured; then he likes this place and its atmosphere and its chanting of Om (Omkara) and devotionalsinging (bhajana) and its peace (prasanthi); he sees Me and observes My movements and words and actions. Hetakes home a picture or a song book, and before']",
"question": " The passage is mainly about _.",
"answers.text": [
" how to become a realised sage"
],
"answers.answer_start": [
0
],
"feat_id": [
1477
],
"feat_Answer_end": [
29
],
"feat_title": [
null
]
},
{
"context": "[\"abidesThey will never enter the world of darkness and wretchedness whose minds are the abode of kindnessWho for undying souls of men provides with gracious zeal, In his own soul the dreaded guilt of sin shall never feel(The wise) say that the evils, which his soul would dread, will never come upon the man who exercises kindness and protects the life (of other creatures)The teeming earth's vast realm, round which the wild winds blow, Is witness, men of 'grace' no woeful want shall knowThis great rich earth over which the wind blows, is a witness that sorrow never comes upon the kind-hearted Gain of true wealth oblivious they eschew,Who 'grace' forsake, and graceless actions do(The wise) say that those who neglect kindness and practise cruelties, neglected virtue (in their former birth), and forgot (the sorrows which they must suffer)As to impoverished men this present world is not; The 'graceless' in you world have neither part nor lotAs this world is not for those who are without wealth, so that world is not for those who are without kindnessWho lose the flower of wealth, when seasons change, again may bloom; Who lose 'benevolence', lose all; nothing can change their doomThose who are without wealth may, at some future time, become prosperous; those who are destitute of kindness are utterly destitute; for them there is no changeWhen souls unwise true wisdom's mystic vision see, The 'graceless' man may work true works of charityIf you consider, the virtue of him who is without kindness is like the perception of the true being by him who is without wisdom0When weaker men you front with threat'ning brow, Think how you felt in presence of some stronger foeWhen a man is about to rush upon those who are weaker than himself, let him remember how he has stood (trembling) before those who are stronger than himself The Renunciation of FleshHow can the wont of 'kindly grace' to him be known, Who other creatures' flesh consumes to feed his own?How can he be possessed of kindness, who to increase his own flesh, eats the flesh of other creaturesNo use of wealth have they who guard not their estate; No use of grace have they with flesh who hunger sateAs those possess no property who do not take care of it, so those possess no kindness who feed on fleshLike heart of them that murderous weapons bear, his mind, Who eats of savoury meat, no joy in good can findLike the (murderous) mind of him who carries a weapon (in his hand), the mind of him who feasts with pleasure on the body of another (creature), has no regard for goodness 'What's grace, or lack of grace'? 'To kill' is this, that 'not to kill'; To eat dead flesh can never worthy end fulfilIf it be asked what is kindness and what its opposite, the answer would be preservation and destruction of life; and therefore it is not right to feed on the flesh (obtained by taking\"]",
"question": "The author's attitude towards the 'graceless' is _.",
"answers.text": [
"objective"
],
"answers.answer_start": [
0
],
"feat_id": [
155
],
"feat_Answer_end": [
8
],
"feat_title": [
null
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)",
"feat_id": "Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)",
"feat_Answer_end": "Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)",
"feat_title": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1499 |
| valid | 375 |
|
Aharneish/dataset-qa-indian-classical-text
|
[
"region:us"
] |
2023-04-05T07:54:25+00:00
|
{}
|
2023-04-05T07:56:45+00:00
|
07637ea37ada11f868467bf679601bd9766625bf
|
# Dataset Card for "multiCorp_tokenized_split_LabelNorm_0404_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Brizape/multiCorp_tokenized_split_LabelNorm_0404_dev
|
[
"region:us"
] |
2023-04-05T08:12:12+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "texts", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "test", "num_bytes": 6729090, "num_examples": 2035}, {"name": "train", "num_bytes": 19925705, "num_examples": 5165}, {"name": "validation", "num_bytes": 5290716, "num_examples": 1293}], "download_size": 5679113, "dataset_size": 31945511}}
|
2023-04-05T10:06:41+00:00
|
0ddd016cefa58abacbb7606ccdd337a9bd4bc02f
|
# Dataset Card for "ag_news_pt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
christykoh/ag_news_pt
|
[
"region:us"
] |
2023-04-05T08:25:19+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Mundo", "1": "Esportes", "2": "Neg\u00f3cios", "3": "Tecnologia"}}}}], "splits": [{"name": "test", "num_bytes": 2484768, "num_examples": 7600}, {"name": "train", "num_bytes": 39309192, "num_examples": 120000}], "download_size": 25945459, "dataset_size": 41793960}}
|
2023-04-21T11:00:19+00:00
|
9252c90e879ab819bef494391c86ba543d9ec2b8
|
wybxc/books-ext
|
[
"license:odc-by",
"region:us"
] |
2023-04-05T09:34:43+00:00
|
{"license": "odc-by"}
|
2023-04-05T09:35:13+00:00
|
|
ce89d63a04aced3ede799e4db45799e4e842984a
|
# Dataset Card for "hpqa_ret2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/hpqa_ret2
|
[
"region:us"
] |
2023-04-05T09:41:04+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "dtype": "int64"}, {"name": "token_type_ids", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2587033112, "num_examples": 836686}, {"name": "validation", "num_bytes": 194737252, "num_examples": 62981}, {"name": "test", "num_bytes": 227880400, "num_examples": 73700}], "download_size": 262776341, "dataset_size": 3009650764}}
|
2023-04-05T09:42:24+00:00
|
56bbaf96718e0c20a62b4c5277382f103216ff53
|
# Abalone
The [Abalone dataset](https://archive-beta.ics.uci.edu/dataset/1/abalone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Predict the age of the given abalone.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------|
| abalone | Regression | Predict the age of the abalone. |
| binary | Binary classification | Does the abalone have more than 9 rings?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/abalone")["train"]
```
# Features
Target feature in bold.
|**Feature** |**Type** |
|-----------------------|---------------|
| sex | `[string]` |
| length | `[float64]` |
| diameter | `[float64]` |
| height | `[float64]` |
| whole_weight | `[float64]` |
| shucked_weight | `[float64]` |
| viscera_weight | `[float64]` |
| shell_weight | `[float64]` |
| **number_of_rings** | `[int8]` |
|
mstz/abalone
|
[
"task_categories:tabular-regression",
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"abalone",
"tabular_regression",
"regression",
"binary_classification",
"region:us"
] |
2023-04-05T09:59:09+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["1K<n<10K"], "task_categories": ["tabular-regression", "tabular-classification"], "pretty_name": "Abalone", "tags": ["abalone", "tabular_regression", "regression", "binary_classification"], "configs": ["abalone", "binary"]}
|
2023-04-15T10:04:08+00:00
|
3f849a6f1c7b39dc81167ae0eef0554baa449f6f
|
# Dataset Card for "my-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
frascuchon/my-dataset
|
[
"region:us"
] |
2023-04-05T10:03:54+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 478527, "num_examples": 100}], "download_size": 0, "dataset_size": 478527}}
|
2023-04-05T10:40:09+00:00
|
9c1b7990906f429d6c87a6852017fd7b01091a42
|
# Dataset Card for "hpqa_ret_real"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/hpqa_ret_real
|
[
"region:us"
] |
2023-04-05T10:10:37+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "dtype": "int64"}, {"name": "token_type_ids", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2586714636, "num_examples": 836583}, {"name": "validation", "num_bytes": 195055728, "num_examples": 63084}, {"name": "test", "num_bytes": 227880400, "num_examples": 73700}], "download_size": 262972143, "dataset_size": 3009650764}}
|
2023-04-05T10:11:35+00:00
|
0c704a0d13ffeac98d5477ca06d49048e1ace460
|
# Acute Inflammation
The [Acute Inflammation dataset](https://archive.ics.uci.edu/ml/datasets/Acute+Inflammations) from the [UCI ML repository](https://archive-beta.ics.uci.edu).
Predict whether the patient has an acute inflammation.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| inflammation | Binary classification | Does the patient have an acute inflammation? |
| nephritis | Binary classification | Does the patient have a nephritic pelvis? |
| bladder | Binary classification | Does the patient have bladder inflammation? |
nephritis
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/acute_inflammation", "inflammation")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
| **Feature** | **Type** |
|---------------------------------------|---------------|
| `temperature` | `[float64]` |
| `has_nausea` | `[bool]` |
| `has_lumbar_pain` | `[bool]` |
| `has_urine_pushing` | `[bool]` |
| `has_micturition_pains` | `[bool]` |
| `has_burnt_urethra` | `[bool]` |
| `has_inflammed_bladder` | `[bool]` |
| `has_nephritis_of_renal_pelvis` | `[bool]` |
| `has_acute_inflammation` | `[int8]` |
|
mstz/acute_inflammation
|
[
"task_categories:tabular-classification",
"size_categories:100<n<1K",
"language:en",
"acute_inflammation",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
2023-04-05T10:13:27+00:00
|
{"language": ["en"], "size_categories": ["100<n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Acute Inflammation", "tags": ["acute_inflammation", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["inflammation", "nephritis", "bladder"]}
|
2023-04-15T10:37:39+00:00
|
b91923216e61ded52ec77316cb329b47227e4955
|
# Dataset Card for JaNLI
## Table of Contents
- [Dataset Card for JaNLI](#dataset-card-for-janli)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [base](#base)
- [original](#original)
- [Data Fields](#data-fields)
- [base](#base-1)
- [original](#original-1)
- [Data Splits](#data-splits)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/verypluming/JaNLI
- **Repository:** https://github.com/verypluming/JaNLI
- **Paper:** https://aclanthology.org/2021.blackboxnlp-1.26/
### Dataset Summary
The JaNLI (Japanese Adversarial NLI) dataset, inspired by the English HANS dataset, is designed to necessitate an understanding of Japanese linguistic phenomena and to illuminate the vulnerabilities of models.
### Languages
The language data in JaNLI is in Japanese (BCP-47 [ja-JP](https://www.rfc-editor.org/info/bcp47)).
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
```python
import datasets as ds
dataset: ds.DatasetDict = ds.load_dataset("hpprc/janli")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
# num_rows: 13680
# })
# test: Dataset({
# features: ['id', 'premise', 'hypothesis', 'label', 'heuristics', 'number_of_NPs', 'semtag'],
# num_rows: 720
# })
# })
dataset: ds.DatasetDict = ds.load_dataset("hpprc/janli", name="original")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
# num_rows: 13680
# })
# test: Dataset({
# features: ['id', 'sentence_A_Ja', 'sentence_B_Ja', 'entailment_label_Ja', 'heuristics', 'number_of_NPs', 'semtag'],
# num_rows: 720
# })
# })
```
#### base
An example of looks as follows:
```json
{
'id': 12,
'premise': '若者がフットボール選手を見ている',
'hypothesis': 'フットボール選手を若者が見ている',
'label': 0,
'heuristics': 'overlap-full',
'number_of_NPs': 2,
'semtag': 'scrambling'
}
```
#### original
An example of looks as follows:
```json
{
'id': 12,
'sentence_A_Ja': '若者がフットボール選手を見ている',
'sentence_B_Ja': 'フットボール選手を若者が見ている',
'entailment_label_Ja': 0,
'heuristics': 'overlap-full',
'number_of_NPs': 2,
'semtag': 'scrambling'
}
```
### Data Fields
#### base
A version adopting the column names of a typical NLI dataset.
| Name | Description |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| id | The number of the sentence pair. |
| premise | The premise (sentence_A_Ja). |
| hypothesis | The hypothesis (sentence_B_Ja). |
| label | The correct label for the sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction (entailment_label_Ja). |
| heuristics | The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset. |
| number_of_NPs | The number of noun phrase in a sentence. |
| semtag | The linguistic phenomena tag. |
#### original
The original version retaining the unaltered column names.
| Name | Description |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| id | The number of the sentence pair. |
| sentence_A_Ja | The premise. |
| sentence_B_Ja | The hypothesis. |
| entailment_label_Ja | The correct label for this sentence pair (either `entailment` or `non-entailment`); in the setting described in the paper, non-entailment = neutral + contradiction |
| heuristics | The heuristics (structural pattern) tag. The tags are: subsequence, constituent, full-overlap, order-subset, and mixed-subset. |
| number_of_NPs | The number of noun phrase in a sentence. |
| semtag | The linguistic phenomena tag. |
### Data Splits
| name | train | validation | test |
| -------- | -----: | ---------: | ---: |
| base | 13,680 | | 720 |
| original | 13,680 | | 720 |
### Annotations
The annotation process for this Japanese NLI dataset involves tagging each pair (P, H) of a premise and hypothesis with a label for structural pattern and linguistic phenomenon.
The structural relationship between premise and hypothesis sentences is classified into five patterns, with each pattern associated with a type of heuristic that can lead to incorrect predictions of the entailment relation.
Additionally, 11 categories of Japanese linguistic phenomena and constructions are focused on for generating the five patterns of adversarial inferences.
For each linguistic phenomenon, a template for the premise sentence P is fixed, and multiple templates for hypothesis sentences H are created.
In total, 144 templates for (P, H) pairs are produced.
Each pair of premise and hypothesis sentences is tagged with an entailment label (`entailment` or `non-entailment`), a structural pattern, and a linguistic phenomenon label.
The JaNLI dataset is generated by instantiating each template 100 times, resulting in a total of 14,400 examples.
The same number of entailment and non-entailment examples are generated for each phenomenon.
The structural patterns are annotated with the templates for each linguistic phenomenon, and the ratio of `entailment` and `non-entailment` examples is not necessarily 1:1 for each pattern.
The dataset uses a total of 158 words (nouns and verbs), which occur more than 20 times in the JSICK and JSNLI datasets.
## Additional Information
- [verypluming/JaNLI](https://github.com/verypluming/JaNLI)
- [Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference](https://aclanthology.org/2021.blackboxnlp-1.26/)
### Licensing Information
CC BY-SA 4.0
### Citation Information
```bibtex
@InProceedings{yanaka-EtAl:2021:blackbox,
author = {Yanaka, Hitomi and Mineshima, Koji},
title = {Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference},
booktitle = {Proceedings of the 2021 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP2021)},
url = {https://aclanthology.org/2021.blackboxnlp-1.26/},
year = {2021},
}
```
### Contributions
Thanks to [Hitomi Yanaka](https://hitomiyanaka.mystrikingly.com/) and [Koji Mineshima](https://abelard.flet.keio.ac.jp/person/minesima/index-j.html) for creating this dataset.
|
hpprc/janli
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language_creators:other",
"multilinguality:monolingual",
"language:ja",
"license:cc-by-sa-4.0",
"region:us"
] |
2023-04-05T11:25:01+00:00
|
{"language_creators": ["other"], "language": ["ja"], "license": "cc-by-sa-4.0", "multilinguality": ["monolingual"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "JaNLI"}
|
2023-04-11T03:40:37+00:00
|
b073721e8b9f5a5fd8b71ac69a218f1ca76adaf2
|
HanselYu/ThermoSeqNet
|
[
"license:gpl-3.0",
"region:us"
] |
2023-04-05T11:32:39+00:00
|
{"license": "gpl-3.0"}
|
2023-04-05T11:35:48+00:00
|
|
df2f9fc9fe7a3b09310286f5ca96ac38b7a298ac
|
celta/carla
|
[
"license:other",
"region:us"
] |
2023-04-05T11:35:42+00:00
|
{"license": "other"}
|
2023-04-05T11:35:42+00:00
|
|
86e4a91905d59e84395c71b04e9ab9b77a860e2d
|
# Arhythmia
The [Arrhythmia dataset](https://archive.ics.uci.edu/ml/datasets/Arrhythmia) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Does the patient have arhythmia? If so, what type?
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| arhytmia | Multiclass classification | What type of arhythmia does the patient have? |
| has_arhytmia | Binary classification | Does the patient have arhythmia? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/arhythmia", "arhythmia")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
|
mstz/arhythmia
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"arrhythmia",
"tabular_classification",
"multiclass_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-05T11:44:38+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Arhythmia", "tags": ["arrhythmia", "tabular_classification", "multiclass_classification", "binary_classification", "UCI"], "configs": ["arhytmia", "has_arhytmia"]}
|
2023-04-15T10:37:57+00:00
|
9603fa442dd511554bc32ffeab2b677da8cda848
|
# Peanut Comic Strip Dataset (Snoopy & Co.)
This is a dataset Peanuts comic strips from `1950/10/02` to `2000/02/13`.
There are `77,456` panels extracted from `17,816` comic strips.
The dataset size is approximately `4.4G`.
Each row in the dataset contains the following fields:
- `image`: `PIL.Image` containing the extracted panel.
- `panel_name`: unique identifier for the row.
- `characters`: `tuple[str, ...]` of characters included in the comic strip the panel is part of.
- `themes`: `tuple[str, ...]` of theme in the comic strip the panel is part of.
- `color`: `str` indicating whether the panel is grayscale or in color.
- `caption`: [BLIP-2_FLAN-T5-XL](https://huggingface.co/docs/transformers/main/model_doc/blip-2) generated caption from the panel.
- `year`: `int` storing the year the specific panel was released.
> **FLAN-T5-XL has a commercial use license and so this dataset can be used for commercial projects. Alternatively use [this similar dataset](https://huggingface.co/datasets/afmck/peanuts-opt-6.7b) that uses OPT-6.7B as the caption pipeline's text model, however it does not permit commercial use.**
Character and theme information was extracted from [Peanuts Wiki (Fandom)](https://peanuts.fandom.com/wiki/Peanuts_Wiki) using [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/).
Images were extracted from [Peanuts Search](https://peanuts-search.com/).
Only strips with the following characters were extracted:
```
- "Charlie Brown"
- "Sally Brown"
- "Joe Cool" # Snoopy alter-ego
- "Franklin"
- "Violet Gray"
- "Eudora"
- "Frieda"
- "Marcie"
- "Peppermint Patty"
- "Patty"
- "Pig-Pen"
- "Linus van Pelt"
- "Lucy van Pelt"
- "Rerun van Pelt"
- "Schroeder"
- "Snoopy"
- "Shermy"
- "Spike"
- "Woodstock"
- "the World War I Flying Ace" # Snoopy alter-ego
```
### Extraction Details
Panel detection and extraction was done using the following codeblock:
```python
def check_contour(cnt):
area = cv2.contourArea(cnt)
if area < 600:
return False
_, _, w, h = cv2.boundingRect(cnt)
if w / h < 1 / 2: return False
if w / h > 2 / 1: return False
return True
def get_panels_from_image(path):
panels = []
original_img = cv2.imread(path)
gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
invert = 255 - opening
cnts, _ = cv2.findContours(invert, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
idx = 0
for cnt in cnts:
if not check_contour(cnt): continue
idx += 1
x,y,w,h = cv2.boundingRect(cnt)
roi = original_img[y:y+h,x:x+w]
panels.append(roi)
return panels
```
`check_contour` will reject panels with `area < 600` or with aspect ratios larger than `2` or smaller than `0.5`.
Grayscale detection was done using the following codeblock:
```python
def is_grayscale(panel):
LAB_THRESHOLD = 10.
img = cv2.cvtColor(panel, cv2.COLOR_RGB2LAB)
_, ea, eb = cv2.split(img)
de = abs(ea - eb)
mean_e = np.mean(de)
return mean_e < LAB_THRESHOLD
```
Captioning was done using the standard BLIP-2 pipeline shown in the [Huggingface docs](https://huggingface.co/docs/transformers/main/model_doc/blip-2) using beam search over 10 beams and a repetition penalty of `2.0`.
Raw captions are extracted and no postprocessing is applied. You may wish to normalise captions (such as replacing "cartoon" with "peanuts cartoon") or incorporate extra metadata into prompts.
|
afmck/peanuts-flan-t5-xl
|
[
"task_categories:text-to-image",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-04-05T12:16:59+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-image"], "pretty_name": "Peanuts Dataset (Snoopy and Co.)", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "panel_name", "dtype": "string"}, {"name": "characters", "sequence": "string"}, {"name": "themes", "sequence": "string"}, {"name": "color", "dtype": "string"}, {"name": "year", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2947874869.848, "num_examples": 77456}], "download_size": 0, "dataset_size": 2947874869.848}}
|
2023-04-05T13:09:59+00:00
|
60a184abdb556c81d56e02eb2f8f8a7a9a249c4c
|
# Balance scale
The [Balance scale dataset](https://archive-beta.ics.uci.edu/dataset/12/balance+scale) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Two weights are put on the arms of a scale. Where does the scale tilt?
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| balance | Multiclass classification | Where does the scale tilt? |
| is_balanced | Binary classification | Does the scale tilt? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/balance_scale", "balance")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
|
mstz/balance_scale
|
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"balance_scale",
"tabular_classification",
"multiclass_classification",
"binary_classification",
"UCI",
"region:us"
] |
2023-04-05T12:38:46+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Balance", "tags": ["balance_scale", "tabular_classification", "multiclass_classification", "binary_classification", "UCI"], "configs": ["balance", "is_balanced"]}
|
2023-04-15T10:14:55+00:00
|
44ff921d19683a05008fc79b7cd6b76a4720c4d4
|
# Dataset Card for "m2e_5_4_merged_tags"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sunbird/m2e_5_4_merged_tags
|
[
"region:us"
] |
2023-04-05T13:17:31+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 895822802, "num_examples": 2276110}, {"name": "valid", "num_bytes": 557490, "num_examples": 2500}], "download_size": 321423120, "dataset_size": 896380292}}
|
2023-04-05T13:18:23+00:00
|
a4d55d92425f062c4e51c37e08045df5fae4fc2f
|
# Dataset Card for "LogicInference_OA"
This is an re-produce of the dataset from LogicInference Dataset in paper: https://openreview.net/pdf?id=HAGeIS_Lcg9.
The github page of LogicInference Dataset: https://github.com/google-research/google-research/tree/master/logic_inference_dataset.
This dataset is aimed to offer more dataset for Open Assistant project, depending on their demands, there three columns: INSTRUCTION, RESPONSE, SOURCE.
The results in this dataset is a little different from which was introduced in the original paper:
1.For all three splits (IID/OOD/length), only IID is used. In the original paper, it seems that model can reach better performance with data generated by this split method.
2.In the original paper, there are two form of responses: LOGICINFERENCE<sub>b</sub> (with the answer at the beginning) and LOGICINFERENCE<sub>e</sub> (with the answer at the end). This dataset uses LOGICINFERENCE<sub>e</sub>, that means: for all questions, the model will first do logic inference, and give the final answer at the end.
3.The original paper, some parameters in generate_dataset.py are:
N_INFERENCE_PROBLEMS = 5000
N_VARIATIONS = 25
N_EXAMPLES = 200000
TRAIN_RATIO = 0.9
LENGTH_SPLIT_THRESHOLD = 4
RANDOM_SEED = 0
I choose some new parameters:
N_INFERENCE_PROBLEMS = 10000
N_VARIATIONS = 25
N_EXAMPLES = 55000
TRAIN_RATIO = 1
LENGTH_SPLIT_THRESHOLD = 4
RANDOM_SEED = 1111
The original script generated 4814 different inference problems and extended all those inference problems to around 200,000 Q-A pairs. My settings generated 5491 different inference problems and extended them to around 54,607 Instruction-Response pairs. I think for Open Assistant projects, maybe the number of different inference problems is more important, and generated many similar Instruction-Response pairs will only add training time and doesn't make much sense.
|
KK04/LogicInference_OA
|
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"Logic Inference",
"region:us"
] |
2023-04-05T13:35:16+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30414202, "num_examples": 54607}], "download_size": 7588805, "dataset_size": 30414202}, "tags": ["Logic Inference"]}
|
2023-04-05T14:38:22+00:00
|
b0bd14a508f2e0312dc58d0f4936f7495845bd53
|
# Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_100
|
[
"region:us"
] |
2023-04-05T13:44:20+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 37864, "num_examples": 100}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 73368, "num_examples": 100}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 145031, "num_examples": 100}], "download_size": 51824, "dataset_size": 256263}}
|
2023-04-05T13:51:36+00:00
|
93e85b1025990ce55c9e81aca7b539a5d5bf4271
|
# Dataset Card for intro_prog
## Dataset Description
### Dataset Summary
IntroProg is a collection of students' submissions to assignments in various introductory programming courses offered at different universities.
Currently, the dataset contains submissions collected from Dublin City University, and the University of Singapore.
#### Dublin
The Dublin programming dataset is a dataset composed of students' submissions to introductory programming assignments at the University of Dublin.
Students submitted these programs for multiple programming courses over the duration of three academic years.
#### Singapore
The Singapore dataset contains 2442 correct and 1783 buggy program attempts by 361 undergraduate students
crediting an introduction to Python programming course at NUS (National University of Singapore).
### Supported Tasks and Leaderboards
#### "Metadata": Program synthesis
Similarly to the [Most Basic Python Programs](https://huggingface.co/datasets/mbpp) (mbpp), the data split can be used to evaluate
code generations models.
#### "Data"
The data configuration contains all the submissions as well as an indicator of whether these passed the required test.
#### "repair": Program refinement/repair
The "repair" configuration of each dataset is a subset of the "data" configuration
augmented with educators' annotations on the corrections to the buggy programs.
This configuration can be used for the task of program refinement. In [Computing Education Research](https://faculty.washington.edu/ajko/cer/) (CER),
methods for automatically repairing student programs are used to provide students with feedback and help them debug their code.
#### "bug": Bug classification
[Coming soon]
### Languages
The assignments were written in Python.
## Dataset Structure
One configuration is defined by one source dataset *dublin* or *singapore* and one subconfiguration ("metadata", "data", or "repair"):
* "dublin_metadata"
* "dublin_data"
* "dublin_repair"
* "singapore_metadata"
* "singapore_data"
* "singapore_repair"
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
Some of the fields are configuration specific
* submission_id: a unique number identifying the submission
* user: a unique string identifying the (anonymized) student who submitted the solution
* date: the timestamp at which the grading server received the submission
* func_code: the cleaned code submitted
* func_name: the name of the function that had to be implemented
* assingment_id: the unique (string) identifier of the assignment that had to be completed
* academic_year: the starting year of the academic year (e.g. 2015 for the academic year 2015-2016)
* module: the course/module
* test: a human eval-style string which can be used to execute the submitted solution on the provided test cases
* Description: a description of what the function is supposed to achieve
* correct: whether the solution passed all tests or not
### Data Splits
#### Dublin
The Dublin dataset is split into a training and validation set. The training set contains the submissions to the assignments
written during the academic years 2015-2016, and 2016-2017, while the test set contains programs written during the academic year 2017-2018.
#### Singapore
The Singapore dataset only contains a training split, which can be used as a test split for evaluating how your feedback
methods perform on an unseen dataset (if, for instance, you train your methods on the Dublin Dataset).
## 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
#### Dublin
#### Singapore
The data was released under a [GNU Lesser General Public License v3.0](https://github.com/githubhuyang/refactory/blob/master/LICENSE) license
### Citation Information
```
@inproceedings{azcona2019user2code2vec,
title={user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code},
author={Azcona, David and Arora, Piyush and Hsiao, I-Han and Smeaton, Alan},
booktitle={Proceedings of the 9th International Learning Analytics & Knowledge Conference (LAK’19)},
year={2019},
organization={ACM}
}
@inproceedings{DBLP:conf/edm/CleuziouF21,
author = {Guillaume Cleuziou and
Fr{\'{e}}d{\'{e}}ric Flouvat},
editor = {Sharon I{-}Han Hsiao and
Shaghayegh (Sherry) Sahebi and
Fran{\c{c}}ois Bouchet and
Jill{-}J{\^{e}}nn Vie},
title = {Learning student program embeddings using abstract execution traces},
booktitle = {Proceedings of the 14th International Conference on Educational Data
Mining, {EDM} 2021, virtual, June 29 - July 2, 2021},
publisher = {International Educational Data Mining Society},
year = {2021},
timestamp = {Wed, 09 Mar 2022 16:47:22 +0100},
biburl = {https://dblp.org/rec/conf/edm/CleuziouF21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
[More Information Needed]
|
koutch/intro_prog
|
[
"region:us"
] |
2023-04-05T13:44:41+00:00
|
{"dataset_info": [{"config_name": "dublin_metadata", "features": [{"name": "assignment_id", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "reference_solution", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18983, "num_examples": 36}, {"name": "test", "num_bytes": 17403, "num_examples": 35}], "download_size": 41873, "dataset_size": 36386}, {"config_name": "singapore_metadata", "features": [{"name": "assignment_id", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "reference_solution", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5577, "num_examples": 5}], "download_size": 6139, "dataset_size": 5577}, {"config_name": "dublin_data", "features": [{"name": "submission_id", "dtype": "int32"}, {"name": "func_code", "dtype": "string"}, {"name": "assignment_id", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "correct", "dtype": "bool"}, {"name": "user", "dtype": "string"}, {"name": "academic_year", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 4412068, "num_examples": 7486}, {"name": "test", "num_bytes": 7737585, "num_examples": 14259}], "download_size": 15756562, "dataset_size": 12149653}, {"config_name": "singapore_data", "features": [{"name": "submission_id", "dtype": "int32"}, {"name": "func_code", "dtype": "string"}, {"name": "assignment_id", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "correct", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 5098928, "num_examples": 4394}], "download_size": 5705043, "dataset_size": 5098928}, {"config_name": "dublin_repair", "features": [{"name": "submission_id", "dtype": "int32"}, {"name": "func_code", "dtype": "string"}, {"name": "assignment_id", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "user", "dtype": "string"}, {"name": "academic_year", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 229683, "num_examples": 307}, {"name": "test", "num_bytes": 1451820, "num_examples": 1698}], "download_size": 1929518, "dataset_size": 1681503}, {"config_name": "singapore_repair", "features": [{"name": "submission_id", "dtype": "int32"}, {"name": "func_code", "dtype": "string"}, {"name": "assignment_id", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "annotation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18979, "num_examples": 18}], "download_size": 21737, "dataset_size": 18979}, {"config_name": "newcaledonia_metadata", "features": [{"name": "assignment_id", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "reference_solution", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9053, "num_examples": 9}], "download_size": 9760, "dataset_size": 9053}, {"config_name": "newcaledonia_data", "features": [{"name": "submission_id", "dtype": "int32"}, {"name": "func_code", "dtype": "string"}, {"name": "assignment_id", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "test", "dtype": "string"}, {"name": "correct", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 932024, "num_examples": 1201}], "download_size": 1198518, "dataset_size": 932024}]}
|
2023-06-05T07:45:02+00:00
|
8d69b196b94ac47b22e9c8f450809282690fc68b
|
LiangMen/jaychou
|
[
"license:other",
"region:us"
] |
2023-04-05T13:56:16+00:00
|
{"license": "other"}
|
2023-04-05T13:57:40+00:00
|
|
57338e385f002ee4e75eb973369f9667708d0d6b
|
# 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]
|
Jornt/calculations
|
[
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"finance",
"region:us"
] |
2023-04-05T14:18:49+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "calculations", "tags": ["finance"]}
|
2023-04-07T11:42:29+00:00
|
26ab213089edd0fc30f5418bfce4188e454360dd
|
A dataset containing both DGA and normal domain names. The normal domain names were taken from the Alexa top one million domains.
An additional 3,161 normal domains were included in the dataset, provided by the Bambenek Consulting feed. This later group is particularly interesting since it consists
of suspicious domain names that were not generated by DGA. Therefore, the total amount of domains normal in the dataset is 1,003,161. DGA domains
were obtained from the repositories of DGA domains of [Andrey Abakumov](https://github.com/andrewaeva/DGA) and [John Bambenek](http://osint.bambenekconsulting.com/feeds/).
The total amount of DGA domains is 1,915,335, and they correspond to 51 different malware families. DGA domains were generated by 51 different malware families.
About the 55% of of the DGA portion of dataset is composed of samples from the Banjori, Post, Timba, Cryptolocker, Ramdo and Conficker malware.
The DGA generation scheme followed by the malware families includes the simple arithmetical (A) and the recent word based (W) schemes.
Under the arithmetic scheme, the algorithm usually calculates a sequence of values that have a direct ASCII representation usable for a domain name.
On the other hand, word-based consists of concatenating a sequence of words from one or more wordlists.
|
harpomaxx/dga-detection
|
[
"license:cc-by-2.0",
"region:us"
] |
2023-04-05T14:22:41+00:00
|
{"license": "cc-by-2.0"}
|
2023-05-10T12:32:11+00:00
|
6ad44cf6f5f1d6a05b78f7916c69cb0dee3df852
|
# Dataset Card for "Caltech101_with_background_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_6084"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Caltech101_with_background_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_6084
|
[
"region:us"
] |
2023-04-05T14:23:40+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2520812, "num_examples": 6084}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4868154, "num_examples": 6084}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 9570134, "num_examples": 6084}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2409382, "num_examples": 6084}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 4649134, "num_examples": 6084}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 9130589, "num_examples": 6084}], "download_size": 4880166, "dataset_size": 33148205}}
|
2023-05-05T22:45:11+00:00
|
6501d09ee825fe9f93428e1c2b5eb5f38e73719e
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_6149"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_6149
|
[
"region:us"
] |
2023-04-05T14:36:21+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2741695, "num_examples": 6149}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 5301717, "num_examples": 6149}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 10412557, "num_examples": 6149}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 2609943, "num_examples": 6149}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 5047641, "num_examples": 6149}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 9920082, "num_examples": 6149}], "download_size": 3467710, "dataset_size": 36033635}}
|
2023-05-13T08:22:32+00:00
|
4c8a851d411ed1707deca4392fc39f2b594a196d
|
AyoubChLin/Cnn_news_article_Sembedding
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-05T14:36:58+00:00
|
{"license": "apache-2.0"}
|
2023-04-05T14:36:58+00:00
|
|
a0bac4bba5d47228fd6c03a2b0b4f9baa51cc2ce
|
# Dataset Card for "bioBERT-ner-biomedical-text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dylanmontoya22/bioBERT-ner-biomedical-text
|
[
"region:us"
] |
2023-04-05T14:39:20+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "annotation", "list": [{"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 148595, "num_examples": 710}], "download_size": 24684, "dataset_size": 148595}}
|
2023-04-05T14:39:21+00:00
|
4a34482d832e01ff4c9a0398653881a69d510c4c
|
# Dataset Card for "camoscio_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
teelinsan/camoscio_cleaned
|
[
"language:it",
"region:us"
] |
2023-04-05T14:42:59+00:00
|
{"language": "it", "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20903457.244625207, "num_examples": 50245}], "download_size": 13083590, "dataset_size": 20903457.244625207}}
|
2023-11-06T18:03:28+00:00
|
d451c3594fdf39a29ffaa2f78532f475847a412d
|
Heeheeeeeeeeee/fuego-20230405-175749-71fa61
|
[
"fuego",
"region:us"
] |
2023-04-05T14:57:51+00:00
|
{"tags": ["fuego"], "fuego": {"id": "20230405-175749-71fa61", "status": "done", "script": "run_glue.py", "requirements_file": "requirements.txt", "space_id": "Heeheeeeeeeeee/fuego-20230405-175749-71fa61", "space_hardware": "cpu-basic", "github_repo_id": "huggingface/transformers", "github_repo_branch": "main", "github_repo_sha": "176ceff91f5e5ff15922715e5a4a4d9f66b92d14"}}
|
2023-04-05T16:39:58+00:00
|
|
449017a72921af200cc71daa9f4c1ebd97a5a70f
|
# Dataset Card for "StanfordCars_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_8041"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/StanfordCars_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_8041
|
[
"region:us"
] |
2023-04-05T15:06:09+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4049401, "num_examples": 8041}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 7738085, "num_examples": 8041}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 15120045, "num_examples": 8041}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 4296917, "num_examples": 8041}, {"name": "fewshot_0__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 4323031, "num_examples": 8041}, {"name": "fewshot_1__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 8284942, "num_examples": 8041}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 8232521, "num_examples": 8041}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 16110329, "num_examples": 8041}, {"name": "fewshot_3__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 16213140, "num_examples": 8041}], "download_size": 15192803, "dataset_size": 84368411}}
|
2023-06-12T08:34:36+00:00
|
aac386edcd00318c257b9e280577fc2dd1a28025
|
# Dataset Card for "tawikidump_20230320_sent_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnanthZeke/tawikidump_20230320_sent_cleaned
|
[
"region:us"
] |
2023-04-05T15:13:54+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "sent_token", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2062743668, "num_examples": 572538}], "download_size": 619146811, "dataset_size": 2062743668}}
|
2023-04-05T15:14:29+00:00
|
f37166e3e48be25ea4e976e2cfdae2c3208f9b90
|
# Dataset Card for "common_voice_6_1_th_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DylanonWic/common_voice_6_1_th_test
|
[
"region:us"
] |
2023-04-05T15:18:13+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "input_values", "sequence": "float32"}], "splits": [{"name": "test", "num_bytes": 586434697, "num_examples": 2050}], "download_size": 559868982, "dataset_size": 586434697}}
|
2023-04-05T15:19:06+00:00
|
6b01285ae44d12350034b9256e4da777d0fc9188
|
# Dataset Card for "imdb_pt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
christykoh/imdb_pt
|
[
"region:us"
] |
2023-04-05T15:27:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negativo", "1": "positivo"}}}}], "splits": [{"name": "train", "num_bytes": 33225773, "num_examples": 25000}, {"name": "test", "num_bytes": 6503491, "num_examples": 5000}, {"name": "test_all", "num_bytes": 32638767, "num_examples": 25000}], "download_size": 44980841, "dataset_size": 72368031}}
|
2023-04-05T15:28:11+00:00
|
a2f4d6fbcc81ba84d4dee9fd2c33ea8ba1914a74
|
ColtonAi/Oi
|
[
"task_categories:question-answering",
"size_categories:n>1T",
"language:en",
"license:gpl",
"not-for-all-audiences",
"legal",
"chemistry",
"biology",
"medical",
"region:us"
] |
2023-04-05T15:30:34+00:00
|
{"language": ["en"], "license": "gpl", "size_categories": ["n>1T"], "task_categories": ["question-answering"], "pretty_name": "kudurru", "tags": ["not-for-all-audiences", "legal", "chemistry", "biology", "medical"]}
|
2023-04-05T15:33:07+00:00
|
|
5a50ca840dead34931f07f79232c6201d86f7d24
|
# Dataset Card for "Food101_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_25250"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Food101_test_google_flan_t5_xl_mode_T_SPECIFIC_A_ns_25250
|
[
"region:us"
] |
2023-04-05T15:39:10+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 10963740, "num_examples": 25250}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 10963611, "num_examples": 25250}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 21186073, "num_examples": 25250}, {"name": "fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 41629357, "num_examples": 25250}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 10655758, "num_examples": 25250}, {"name": "fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 20601550, "num_examples": 25250}, {"name": "fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 40504304, "num_examples": 25250}, {"name": "fewshot_0__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 11495378, "num_examples": 25250}, {"name": "fewshot_1__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 22272662, "num_examples": 25250}, {"name": "fewshot_3__Attributes_ViT_L_14_descriptors_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 43826791, "num_examples": 25250}], "download_size": 26062627, "dataset_size": 234099224}}
|
2023-06-12T12:51:12+00:00
|
946c6f972918a2c114031e40da0a55b9d62cce60
|
gracebwu/amazon-toys
|
[
"license:unknown",
"region:us"
] |
2023-04-05T16:09:56+00:00
|
{"license": "unknown"}
|
2023-04-05T21:28:17+00:00
|
|
966643635aca47e0f55fe619653a659495e48322
|
# Dataset Card for "tamil_combined_sentences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnanthZeke/tamil_sentences_sample
|
[
"task_categories:sentence-similarity",
"task_categories:zero-shot-classification",
"size_categories:1M<n<10M",
"language:ta",
"license:mit",
"OSCAR",
"Wikipedia",
"Tamil",
"region:us"
] |
2023-04-05T16:12:47+00:00
|
{"language": ["ta"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["sentence-similarity", "zero-shot-classification"], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1164550978, "num_examples": 2391475}], "download_size": 347960778, "dataset_size": 1164550978}, "tags": ["OSCAR", "Wikipedia", "Tamil"]}
|
2023-10-13T06:18:48+00:00
|
2ffe3afe1a8431316856ca83b462f6dbe63eb9a8
|
# Dataset Card for "Food101_test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/Food101_test_embeddings
|
[
"region:us"
] |
2023-04-05T16:25:39+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 1352851340.5, "num_examples": 25250}], "download_size": 1355827682, "dataset_size": 1352851340.5}}
|
2023-04-05T16:27:44+00:00
|
b43f085d0a3f21e0a4c36b8b8884e333a75ff097
|
# Dataset Card for "Food101_train_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/Food101_train_embeddings
|
[
"region:us"
] |
2023-04-05T16:29:33+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 4075664187.0, "num_examples": 75750}], "download_size": 4082066204, "dataset_size": 4075664187.0}}
|
2023-04-26T01:27:45+00:00
|
46fa031e4283564670a6a81a4876a4b423d49f3a
|
ealtan/MoodBooster
|
[
"license:mit",
"region:us"
] |
2023-04-05T16:45:33+00:00
|
{"license": "mit"}
|
2023-04-05T16:45:33+00:00
|
|
b28e388fb0b0a8cf7aadd049991ec4fedfc46087
|
tthoraldson/OasisLyrics
|
[
"license:cc",
"region:us"
] |
2023-04-05T16:48:14+00:00
|
{"license": "cc"}
|
2023-04-05T17:26:19+00:00
|
|
e6ec7b07313b4e5e281d8e9f095f64e9683bafa8
|
Dump of some lyrics by the amazing Beatles!
|
Beatcenture/beatles_lyrics
|
[
"license:apache-2.0",
"region:us"
] |
2023-04-05T16:50:48+00:00
|
{"license": "apache-2.0"}
|
2023-04-05T19:24:52+00:00
|
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