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# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Attributes_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Attributes_ns_5647
[ "region:us" ]
2023-01-26T17:32:11+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84091856.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 85276115.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 87656033.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 90033855.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 93580332.125, "num_examples": 5647}], "download_size": 415578350, "dataset_size": 440638191.625}}
2023-01-27T09:56:58+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Attributes_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Attributes_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Attributes_ns_5647\"\n\nMore Information needed" ]
[ 6, 37 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Attributes_ns_5647\"\n\nMore Information needed" ]
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17fca26fa60bd5b68e3b931516a83adcc1404004
# Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Attributes_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_1.3b_Attributes_ns_5647
[ "region:us" ]
2023-01-26T17:41:52+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84091875.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 85275969.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 87655782.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 90033051.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 93578765.125, "num_examples": 5647}], "download_size": 415550337, "dataset_size": 440635442.625}}
2023-01-27T10:29:33+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Attributes_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Attributes_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Attributes_ns_5647\"\n\nMore Information needed" ]
[ 6, 37 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Attributes_ns_5647\"\n\nMore Information needed" ]
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3fee92c9d1d9aac33c567e169877f73b3656a9c9
# Dataset Card for "Caltech101_not_background_test_facebook_opt_2.7b_Attributes_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Attributes_ns_5647
[ "region:us" ]
2023-01-26T17:54:58+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84092194.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 85276120.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 87656119.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 90033173.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 93579321.125, "num_examples": 5647}], "download_size": 415540728, "dataset_size": 440636927.625}}
2023-01-27T11:22:15+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_2.7b_Attributes_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Attributes_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Attributes_ns_5647\"\n\nMore Information needed" ]
[ 6, 38 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Attributes_ns_5647\"\n\nMore Information needed" ]
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2dcadcaa34528d6688508d5da3cc5a1416a516cd
# Dataset Card for "Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647
[ "region:us" ]
2023-01-26T18:09:31+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84344156.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 85792185.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 88692887.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 91584891.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 95914176.125, "num_examples": 5647}], "download_size": 416469739, "dataset_size": 446328295.625}}
2023-01-27T11:36:31+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
[ 6, 40 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
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68d58fdd810bd3737c9c98f62721875f14c92052
# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647
[ "region:us" ]
2023-01-26T18:23:45+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84345940.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 85792356.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 88692846.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 91584840.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 95914371.125, "num_examples": 5647}], "download_size": 416501462, "dataset_size": 446330353.625}}
2023-01-27T11:58:07+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
[ 6, 40 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
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3995288fed4000de6792377a98038ccf36c604ea
# Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Attributes_Caption_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_1.3b_Attributes_Caption_ns_5647
[ "region:us" ]
2023-01-26T18:35:01+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84346377.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 85792216.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 88692718.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 91584252.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 95913089.125, "num_examples": 5647}], "download_size": 416449265, "dataset_size": 446328652.625}}
2023-01-27T12:39:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Attributes_Caption_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
[ 6, 40 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
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a26beb13be2d7b00b62c07ce715b877836323b7f
# Dataset Card for "Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647
[ "region:us" ]
2023-01-26T18:52:00+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84346848.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 85792300.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 88692841.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 91584503.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 95913670.125, "num_examples": 5647}], "download_size": 416433288, "dataset_size": 446330162.625}}
2023-01-27T13:47:21+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
[ 6, 41 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647\"\n\nMore Information needed" ]
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3e1ff12ee975fa866b8c6d0352b0384ea7a3ef4e
# Dataset Card for "test_meta_dump" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
polinaeterna/test_meta_dump
[ "region:us" ]
2023-01-26T18:54:51+00:00
{"dataset_info": [{"config_name": "single", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 360, "num_examples": 20}], "download_size": 0, "dataset_size": 360}, {"config_name": "v1", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 93, "num_examples": 6}, {"name": "test", "num_bytes": 28, "num_examples": 2}], "download_size": 0, "dataset_size": 121}, {"config_name": "v2", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 56, "num_examples": 4}, {"name": "test", "num_bytes": 14, "num_examples": 1}], "download_size": 1673, "dataset_size": 70}], "configs_kwargs": [{"config_name": "single", "data_dir": "single"}, {"config_name": "v1", "data_dir": "v1"}, {"config_name": "v2", "data_dir": "v2"}]}
2023-01-27T18:20:18+00:00
[]
[]
TAGS #region-us
# Dataset Card for "test_meta_dump" More Information needed
[ "# Dataset Card for \"test_meta_dump\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"test_meta_dump\"\n\nMore Information needed" ]
[ 6, 16 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"test_meta_dump\"\n\nMore Information needed" ]
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b3831f98a3589ad2083fa27ed7e659ffb2a48ec8
# Dataset Card for "Caltech101_not_background_test_facebook_opt_125m_Visclues_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_125m_Visclues_ns_5647
[ "region:us" ]
2023-01-26T18:58:39+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84810527.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 86719159.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 90542895.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 94355222.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 100059276.125, "num_examples": 5647}], "download_size": 418833711, "dataset_size": 456487079.625}}
2023-01-27T14:07:24+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_125m_Visclues_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_125m_Visclues_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_125m_Visclues_ns_5647\"\n\nMore Information needed" ]
[ 6, 37 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_125m_Visclues_ns_5647\"\n\nMore Information needed" ]
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6d1d7a701f68b1126908169b40a4e00253ae8d53
# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647
[ "region:us" ]
2023-01-26T19:08:18+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84812320.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 86719140.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 90542803.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 94355249.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 100059471.125, "num_examples": 5647}], "download_size": 418860890, "dataset_size": 456488983.625}}
2023-01-27T14:38:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647\"\n\nMore Information needed" ]
[ 6, 37 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647\"\n\nMore Information needed" ]
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2492d638ab77251333818e72e5601df701c91591
# Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647
[ "region:us" ]
2023-01-26T19:22:50+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84811910.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 86719029.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 90542558.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 94354619.125, "num_examples": 5647}, {"name": "fewshot_8_bs_16", "num_bytes": 100058064.125, "num_examples": 5647}], "download_size": 418819193, "dataset_size": 456486180.625}}
2023-01-27T15:39:11+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647\"\n\nMore Information needed" ]
[ 6, 37 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647\"\n\nMore Information needed" ]
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6400a0d39aab6b23746b4d82d3e5261f66d4c127
# Dataset Card for "Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647
[ "region:us" ]
2023-01-26T19:45:27+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84812225.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 86719164.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 90542888.125, "num_examples": 5647}, {"name": "fewshot_5_bs_16", "num_bytes": 94354856.125, "num_examples": 5647}], "download_size": 333860025, "dataset_size": 356429133.5}}
2023-01-27T09:25:26+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647" More Information needed
[ "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647\"\n\nMore Information needed" ]
[ 6, 38 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_2.7b_Visclues_ns_5647\"\n\nMore Information needed" ]
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6c258c8c477a799c95246cca4cf8bf734d3c36ad
# ChatGPT-Prompts Dataset ## Description This dataset aims to provide an evaluation data for the Language Models to come. It has been generated using [LearnGPT website](https://www.emergentmind.com/).
MohamedRashad/ChatGPT-prompts
[ "region:us" ]
2023-01-26T22:32:41+00:00
{}
2023-01-26T22:54:31+00:00
[]
[]
TAGS #region-us
# ChatGPT-Prompts Dataset ## Description This dataset aims to provide an evaluation data for the Language Models to come. It has been generated using LearnGPT website.
[ "# ChatGPT-Prompts Dataset", "## Description\n\nThis dataset aims to provide an evaluation data for the Language Models to come. It has been generated using LearnGPT website." ]
[ "TAGS\n#region-us \n", "# ChatGPT-Prompts Dataset", "## Description\n\nThis dataset aims to provide an evaluation data for the Language Models to come. It has been generated using LearnGPT website." ]
[ 6, 10, 30 ]
[ "passage: TAGS\n#region-us \n# ChatGPT-Prompts Dataset## Description\n\nThis dataset aims to provide an evaluation data for the Language Models to come. It has been generated using LearnGPT website." ]
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aede56159c7772e274dc42493b93348e3c0d3575
# Dataset Card for "mls_ada_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
juancopi81/mls_ada_embeddings
[ "region:us" ]
2023-01-26T22:34:59+00:00
{"dataset_info": {"features": [{"name": "TITLE", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "transcription_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "ada_embedding", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16124584, "num_examples": 410}], "download_size": 10343619, "dataset_size": 16124584}}
2023-01-26T22:35:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "mls_ada_embeddings" More Information needed
[ "# Dataset Card for \"mls_ada_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"mls_ada_embeddings\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"mls_ada_embeddings\"\n\nMore Information needed" ]
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169da140fe10e57ca303c4f9b0d309be088bb309
# Dataset Card for "methods2test_raw_grouped_tok" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dembastu/methods2test_raw_grouped_tok
[ "region:us" ]
2023-01-26T23:43:03+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 1110668740, "num_examples": 631120}], "download_size": 361945934, "dataset_size": 1110668740}}
2023-01-26T23:43:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "methods2test_raw_grouped_tok" More Information needed
[ "# Dataset Card for \"methods2test_raw_grouped_tok\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"methods2test_raw_grouped_tok\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"methods2test_raw_grouped_tok\"\n\nMore Information needed" ]
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b53a0458983714e1488b7a781a460232af20c6f0
# Dataset Card for "methods2test_raw_grouped_block_tok" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dembastu/methods2test_raw_grouped_block_tok
[ "region:us" ]
2023-01-26T23:44:42+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3112626976, "num_examples": 1857176}], "download_size": 818802194, "dataset_size": 3112626976}}
2023-01-27T01:31:14+00:00
[]
[]
TAGS #region-us
# Dataset Card for "methods2test_raw_grouped_block_tok" More Information needed
[ "# Dataset Card for \"methods2test_raw_grouped_block_tok\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"methods2test_raw_grouped_block_tok\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"methods2test_raw_grouped_block_tok\"\n\nMore Information needed" ]
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09d397ade5a344c568b4cea7936693a030e742cf
RoRuDi - Romanian Rules for Dialects
fmi-unibuc/RoRuDi
[ "task_categories:translation", "size_categories:n<1K", "language:ro", "region:us" ]
2023-01-27T00:07:04+00:00
{"language": ["ro"], "size_categories": ["n<1K"], "task_categories": ["translation"]}
2023-01-27T15:29:04+00:00
[]
[ "ro" ]
TAGS #task_categories-translation #size_categories-n<1K #language-Romanian #region-us
RoRuDi - Romanian Rules for Dialects
[]
[ "TAGS\n#task_categories-translation #size_categories-n<1K #language-Romanian #region-us \n" ]
[ 30 ]
[ "passage: TAGS\n#task_categories-translation #size_categories-n<1K #language-Romanian #region-us \n" ]
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205ba26c7c8c4f60ba8278be334e8f1ec9209f52
RoAcReL - Romanian Archaisms Regionalisms Lexicon
fmi-unibuc/RoAcReL
[ "language:ro", "region:us" ]
2023-01-27T00:08:17+00:00
{"language": ["ro"]}
2023-01-27T15:27:46+00:00
[]
[ "ro" ]
TAGS #language-Romanian #region-us
RoAcReL - Romanian Archaisms Regionalisms Lexicon
[]
[ "TAGS\n#language-Romanian #region-us \n" ]
[ 11 ]
[ "passage: TAGS\n#language-Romanian #region-us \n" ]
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af4de0a7631e69ee6c0691ece10b776e73b04347
# Dataset Card for WikiHow Lists ### Dataset Summary Contains CSV of a subset of WikiHow articles. Subsets include articles that have summaries in numbered list format, unordered list of ingredients, or unordered list of items needed for the article. CSV contains a pageId to reference back to the source, title of the article, result with the list data, and a column specifying the result type (ingredient, needed items, summary) ### Licensing Information Data is from WikiHow, license for content is located here https://www.wikihow.com/wikiHow:Creative-Commons
b-mc2/wikihow_lists
[ "task_categories:summarization", "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "license:cc-by-nc-sa-3.0", "lists", "bullets", "steps", "summary", "region:us" ]
2023-01-27T00:36:11+00:00
{"language": ["en"], "license": "cc-by-nc-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["summarization", "question-answering"], "pretty_name": "wikihow_lists", "tags": ["lists", "bullets", "steps", "summary"]}
2023-01-27T00:50:59+00:00
[]
[ "en" ]
TAGS #task_categories-summarization #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-sa-3.0 #lists #bullets #steps #summary #region-us
# Dataset Card for WikiHow Lists ### Dataset Summary Contains CSV of a subset of WikiHow articles. Subsets include articles that have summaries in numbered list format, unordered list of ingredients, or unordered list of items needed for the article. CSV contains a pageId to reference back to the source, title of the article, result with the list data, and a column specifying the result type (ingredient, needed items, summary) ### Licensing Information Data is from WikiHow, license for content is located here URL
[ "# Dataset Card for WikiHow Lists", "### Dataset Summary\n\nContains CSV of a subset of WikiHow articles.\n\nSubsets include articles that have summaries in numbered list format, unordered list of ingredients, or unordered list of items needed for the article. \n\nCSV contains a pageId to reference back to the source, title of the article, result with the list data, and a column specifying the result type (ingredient, needed items, summary)", "### Licensing Information\n\nData is from WikiHow, license for content is located here\nURL" ]
[ "TAGS\n#task_categories-summarization #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-sa-3.0 #lists #bullets #steps #summary #region-us \n", "# Dataset Card for WikiHow Lists", "### Dataset Summary\n\nContains CSV of a subset of WikiHow articles.\n\nSubsets include articles that have summaries in numbered list format, unordered list of ingredients, or unordered list of items needed for the article. \n\nCSV contains a pageId to reference back to the source, title of the article, result with the list data, and a column specifying the result type (ingredient, needed items, summary)", "### Licensing Information\n\nData is from WikiHow, license for content is located here\nURL" ]
[ 69, 9, 99, 19 ]
[ "passage: TAGS\n#task_categories-summarization #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-sa-3.0 #lists #bullets #steps #summary #region-us \n# Dataset Card for WikiHow Lists### Dataset Summary\n\nContains CSV of a subset of WikiHow articles.\n\nSubsets include articles that have summaries in numbered list format, unordered list of ingredients, or unordered list of items needed for the article. \n\nCSV contains a pageId to reference back to the source, title of the article, result with the list data, and a column specifying the result type (ingredient, needed items, summary)### Licensing Information\n\nData is from WikiHow, license for content is located here\nURL" ]
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b00c4292fbc2e03be93809ea0c41445f294f81fc
# Dataset Card for "tib_slides" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gigant/tib_slides
[ "region:us" ]
2023-01-27T01:44:02+00:00
{"dataset_info": {"features": [{"name": "Image", "dtype": "image"}, {"name": "file_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 131956494917.654, "num_examples": 484843}], "download_size": 0, "dataset_size": 131956494917.654}}
2023-03-25T14:28:21+00:00
[]
[]
TAGS #region-us
# Dataset Card for "tib_slides" More Information needed
[ "# Dataset Card for \"tib_slides\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"tib_slides\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"tib_slides\"\n\nMore Information needed" ]
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1d1a6ceafa2c2c9d4dced64b2f70480677d051db
# Dataset Card for "trading_card_display_classification_1_5k_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
connorhoehn/trading_card_display_classification_1_5k_v3
[ "region:us" ]
2023-01-27T02:01:08+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "grid", "1": "solo", "2": "spread", "3": "stack"}}}}], "splits": [{"name": "train", "num_bytes": 1127230775.103, "num_examples": 1249}, {"name": "test", "num_bytes": 155934991.0, "num_examples": 307}], "download_size": 1317201819, "dataset_size": 1283165766.103}}
2023-01-27T02:06:10+00:00
[]
[]
TAGS #region-us
# Dataset Card for "trading_card_display_classification_1_5k_v3" More Information needed
[ "# Dataset Card for \"trading_card_display_classification_1_5k_v3\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"trading_card_display_classification_1_5k_v3\"\n\nMore Information needed" ]
[ 6, 27 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"trading_card_display_classification_1_5k_v3\"\n\nMore Information needed" ]
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e389fcd654e99d14033325da41cc0369a8181c5a
# Wadhwani AI Pest Management Open Data This dataset is a Hugging Face adaptor to the official dataset [hosted on Github](https://github.com/wadhwani-ai/pest-management-opendata). Please refer to that repository for detailed and up-to-date documentation. ## Usage This dataset is large. It is strongly recommended users access it as a stream: ```python from datasets import load_dataset dataset = load_dataset('wadhwani-ai/pest-management-opendata', streaming=True) ``` Bounding boxes are stored as geospatial types. Once loaded, they can be read as follows: ```python from shapely.wkb import loads for (s, data) in dataset.items(): for d in data: pests = d['pests'] iterable = map(pests.get, ('label', 'geometry')) for (i, j) in zip(*iterable): geom = loads(j) print(i, geom.bounds) ``` The bounds of a geometry are what most object detection systems require. See the [Shapely documentation](https://shapely.readthedocs.io/en/stable/manual.html#object.bounds) for more.
wadhwani-ai/pest-management-opendata
[ "license:apache-2.0", "region:us" ]
2023-01-27T04:33:23+00:00
{"license": "apache-2.0"}
2023-06-02T08:25:17+00:00
[]
[]
TAGS #license-apache-2.0 #region-us
# Wadhwani AI Pest Management Open Data This dataset is a Hugging Face adaptor to the official dataset hosted on Github. Please refer to that repository for detailed and up-to-date documentation. ## Usage This dataset is large. It is strongly recommended users access it as a stream: Bounding boxes are stored as geospatial types. Once loaded, they can be read as follows: The bounds of a geometry are what most object detection systems require. See the Shapely documentation for more.
[ "# Wadhwani AI Pest Management Open Data\n\nThis dataset is a Hugging Face adaptor to the official dataset hosted\non\nGithub. Please\nrefer to that repository for detailed and up-to-date documentation.", "## Usage\n\nThis dataset is large. It is strongly recommended users access it as a\nstream:\n\n\n\nBounding boxes are stored as geospatial types. Once loaded, they can be\nread as follows:\n\n\n\nThe bounds of a geometry are what most object detection systems\nrequire. See the Shapely\ndocumentation\nfor more." ]
[ "TAGS\n#license-apache-2.0 #region-us \n", "# Wadhwani AI Pest Management Open Data\n\nThis dataset is a Hugging Face adaptor to the official dataset hosted\non\nGithub. Please\nrefer to that repository for detailed and up-to-date documentation.", "## Usage\n\nThis dataset is large. It is strongly recommended users access it as a\nstream:\n\n\n\nBounding boxes are stored as geospatial types. Once loaded, they can be\nread as follows:\n\n\n\nThe bounds of a geometry are what most object detection systems\nrequire. See the Shapely\ndocumentation\nfor more." ]
[ 14, 48, 74 ]
[ "passage: TAGS\n#license-apache-2.0 #region-us \n# Wadhwani AI Pest Management Open Data\n\nThis dataset is a Hugging Face adaptor to the official dataset hosted\non\nGithub. Please\nrefer to that repository for detailed and up-to-date documentation.## Usage\n\nThis dataset is large. It is strongly recommended users access it as a\nstream:\n\n\n\nBounding boxes are stored as geospatial types. Once loaded, they can be\nread as follows:\n\n\n\nThe bounds of a geometry are what most object detection systems\nrequire. See the Shapely\ndocumentation\nfor more." ]
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1a56be70e435aff162234895c04b269d0e9e9700
# Dataset Card for "OxfordPets_test_facebook_opt_125m_Attributes_ns_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/OxfordPets_test_facebook_opt_125m_Attributes_ns_10
[ "region:us" ]
2023-01-27T05:57:37+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 123008.0, "num_examples": 10}], "download_size": 0, "dataset_size": 123008.0}}
2023-01-27T06:01:48+00:00
[]
[]
TAGS #region-us
# Dataset Card for "OxfordPets_test_facebook_opt_125m_Attributes_ns_10" More Information needed
[ "# Dataset Card for \"OxfordPets_test_facebook_opt_125m_Attributes_ns_10\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_test_facebook_opt_125m_Attributes_ns_10\"\n\nMore Information needed" ]
[ 6, 31 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_facebook_opt_125m_Attributes_ns_10\"\n\nMore Information needed" ]
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0929957d884b2e67e49ad3de65ea0c5eaf6747c5
# Dataset Card for "OxfordPets_test_facebook_opt_6.7b_Attributes_ns_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/OxfordPets_test_facebook_opt_6.7b_Attributes_ns_10
[ "region:us" ]
2023-01-27T06:24:50+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 123008.0, "num_examples": 10}], "download_size": 122342, "dataset_size": 123008.0}}
2023-01-27T06:24:53+00:00
[]
[]
TAGS #region-us
# Dataset Card for "OxfordPets_test_facebook_opt_6.7b_Attributes_ns_10" More Information needed
[ "# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Attributes_ns_10\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Attributes_ns_10\"\n\nMore Information needed" ]
[ 6, 31 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Attributes_ns_10\"\n\nMore Information needed" ]
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e1a830f60d8d8557c2b76f48712f15bc5e1193c7
# Dataset Card for "OxfordPets_test_facebook_opt_125m_Attributes_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/OxfordPets_test_facebook_opt_125m_Attributes_ns_1000
[ "region:us" ]
2023-01-27T06:53:14+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 32169219.0, "num_examples": 1000}], "download_size": 31715574, "dataset_size": 32169219.0}}
2023-01-27T06:56:38+00:00
[]
[]
TAGS #region-us
# Dataset Card for "OxfordPets_test_facebook_opt_125m_Attributes_ns_1000" More Information needed
[ "# Dataset Card for \"OxfordPets_test_facebook_opt_125m_Attributes_ns_1000\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"OxfordPets_test_facebook_opt_125m_Attributes_ns_1000\"\n\nMore Information needed" ]
[ 6, 31 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_facebook_opt_125m_Attributes_ns_1000\"\n\nMore Information needed" ]
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bd10e70d61c4e6c475c382a7f04c25f37524c587
# Dataset Card for "turkishReviews-ds-mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
erytrn/turkishReviews-ds-mini
[ "region:us" ]
2023-01-27T09:01:48+00:00
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1252876.2642514652, "num_examples": 3378}, {"name": "validation", "num_bytes": 139455.7357485349, "num_examples": 376}], "download_size": 895863, "dataset_size": 1392332.0}}
2023-01-27T09:01:51+00:00
[]
[]
TAGS #region-us
# Dataset Card for "turkishReviews-ds-mini" More Information needed
[ "# Dataset Card for \"turkishReviews-ds-mini\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"turkishReviews-ds-mini\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"turkishReviews-ds-mini\"\n\nMore Information needed" ]
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1f24d8877e93f2e33ac68a6968dcdc14d0080b87
# Dataset Card for "turkishReviews-ds-mini2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
erytrn/turkishReviews-ds-mini2
[ "region:us" ]
2023-01-27T09:06:41+00:00
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1252876.2642514652, "num_examples": 3378}, {"name": "validation", "num_bytes": 139455.7357485349, "num_examples": 376}], "download_size": 895863, "dataset_size": 1392332.0}}
2023-01-27T09:06:47+00:00
[]
[]
TAGS #region-us
# Dataset Card for "turkishReviews-ds-mini2" More Information needed
[ "# Dataset Card for \"turkishReviews-ds-mini2\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"turkishReviews-ds-mini2\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"turkishReviews-ds-mini2\"\n\nMore Information needed" ]
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f3add750e347e43faac35abf741ecfd943e6a4e4
# Dataset Card for "textual-explanations-702010" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
james-burton/textual-explanations-702010
[ "region:us" ]
2023-01-27T10:01:59+00:00
{"dataset_info": {"features": [{"name": "model_name", "dtype": "string"}, {"name": "predicted_class", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "narration", "dtype": "string"}, {"name": "values", "sequence": "string"}, {"name": "sign", "sequence": "string"}, {"name": "narrative_id", "dtype": "int32"}, {"name": "unique_id", "dtype": "int32"}, {"name": "classes_dict", "dtype": "string"}, {"name": "narrative_questions", "sequence": "string"}, {"name": "feature_nums", "sequence": "string"}, {"name": "ft_num2name", "dtype": "string"}, {"name": "old2new_ft_nums", "dtype": "string"}, {"name": "old2new_classes", "dtype": "string"}, {"name": "predicted_class_label", "dtype": "string"}, {"name": "class2name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 865101, "num_examples": 328}, {"name": "validation", "num_bytes": 121591, "num_examples": 47}, {"name": "test", "num_bytes": 252513, "num_examples": 94}], "download_size": 380420, "dataset_size": 1239205}}
2023-01-27T10:02:13+00:00
[]
[]
TAGS #region-us
# Dataset Card for "textual-explanations-702010" More Information needed
[ "# Dataset Card for \"textual-explanations-702010\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"textual-explanations-702010\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"textual-explanations-702010\"\n\nMore Information needed" ]
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c6f8545fefcebe755633aecc174a0a2ff73f774f
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: alex-apostolo/legal-bert-small-filtered-cuad * Dataset: alex-apostolo/filtered-cuad * Config: alex-apostolo--filtered-cuad * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pankajm](https://huggingface.co/pankajm) for evaluating this model.
autoevaluate/autoeval-eval-alex-apostolo__filtered-cuad-alex-apostolo__filtered-cu-fd7768-3096988007
[ "autotrain", "evaluation", "region:us" ]
2023-01-27T11:08:29+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["alex-apostolo/filtered-cuad"], "eval_info": {"task": "extractive_question_answering", "model": "alex-apostolo/legal-bert-small-filtered-cuad", "metrics": ["accuracy"], "dataset_name": "alex-apostolo/filtered-cuad", "dataset_config": "alex-apostolo--filtered-cuad", "dataset_split": "test", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
2023-01-27T11:13:48+00:00
[]
[]
TAGS #autotrain #evaluation #region-us
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Question Answering * Model: alex-apostolo/legal-bert-small-filtered-cuad * Dataset: alex-apostolo/filtered-cuad * Config: alex-apostolo--filtered-cuad * Split: test To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @pankajm for evaluating this model.
[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-bert-small-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pankajm for evaluating this model." ]
[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-bert-small-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pankajm for evaluating this model." ]
[ 13, 113, 16 ]
[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-bert-small-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pankajm for evaluating this model." ]
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bc3f83e09363e08df6bfe2d336fe0f8adf7db76c
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: alex-apostolo/legal-roberta-base-filtered-cuad * Dataset: alex-apostolo/filtered-cuad * Config: alex-apostolo--filtered-cuad * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pankajm](https://huggingface.co/pankajm) for evaluating this model.
autoevaluate/autoeval-eval-alex-apostolo__filtered-cuad-alex-apostolo__filtered-cu-fd7768-3096988008
[ "autotrain", "evaluation", "region:us" ]
2023-01-27T11:08:34+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["alex-apostolo/filtered-cuad"], "eval_info": {"task": "extractive_question_answering", "model": "alex-apostolo/legal-roberta-base-filtered-cuad", "metrics": ["accuracy"], "dataset_name": "alex-apostolo/filtered-cuad", "dataset_config": "alex-apostolo--filtered-cuad", "dataset_split": "test", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
2023-01-27T11:14:59+00:00
[]
[]
TAGS #autotrain #evaluation #region-us
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Question Answering * Model: alex-apostolo/legal-roberta-base-filtered-cuad * Dataset: alex-apostolo/filtered-cuad * Config: alex-apostolo--filtered-cuad * Split: test To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @pankajm for evaluating this model.
[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-roberta-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pankajm for evaluating this model." ]
[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-roberta-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pankajm for evaluating this model." ]
[ 13, 113, 16 ]
[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-roberta-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pankajm for evaluating this model." ]
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3eb717e719ef749a17895433df739932cf81f55c
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: alex-apostolo/roberta-base-filtered-cuad * Dataset: alex-apostolo/filtered-cuad * Config: alex-apostolo--filtered-cuad * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pankajm](https://huggingface.co/pankajm) for evaluating this model.
autoevaluate/autoeval-eval-alex-apostolo__filtered-cuad-alex-apostolo__filtered-cu-fd7768-3096988009
[ "autotrain", "evaluation", "region:us" ]
2023-01-27T11:08:40+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["alex-apostolo/filtered-cuad"], "eval_info": {"task": "extractive_question_answering", "model": "alex-apostolo/roberta-base-filtered-cuad", "metrics": ["accuracy"], "dataset_name": "alex-apostolo/filtered-cuad", "dataset_config": "alex-apostolo--filtered-cuad", "dataset_split": "test", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
2023-01-27T11:15:06+00:00
[]
[]
TAGS #autotrain #evaluation #region-us
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Question Answering * Model: alex-apostolo/roberta-base-filtered-cuad * Dataset: alex-apostolo/filtered-cuad * Config: alex-apostolo--filtered-cuad * Split: test To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @pankajm for evaluating this model.
[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/roberta-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pankajm for evaluating this model." ]
[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/roberta-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pankajm for evaluating this model." ]
[ 13, 111, 16 ]
[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/roberta-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pankajm for evaluating this model." ]
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58b824f3c33818ffd6b5e5908283f648899597e0
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: alex-apostolo/legal-bert-base-filtered-cuad * Dataset: alex-apostolo/filtered-cuad * Config: alex-apostolo--filtered-cuad * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pankajm](https://huggingface.co/pankajm) for evaluating this model.
autoevaluate/autoeval-eval-alex-apostolo__filtered-cuad-alex-apostolo__filtered-cu-fd7768-3096988010
[ "autotrain", "evaluation", "region:us" ]
2023-01-27T11:08:45+00:00
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["alex-apostolo/filtered-cuad"], "eval_info": {"task": "extractive_question_answering", "model": "alex-apostolo/legal-bert-base-filtered-cuad", "metrics": ["accuracy"], "dataset_name": "alex-apostolo/filtered-cuad", "dataset_config": "alex-apostolo--filtered-cuad", "dataset_split": "test", "col_mapping": {"context": "context", "question": "question", "answers-text": "answers.text", "answers-answer_start": "answers.answer_start"}}}
2023-01-27T11:15:01+00:00
[]
[]
TAGS #autotrain #evaluation #region-us
# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Question Answering * Model: alex-apostolo/legal-bert-base-filtered-cuad * Dataset: alex-apostolo/filtered-cuad * Config: alex-apostolo--filtered-cuad * Split: test To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @pankajm for evaluating this model.
[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-bert-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pankajm for evaluating this model." ]
[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-bert-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @pankajm for evaluating this model." ]
[ 13, 112, 16 ]
[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Question Answering\n* Model: alex-apostolo/legal-bert-base-filtered-cuad\n* Dataset: alex-apostolo/filtered-cuad\n* Config: alex-apostolo--filtered-cuad\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @pankajm for evaluating this model." ]
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bf5ce2eb6436b1bcc3cccceb4b0f700f0b9dbcae
# Dataset Card for "PatternNet" ## Dataset Description - **Paper** [PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval](https://www.sciencedirect.com/science/article/pii/S0924271618300042) ### Licensing Information For research purposes. ## Citation Information [PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval](https://www.sciencedirect.com/science/article/pii/S0924271618300042) ``` @article{zhou2018patternnet, title = {PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval}, author = {Zhou, Weixun and Newsam, Shawn and Li, Congmin and Shao, Zhenfeng}, year = 2018, journal = {ISPRS journal of photogrammetry and remote sensing}, publisher = {Elsevier}, volume = 145, pages = {197--209} } ```
jonathan-roberts1/PatternNet
[ "task_categories:image-classification", "task_categories:zero-shot-image-classification", "license:other", "region:us" ]
2023-01-27T12:46:23+00:00
{"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "airplane", "1": "baseball field", "2": "basketball court", "3": "beach", "4": "bridge", "5": "cemetery", "6": "chaparral", "7": "christmas tree farm", "8": "closed road", "9": "coastal mansion", "10": "crosswalk", "11": "dense residential", "12": "ferry terminal", "13": "football field", "14": "forest", "15": "freeway", "16": "golf course", "17": "harbor", "18": "intersection", "19": "mobile home park", "20": "nursing home", "21": "oil gas field", "22": "oil well", "23": "overpass", "24": "parking lot", "25": "parking space", "26": "railway", "27": "river", "28": "runway", "29": "runway marking", "30": "shipping yard", "31": "solar panel", "32": "sparse residential", "33": "storage tank", "34": "swimming pool", "35": "tennis court", "36": "transformer station", "37": "wastewater treatment plant"}}}}], "splits": [{"name": "train", "num_bytes": 821222673.6, "num_examples": 30400}], "download_size": 1422129774, "dataset_size": 821222673.6}}
2023-03-31T16:06:42+00:00
[]
[]
TAGS #task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us
# Dataset Card for "PatternNet" ## Dataset Description - Paper PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval ### Licensing Information For research purposes. PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
[ "# Dataset Card for \"PatternNet\"", "## Dataset Description\n\n- Paper PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval", "### Licensing Information\n\nFor research purposes.\n\n\n\nPatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval" ]
[ "TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n", "# Dataset Card for \"PatternNet\"", "## Dataset Description\n\n- Paper PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval", "### Licensing Information\n\nFor research purposes.\n\n\n\nPatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval" ]
[ 37, 11, 25, 30 ]
[ "passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n# Dataset Card for \"PatternNet\"## Dataset Description\n\n- Paper PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval### Licensing Information\n\nFor research purposes.\n\n\n\nPatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval" ]
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d2f1a78496031190621c8d56b9f390b00fec597d
# Dataset Card for "github-ds-tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
threite/github-ds-tokenized
[ "region:us" ]
2023-01-27T13:12:40+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 8618263476, "num_examples": 16702061}, {"name": "valid", "num_bytes": 48072624, "num_examples": 93164}], "download_size": 3804663704, "dataset_size": 8666336100}}
2023-01-27T13:47:09+00:00
[]
[]
TAGS #region-us
# Dataset Card for "github-ds-tokenized" More Information needed
[ "# Dataset Card for \"github-ds-tokenized\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"github-ds-tokenized\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"github-ds-tokenized\"\n\nMore Information needed" ]
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0453ba70ecb871cc228585fc9d4b75e5b0f40552
# Dataset Card for "nordstrom96568" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jiwnchoi/nordstrom96568
[ "region:us" ]
2023-01-27T13:44:22+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1717226199.24, "num_examples": 96568}], "download_size": 1627696261, "dataset_size": 1717226199.24}}
2023-01-27T13:47:23+00:00
[]
[]
TAGS #region-us
# Dataset Card for "nordstrom96568" More Information needed
[ "# Dataset Card for \"nordstrom96568\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"nordstrom96568\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"nordstrom96568\"\n\nMore Information needed" ]
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04f959cc293074479c654af4febabaa859483769
<div align="center"> <img width="640" alt="keremberke/pcb-defect-segmentation" src="https://huggingface.co/datasets/keremberke/pcb-defect-segmentation/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['dry_joint', 'incorrect_installation', 'pcb_damage', 'short_circuit'] ``` ### Number of Images ```json {'valid': 25, 'train': 128, 'test': 36} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/pcb-defect-segmentation", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8](https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8?ref=roboflow2huggingface) ### Citation ``` @misc{ defects-2q87r_dataset, title = { Defects Dataset }, type = { Open Source Dataset }, author = { Diplom }, howpublished = { \\url{ https://universe.roboflow.com/diplom-qz7q6/defects-2q87r } }, url = { https://universe.roboflow.com/diplom-qz7q6/defects-2q87r }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { jan }, note = { visited on 2023-01-27 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on January 27, 2023 at 1:45 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com The dataset includes 189 images. Defect are annotated in COCO format. The following pre-processing was applied to each image: No image augmentation techniques were applied.
keremberke/pcb-defect-segmentation
[ "task_categories:image-segmentation", "roboflow", "roboflow2huggingface", "region:us" ]
2023-01-27T13:45:20+00:00
{"task_categories": ["image-segmentation"], "tags": ["roboflow", "roboflow2huggingface"]}
2023-01-27T13:45:36+00:00
[]
[]
TAGS #task_categories-image-segmentation #roboflow #roboflow2huggingface #region-us
<div align="center"> <img width="640" alt="keremberke/pcb-defect-segmentation" src="URL </div> ### Dataset Labels ### Number of Images ### How to Use - Install datasets: - Load the dataset: ### Roboflow Dataset Page URL ### License CC BY 4.0 ### Dataset Summary This dataset was exported via URL on January 27, 2023 at 1:45 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit URL To find over 100k other datasets and pre-trained models, visit URL The dataset includes 189 images. Defect are annotated in COCO format. The following pre-processing was applied to each image: No image augmentation techniques were applied.
[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on January 27, 2023 at 1:45 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand and search unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nFor state of the art Computer Vision training notebooks you can use with this dataset,\nvisit URL\n\nTo find over 100k other datasets and pre-trained models, visit URL\n\nThe dataset includes 189 images.\nDefect are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n\nNo image augmentation techniques were applied." ]
[ "TAGS\n#task_categories-image-segmentation #roboflow #roboflow2huggingface #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on January 27, 2023 at 1:45 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand and search unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nFor state of the art Computer Vision training notebooks you can use with this dataset,\nvisit URL\n\nTo find over 100k other datasets and pre-trained models, visit URL\n\nThe dataset includes 189 images.\nDefect are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n\nNo image augmentation techniques were applied." ]
[ 28, 5, 5, 18, 8, 6, 171 ]
[ "passage: TAGS\n#task_categories-image-segmentation #roboflow #roboflow2huggingface #region-us \n### Dataset Labels### Number of Images### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:### Roboflow Dataset Page\nURL### License\nCC BY 4.0### Dataset Summary\nThis dataset was exported via URL on January 27, 2023 at 1:45 PM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand and search unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nFor state of the art Computer Vision training notebooks you can use with this dataset,\nvisit URL\n\nTo find over 100k other datasets and pre-trained models, visit URL\n\nThe dataset includes 189 images.\nDefect are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n\nNo image augmentation techniques were applied." ]
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0ee061abef02a0c7c51ca8a398746004dce4cc72
<div align="center"> <img width="640" alt="keremberke/shoe-classification" src="https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['converse', 'adidas', 'nike'] ``` ### Number of Images ```json {'train': 576, 'test': 83, 'valid': 166} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/shoe-classification", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification/dataset/4](https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification/dataset/4?ref=roboflow2huggingface) ### Citation ``` ``` ### License Public Domain ### Dataset Summary This dataset was exported via roboflow.com on October 28, 2022 at 2:38 AM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time It includes 825 images. Shoes are annotated in folder format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.
keremberke/shoe-classification
[ "task_categories:image-classification", "roboflow", "roboflow2huggingface", "Sports", "Retail", "Benchmark", "region:us" ]
2023-01-27T13:46:37+00:00
{"task_categories": ["image-classification"], "tags": ["roboflow", "roboflow2huggingface", "Sports", "Retail", "Benchmark"]}
2023-01-27T13:46:52+00:00
[]
[]
TAGS #task_categories-image-classification #roboflow #roboflow2huggingface #Sports #Retail #Benchmark #region-us
<div align="center"> <img width="640" alt="keremberke/shoe-classification" src="URL </div> ### Dataset Labels ### Number of Images ### How to Use - Install datasets: - Load the dataset: ### Roboflow Dataset Page URL ### License Public Domain ### Dataset Summary This dataset was exported via URL on October 28, 2022 at 2:38 AM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time It includes 825 images. Shoes are annotated in folder format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.
[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nPublic Domain", "### Dataset Summary\nThis dataset was exported via URL on October 28, 2022 at 2:38 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 825 images.\nShoes are annotated in folder format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]
[ "TAGS\n#task_categories-image-classification #roboflow #roboflow2huggingface #Sports #Retail #Benchmark #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nPublic Domain", "### Dataset Summary\nThis dataset was exported via URL on October 28, 2022 at 2:38 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 825 images.\nShoes are annotated in folder format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]
[ 37, 5, 5, 18, 8, 5, 145 ]
[ "passage: TAGS\n#task_categories-image-classification #roboflow #roboflow2huggingface #Sports #Retail #Benchmark #region-us \n### Dataset Labels### Number of Images### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:### Roboflow Dataset Page\nURL### License\nPublic Domain### Dataset Summary\nThis dataset was exported via URL on October 28, 2022 at 2:38 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nIt includes 825 images.\nShoes are annotated in folder format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n\nNo image augmentation techniques were applied." ]
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63b6b4d310e1f63c777407ea64dfb4490341b0b5
wids2023 train and test datasets
kennethlongshaw/wids2023
[ "license:unknown", "region:us" ]
2023-01-27T14:13:55+00:00
{"license": "unknown"}
2023-01-27T14:16:17+00:00
[]
[]
TAGS #license-unknown #region-us
wids2023 train and test datasets
[]
[ "TAGS\n#license-unknown #region-us \n" ]
[ 13 ]
[ "passage: TAGS\n#license-unknown #region-us \n" ]
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8417143bdbf9658a2483c8a09da5c8aed7882460
# Dataset Card for "diana_uribe_ada_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
juancopi81/diana_uribe_ada_embeddings
[ "region:us" ]
2023-01-27T14:47:53+00:00
{"dataset_info": {"features": [{"name": "TITLE", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "transcription_length", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "ada_embedding", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 130390221, "num_examples": 3271}], "download_size": 84762133, "dataset_size": 130390221}}
2023-01-27T14:47:58+00:00
[]
[]
TAGS #region-us
# Dataset Card for "diana_uribe_ada_embeddings" More Information needed
[ "# Dataset Card for \"diana_uribe_ada_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"diana_uribe_ada_embeddings\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"diana_uribe_ada_embeddings\"\n\nMore Information needed" ]
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0bd8f216a689175651df5bad2f88e570e9052930
# Датасет для задачи раскрытия неполных реплик в контексте диалога Подробное описание задачи "Incomplete Utterance Restoration" можно найти в карточке генеративной модели [inkoziev/rugpt_interpreter](https://huggingface.co/inkoziev/rugpt_interpreter), которая обучена на аугментированном варианте этого датасета. В датасете содержатся фрагменты диалогов длиной от 1 до 3 последовательных реплик. Для последней реплики дается ее полный вариант с раскрытыми анафорами, эллипсисами и т.д. Например, следующий сэмпл: ``` { "context": ["Добрый день, давай поговорим?"], "short_phrase": "добрый день, давай", "expanded_phrase": "Давай поговорим" } ``` Соответствует фрагменту диалога: ``` - Добрый день, давай поговорим? - добрый день, давай ``` Для последней реплики "_добрый день, давай_" приводится ее полный вариант "_Давай поговорим_". Для фрагментов длиной 3 реплики в поле ```context``` будет 2 элемента: ``` { "context": [ "Что-то зрителей полный инет, а изучителей не видать.", "Ты кем себя считаешь: зрителем или изучателем?" ], "short_phrase": "изучателем", "expanded_phrase": "я считаю себя изучателем" }, ``` Этот сэмпл соответствует фрагменту диалога: ``` - Что-то зрителей полный инет, а изучителей не видать. - Ты кем себя считаешь: зрителем или изучателем? - изучателем ``` Для последней реплики дана полная форма "_я считаю себя изучателем_". ## Особенности датасета 1) Для многих сэмплов вежливая форма 2л. мн.ч. "Вы" в раскрытом варианте нормализована до "ты". 2) Есть нецензурная лексика, оскорбления и т.п. ## Ссылки, цитирование Этот датасет я собирал сам для экспериментов с [диалоговыми системами](https://github.com/Koziev/chatbot). Если вы используете его в каком-то виде, буду благодарен за ссылки на эту страницу.
inkoziev/incomplete_utterance_restoration
[ "task_categories:conversational", "task_categories:question-answering", "task_ids:dialogue-generation", "task_ids:open-domain-qa", "multilinguality:monolingual", "source_datasets:original", "language:ru", "license:cc-by-nc-4.0", "region:us" ]
2023-01-27T15:19:28+00:00
{"language_creators": "expert", "language": "ru", "license": "cc-by-nc-4.0", "multilinguality": "monolingual", "source_datasets": "original", "task_categories": ["conversational", "question-answering"], "task_ids": ["dialogue-generation", "open-domain-qa"]}
2023-01-27T15:56:24+00:00
[]
[ "ru" ]
TAGS #task_categories-conversational #task_categories-question-answering #task_ids-dialogue-generation #task_ids-open-domain-qa #multilinguality-monolingual #source_datasets-original #language-Russian #license-cc-by-nc-4.0 #region-us
# Датасет для задачи раскрытия неполных реплик в контексте диалога Подробное описание задачи "Incomplete Utterance Restoration" можно найти в карточке генеративной модели inkoziev/rugpt_interpreter, которая обучена на аугментированном варианте этого датасета. В датасете содержатся фрагменты диалогов длиной от 1 до 3 последовательных реплик. Для последней реплики дается ее полный вариант с раскрытыми анафорами, эллипсисами и т.д. Например, следующий сэмпл: Соответствует фрагменту диалога: Для последней реплики "_добрый день, давай_" приводится ее полный вариант "_Давай поговорим_". Для фрагментов длиной 3 реплики в поле будет 2 элемента: Этот сэмпл соответствует фрагменту диалога: Для последней реплики дана полная форма "_я считаю себя изучателем_". ## Особенности датасета 1) Для многих сэмплов вежливая форма 2л. мн.ч. "Вы" в раскрытом варианте нормализована до "ты". 2) Есть нецензурная лексика, оскорбления и т.п. ## Ссылки, цитирование Этот датасет я собирал сам для экспериментов с диалоговыми системами. Если вы используете его в каком-то виде, буду благодарен за ссылки на эту страницу.
[ "# Датасет для задачи раскрытия неполных реплик в контексте диалога\n\nПодробное описание задачи \"Incomplete Utterance Restoration\" можно найти в карточке генеративной модели inkoziev/rugpt_interpreter, которая обучена на аугментированном варианте этого датасета.\n\nВ датасете содержатся фрагменты диалогов длиной от 1 до 3 последовательных реплик. Для последней реплики дается ее полный вариант с раскрытыми анафорами, эллипсисами и т.д.\n\nНапример, следующий сэмпл:\n\n\n\nСоответствует фрагменту диалога:\n\n\n\nДля последней реплики \"_добрый день, давай_\" приводится ее полный вариант \"_Давай поговорим_\".\n\nДля фрагментов длиной 3 реплики в поле будет 2 элемента:\n\n\n\nЭтот сэмпл соответствует фрагменту диалога:\n\n\n\nДля последней реплики дана полная форма \"_я считаю себя изучателем_\".", "## Особенности датасета\n\n1) Для многих сэмплов вежливая форма 2л. мн.ч. \"Вы\" в раскрытом варианте нормализована до \"ты\".\n\n2) Есть нецензурная лексика, оскорбления и т.п.", "## Ссылки, цитирование\n\nЭтот датасет я собирал сам для экспериментов с диалоговыми системами. Если вы используете его в каком-то виде, буду благодарен за ссылки на эту страницу." ]
[ "TAGS\n#task_categories-conversational #task_categories-question-answering #task_ids-dialogue-generation #task_ids-open-domain-qa #multilinguality-monolingual #source_datasets-original #language-Russian #license-cc-by-nc-4.0 #region-us \n", "# Датасет для задачи раскрытия неполных реплик в контексте диалога\n\nПодробное описание задачи \"Incomplete Utterance Restoration\" можно найти в карточке генеративной модели inkoziev/rugpt_interpreter, которая обучена на аугментированном варианте этого датасета.\n\nВ датасете содержатся фрагменты диалогов длиной от 1 до 3 последовательных реплик. Для последней реплики дается ее полный вариант с раскрытыми анафорами, эллипсисами и т.д.\n\nНапример, следующий сэмпл:\n\n\n\nСоответствует фрагменту диалога:\n\n\n\nДля последней реплики \"_добрый день, давай_\" приводится ее полный вариант \"_Давай поговорим_\".\n\nДля фрагментов длиной 3 реплики в поле будет 2 элемента:\n\n\n\nЭтот сэмпл соответствует фрагменту диалога:\n\n\n\nДля последней реплики дана полная форма \"_я считаю себя изучателем_\".", "## Особенности датасета\n\n1) Для многих сэмплов вежливая форма 2л. мн.ч. \"Вы\" в раскрытом варианте нормализована до \"ты\".\n\n2) Есть нецензурная лексика, оскорбления и т.п.", "## Ссылки, цитирование\n\nЭтот датасет я собирал сам для экспериментов с диалоговыми системами. Если вы используете его в каком-то виде, буду благодарен за ссылки на эту страницу." ]
[ 84, 204, 60, 45 ]
[ "passage: TAGS\n#task_categories-conversational #task_categories-question-answering #task_ids-dialogue-generation #task_ids-open-domain-qa #multilinguality-monolingual #source_datasets-original #language-Russian #license-cc-by-nc-4.0 #region-us \n# Датасет для задачи раскрытия неполных реплик в контексте диалога\n\nПодробное описание задачи \"Incomplete Utterance Restoration\" можно найти в карточке генеративной модели inkoziev/rugpt_interpreter, которая обучена на аугментированном варианте этого датасета.\n\nВ датасете содержатся фрагменты диалогов длиной от 1 до 3 последовательных реплик. Для последней реплики дается ее полный вариант с раскрытыми анафорами, эллипсисами и т.д.\n\nНапример, следующий сэмпл:\n\n\n\nСоответствует фрагменту диалога:\n\n\n\nДля последней реплики \"_добрый день, давай_\" приводится ее полный вариант \"_Давай поговорим_\".\n\nДля фрагментов длиной 3 реплики в поле будет 2 элемента:\n\n\n\nЭтот сэмпл соответствует фрагменту диалога:\n\n\n\nДля последней реплики дана полная форма \"_я считаю себя изучателем_\".## Особенности датасета\n\n1) Для многих сэмплов вежливая форма 2л. мн.ч. \"Вы\" в раскрытом варианте нормализована до \"ты\".\n\n2) Есть нецензурная лексика, оскорбления и т.п.## Ссылки, цитирование\n\nЭтот датасет я собирал сам для экспериментов с диалоговыми системами. Если вы используете его в каком-то виде, буду благодарен за ссылки на эту страницу." ]
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0a51889b340037bb75a9a0858af2e4ece21f7f89
# Dataset Card for MusicCaps ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://kaggle.com/datasets/googleai/musiccaps - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The MusicCaps dataset contains **5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians**. An aspect list is for example *"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead"*, while the caption consists of multiple sentences about the music, e.g., *"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar."* The text is solely focused on describing *how* the music sounds, not the metadata like the artist name. The labeled examples are 10s music clips from the [**AudioSet**](https://research.google.com/audioset/) dataset (2,858 from the eval and 2,663 from the train split). Please cite the corresponding paper, when using this dataset: http://arxiv.org/abs/2301.11325 (DOI: `10.48550/arXiv.2301.11325`) ### Dataset Usage The published dataset takes the form of a `.csv` file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times. The following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: https://github.com/nateraw/download-musiccaps-dataset ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields #### ytid YT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening https://youtu.be/watch?v={ytid}&start={start_s} #### start_s Position in the YouTube video at which the music starts. #### end_s Position in the YouTube video at which the music end. All clips are 10s long. #### audioset_positive_labels Labels for this segment from the AudioSet (https://research.google.com/audioset/) dataset. #### aspect_list A list of aspects describing the music. #### caption A multi-sentence free text caption describing the music. #### author_id An integer for grouping samples by who wrote them. #### is_balanced_subset If this value is true, the row is a part of the 1k subset which is genre-balanced. #### is_audioset_eval If this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split. ### 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 This dataset was shared by [@googleai](https://ai.google/research/) ### Licensing Information The license for this dataset is cc-by-sa-4.0 ### Citation Information ```bibtex [More Information Needed] ``` ### Contributions [More Information Needed]
google/MusicCaps
[ "task_categories:text-to-speech", "language:en", "license:cc-by-sa-4.0", "arxiv:2301.11325", "region:us" ]
2023-01-27T16:26:11+00:00
{"language": ["en"], "license": ["cc-by-sa-4.0"], "task_categories": ["text-to-speech"], "converted_from": "kaggle", "kaggle_id": "googleai/musiccaps"}
2023-03-08T14:37:09+00:00
[ "2301.11325" ]
[ "en" ]
TAGS #task_categories-text-to-speech #language-English #license-cc-by-sa-4.0 #arxiv-2301.11325 #region-us
# Dataset Card for MusicCaps ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The MusicCaps dataset contains 5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians. An aspect list is for example *"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead"*, while the caption consists of multiple sentences about the music, e.g., *"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar."* The text is solely focused on describing *how* the music sounds, not the metadata like the artist name. The labeled examples are 10s music clips from the AudioSet dataset (2,858 from the eval and 2,663 from the train split). Please cite the corresponding paper, when using this dataset: URL (DOI: '10.48550/arXiv.2301.11325') ### Dataset Usage The published dataset takes the form of a '.csv' file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times. The following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: URL ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields #### ytid YT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening URL #### start_s Position in the YouTube video at which the music starts. #### end_s Position in the YouTube video at which the music end. All clips are 10s long. #### audioset_positive_labels Labels for this segment from the AudioSet (URL dataset. #### aspect_list A list of aspects describing the music. #### caption A multi-sentence free text caption describing the music. #### author_id An integer for grouping samples by who wrote them. #### is_balanced_subset If this value is true, the row is a part of the 1k subset which is genre-balanced. #### is_audioset_eval If this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split. ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators This dataset was shared by @googleai ### Licensing Information The license for this dataset is cc-by-sa-4.0 ### Contributions
[ "# Dataset Card for MusicCaps", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe MusicCaps dataset contains 5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians. An aspect list is for example *\"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead\"*, while the caption consists of multiple sentences about the music, e.g., \n\n*\"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar.\"*\n\nThe text is solely focused on describing *how* the music sounds, not the metadata like the artist name.\n\nThe labeled examples are 10s music clips from the AudioSet dataset (2,858 from the eval and 2,663 from the train split).\n\nPlease cite the corresponding paper, when using this dataset: URL (DOI: '10.48550/arXiv.2301.11325')", "### Dataset Usage\n\nThe published dataset takes the form of a '.csv' file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times.\n\nThe following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: URL", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "#### ytid \nYT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening URL", "#### start_s\nPosition in the YouTube video at which the music starts.", "#### end_s\nPosition in the YouTube video at which the music end. All clips are 10s long.", "#### audioset_positive_labels\nLabels for this segment from the AudioSet (URL dataset.", "#### aspect_list\nA list of aspects describing the music.", "#### caption\nA multi-sentence free text caption describing the music.", "#### author_id\nAn integer for grouping samples by who wrote them.", "#### is_balanced_subset\nIf this value is true, the row is a part of the 1k subset which is genre-balanced.", "#### is_audioset_eval\nIf this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @googleai", "### Licensing Information\n\nThe license for this dataset is cc-by-sa-4.0", "### Contributions" ]
[ "TAGS\n#task_categories-text-to-speech #language-English #license-cc-by-sa-4.0 #arxiv-2301.11325 #region-us \n", "# Dataset Card for MusicCaps", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nThe MusicCaps dataset contains 5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians. An aspect list is for example *\"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead\"*, while the caption consists of multiple sentences about the music, e.g., \n\n*\"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar.\"*\n\nThe text is solely focused on describing *how* the music sounds, not the metadata like the artist name.\n\nThe labeled examples are 10s music clips from the AudioSet dataset (2,858 from the eval and 2,663 from the train split).\n\nPlease cite the corresponding paper, when using this dataset: URL (DOI: '10.48550/arXiv.2301.11325')", "### Dataset Usage\n\nThe published dataset takes the form of a '.csv' file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times.\n\nThe following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: URL", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "#### ytid \nYT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening URL", "#### start_s\nPosition in the YouTube video at which the music starts.", "#### end_s\nPosition in the YouTube video at which the music end. All clips are 10s long.", "#### audioset_positive_labels\nLabels for this segment from the AudioSet (URL dataset.", "#### aspect_list\nA list of aspects describing the music.", "#### caption\nA multi-sentence free text caption describing the music.", "#### author_id\nAn integer for grouping samples by who wrote them.", "#### is_balanced_subset\nIf this value is true, the row is a part of the 1k subset which is genre-balanced.", "#### is_audioset_eval\nIf this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split.", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators\n\nThis dataset was shared by @googleai", "### Licensing Information\n\nThe license for this dataset is cc-by-sa-4.0", "### Contributions" ]
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[ "passage: TAGS\n#task_categories-text-to-speech #language-English #license-cc-by-sa-4.0 #arxiv-2301.11325 #region-us \n# Dataset Card for MusicCaps## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: URL\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary\n\nThe MusicCaps dataset contains 5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians. An aspect list is for example *\"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead\"*, while the caption consists of multiple sentences about the music, e.g., \n\n*\"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar.\"*\n\nThe text is solely focused on describing *how* the music sounds, not the metadata like the artist name.\n\nThe labeled examples are 10s music clips from the AudioSet dataset (2,858 from the eval and 2,663 from the train split).\n\nPlease cite the corresponding paper, when using this dataset: URL (DOI: '10.48550/arXiv.2301.11325')" ]
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1cbcd93f538ff1f833929741326579cc74680348
### To generate the dataset Register for an account at https://www.pdbbind.org.cn/, confirm the validation email, then login and download the Index files (1) the general protein-ligand complexes (2) the refined protein-ligand complexes (3) Extract those files in pdbbind_complex_GB2022/data Run the script pdbbind.py in a compute job on an MPI-enabled cluster (e.g., mpirun -n 64 pdbbind.py). Output will be tar files in `train/`, `val/` and `test/` folders, following the split direction which is from EquiBind manuscript.
djh992/pdbbind_complex_GB2022
[ "molecules", "Chemistry", "SMILES", "Protein", "Ligand", "Complexes", "region:us" ]
2023-01-27T17:13:36+00:00
{"tags": ["molecules", "Chemistry", "SMILES", "Protein", "Ligand", "Complexes"]}
2023-02-20T03:23:09+00:00
[]
[]
TAGS #molecules #Chemistry #SMILES #Protein #Ligand #Complexes #region-us
### To generate the dataset Register for an account at URL confirm the validation email, then login and download the Index files (1) the general protein-ligand complexes (2) the refined protein-ligand complexes (3) Extract those files in pdbbind_complex_GB2022/data Run the script URL in a compute job on an MPI-enabled cluster (e.g., mpirun -n 64 URL). Output will be tar files in 'train/', 'val/' and 'test/' folders, following the split direction which is from EquiBind manuscript.
[ "### To generate the dataset\n\nRegister for an account at URL confirm the validation email, then login and download\n\nthe Index files (1)\nthe general protein-ligand complexes (2)\nthe refined protein-ligand complexes (3)\nExtract those files in pdbbind_complex_GB2022/data\n\nRun the script URL in a compute job on an MPI-enabled cluster (e.g., mpirun -n 64 URL).\n\nOutput will be tar files in 'train/', 'val/' and 'test/' folders, following the split direction which is from EquiBind manuscript." ]
[ "TAGS\n#molecules #Chemistry #SMILES #Protein #Ligand #Complexes #region-us \n", "### To generate the dataset\n\nRegister for an account at URL confirm the validation email, then login and download\n\nthe Index files (1)\nthe general protein-ligand complexes (2)\nthe refined protein-ligand complexes (3)\nExtract those files in pdbbind_complex_GB2022/data\n\nRun the script URL in a compute job on an MPI-enabled cluster (e.g., mpirun -n 64 URL).\n\nOutput will be tar files in 'train/', 'val/' and 'test/' folders, following the split direction which is from EquiBind manuscript." ]
[ 30, 132 ]
[ "passage: TAGS\n#molecules #Chemistry #SMILES #Protein #Ligand #Complexes #region-us \n### To generate the dataset\n\nRegister for an account at URL confirm the validation email, then login and download\n\nthe Index files (1)\nthe general protein-ligand complexes (2)\nthe refined protein-ligand complexes (3)\nExtract those files in pdbbind_complex_GB2022/data\n\nRun the script URL in a compute job on an MPI-enabled cluster (e.g., mpirun -n 64 URL).\n\nOutput will be tar files in 'train/', 'val/' and 'test/' folders, following the split direction which is from EquiBind manuscript." ]
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a2768bceef2fa92addf00220c356d92e6d76aa45
This repository is for the paper "Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement". To use the data, there are two directories: 1. language datasets: Contains the necessary Tatoeba files used for the experiments. We have experimented with 4 languages(English, French, Italian and German). 2. language_models: Contains all trained language models and scripts to train them. It's organized in this way: language_models/{X}: contains language models for X, X={Vanilla RNN,LSTM,GRU}. language_models/{X}/motivating example models/problem1: Contains models needed to reproduce motivating example Problem 1. language_models/{X}/motivating example models/problem2: Contains models needed to reproduce motivating example Problem 2. language_models/{X}/reuse models: Contains combined models needed for reuse experiments. language_models/{X}/rq1 models: Contains all models needed for rq1. language_models/{X}/training script: Contains the training script to get the models. Note: To run the training scripts, direct the source path to /language datasets/Tatoeba/<name.txt> depending on the requirement.
astha/languagemodelsforRNNdecomposition
[ "license:mit", "doi:10.57967/hf/0307", "region:us" ]
2023-01-27T17:14:35+00:00
{"license": "mit"}
2023-01-27T20:38:51+00:00
[]
[]
TAGS #license-mit #doi-10.57967/hf/0307 #region-us
This repository is for the paper "Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement". To use the data, there are two directories: 1. language datasets: Contains the necessary Tatoeba files used for the experiments. We have experimented with 4 languages(English, French, Italian and German). 2. language_models: Contains all trained language models and scripts to train them. It's organized in this way: language_models/{X}: contains language models for X, X={Vanilla RNN,LSTM,GRU}. language_models/{X}/motivating example models/problem1: Contains models needed to reproduce motivating example Problem 1. language_models/{X}/motivating example models/problem2: Contains models needed to reproduce motivating example Problem 2. language_models/{X}/reuse models: Contains combined models needed for reuse experiments. language_models/{X}/rq1 models: Contains all models needed for rq1. language_models/{X}/training script: Contains the training script to get the models. Note: To run the training scripts, direct the source path to /language datasets/Tatoeba/<URL> depending on the requirement.
[]
[ "TAGS\n#license-mit #doi-10.57967/hf/0307 #region-us \n" ]
[ 23 ]
[ "passage: TAGS\n#license-mit #doi-10.57967/hf/0307 #region-us \n" ]
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0579c1773b12b8ba992060fb22586cc4db7f5336
# 概要 - 私自身から作成した人工・架空の東アジア系美人(あたし/Atashi)の動画セット。 - 名称はnewest順。1~4は約500本、5は約400本の動画。 - 無加工の原データ(タグ付けもこちらでは行わず)。画像生成AIのみならず、将来的に動画生成AIの学習原データとしても使えるように想定。 - 顔の合成にはFaceApp, Meitu, Faceplayを利用。 - Faceplayのビデオ音声をそのまま利用しているため、Audioについては第三者が著作権を保持している可能性がある。ただし、その場合であっても、日本国法ではAudioの学習も合法である。 - Visualな側面を切り出したい場合は、どちらにせよAudioは使わないはずなので、実質関係ないとみてよい。 - Faceplayの置換漏れフレーム・人物、男性化されたあたしが含まれている可能性があるため、必要であればそのチェックや除去は各自で行うこと。 - 実写タッチを強化したいが、実在人物を使うことで肖像権がらみの問題が発生することを避けたい人向け。 # About - A video set of an artificial and fictional East Asian beauty (Atashi), that was created from myself. - The file name is sorted by newest. No. 1 ~ 4 contains about 500, and no. 5 around 400. - The videos are raw data that haven't been modified (not even tagged). I have uploaded them as is so that it could potentially be trained for a generative AI for videos, as well as those for images. - The face of Atashi was created using FaceApp, Meitu, and Faceplay. - Since the audios originate from faceplay videos, it might be copyrighted by others. However, note that it is legal, at least in Japan, to use it for training an AI. - If you want to use it for visual purposes (images and/or videos), it doesn't really matter because the audios are simply unnecessary. - Since they are uploaded as is, it might contain unswapped frames, person, and Atashi transformed into a male. Make sure to check and remove such data by yourself if necessary. - The dataset is targeted for those who do not want to use real personal photos but want to train an AI to generate a more photorealistic style. # 外見サンプル / Sample on how she looks - **このビデオ自体はD-IDで生成したもので、zipには含めていない。が、必要なら直接ダウンロードの上、利用してよい。** - **This video (generated by D-ID) itself is not included in the zips. You may download and use it directly if necessary.** <video width="480" controls> <source src="https://huggingface.co/datasets/ThePioneer/FictionalAsianBeautyCollection/resolve/main/Sample%20view%20of%20Atashi%20with%20her%20explanation%20about%20herself.mp4" type="video/mp4"> </video>
ThePioneer/FictionalAsianBeautyCollection
[ "size_categories:1K<n<10K", "license:cc0-1.0", "Raw data for training", "video", "art", "region:us" ]
2023-01-27T17:22:51+00:00
{"license": "cc0-1.0", "size_categories": ["1K<n<10K"], "pretty_name": "AtashiCollection", "tags": ["Raw data for training", "video", "art"]}
2023-01-27T18:27:11+00:00
[]
[]
TAGS #size_categories-1K<n<10K #license-cc0-1.0 #Raw data for training #video #art #region-us
# 概要 - 私自身から作成した人工・架空の東アジア系美人(あたし/Atashi)の動画セット。 - 名称はnewest順。1~4は約500本、5は約400本の動画。 - 無加工の原データ(タグ付けもこちらでは行わず)。画像生成AIのみならず、将来的に動画生成AIの学習原データとしても使えるように想定。 - 顔の合成にはFaceApp, Meitu, Faceplayを利用。 - Faceplayのビデオ音声をそのまま利用しているため、Audioについては第三者が著作権を保持している可能性がある。ただし、その場合であっても、日本国法ではAudioの学習も合法である。 - Visualな側面を切り出したい場合は、どちらにせよAudioは使わないはずなので、実質関係ないとみてよい。 - Faceplayの置換漏れフレーム・人物、男性化されたあたしが含まれている可能性があるため、必要であればそのチェックや除去は各自で行うこと。 - 実写タッチを強化したいが、実在人物を使うことで肖像権がらみの問題が発生することを避けたい人向け。 # About - A video set of an artificial and fictional East Asian beauty (Atashi), that was created from myself. - The file name is sorted by newest. No. 1 ~ 4 contains about 500, and no. 5 around 400. - The videos are raw data that haven't been modified (not even tagged). I have uploaded them as is so that it could potentially be trained for a generative AI for videos, as well as those for images. - The face of Atashi was created using FaceApp, Meitu, and Faceplay. - Since the audios originate from faceplay videos, it might be copyrighted by others. However, note that it is legal, at least in Japan, to use it for training an AI. - If you want to use it for visual purposes (images and/or videos), it doesn't really matter because the audios are simply unnecessary. - Since they are uploaded as is, it might contain unswapped frames, person, and Atashi transformed into a male. Make sure to check and remove such data by yourself if necessary. - The dataset is targeted for those who do not want to use real personal photos but want to train an AI to generate a more photorealistic style. # 外見サンプル / Sample on how she looks - このビデオ自体はD-IDで生成したもので、zipには含めていない。が、必要なら直接ダウンロードの上、利用してよい。 - This video (generated by D-ID) itself is not included in the zips. You may download and use it directly if necessary. <video width="480" controls> <source src="URL type="video/mp4"> </video>
[ "# 概要\n- 私自身から作成した人工・架空の東アジア系美人(あたし/Atashi)の動画セット。\n - 名称はnewest順。1~4は約500本、5は約400本の動画。\n - 無加工の原データ(タグ付けもこちらでは行わず)。画像生成AIのみならず、将来的に動画生成AIの学習原データとしても使えるように想定。\n - 顔の合成にはFaceApp, Meitu, Faceplayを利用。\n - Faceplayのビデオ音声をそのまま利用しているため、Audioについては第三者が著作権を保持している可能性がある。ただし、その場合であっても、日本国法ではAudioの学習も合法である。\n - Visualな側面を切り出したい場合は、どちらにせよAudioは使わないはずなので、実質関係ないとみてよい。\n - Faceplayの置換漏れフレーム・人物、男性化されたあたしが含まれている可能性があるため、必要であればそのチェックや除去は各自で行うこと。\n- 実写タッチを強化したいが、実在人物を使うことで肖像権がらみの問題が発生することを避けたい人向け。", "# About\n- A video set of an artificial and fictional East Asian beauty (Atashi), that was created from myself.\n - The file name is sorted by newest. No. 1 ~ 4 contains about 500, and no. 5 around 400.\n - The videos are raw data that haven't been modified (not even tagged). I have uploaded them as is so that it could potentially be trained for a generative AI for videos, as well as those for images.\n - The face of Atashi was created using FaceApp, Meitu, and Faceplay.\n - Since the audios originate from faceplay videos, it might be copyrighted by others. However, note that it is legal, at least in Japan, to use it for training an AI.\n - If you want to use it for visual purposes (images and/or videos), it doesn't really matter because the audios are simply unnecessary.\n - Since they are uploaded as is, it might contain unswapped frames, person, and Atashi transformed into a male. Make sure to check and remove such data by yourself if necessary.\n- The dataset is targeted for those who do not want to use real personal photos but want to train an AI to generate a more photorealistic style.", "# 外見サンプル / Sample on how she looks\n- このビデオ自体はD-IDで生成したもので、zipには含めていない。が、必要なら直接ダウンロードの上、利用してよい。\n- This video (generated by D-ID) itself is not included in the zips. You may download and use it directly if necessary.\n<video width=\"480\" controls>\n <source src=\"URL type=\"video/mp4\">\n</video>" ]
[ "TAGS\n#size_categories-1K<n<10K #license-cc0-1.0 #Raw data for training #video #art #region-us \n", "# 概要\n- 私自身から作成した人工・架空の東アジア系美人(あたし/Atashi)の動画セット。\n - 名称はnewest順。1~4は約500本、5は約400本の動画。\n - 無加工の原データ(タグ付けもこちらでは行わず)。画像生成AIのみならず、将来的に動画生成AIの学習原データとしても使えるように想定。\n - 顔の合成にはFaceApp, Meitu, Faceplayを利用。\n - Faceplayのビデオ音声をそのまま利用しているため、Audioについては第三者が著作権を保持している可能性がある。ただし、その場合であっても、日本国法ではAudioの学習も合法である。\n - Visualな側面を切り出したい場合は、どちらにせよAudioは使わないはずなので、実質関係ないとみてよい。\n - Faceplayの置換漏れフレーム・人物、男性化されたあたしが含まれている可能性があるため、必要であればそのチェックや除去は各自で行うこと。\n- 実写タッチを強化したいが、実在人物を使うことで肖像権がらみの問題が発生することを避けたい人向け。", "# About\n- A video set of an artificial and fictional East Asian beauty (Atashi), that was created from myself.\n - The file name is sorted by newest. No. 1 ~ 4 contains about 500, and no. 5 around 400.\n - The videos are raw data that haven't been modified (not even tagged). I have uploaded them as is so that it could potentially be trained for a generative AI for videos, as well as those for images.\n - The face of Atashi was created using FaceApp, Meitu, and Faceplay.\n - Since the audios originate from faceplay videos, it might be copyrighted by others. However, note that it is legal, at least in Japan, to use it for training an AI.\n - If you want to use it for visual purposes (images and/or videos), it doesn't really matter because the audios are simply unnecessary.\n - Since they are uploaded as is, it might contain unswapped frames, person, and Atashi transformed into a male. Make sure to check and remove such data by yourself if necessary.\n- The dataset is targeted for those who do not want to use real personal photos but want to train an AI to generate a more photorealistic style.", "# 外見サンプル / Sample on how she looks\n- このビデオ自体はD-IDで生成したもので、zipには含めていない。が、必要なら直接ダウンロードの上、利用してよい。\n- This video (generated by D-ID) itself is not included in the zips. You may download and use it directly if necessary.\n<video width=\"480\" controls>\n <source src=\"URL type=\"video/mp4\">\n</video>" ]
[ 36, 247, 273, 100 ]
[ "passage: TAGS\n#size_categories-1K<n<10K #license-cc0-1.0 #Raw data for training #video #art #region-us \n# 概要\n- 私自身から作成した人工・架空の東アジア系美人(あたし/Atashi)の動画セット。\n - 名称はnewest順。1~4は約500本、5は約400本の動画。\n - 無加工の原データ(タグ付けもこちらでは行わず)。画像生成AIのみならず、将来的に動画生成AIの学習原データとしても使えるように想定。\n - 顔の合成にはFaceApp, Meitu, Faceplayを利用。\n - Faceplayのビデオ音声をそのまま利用しているため、Audioについては第三者が著作権を保持している可能性がある。ただし、その場合であっても、日本国法ではAudioの学習も合法である。\n - Visualな側面を切り出したい場合は、どちらにせよAudioは使わないはずなので、実質関係ないとみてよい。\n - Faceplayの置換漏れフレーム・人物、男性化されたあたしが含まれている可能性があるため、必要であればそのチェックや除去は各自で行うこと。\n- 実写タッチを強化したいが、実在人物を使うことで肖像権がらみの問題が発生することを避けたい人向け。" ]
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5f83e2a9c1310e1743dc08ffe26dbb07c836294d
--- language_creators: - other license: - mit multilinguality: - monolingual pretty_name: narrative-arc size_categories: [] source_datasets: [] tags: [] task_categories: - text-classification task_ids: [] --- # Dataset Card for [narrative-arc] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Dataset of stories used for Narrative Arc post-processing. An instance of a story in this dataset will include the original text and its metadata, the transformer model used to make the embeddings, the model's checkpoint, the window indices of the stored embeddings, and the embeddings. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields An example story will look like the following: { "book name": "", "book meta data": "", "full text": "", "model": { "distilbert-base-cased": { "window indices": (first_index, last_index), "embeddings": [[]] }, "distilbert-base-uncased": { "window indices": (first_index, last_index), "embeddings": [[]] } }, } ... } ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale The processed text needs to be stored somewhere that is both accessible and can accomodate the large amount of data generated. ### Source Data #### Initial Data Collection and Normalization The data were sourced from the Project Gutenberg[https://www.gutenberg.org/] library. #### Who are the source language producers? Each instance in the dataset represents a text written by a human author. At present, data selected for processing are English-language short stories. ### Personal and Sensitive Information Not applicable. ## 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 Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
jnieus01/narrative-arc
[ "license:mit", "region:us" ]
2023-01-27T19:13:58+00:00
{"license": "mit", "dataset_info": {"features": [{"name": "distilbert-base-cased", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32, "num_examples": 2}], "download_size": 631, "dataset_size": 32}}
2023-02-02T08:43:11+00:00
[]
[]
TAGS #license-mit #region-us
--- language_creators: - other license: - mit multilinguality: - monolingual pretty_name: narrative-arc size_categories: [] source_datasets: [] tags: [] task_categories: - text-classification task_ids: [] --- # Dataset Card for [narrative-arc] ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary Dataset of stories used for Narrative Arc post-processing. An instance of a story in this dataset will include the original text and its metadata, the transformer model used to make the embeddings, the model's checkpoint, the window indices of the stored embeddings, and the embeddings. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields An example story will look like the following: { "book name": "", "book meta data": "", "full text": "", "model": { "distilbert-base-cased": { "window indices": (first_index, last_index), "embeddings": [[]] }, "distilbert-base-uncased": { "window indices": (first_index, last_index), "embeddings": [[]] } }, } ... } ### Data Splits ## Dataset Creation ### Curation Rationale The processed text needs to be stored somewhere that is both accessible and can accomodate the large amount of data generated. ### Source Data #### Initial Data Collection and Normalization The data were sourced from the Project Gutenberg[URL library. #### Who are the source language producers? Each instance in the dataset represents a text written by a human author. At present, data selected for processing are English-language short stories. ### Personal and Sensitive Information Not applicable. ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions Thanks to @github-username for adding this dataset.
[ "# Dataset Card for [narrative-arc]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nDataset of stories used for Narrative Arc post-processing. An instance of a story in this dataset will include the original text and its metadata, the transformer model used to make the embeddings, the model's checkpoint, the window indices of the stored embeddings, and the embeddings.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nAn example story will look like the following:\n\n {\n \"book name\": \"\",\n \"book meta data\": \"\",\n \"full text\": \"\",\n \"model\": {\n \"distilbert-base-cased\": {\n \"window indices\": (first_index, last_index),\n \"embeddings\": [[]] },\n \n \"distilbert-base-uncased\": {\n \"window indices\": (first_index, last_index),\n \"embeddings\": [[]] \n }\n },\n }\n ...\n }", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nThe processed text needs to be stored somewhere that is both accessible and can accomodate the large amount of data generated.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data were sourced from the Project Gutenberg[URL library.", "#### Who are the source language producers?\n\nEach instance in the dataset represents a text written by a human author. At present, data selected for processing are English-language short stories.", "### Personal and Sensitive Information\n\nNot applicable.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]
[ "TAGS\n#license-mit #region-us \n", "# Dataset Card for [narrative-arc]", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:", "### Dataset Summary\n\nDataset of stories used for Narrative Arc post-processing. An instance of a story in this dataset will include the original text and its metadata, the transformer model used to make the embeddings, the model's checkpoint, the window indices of the stored embeddings, and the embeddings.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields\n\nAn example story will look like the following:\n\n {\n \"book name\": \"\",\n \"book meta data\": \"\",\n \"full text\": \"\",\n \"model\": {\n \"distilbert-base-cased\": {\n \"window indices\": (first_index, last_index),\n \"embeddings\": [[]] },\n \n \"distilbert-base-uncased\": {\n \"window indices\": (first_index, last_index),\n \"embeddings\": [[]] \n }\n },\n }\n ...\n }", "### Data Splits", "## Dataset Creation", "### Curation Rationale\n\nThe processed text needs to be stored somewhere that is both accessible and can accomodate the large amount of data generated.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe data were sourced from the Project Gutenberg[URL library.", "#### Who are the source language producers?\n\nEach instance in the dataset represents a text written by a human author. At present, data selected for processing are English-language short stories.", "### Personal and Sensitive Information\n\nNot applicable.", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @github-username for adding this dataset." ]
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[ "passage: TAGS\n#license-mit #region-us \n# Dataset Card for [narrative-arc]## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary\n\nDataset of stories used for Narrative Arc post-processing. An instance of a story in this dataset will include the original text and its metadata, the transformer model used to make the embeddings, the model's checkpoint, the window indices of the stored embeddings, and the embeddings.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields\n\nAn example story will look like the following:\n\n {\n \"book name\": \"\",\n \"book meta data\": \"\",\n \"full text\": \"\",\n \"model\": {\n \"distilbert-base-cased\": {\n \"window indices\": (first_index, last_index),\n \"embeddings\": [[]] },\n \n \"distilbert-base-uncased\": {\n \"window indices\": (first_index, last_index),\n \"embeddings\": [[]] \n }\n },\n }\n ...\n }### Data Splits## Dataset Creation### Curation Rationale\n\nThe processed text needs to be stored somewhere that is both accessible and can accomodate the large amount of data generated.### Source Data#### Initial Data Collection and Normalization\n\nThe data were sourced from the Project Gutenberg[URL library." ]
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f1258848a0f92468596a743b0ca359d7791c7b40
# Dataset Card for "Aivazovsky-captions-BLIP" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Outrun32/Aivazovsky-captions-BLIP
[ "region:us" ]
2023-01-27T20:24:15+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3610951.0, "num_examples": 50}], "download_size": 3612823, "dataset_size": 3610951.0}}
2023-03-14T11:11:02+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Aivazovsky-captions-BLIP" More Information needed
[ "# Dataset Card for \"Aivazovsky-captions-BLIP\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Aivazovsky-captions-BLIP\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Aivazovsky-captions-BLIP\"\n\nMore Information needed" ]
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2043785edd3655b50d3dba696fd13b4e6088ad48
# Dataset Card for "imdb_mlm_downsampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Jedalc/imdb_mlm_downsampled
[ "region:us" ]
2023-01-27T20:36:47+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "word_ids", "sequence": "int64"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 27200000.0, "num_examples": 10000}, {"name": "test", "num_bytes": 2720000.0, "num_examples": 1000}], "download_size": 6741894, "dataset_size": 29920000.0}}
2023-01-27T20:36:56+00:00
[]
[]
TAGS #region-us
# Dataset Card for "imdb_mlm_downsampled" More Information needed
[ "# Dataset Card for \"imdb_mlm_downsampled\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"imdb_mlm_downsampled\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"imdb_mlm_downsampled\"\n\nMore Information needed" ]
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d6601c0b99a323fc295b32d8061df32eabe1ba05
# Dataset Card for "Intent-Classification-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dipesh/Intent-Classification-small
[ "region:us" ]
2023-01-27T22:08:07+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "goodbye", "1": "volume control", "2": "play games", "3": "covid cases", "4": "open website", "5": "tell me joke", "6": "play on youtube", "7": "places near me", "8": "greet and hello hi kind of things, general check in", "9": "asking time", "10": "asking date", "11": "tell me news", "12": "asking weather", "13": "download youtube video", "14": "what can you do", "15": "take screenshot", "16": "send email", "17": "i am bored", "18": "click photo", "19": "tell me about", "20": "send whatsapp message"}}}}], "splits": [{"name": "train", "num_bytes": 630723, "num_examples": 6153}, {"name": "validation", "num_bytes": 71230, "num_examples": 684}], "download_size": 201336, "dataset_size": 701953}}
2023-01-27T22:08:26+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Intent-Classification-small" More Information needed
[ "# Dataset Card for \"Intent-Classification-small\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Intent-Classification-small\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Intent-Classification-small\"\n\nMore Information needed" ]
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4a958e109d90b4be985ba1d5caac65f75a051b3a
Disclaimer: This dataset, 'tiny-amaradeva', is created for educational and research purposes only. The song lyrics included in the dataset are the property of W. D. Amaradeva's estate and the songwriters, and should not be used for commercial or any other unauthorized purposes. Credit Note: The 'tiny-amaradeva' dataset is created by Hasarindu Perera. I would like to extend my gratitude to the estate of W. D. Amaradeva and the songwriters for their contributions to the music industry and for allowing the use of their song lyrics in this dataset.
hasarinduperera/tiny-amaradeva
[ "license:openrail", "region:us" ]
2023-01-27T23:01:49+00:00
{"license": "openrail"}
2023-01-27T23:16:50+00:00
[]
[]
TAGS #license-openrail #region-us
Disclaimer: This dataset, 'tiny-amaradeva', is created for educational and research purposes only. The song lyrics included in the dataset are the property of W. D. Amaradeva's estate and the songwriters, and should not be used for commercial or any other unauthorized purposes. Credit Note: The 'tiny-amaradeva' dataset is created by Hasarindu Perera. I would like to extend my gratitude to the estate of W. D. Amaradeva and the songwriters for their contributions to the music industry and for allowing the use of their song lyrics in this dataset.
[]
[ "TAGS\n#license-openrail #region-us \n" ]
[ 12 ]
[ "passage: TAGS\n#license-openrail #region-us \n" ]
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bb34bc3751411ac6e524d7bb865e701fd2aaee94
# Fair Ensembles The repo contains the benchmark models, datasets and results for the following paper submission at ICSE 2023. **Title**: Towards Understanding Fairness and its Composition in Ensemble Machine Learning **Abstract** Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. \textit{How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble?} Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers in designing fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: bagging, boosting, stacking and voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature. ## Index > 1. Datasets <br> - [Adult Census](https://gitlab.com/anonymousdot/fair-ensemble/-/tree/main/AdultNoteBook/Data) <br> - [Bank Marketing](BankMarketingNoteBook/Data) <br> - [Titanic](Titanic/Data) <br> - [German Credit](GermanCredit/Data) <br> > 2. Benchmark - All the ensemble models used in the study and their scripts are shared in the following directories: <br> - [Adult Census](https://gitlab.com/anonymousdot/fair-ensemble/-/tree/main/AdultNoteBook/Kernels) <br> - [Bank Marketing](BankMarketingNoteBook/Kernels) <br> - [Titanic](Titanic/Kernels) <br> - [German Credit](GermanCredit/Kernels) <br> > 3. Results - For reproduction and validation of results, we provide the exact train/test splits, saved model (.pkl) and measures in a .csv. Note: Due to randomization in some models, output might vary a bit. We take mean of 10 runs to reduce the variance <br> - [Adult Census](https://gitlab.com/anonymousdot/fair-ensemble/-/tree/main/AdultNoteBook/Results) <br> - [Bank Marketing](BankMarketingNoteBook/Results) <br> - [Titanic](Titanic/Results) <br> - [German Credit](GermanCredit/Results) <br>
UsmanGohar/FairEnsemble
[ "region:us" ]
2023-01-28T00:56:49+00:00
{}
2023-01-28T04:47:21+00:00
[]
[]
TAGS #region-us
# Fair Ensembles The repo contains the benchmark models, datasets and results for the following paper submission at ICSE 2023. Title: Towards Understanding Fairness and its Composition in Ensemble Machine Learning Abstract Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. \textit{How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble?} Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers in designing fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: bagging, boosting, stacking and voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature. ## Index > 1. Datasets <br> - Adult Census <br> - Bank Marketing <br> - Titanic <br> - German Credit <br> > 2. Benchmark - All the ensemble models used in the study and their scripts are shared in the following directories: <br> - Adult Census <br> - Bank Marketing <br> - Titanic <br> - German Credit <br> > 3. Results - For reproduction and validation of results, we provide the exact train/test splits, saved model (.pkl) and measures in a .csv. Note: Due to randomization in some models, output might vary a bit. We take mean of 10 runs to reduce the variance <br> - Adult Census <br> - Bank Marketing <br> - Titanic <br> - German Credit <br>
[ "# Fair Ensembles\nThe repo contains the benchmark models, datasets and results for the following paper submission at ICSE 2023.\n\nTitle: Towards Understanding Fairness and its Composition in Ensemble Machine Learning\n\nAbstract Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. \\textit{How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble?} Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers in designing fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: bagging, boosting, stacking and voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature.", "## Index\n\n> 1. Datasets <br>\n\t- Adult Census <br>\n\t- Bank Marketing <br>\n\t- Titanic <br>\n\t- German Credit <br>\n\n> 2. Benchmark\n\t- All the ensemble models used in the study and their scripts are shared in the following directories: <br>\n\t\t- Adult Census <br>\n\t\t- Bank Marketing <br>\n\t\t- Titanic <br>\n\t\t- German Credit <br>\n\n> 3. Results\n\t- For reproduction and validation of results, we provide the exact train/test splits, saved model (.pkl) and measures in a .csv. Note: Due to randomization in some models, output might vary a bit. We take mean of 10 runs to reduce the variance <br>\n\t\t- Adult Census <br>\n\t\t- Bank Marketing <br>\n\t\t- Titanic <br>\n\t\t- German Credit <br>" ]
[ "TAGS\n#region-us \n", "# Fair Ensembles\nThe repo contains the benchmark models, datasets and results for the following paper submission at ICSE 2023.\n\nTitle: Towards Understanding Fairness and its Composition in Ensemble Machine Learning\n\nAbstract Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. \\textit{How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble?} Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers in designing fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: bagging, boosting, stacking and voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature.", "## Index\n\n> 1. Datasets <br>\n\t- Adult Census <br>\n\t- Bank Marketing <br>\n\t- Titanic <br>\n\t- German Credit <br>\n\n> 2. Benchmark\n\t- All the ensemble models used in the study and their scripts are shared in the following directories: <br>\n\t\t- Adult Census <br>\n\t\t- Bank Marketing <br>\n\t\t- Titanic <br>\n\t\t- German Credit <br>\n\n> 3. Results\n\t- For reproduction and validation of results, we provide the exact train/test splits, saved model (.pkl) and measures in a .csv. Note: Due to randomization in some models, output might vary a bit. We take mean of 10 runs to reduce the variance <br>\n\t\t- Adult Census <br>\n\t\t- Bank Marketing <br>\n\t\t- Titanic <br>\n\t\t- German Credit <br>" ]
[ 6, 439, 184 ]
[ "passage: TAGS\n#region-us \n# Fair Ensembles\nThe repo contains the benchmark models, datasets and results for the following paper submission at ICSE 2023.\n\nTitle: Towards Understanding Fairness and its Composition in Ensemble Machine Learning\n\nAbstract Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. \\textit{How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble?} Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers in designing fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: bagging, boosting, stacking and voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature." ]
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c5085436a40d8df1d73103911f642f7237b3fdeb
# Dataset Card for "mitchell" 11 Joan Mitchell's work and descriptions (image-text pairs). Texts are from the Collection in the [Foundation Louis Vuitton](https://www.fondationlouisvuitton.fr/en/collection/artists/joan-mitchell). Images are from the [Joan Mitchell Foundation](https://www.joanmitchellfoundation.org/joan-mitchell/artwork).
maximalmargin/mitchell
[ "region:us" ]
2023-01-28T01:48:02+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20892005.0, "num_examples": 11}], "download_size": 20893901, "dataset_size": 20892005.0}}
2023-01-29T03:40:56+00:00
[]
[]
TAGS #region-us
# Dataset Card for "mitchell" 11 Joan Mitchell's work and descriptions (image-text pairs). Texts are from the Collection in the Foundation Louis Vuitton. Images are from the Joan Mitchell Foundation.
[ "# Dataset Card for \"mitchell\"\n\n11 Joan Mitchell's work and descriptions (image-text pairs). \nTexts are from the Collection in the Foundation Louis Vuitton.\nImages are from the Joan Mitchell Foundation." ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"mitchell\"\n\n11 Joan Mitchell's work and descriptions (image-text pairs). \nTexts are from the Collection in the Foundation Louis Vuitton.\nImages are from the Joan Mitchell Foundation." ]
[ 6, 46 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"mitchell\"\n\n11 Joan Mitchell's work and descriptions (image-text pairs). \nTexts are from the Collection in the Foundation Louis Vuitton.\nImages are from the Joan Mitchell Foundation." ]
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21cd79d264a3cfb7c2fa1fed864d55d629ac50a8
# Dataset Card for "Caltech101_with_background_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_with_background_train
[ "region:us" ]
2023-01-28T02:21:00+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "accordion", "1": "airplanes", "2": "anchor", "3": "ant", "4": "background google", "5": "barrel", "6": "bass", "7": "beaver", "8": "binocular", "9": "bonsai", "10": "brain", "11": "brontosaurus", "12": "buddha", "13": "butterfly", "14": "camera", "15": "cannon", "16": "car side", "17": "ceiling fan", "18": "cellphone", "19": "chair", "20": "chandelier", "21": "cougar body", "22": "cougar face", "23": "crab", "24": "crayfish", "25": "crocodile", "26": "crocodile head", "27": "cup", "28": "dalmatian", "29": "dollar bill", "30": "dolphin", "31": "dragonfly", "32": "electric guitar", "33": "elephant", "34": "emu", "35": "euphonium", "36": "ewer", "37": "faces", "38": "faces easy", "39": "ferry", "40": "flamingo", "41": "flamingo head", "42": "garfield", "43": "gerenuk", "44": "gramophone", "45": "grand piano", "46": "hawksbill", "47": "headphone", "48": "hedgehog", "49": "helicopter", "50": "ibis", "51": "inline skate", "52": "joshua tree", "53": "kangaroo", "54": "ketch", "55": "lamp", "56": "laptop", "57": "leopards", "58": "llama", "59": "lobster", "60": "lotus", "61": "mandolin", "62": "mayfly", "63": "menorah", "64": "metronome", "65": "minaret", "66": "motorbikes", "67": "nautilus", "68": "octopus", "69": "okapi", "70": "pagoda", "71": "panda", "72": "pigeon", "73": "pizza", "74": "platypus", "75": "pyramid", "76": "revolver", "77": "rhino", "78": "rooster", "79": "saxophone", "80": "schooner", "81": "scissors", "82": "scorpion", "83": "sea horse", "84": "snoopy", "85": "soccer ball", "86": "stapler", "87": "starfish", "88": "stegosaurus", "89": "stop sign", "90": "strawberry", "91": "sunflower", "92": "tick", "93": "trilobite", "94": "umbrella", "95": "watch", "96": "water lilly", "97": "wheelchair", "98": "wild cat", "99": "windsor chair", "100": "wrench", "101": "yin yang"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_opt175b_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_caltech101", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49965015.0, "num_examples": 3060}], "download_size": 45077220, "dataset_size": 49965015.0}}
2023-05-04T04:01:53+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_with_background_train" More Information needed
[ "# Dataset Card for \"Caltech101_with_background_train\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_with_background_train\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_train\"\n\nMore Information needed" ]
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04bc12c6fe399c15626615bbc00d273e32a94a50
# Dataset Card for "Caltech101_with_background_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_with_background_test
[ "region:us" ]
2023-01-28T02:21:07+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "accordion", "1": "airplanes", "2": "anchor", "3": "ant", "4": "background google", "5": "barrel", "6": "bass", "7": "beaver", "8": "binocular", "9": "bonsai", "10": "brain", "11": "brontosaurus", "12": "buddha", "13": "butterfly", "14": "camera", "15": "cannon", "16": "car side", "17": "ceiling fan", "18": "cellphone", "19": "chair", "20": "chandelier", "21": "cougar body", "22": "cougar face", "23": "crab", "24": "crayfish", "25": "crocodile", "26": "crocodile head", "27": "cup", "28": "dalmatian", "29": "dollar bill", "30": "dolphin", "31": "dragonfly", "32": "electric guitar", "33": "elephant", "34": "emu", "35": "euphonium", "36": "ewer", "37": "faces", "38": "faces easy", "39": "ferry", "40": "flamingo", "41": "flamingo head", "42": "garfield", "43": "gerenuk", "44": "gramophone", "45": "grand piano", "46": "hawksbill", "47": "headphone", "48": "hedgehog", "49": "helicopter", "50": "ibis", "51": "inline skate", "52": "joshua tree", "53": "kangaroo", "54": "ketch", "55": "lamp", "56": "laptop", "57": "leopards", "58": "llama", "59": "lobster", "60": "lotus", "61": "mandolin", "62": "mayfly", "63": "menorah", "64": "metronome", "65": "minaret", "66": "motorbikes", "67": "nautilus", "68": "octopus", "69": "okapi", "70": "pagoda", "71": "panda", "72": "pigeon", "73": "pizza", "74": "platypus", "75": "pyramid", "76": "revolver", "77": "rhino", "78": "rooster", "79": "saxophone", "80": "schooner", "81": "scissors", "82": "scorpion", "83": "sea horse", "84": "snoopy", "85": "soccer ball", "86": "stapler", "87": "starfish", "88": "stegosaurus", "89": "stop sign", "90": "strawberry", "91": "sunflower", "92": "tick", "93": "trilobite", "94": "umbrella", "95": "watch", "96": "water lilly", "97": "wheelchair", "98": "wild cat", "99": "windsor chair", "100": "wrench", "101": "yin yang"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_opt175b_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_caltech101", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 105713723.0, "num_examples": 6084}], "download_size": 95507836, "dataset_size": 105713723.0}}
2023-05-04T04:04:14+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_with_background_test" More Information needed
[ "# Dataset Card for \"Caltech101_with_background_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_with_background_test\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test\"\n\nMore Information needed" ]
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bac84af4fe88dddd3ac5867e4d67e76924b28659
# Dataset Card for Bio-ID ## Dataset Description - **Homepage:** https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-1/ - **Pubmed:** True - **Public:** True - **Tasks:** NER,NED The Bio-ID track focuses on entity tagging and ID assignment to selected bioentity types. The task is to annotate text from figure legends with the entity types and IDs for taxon (organism), gene, protein, miRNA, small molecules, cellular components, cell types and cell lines, tissues and organs. The track draws on SourceData annotated figure legends (by panel), in BioC format, and the corresponding full text articles (also BioC format) provided for context. ## Citation Information ``` @inproceedings{arighi2017bio, title={Bio-ID track overview}, author={Arighi, Cecilia and Hirschman, Lynette and Lemberger, Thomas and Bayer, Samuel and Liechti, Robin and Comeau, Donald and Wu, Cathy}, booktitle={Proc. BioCreative Workshop}, volume={482}, pages={376}, year={2017} } ```
bigbio/bioid
[ "multilinguality:monolingual", "language:en", "license:other", "region:us" ]
2023-01-28T02:24:51+00:00
{"language": ["en"], "license": "other", "multilinguality": "monolingual", "pretty_name": "Bio-ID", "bigbio_language": ["English"], "bigbio_license_shortname": "UNKNOWN", "homepage": "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-1/", "bigbio_pubmed": true, "bigbio_public": true, "bigbio_tasks": ["NAMED_ENTITY_RECOGNITION", "NAMED_ENTITY_DISAMBIGUATION"]}
2023-02-17T14:54:28+00:00
[]
[ "en" ]
TAGS #multilinguality-monolingual #language-English #license-other #region-us
# Dataset Card for Bio-ID ## Dataset Description - Homepage: URL - Pubmed: True - Public: True - Tasks: NER,NED The Bio-ID track focuses on entity tagging and ID assignment to selected bioentity types. The task is to annotate text from figure legends with the entity types and IDs for taxon (organism), gene, protein, miRNA, small molecules, cellular components, cell types and cell lines, tissues and organs. The track draws on SourceData annotated figure legends (by panel), in BioC format, and the corresponding full text articles (also BioC format) provided for context.
[ "# Dataset Card for Bio-ID", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED \n\n\nThe Bio-ID track focuses on entity tagging and ID assignment to selected bioentity types.\nThe task is to annotate text from figure legends with the entity types and IDs for taxon (organism), gene, protein, miRNA, small molecules,\ncellular components, cell types and cell lines, tissues and organs. The track draws on SourceData annotated figure\nlegends (by panel), in BioC format, and the corresponding full text articles (also BioC format) provided for context." ]
[ "TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n", "# Dataset Card for Bio-ID", "## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED \n\n\nThe Bio-ID track focuses on entity tagging and ID assignment to selected bioentity types.\nThe task is to annotate text from figure legends with the entity types and IDs for taxon (organism), gene, protein, miRNA, small molecules,\ncellular components, cell types and cell lines, tissues and organs. The track draws on SourceData annotated figure\nlegends (by panel), in BioC format, and the corresponding full text articles (also BioC format) provided for context." ]
[ 23, 8, 142 ]
[ "passage: TAGS\n#multilinguality-monolingual #language-English #license-other #region-us \n# Dataset Card for Bio-ID## Dataset Description\n\n- Homepage: URL\n- Pubmed: True\n- Public: True\n- Tasks: NER,NED \n\n\nThe Bio-ID track focuses on entity tagging and ID assignment to selected bioentity types.\nThe task is to annotate text from figure legends with the entity types and IDs for taxon (organism), gene, protein, miRNA, small molecules,\ncellular components, cell types and cell lines, tissues and organs. The track draws on SourceData annotated figure\nlegends (by panel), in BioC format, and the corresponding full text articles (also BioC format) provided for context." ]
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31847c49b43333c42e492bb31a21d3fcfcddab55
# Dataset Card for "StanfordCars_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/StanfordCars_train
[ "region:us" ]
2023-01-28T02:30:01+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "am general hummer suv 2000", "1": "acura rl sedan 2012", "2": "acura tl sedan 2012", "3": "acura tl type-s 2008", "4": "acura tsx sedan 2012", "5": "acura integra type r 2001", "6": "acura zdx hatchback 2012", "7": "aston martin v8 vantage convertible 2012", "8": "aston martin v8 vantage coupe 2012", "9": "aston martin virage convertible 2012", "10": "aston martin virage coupe 2012", "11": "audi rs 4 convertible 2008", "12": "audi a5 coupe 2012", "13": "audi tts coupe 2012", "14": "audi r8 coupe 2012", "15": "audi v8 sedan 1994", "16": "audi 100 sedan 1994", "17": "audi 100 wagon 1994", "18": "audi tt hatchback 2011", "19": "audi s6 sedan 2011", "20": "audi s5 convertible 2012", "21": "audi s5 coupe 2012", "22": "audi s4 sedan 2012", "23": "audi s4 sedan 2007", "24": "audi tt rs coupe 2012", "25": "bmw activehybrid 5 sedan 2012", "26": "bmw 1 series 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and country minivan 2012", "78": "chrysler 300 srt-8 2010", "79": "chrysler crossfire convertible 2008", "80": "chrysler pt cruiser convertible 2008", "81": "daewoo nubira wagon 2002", "82": "dodge caliber wagon 2012", "83": "dodge caliber wagon 2007", "84": "dodge caravan minivan 1997", "85": "dodge ram pickup 3500 crew cab 2010", "86": "dodge ram pickup 3500 quad cab 2009", "87": "dodge sprinter cargo van 2009", "88": "dodge journey suv 2012", "89": "dodge dakota crew cab 2010", "90": "dodge dakota club cab 2007", "91": "dodge magnum wagon 2008", "92": "dodge challenger srt8 2011", "93": "dodge durango suv 2012", "94": "dodge durango suv 2007", "95": "dodge charger sedan 2012", "96": "dodge charger srt-8 2009", "97": "eagle talon hatchback 1998", "98": "fiat 500 abarth 2012", "99": "fiat 500 convertible 2012", "100": "ferrari ff coupe 2012", "101": "ferrari california convertible 2012", "102": "ferrari 458 italia convertible 2012", "103": "ferrari 458 italia coupe 2012", "104": 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"mazda tribute suv 2011", "159": "mclaren mp4-12c coupe 2012", "160": "mercedes-benz 300-class convertible 1993", "161": "mercedes-benz c-class sedan 2012", "162": "mercedes-benz sl-class coupe 2009", "163": "mercedes-benz e-class sedan 2012", "164": "mercedes-benz s-class sedan 2012", "165": "mercedes-benz sprinter van 2012", "166": "mitsubishi lancer sedan 2012", "167": "nissan leaf hatchback 2012", "168": "nissan nv passenger van 2012", "169": "nissan juke hatchback 2012", "170": "nissan 240sx coupe 1998", "171": "plymouth neon coupe 1999", "172": "porsche panamera sedan 2012", "173": "ram c/v cargo van minivan 2012", "174": "rolls-royce phantom drophead coupe convertible 2012", "175": "rolls-royce ghost sedan 2012", "176": "rolls-royce phantom sedan 2012", "177": "scion xd hatchback 2012", "178": "spyker c8 convertible 2009", "179": "spyker c8 coupe 2009", "180": "suzuki aerio sedan 2007", "181": "suzuki kizashi sedan 2012", "182": "suzuki sx4 hatchback 2012", "183": "suzuki sx4 sedan 2012", "184": "tesla model s sedan 2012", "185": "toyota sequoia suv 2012", "186": "toyota camry sedan 2012", "187": "toyota corolla sedan 2012", "188": "toyota 4runner suv 2012", "189": "volkswagen golf hatchback 2012", "190": "volkswagen golf hatchback 1991", "191": "volkswagen beetle hatchback 2012", "192": "volvo c30 hatchback 2012", "193": "volvo 240 sedan 1993", "194": "volvo xc90 suv 2007", "195": "smart fortwo convertible 2012"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_stanfordcars", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": 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2023-06-12T05:26:48+00:00
[]
[]
TAGS #region-us
# Dataset Card for "StanfordCars_train" More Information needed
[ "# Dataset Card for \"StanfordCars_train\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"StanfordCars_train\"\n\nMore Information needed" ]
[ 6, 18 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"StanfordCars_train\"\n\nMore Information needed" ]
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44480308e3192efdfbb2ec4cec40b36feacbfbb7
# Dataset Card for "StanfordCars_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/StanfordCars_test
[ "region:us" ]
2023-01-28T02:30:24+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "am general hummer suv 2000", "1": "acura rl sedan 2012", "2": "acura tl sedan 2012", "3": "acura tl type-s 2008", "4": "acura tsx sedan 2012", "5": "acura integra type r 2001", "6": "acura zdx hatchback 2012", "7": "aston martin v8 vantage convertible 2012", "8": "aston martin v8 vantage coupe 2012", "9": "aston martin virage convertible 2012", "10": "aston martin virage coupe 2012", "11": "audi rs 4 convertible 2008", "12": "audi a5 coupe 2012", "13": "audi tts coupe 2012", "14": "audi r8 coupe 2012", "15": "audi v8 sedan 1994", "16": "audi 100 sedan 1994", "17": "audi 100 wagon 1994", "18": "audi tt hatchback 2011", "19": "audi s6 sedan 2011", "20": "audi s5 convertible 2012", "21": "audi s5 coupe 2012", "22": "audi s4 sedan 2012", "23": "audi s4 sedan 2007", "24": "audi tt rs coupe 2012", "25": "bmw activehybrid 5 sedan 2012", "26": "bmw 1 series 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"string"}], "splits": [{"name": "test", "num_bytes": 1016320238.0, "num_examples": 8041}], "download_size": 989991348, "dataset_size": 1016320238.0}}
2023-06-12T01:33:45+00:00
[]
[]
TAGS #region-us
# Dataset Card for "StanfordCars_test" More Information needed
[ "# Dataset Card for \"StanfordCars_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"StanfordCars_test\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"StanfordCars_test\"\n\nMore Information needed" ]
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7aed01f3cbf8565946dff6ab186e637b31999ed2
# Dataset Card for "FGVC_Aircraft_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/FGVC_Aircraft_test
[ "region:us" ]
2023-01-28T02:49:32+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "family", "dtype": {"class_label": {"names": {"0": "A300", "1": "A310", "2": "A320", "3": "A330", "4": "A340", "5": "A380", "6": "ATR-42", "7": "ATR-72", "8": "An-12", "9": "BAE 146", "10": "BAE-125", "11": "Beechcraft 1900", "12": "Boeing 707", "13": "Boeing 717", "14": "Boeing 727", "15": "Boeing 737", "16": "Boeing 747", "17": "Boeing 757", "18": "Boeing 767", "19": "Boeing 777", "20": "C-130", "21": "C-47", "22": "CRJ-200", "23": "CRJ-700", "24": "Cessna 172", "25": "Cessna 208", "26": "Cessna Citation", "27": "Challenger 600", "28": "DC-10", "29": "DC-3", "30": "DC-6", "31": "DC-8", "32": "DC-9", "33": "DH-82", "34": "DHC-1", "35": "DHC-6", "36": "DR-400", "37": "Dash 8", "38": "Dornier 328", "39": "EMB-120", "40": "Embraer E-Jet", "41": "Embraer ERJ 145", "42": "Embraer Legacy 600", "43": "Eurofighter Typhoon", "44": "F-16", "45": "F/A-18", "46": "Falcon 2000", "47": "Falcon 900", "48": "Fokker 100", 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"28": "Yakovlev", "29": "de Havilland"}}}}, {"name": "label", "dtype": {"class_label": {"names": {"0": "707-320", "1": "727-200", "2": "737-200", "3": "737-300", "4": "737-400", "5": "737-500", "6": "737-600", "7": "737-700", "8": "737-800", "9": "737-900", "10": "747-100", "11": "747-200", "12": "747-300", "13": "747-400", "14": "757-200", "15": "757-300", "16": "767-200", "17": "767-300", "18": "767-400", "19": "777-200", "20": "777-300", "21": "A300B4", "22": "A310", "23": "A318", "24": "A319", "25": "A320", "26": "A321", "27": "A330-200", "28": "A330-300", "29": "A340-200", "30": "A340-300", "31": "A340-500", "32": "A340-600", "33": "A380", "34": "ATR-42", "35": "ATR-72", "36": "An-12", "37": "BAE 146-200", "38": "BAE 146-300", "39": "BAE-125", "40": "Beechcraft 1900", "41": "Boeing 717", "42": "C-130", "43": "C-47", "44": "CRJ-200", "45": "CRJ-700", "46": "CRJ-900", "47": "Cessna 172", "48": "Cessna 208", "49": "Cessna 525", "50": "Cessna 560", "51": "Challenger 600", "52": 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"sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "clip_tag_ViT_L_14_specific", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_fgvc", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 929803718.0, "num_examples": 3333}], "download_size": 923279914, "dataset_size": 929803718.0}}
2023-06-02T01:15:19+00:00
[]
[]
TAGS #region-us
# Dataset Card for "FGVC_Aircraft_test" More Information needed
[ "# Dataset Card for \"FGVC_Aircraft_test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"FGVC_Aircraft_test\"\n\nMore Information needed" ]
[ 6, 17 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test\"\n\nMore Information needed" ]
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1b0f7054c795b945a77ce0246d43c9597f602c59
Collect images from [danbooru website](https://danbooru.donmai.us/). Post id range: 6019085 - 1019085 About 4M+ images. All images with the shortest edge greater than 768 are scaled to the shortest edge equal to 768. Some images not download in the range: - need gold account - removed - over 25000000 pixels - has one of ['furry', "realistic", "3d", "1940s_(style)","1950s_(style)","1960s_(style)","1970s_(style)","1980s_(style)","1990s_(style)","retro_artstyle","screentones","pixel_art","magazine_scan","scan"] tag.
animelover/danbooru2022
[ "task_categories:text-to-image", "size_categories:1M<n<10M", "language:en", "license:cc0-1.0", "art", "doi:10.57967/hf/0425", "region:us" ]
2023-01-28T03:34:32+00:00
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-to-image"], "pretty_name": "Danbooru 2022", "tags": ["art"]}
2023-12-04T11:26:03+00:00
[]
[ "en" ]
TAGS #task_categories-text-to-image #size_categories-1M<n<10M #language-English #license-cc0-1.0 #art #doi-10.57967/hf/0425 #region-us
Collect images from danbooru website. Post id range: 6019085 - 1019085 About 4M+ images. All images with the shortest edge greater than 768 are scaled to the shortest edge equal to 768. Some images not download in the range: - need gold account - removed - over 25000000 pixels - has one of ['furry', "realistic", "3d", "1940s_(style)","1950s_(style)","1960s_(style)","1970s_(style)","1980s_(style)","1990s_(style)","retro_artstyle","screentones","pixel_art","magazine_scan","scan"] tag.
[]
[ "TAGS\n#task_categories-text-to-image #size_categories-1M<n<10M #language-English #license-cc0-1.0 #art #doi-10.57967/hf/0425 #region-us \n" ]
[ 56 ]
[ "passage: TAGS\n#task_categories-text-to-image #size_categories-1M<n<10M #language-English #license-cc0-1.0 #art #doi-10.57967/hf/0425 #region-us \n" ]
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464097e2d324b4745799aa7b71fe327d901cd0a4
# Stable Diffusion web UI A browser interface based on Gradio library for Stable Diffusion. ![](txt2img_Screenshot.png) Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) wiki page for extra scripts developed by users. ## Features [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): - Original txt2img and img2img modes - One click install and run script (but you still must install python and git) - Outpainting - Inpainting - Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - a man in a ((tuxedo)) - will pay more attention to tuxedo - a man in a (tuxedo:1.21) - alternative syntax - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y plot, a way to draw a 2 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection - Adjust sampler eta values (noise multiplier) - More advanced noise setting options - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches - Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings - drag and drop an image/text-parameters to promptbox - Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page - Running arbitrary python code from UI (must run with --allow-code to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config - Random artist button - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences - Seed resizing, a way to generate same image but at slightly different resolution - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img - Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI - Generate forever option - Training tab - hypernetworks and embeddings options - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) - Clip skip - Use Hypernetworks - Use VAEs - Estimated completion time in progress bar - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) ### Automatic Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH" 2. Install [git](https://git-scm.com/download/win). 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. 4. Place `model.ckpt` in the `models` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it). 5. _*(Optional)*_ Place `GFPGANv1.4.pth` in the base directory, alongside `webui.py` (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it). 6. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. ### Automatic Installation on Linux 1. Install the dependencies: ```bash # Debian-based: sudo apt install wget git python3 python3-venv # Red Hat-based: sudo dnf install wget git python3 # Arch-based: sudo pacman -S wget git python3 ``` 2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run: ```bash bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh) ``` ### Installation on Apple Silicon Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). ## Contributing Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). ## Credits - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers - k-diffusion - https://github.com/crowsonkb/k-diffusion.git - GFPGAN - https://github.com/TencentARC/GFPGAN.git - CodeFormer - https://github.com/sczhou/CodeFormer - ESRGAN - https://github.com/xinntao/ESRGAN - SwinIR - https://github.com/JingyunLiang/SwinIR - Swin2SR - https://github.com/mv-lab/swin2sr - LDSR - https://github.com/Hafiidz/latent-diffusion - MiDaS - https://github.com/isl-org/MiDaS - Ideas for optimizations - https://github.com/basujindal/stable-diffusion - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - xformers - https://github.com/facebookresearch/xformers - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru - Security advice - RyotaK - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You)
Kain1999/stable-diffusion-webui
[ "region:us" ]
2023-01-28T04:59:37+00:00
{}
2023-07-16T05:01:16+00:00
[]
[]
TAGS #region-us
# Stable Diffusion web UI A browser interface based on Gradio library for Stable Diffusion. ![](txt2img_Screenshot.png) Check the custom scripts wiki page for extra scripts developed by users. ## Features Detailed feature showcase with images: - Original txt2img and img2img modes - One click install and run script (but you still must install python and git) - Outpainting - Inpainting - Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - a man in a ((tuxedo)) - will pay more attention to tuxedo - a man in a (tuxedo:1.21) - alternative syntax - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y plot, a way to draw a 2 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - SwinIR and Swin2SR(see here), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection - Adjust sampler eta values (noise multiplier) - More advanced noise setting options - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches - Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings - drag and drop an image/text-parameters to promptbox - Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page - Running arbitrary python code from UI (must run with --allow-code to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config - Random artist button - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences - Seed resizing, a way to generate same image but at slightly different resolution - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img - Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - Custom scripts with many extensions from community - Composable-Diffusion, a way to use multiple prompts at once - separate prompts using uppercase 'AND' - also supports weights for prompts: 'a cat :1.2 AND a dog AND a penguin :2.2' - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts - xformers, major speed increase for select cards: (add --xformers to commandline args) - via extension: History tab: view, direct and delete images conveniently within the UI - Generate forever option - Training tab - hypernetworks and embeddings options - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) - Clip skip - Use Hypernetworks - Use VAEs - Estimated completion time in progress bar - API - Support for dedicated inpainting model by RunwayML. - via extension: Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of URL - Stable Diffusion 2.0 support - see wiki for instructions ## Installation and Running Make sure the required dependencies are met and follow the instructions available for both NVidia (recommended) and AMD GPUs. Alternatively, use online services (like Google Colab): - List of Online Services ### Automatic Installation on Windows 1. Install Python 3.10.6, checking "Add Python to PATH" 2. Install git. 3. Download the stable-diffusion-webui repository, for example by running 'git clone URL 4. Place 'URL' in the 'models' directory (see dependencies for where to get it). 5. _*(Optional)*_ Place 'GFPGANv1.4.pth' in the base directory, alongside 'URL' (see dependencies for where to get it). 6. Run 'URL' from Windows Explorer as normal, non-administrator, user. ### Automatic Installation on Linux 1. Install the dependencies: 2. To install in '/home/$(whoami)/stable-diffusion-webui/', run: ### Installation on Apple Silicon Find the instructions here. ## Contributing Here's how to add code to this repo: Contributing ## Documentation The documentation was moved from this README over to the project's wiki. ## Credits - Stable Diffusion - URL URL - k-diffusion - URL - GFPGAN - URL - CodeFormer - URL - ESRGAN - URL - SwinIR - URL - Swin2SR - URL - LDSR - URL - MiDaS - URL - Ideas for optimizations - URL - Cross Attention layer optimization - Doggettx - URL original idea for prompt editing. - Cross Attention layer optimization - InvokeAI, lstein - URL (originally URL - Textual Inversion - Rinon Gal - URL (we're not using his code, but we are using his ideas). - Idea for SD upscale - URL - Noise generation for outpainting mk2 - URL - CLIP interrogator idea and borrowing some code - URL - Idea for Composable Diffusion - URL - xformers - URL - DeepDanbooru - interrogator for anime diffusers URL - Security advice - RyotaK - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You)
[ "# Stable Diffusion web UI\nA browser interface based on Gradio library for Stable Diffusion.\n\n![](txt2img_Screenshot.png)\n\nCheck the custom scripts wiki page for extra scripts developed by users.", "## Features\nDetailed feature showcase with images:\n- Original txt2img and img2img modes\n- One click install and run script (but you still must install python and git)\n- Outpainting\n- Inpainting\n- Color Sketch\n- Prompt Matrix\n- Stable Diffusion Upscale\n- Attention, specify parts of text that the model should pay more attention to\n - a man in a ((tuxedo)) - will pay more attention to tuxedo\n - a man in a (tuxedo:1.21) - alternative syntax\n - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)\n- Loopback, run img2img processing multiple times\n- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters\n- Textual Inversion\n - have as many embeddings as you want and use any names you like for them\n - use multiple embeddings with different numbers of vectors per token\n - works with half precision floating point numbers\n - train embeddings on 8GB (also reports of 6GB working)\n- Extras tab with:\n - GFPGAN, neural network that fixes faces\n - CodeFormer, face restoration tool as an alternative to GFPGAN\n - RealESRGAN, neural network upscaler\n - ESRGAN, neural network upscaler with a lot of third party models\n - SwinIR and Swin2SR(see here), neural network upscalers\n - LDSR, Latent diffusion super resolution upscaling\n- Resizing aspect ratio options\n- Sampling method selection\n - Adjust sampler eta values (noise multiplier)\n - More advanced noise setting options\n- Interrupt processing at any time\n- 4GB video card support (also reports of 2GB working)\n- Correct seeds for batches\n- Live prompt token length validation\n- Generation parameters\n - parameters you used to generate images are saved with that image\n - in PNG chunks for PNG, in EXIF for JPEG\n - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI\n - can be disabled in settings\n - drag and drop an image/text-parameters to promptbox\n- Read Generation Parameters Button, loads parameters in promptbox to UI\n- Settings page\n- Running arbitrary python code from UI (must run with --allow-code to enable)\n- Mouseover hints for most UI elements\n- Possible to change defaults/mix/max/step values for UI elements via text config\n- Random artist button\n- Tiling support, a checkbox to create images that can be tiled like textures\n- Progress bar and live image generation preview\n- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image\n- Styles, a way to save part of prompt and easily apply them via dropdown later\n- Variations, a way to generate same image but with tiny differences\n- Seed resizing, a way to generate same image but at slightly different resolution\n- CLIP interrogator, a button that tries to guess prompt from an image\n- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway\n- Batch Processing, process a group of files using img2img\n- Img2img Alternative, reverse Euler method of cross attention control\n- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions\n- Reloading checkpoints on the fly\n- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one\n- Custom scripts with many extensions from community\n- Composable-Diffusion, a way to use multiple prompts at once\n - separate prompts using uppercase 'AND'\n - also supports weights for prompts: 'a cat :1.2 AND a dog AND a penguin :2.2'\n- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)\n- DeepDanbooru integration, creates danbooru style tags for anime prompts\n- xformers, major speed increase for select cards: (add --xformers to commandline args)\n- via extension: History tab: view, direct and delete images conveniently within the UI\n- Generate forever option\n- Training tab\n - hypernetworks and embeddings options\n - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)\n- Clip skip\n- Use Hypernetworks\n- Use VAEs\n- Estimated completion time in progress bar\n- API\n- Support for dedicated inpainting model by RunwayML. \n- via extension: Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of URL\n- Stable Diffusion 2.0 support - see wiki for instructions", "## Installation and Running\nMake sure the required dependencies are met and follow the instructions available for both NVidia (recommended) and AMD GPUs.\n\nAlternatively, use online services (like Google Colab):\n\n- List of Online Services", "### Automatic Installation on Windows\n1. Install Python 3.10.6, checking \"Add Python to PATH\"\n2. Install git.\n3. Download the stable-diffusion-webui repository, for example by running 'git clone URL\n4. Place 'URL' in the 'models' directory (see dependencies for where to get it).\n5. _*(Optional)*_ Place 'GFPGANv1.4.pth' in the base directory, alongside 'URL' (see dependencies for where to get it).\n6. Run 'URL' from Windows Explorer as normal, non-administrator, user.", "### Automatic Installation on Linux\n1. Install the dependencies:\n\n2. To install in '/home/$(whoami)/stable-diffusion-webui/', run:", "### Installation on Apple Silicon\n\nFind the instructions here.", "## Contributing\nHere's how to add code to this repo: Contributing", "## Documentation\nThe documentation was moved from this README over to the project's wiki.", "## Credits\n- Stable Diffusion - URL URL\n- k-diffusion - URL\n- GFPGAN - URL\n- CodeFormer - URL\n- ESRGAN - URL\n- SwinIR - URL\n- Swin2SR - URL\n- LDSR - URL\n- MiDaS - URL\n- Ideas for optimizations - URL\n- Cross Attention layer optimization - Doggettx - URL original idea for prompt editing.\n- Cross Attention layer optimization - InvokeAI, lstein - URL (originally URL\n- Textual Inversion - Rinon Gal - URL (we're not using his code, but we are using his ideas).\n- Idea for SD upscale - URL\n- Noise generation for outpainting mk2 - URL\n- CLIP interrogator idea and borrowing some code - URL\n- Idea for Composable Diffusion - URL\n- xformers - URL\n- DeepDanbooru - interrogator for anime diffusers URL\n- Security advice - RyotaK\n- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.\n- (You)" ]
[ "TAGS\n#region-us \n", "# Stable Diffusion web UI\nA browser interface based on Gradio library for Stable Diffusion.\n\n![](txt2img_Screenshot.png)\n\nCheck the custom scripts wiki page for extra scripts developed by users.", "## Features\nDetailed feature showcase with images:\n- Original txt2img and img2img modes\n- One click install and run script (but you still must install python and git)\n- Outpainting\n- Inpainting\n- Color Sketch\n- Prompt Matrix\n- Stable Diffusion Upscale\n- Attention, specify parts of text that the model should pay more attention to\n - a man in a ((tuxedo)) - will pay more attention to tuxedo\n - a man in a (tuxedo:1.21) - alternative syntax\n - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)\n- Loopback, run img2img processing multiple times\n- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters\n- Textual Inversion\n - have as many embeddings as you want and use any names you like for them\n - use multiple embeddings with different numbers of vectors per token\n - works with half precision floating point numbers\n - train embeddings on 8GB (also reports of 6GB working)\n- Extras tab with:\n - GFPGAN, neural network that fixes faces\n - CodeFormer, face restoration tool as an alternative to GFPGAN\n - RealESRGAN, neural network upscaler\n - ESRGAN, neural network upscaler with a lot of third party models\n - SwinIR and Swin2SR(see here), neural network upscalers\n - LDSR, Latent diffusion super resolution upscaling\n- Resizing aspect ratio options\n- Sampling method selection\n - Adjust sampler eta values (noise multiplier)\n - More advanced noise setting options\n- Interrupt processing at any time\n- 4GB video card support (also reports of 2GB working)\n- Correct seeds for batches\n- Live prompt token length validation\n- Generation parameters\n - parameters you used to generate images are saved with that image\n - in PNG chunks for PNG, in EXIF for JPEG\n - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI\n - can be disabled in settings\n - drag and drop an image/text-parameters to promptbox\n- Read Generation Parameters Button, loads parameters in promptbox to UI\n- Settings page\n- Running arbitrary python code from UI (must run with --allow-code to enable)\n- Mouseover hints for most UI elements\n- Possible to change defaults/mix/max/step values for UI elements via text config\n- Random artist button\n- Tiling support, a checkbox to create images that can be tiled like textures\n- Progress bar and live image generation preview\n- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image\n- Styles, a way to save part of prompt and easily apply them via dropdown later\n- Variations, a way to generate same image but with tiny differences\n- Seed resizing, a way to generate same image but at slightly different resolution\n- CLIP interrogator, a button that tries to guess prompt from an image\n- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway\n- Batch Processing, process a group of files using img2img\n- Img2img Alternative, reverse Euler method of cross attention control\n- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions\n- Reloading checkpoints on the fly\n- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one\n- Custom scripts with many extensions from community\n- Composable-Diffusion, a way to use multiple prompts at once\n - separate prompts using uppercase 'AND'\n - also supports weights for prompts: 'a cat :1.2 AND a dog AND a penguin :2.2'\n- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)\n- DeepDanbooru integration, creates danbooru style tags for anime prompts\n- xformers, major speed increase for select cards: (add --xformers to commandline args)\n- via extension: History tab: view, direct and delete images conveniently within the UI\n- Generate forever option\n- Training tab\n - hypernetworks and embeddings options\n - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)\n- Clip skip\n- Use Hypernetworks\n- Use VAEs\n- Estimated completion time in progress bar\n- API\n- Support for dedicated inpainting model by RunwayML. \n- via extension: Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of URL\n- Stable Diffusion 2.0 support - see wiki for instructions", "## Installation and Running\nMake sure the required dependencies are met and follow the instructions available for both NVidia (recommended) and AMD GPUs.\n\nAlternatively, use online services (like Google Colab):\n\n- List of Online Services", "### Automatic Installation on Windows\n1. Install Python 3.10.6, checking \"Add Python to PATH\"\n2. Install git.\n3. Download the stable-diffusion-webui repository, for example by running 'git clone URL\n4. Place 'URL' in the 'models' directory (see dependencies for where to get it).\n5. _*(Optional)*_ Place 'GFPGANv1.4.pth' in the base directory, alongside 'URL' (see dependencies for where to get it).\n6. Run 'URL' from Windows Explorer as normal, non-administrator, user.", "### Automatic Installation on Linux\n1. Install the dependencies:\n\n2. To install in '/home/$(whoami)/stable-diffusion-webui/', run:", "### Installation on Apple Silicon\n\nFind the instructions here.", "## Contributing\nHere's how to add code to this repo: Contributing", "## Documentation\nThe documentation was moved from this README over to the project's wiki.", "## Credits\n- Stable Diffusion - URL URL\n- k-diffusion - URL\n- GFPGAN - URL\n- CodeFormer - URL\n- ESRGAN - URL\n- SwinIR - URL\n- Swin2SR - URL\n- LDSR - URL\n- MiDaS - URL\n- Ideas for optimizations - URL\n- Cross Attention layer optimization - Doggettx - URL original idea for prompt editing.\n- Cross Attention layer optimization - InvokeAI, lstein - URL (originally URL\n- Textual Inversion - Rinon Gal - URL (we're not using his code, but we are using his ideas).\n- Idea for SD upscale - URL\n- Noise generation for outpainting mk2 - URL\n- CLIP interrogator idea and borrowing some code - URL\n- Idea for Composable Diffusion - URL\n- xformers - URL\n- DeepDanbooru - interrogator for anime diffusers URL\n- Security advice - RyotaK\n- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.\n- (You)" ]
[ 6, 55, 1107, 51, 132, 42, 11, 18, 19, 237 ]
[ "passage: TAGS\n#region-us \n# Stable Diffusion web UI\nA browser interface based on Gradio library for Stable Diffusion.\n\n![](txt2img_Screenshot.png)\n\nCheck the custom scripts wiki page for extra scripts developed by users.", "passage: ## Features\nDetailed feature showcase with images:\n- Original txt2img and img2img modes\n- One click install and run script (but you still must install python and git)\n- Outpainting\n- Inpainting\n- Color Sketch\n- Prompt Matrix\n- Stable Diffusion Upscale\n- Attention, specify parts of text that the model should pay more attention to\n - a man in a ((tuxedo)) - will pay more attention to tuxedo\n - a man in a (tuxedo:1.21) - alternative syntax\n - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)\n- Loopback, run img2img processing multiple times\n- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters\n- Textual Inversion\n - have as many embeddings as you want and use any names you like for them\n - use multiple embeddings with different numbers of vectors per token\n - works with half precision floating point numbers\n - train embeddings on 8GB (also reports of 6GB working)\n- Extras tab with:\n - GFPGAN, neural network that fixes faces\n - CodeFormer, face restoration tool as an alternative to GFPGAN\n - RealESRGAN, neural network upscaler\n - ESRGAN, neural network upscaler with a lot of third party models\n - SwinIR and Swin2SR(see here), neural network upscalers\n - LDSR, Latent diffusion super resolution upscaling\n- Resizing aspect ratio options\n- Sampling method selection\n - Adjust sampler eta values (noise multiplier)\n - More advanced noise setting options\n- Interrupt processing at any time\n- 4GB video card support (also reports of 2GB working)\n- Correct seeds for batches\n- Live prompt token length validation\n- Generation parameters\n - parameters you used to generate images are saved with that image\n - in PNG chunks for PNG, in EXIF for JPEG\n - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI\n - can be disabled in settings\n - drag and drop an image/text-parameters to promptbox\n- Read Generation Parameters Button, loads parameters in promptbox to UI\n- Settings page\n- Running arbitrary python code from UI (must run with --allow-code to enable)\n- Mouseover hints for most UI elements\n- Possible to change defaults/mix/max/step values for UI elements via text config\n- Random artist button\n- Tiling support, a checkbox to create images that can be tiled like textures\n- Progress bar and live image generation preview\n- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image\n- Styles, a way to save part of prompt and easily apply them via dropdown later\n- Variations, a way to generate same image but with tiny differences\n- Seed resizing, a way to generate same image but at slightly different resolution\n- CLIP interrogator, a button that tries to guess prompt from an image\n- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway\n- Batch Processing, process a group of files using img2img\n- Img2img Alternative, reverse Euler method of cross attention control\n- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions\n- Reloading checkpoints on the fly\n- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one\n- Custom scripts with many extensions from community\n- Composable-Diffusion, a way to use multiple prompts at once\n - separate prompts using uppercase 'AND'\n - also supports weights for prompts: 'a cat :1.2 AND a dog AND a penguin :2.2'\n- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)\n- DeepDanbooru integration, creates danbooru style tags for anime prompts\n- xformers, major speed increase for select cards: (add --xformers to commandline args)\n- via extension: History tab: view, direct and delete images conveniently within the UI\n- Generate forever option\n- Training tab\n - hypernetworks and embeddings options\n - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)\n- Clip skip\n- Use Hypernetworks\n- Use VAEs\n- Estimated completion time in progress bar\n- API\n- Support for dedicated inpainting model by RunwayML. \n- via extension: Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of URL\n- Stable Diffusion 2.0 support - see wiki for instructions## Installation and Running\nMake sure the required dependencies are met and follow the instructions available for both NVidia (recommended) and AMD GPUs.\n\nAlternatively, use online services (like Google Colab):\n\n- List of Online Services### Automatic Installation on Windows\n1. Install Python 3.10.6, checking \"Add Python to PATH\"\n2. Install git.\n3. Download the stable-diffusion-webui repository, for example by running 'git clone URL\n4. Place 'URL' in the 'models' directory (see dependencies for where to get it).\n5. _*(Optional)*_ Place 'GFPGANv1.4.pth' in the base directory, alongside 'URL' (see dependencies for where to get it).\n6. Run 'URL' from Windows Explorer as normal, non-administrator, user.### Automatic Installation on Linux\n1. Install the dependencies:\n\n2. To install in '/home/$(whoami)/stable-diffusion-webui/', run:### Installation on Apple Silicon\n\nFind the instructions here.## Contributing\nHere's how to add code to this repo: Contributing## Documentation\nThe documentation was moved from this README over to the project's wiki." ]
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32e9d79e78609613d2f6333eb1e95ad9d9d795b0
# Dataset Card for "bookcorpus_compact_1024_shard1_of_10_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saibo/bookcorpus_compact_1024_shard1_of_10_meta
[ "region:us" ]
2023-01-28T08:59:26+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7450626244, "num_examples": 61605}], "download_size": 1631069561, "dataset_size": 7450626244}}
2023-01-29T10:02:31+00:00
[]
[]
TAGS #region-us
# Dataset Card for "bookcorpus_compact_1024_shard1_of_10_meta" More Information needed
[ "# Dataset Card for \"bookcorpus_compact_1024_shard1_of_10_meta\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"bookcorpus_compact_1024_shard1_of_10_meta\"\n\nMore Information needed" ]
[ 6, 29 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_1024_shard1_of_10_meta\"\n\nMore Information needed" ]
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9901e7c37555847ca112a6f055474ff030abc01f
# VG Summarization Dataset The source of this dataset is Norsk Aviskorpus (Norwegian newspaper corpus). This corpus includes articles from Norway’s largest newspaper from 1998 to 2019. In this dataset, we used the first paragraph (lead) of each article as its summary. This dataset only includes articles from the Norwegian newspaper "VG". The quality of the summary-article pairs has not been evaluated. # License Please refer to the license of Norsk Aviskorpus # Citation If you are using this dataset in your work, please cite our master thesis which this dataset was a part of ``` @mastersthesis{navjord2023beyond, title={Beyond extractive: advancing abstractive automatic text summarization in Norwegian with transformers}, author={Navjord, J{\o}rgen Johnsen and Korsvik, Jon-Mikkel Ryen}, year={2023}, school={Norwegian University of Life Sciences, {\AA}s} } ```
navjordj/VG_summarization
[ "task_categories:summarization", "task_categories:text2text-generation", "size_categories:100K<n<1M", "language:no", "language:nb", "region:us" ]
2023-01-28T09:37:23+00:00
{"language": ["no", "nb"], "size_categories": ["100K<n<1M"], "task_categories": ["summarization", "text2text-generation"], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "published", "dtype": "string"}, {"name": "classes", "dtype": "string"}, {"name": "article", "dtype": "string"}, {"name": "ingress", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 482362411.09144986, "num_examples": 157038}, {"name": "validation", "num_bytes": 36309721.60567524, "num_examples": 11821}, {"name": "test", "num_bytes": 57632967.30287493, "num_examples": 18763}], "download_size": 364433583, "dataset_size": 576305100.0}}
2024-01-23T07:20:12+00:00
[]
[ "no", "nb" ]
TAGS #task_categories-summarization #task_categories-text2text-generation #size_categories-100K<n<1M #language-Norwegian #language-Norwegian Bokmål #region-us
# VG Summarization Dataset The source of this dataset is Norsk Aviskorpus (Norwegian newspaper corpus). This corpus includes articles from Norway’s largest newspaper from 1998 to 2019. In this dataset, we used the first paragraph (lead) of each article as its summary. This dataset only includes articles from the Norwegian newspaper "VG". The quality of the summary-article pairs has not been evaluated. # License Please refer to the license of Norsk Aviskorpus If you are using this dataset in your work, please cite our master thesis which this dataset was a part of
[ "# VG Summarization Dataset\n\nThe source of this dataset is Norsk Aviskorpus (Norwegian newspaper corpus). This corpus includes articles from Norway’s largest newspaper from 1998 to 2019. In this dataset, we used\n the first paragraph (lead) of each article as its summary. This dataset only includes articles from the Norwegian newspaper \"VG\".\n\nThe quality of the summary-article pairs has not been evaluated.", "# License\nPlease refer to the license of Norsk Aviskorpus\n\nIf you are using this dataset in your work, please cite our master thesis which this dataset was a part of" ]
[ "TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-100K<n<1M #language-Norwegian #language-Norwegian Bokmål #region-us \n", "# VG Summarization Dataset\n\nThe source of this dataset is Norsk Aviskorpus (Norwegian newspaper corpus). This corpus includes articles from Norway’s largest newspaper from 1998 to 2019. In this dataset, we used\n the first paragraph (lead) of each article as its summary. This dataset only includes articles from the Norwegian newspaper \"VG\".\n\nThe quality of the summary-article pairs has not been evaluated.", "# License\nPlease refer to the license of Norsk Aviskorpus\n\nIf you are using this dataset in your work, please cite our master thesis which this dataset was a part of" ]
[ 55, 88, 36 ]
[ "passage: TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-100K<n<1M #language-Norwegian #language-Norwegian Bokmål #region-us \n# VG Summarization Dataset\n\nThe source of this dataset is Norsk Aviskorpus (Norwegian newspaper corpus). This corpus includes articles from Norway’s largest newspaper from 1998 to 2019. In this dataset, we used\n the first paragraph (lead) of each article as its summary. This dataset only includes articles from the Norwegian newspaper \"VG\".\n\nThe quality of the summary-article pairs has not been evaluated.# License\nPlease refer to the license of Norsk Aviskorpus\n\nIf you are using this dataset in your work, please cite our master thesis which this dataset was a part of" ]
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9cbfdff6956e8b29f27c11f1bd88ba7758f7a055
# Dataset Card for "PickaPic-ft-pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yuvalkirstain/PickaPic-ft-pairs
[ "region:us" ]
2023-01-28T10:32:49+00:00
{"dataset_info": {"features": [{"name": "url_bad", "dtype": "string"}, {"name": "url_good", "dtype": "string"}, {"name": "good_jpg", "dtype": "binary"}, {"name": "caption", "dtype": "string"}, {"name": "user_id", "dtype": "int64"}, {"name": "has_label", "dtype": "bool"}, {"name": "bad_jpg", "dtype": "binary"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6044785615, "num_examples": 27208}, {"name": "validation", "num_bytes": 292430239, "num_examples": 1335}, {"name": "test", "num_bytes": 318053633, "num_examples": 1410}, {"name": "validation_unique", "num_bytes": 54146831, "num_examples": 250}, {"name": "test_unique", "num_bytes": 54986693, "num_examples": 250}], "download_size": 6690503991, "dataset_size": 6764403011}}
2023-02-02T11:26:36+00:00
[]
[]
TAGS #region-us
# Dataset Card for "PickaPic-ft-pairs" More Information needed
[ "# Dataset Card for \"PickaPic-ft-pairs\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"PickaPic-ft-pairs\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"PickaPic-ft-pairs\"\n\nMore Information needed" ]
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275228ff0433b73f18dd69f7c22fcfb6d9a65f98
# Dataset Card for "PickaPic-ft-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yuvalkirstain/PickaPic-ft-pairwise
[ "region:us" ]
2023-01-28T11:05:03+00:00
{"dataset_info": {"features": [{"name": "good_image", "dtype": "image"}, {"name": "bad_image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "good_url", "dtype": "string"}, {"name": "bad_url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8329737112.221633, "num_examples": 10313}, {"name": "test", "num_bytes": 347307956.7783673, "num_examples": 430}], "download_size": 8675583639, "dataset_size": 8677045069.0}}
2023-01-31T09:50:57+00:00
[]
[]
TAGS #region-us
# Dataset Card for "PickaPic-ft-pairwise" More Information needed
[ "# Dataset Card for \"PickaPic-ft-pairwise\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"PickaPic-ft-pairwise\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"PickaPic-ft-pairwise\"\n\nMore Information needed" ]
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4327f8fb5b66ffb06eee1e2fc241afe7a41b6265
# Database of Characters in Mobile Games [![Last Updated](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/narugo1992/254442dea2e77cf46366df97f499242f/raw/data_last_update.json)](https://huggingface.co/datasets/deepghs/game_characters) All the character in the following games are supported: * Arknights (crawled from [https://prts.wiki](https://prts.wiki)) * Fate/Grand Order (crawled from [https://fgo.wiki](https://fgo.wiki)) * Azur Lane (crawled from [https://wiki.biligame.com/blhx](https://wiki.biligame.com/blhx)) * Girls' Front-Line (crawled from [https://iopwiki.com/](https://iopwiki.com/)) * Genshin Impact (crawled from [https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki](https://genshin-impact.fandom.com/ja/wiki/%E5%8E%9F%E7%A5%9E_Wiki)) The source code and python library is hosted on [narugo1992/gchar](https://github.com/narugo1992/gchar), and the scheduled job is configured on Github Action, so the data will be automatically updated to the latest version once a day. More character data for other games is coming...
deepghs/game_characters
[ "license:apache-2.0", "region:us" ]
2023-01-28T11:12:51+00:00
{"license": "apache-2.0"}
2024-02-17T16:59:30+00:00
[]
[]
TAGS #license-apache-2.0 #region-us
# Database of Characters in Mobile Games ![Last Updated](URL All the character in the following games are supported: * Arknights (crawled from URL) * Fate/Grand Order (crawled from URL) * Azur Lane (crawled from URL * Girls' Front-Line (crawled from URL * Genshin Impact (crawled from URL The source code and python library is hosted on narugo1992/gchar, and the scheduled job is configured on Github Action, so the data will be automatically updated to the latest version once a day. More character data for other games is coming...
[ "# Database of Characters in Mobile Games\n\n![Last Updated](URL\n\nAll the character in the following games are supported:\n* Arknights (crawled from URL)\n* Fate/Grand Order (crawled from URL)\n* Azur Lane (crawled from URL\n* Girls' Front-Line (crawled from URL\n* Genshin Impact (crawled from URL\n\nThe source code and python library is hosted on narugo1992/gchar, and the scheduled job is configured on Github Action, so the data will be automatically updated to the latest version once a day.\n\nMore character data for other games is coming..." ]
[ "TAGS\n#license-apache-2.0 #region-us \n", "# Database of Characters in Mobile Games\n\n![Last Updated](URL\n\nAll the character in the following games are supported:\n* Arknights (crawled from URL)\n* Fate/Grand Order (crawled from URL)\n* Azur Lane (crawled from URL\n* Girls' Front-Line (crawled from URL\n* Genshin Impact (crawled from URL\n\nThe source code and python library is hosted on narugo1992/gchar, and the scheduled job is configured on Github Action, so the data will be automatically updated to the latest version once a day.\n\nMore character data for other games is coming..." ]
[ 14, 143 ]
[ "passage: TAGS\n#license-apache-2.0 #region-us \n# Database of Characters in Mobile Games\n\n![Last Updated](URL\n\nAll the character in the following games are supported:\n* Arknights (crawled from URL)\n* Fate/Grand Order (crawled from URL)\n* Azur Lane (crawled from URL\n* Girls' Front-Line (crawled from URL\n* Genshin Impact (crawled from URL\n\nThe source code and python library is hosted on narugo1992/gchar, and the scheduled job is configured on Github Action, so the data will be automatically updated to the latest version once a day.\n\nMore character data for other games is coming..." ]
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# Dataset Card for XStoryCloze ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://cs.rochester.edu/nlp/rocstories/](https://cs.rochester.edu/nlp/rocstories/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Few-shot Learning with Multilingual Generative Language Models](https://arxiv.org/pdf/2112.10668.pdf) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.03 MB - **Size of the generated dataset:** 2.03 MB - **Total amount of disk used:** 2.05 MB ### Dataset Summary XStoryCloze consists of the professionally translated version of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI. ### Supported Tasks and Leaderboards commonsense reasoning ### Languages en, ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my. ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 2.03 MB - **Size of the generated dataset:** 2.03 MB - **Total amount of disk used:** 2.05 MB An example of 'train' looks as follows. ``` {'answer_right_ending': 1, 'input_sentence_1': 'Rick grew up in a troubled household.', 'input_sentence_2': 'He never found good support in family, and turned to gangs.', 'input_sentence_3': "It wasn't long before Rick got shot in a robbery.", 'input_sentence_4': 'The incident caused him to turn a new leaf.', 'sentence_quiz1': 'He is happy now.', 'sentence_quiz2': 'He joined a gang.', 'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'} ``` ### Data Fields The data fields are the same among all splits. - `input_sentence_1`: The first statement in the story. - `input_sentence_2`: The second statement in the story. - `input_sentence_3`: The third statement in the story. - `input_sentence_4`: The forth statement in the story. - `sentence_quiz1`: first possible continuation of the story. - `sentence_quiz2`: second possible continuation of the story. - `answer_right_ending`: correct possible ending; either 1 or 2. - `story_id`: story id. ### Data Splits This dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment. | name |train |test| |-------|-----:|---:| |en|360|1510| |ru|360|1510| |zh|360|1510| |es|360|1510| |ar|360|1510| |hi|360|1510| |id|360|1510| |te|360|1510| |sw|360|1510| |eu|360|1510| |my|360|1510| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information XStoryCloze is opensourced under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode), the same license as the original English StoryCloze. ### Citation Information ``` @article{DBLP:journals/corr/abs-2112-10668, author = {Xi Victoria Lin and Todor Mihaylov and Mikel Artetxe and Tianlu Wang and Shuohui Chen and Daniel Simig and Myle Ott and Naman Goyal and Shruti Bhosale and Jingfei Du and Ramakanth Pasunuru and Sam Shleifer and Punit Singh Koura and Vishrav Chaudhary and Brian O'Horo and Jeff Wang and Luke Zettlemoyer and Zornitsa Kozareva and Mona T. Diab and Veselin Stoyanov and Xian Li}, title = {Few-shot Learning with Multilingual Language Models}, journal = {CoRR}, volume = {abs/2112.10668}, year = {2021}, url = {https://arxiv.org/abs/2112.10668}, eprinttype = {arXiv}, eprint = {2112.10668}, timestamp = {Tue, 04 Jan 2022 15:59:27 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@juletx](https://github.com/juletx).
juletxara/xstory_cloze
[ "task_categories:other", "annotations_creators:found", "language_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:extended|story_cloze", "language:en", "language:ru", "language:zh", "language:es", "language:ar", "language:hi", "language:id", "language:te", "language:sw", "language:eu", "language:my", "license:cc-by-sa-4.0", "arxiv:2112.10668", "region:us" ]
2023-01-28T14:49:52+00:00
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2023-05-21T15:04:36+00:00
[ "2112.10668" ]
[ "en", "ru", "zh", "es", "ar", "hi", "id", "te", "sw", "eu", "my" ]
TAGS #task_categories-other #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|story_cloze #language-English #language-Russian #language-Chinese #language-Spanish #language-Arabic #language-Hindi #language-Indonesian #language-Telugu #language-Swahili (macrolanguage) #language-Basque #language-Burmese #license-cc-by-sa-4.0 #arxiv-2112.10668 #region-us
Dataset Card for XStoryCloze ============================ Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Data Instances + Data Fields + Data Splits * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: URL * Repository: * Paper: Few-shot Learning with Multilingual Generative Language Models * Point of Contact: * Size of downloaded dataset files: 2.03 MB * Size of the generated dataset: 2.03 MB * Total amount of disk used: 2.05 MB ### Dataset Summary XStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI. ### Supported Tasks and Leaderboards commonsense reasoning ### Languages en, ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my. Dataset Structure ----------------- ### Data Instances * Size of downloaded dataset files: 2.03 MB * Size of the generated dataset: 2.03 MB * Total amount of disk used: 2.05 MB An example of 'train' looks as follows. ### Data Fields The data fields are the same among all splits. * 'input\_sentence\_1': The first statement in the story. * 'input\_sentence\_2': The second statement in the story. * 'input\_sentence\_3': The third statement in the story. * 'input\_sentence\_4': The forth statement in the story. * 'sentence\_quiz1': first possible continuation of the story. * 'sentence\_quiz2': second possible continuation of the story. * 'answer\_right\_ending': correct possible ending; either 1 or 2. * 'story\_id': story id. ### Data Splits This dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information XStoryCloze is opensourced under CC BY-SA 4.0, the same license as the original English StoryCloze. ### Contributions Thanks to @juletx.
[ "### Dataset Summary\n\n\nXStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI.", "### Supported Tasks and Leaderboards\n\n\ncommonsense reasoning", "### Languages\n\n\nen, ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 2.03 MB\n* Size of the generated dataset: 2.03 MB\n* Total amount of disk used: 2.05 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'input\\_sentence\\_1': The first statement in the story.\n* 'input\\_sentence\\_2': The second statement in the story.\n* 'input\\_sentence\\_3': The third statement in the story.\n* 'input\\_sentence\\_4': The forth statement in the story.\n* 'sentence\\_quiz1': first possible continuation of the story.\n* 'sentence\\_quiz2': second possible continuation of the story.\n* 'answer\\_right\\_ending': correct possible ending; either 1 or 2.\n* 'story\\_id': story id.", "### Data Splits\n\n\nThis dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nXStoryCloze is opensourced under CC BY-SA 4.0, the same license as the original English StoryCloze.", "### Contributions\n\n\nThanks to @juletx." ]
[ "TAGS\n#task_categories-other #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|story_cloze #language-English #language-Russian #language-Chinese #language-Spanish #language-Arabic #language-Hindi #language-Indonesian #language-Telugu #language-Swahili (macrolanguage) #language-Basque #language-Burmese #license-cc-by-sa-4.0 #arxiv-2112.10668 #region-us \n", "### Dataset Summary\n\n\nXStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI.", "### Supported Tasks and Leaderboards\n\n\ncommonsense reasoning", "### Languages\n\n\nen, ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\n* Size of downloaded dataset files: 2.03 MB\n* Size of the generated dataset: 2.03 MB\n* Total amount of disk used: 2.05 MB\n\n\nAn example of 'train' looks as follows.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'input\\_sentence\\_1': The first statement in the story.\n* 'input\\_sentence\\_2': The second statement in the story.\n* 'input\\_sentence\\_3': The third statement in the story.\n* 'input\\_sentence\\_4': The forth statement in the story.\n* 'sentence\\_quiz1': first possible continuation of the story.\n* 'sentence\\_quiz2': second possible continuation of the story.\n* 'answer\\_right\\_ending': correct possible ending; either 1 or 2.\n* 'story\\_id': story id.", "### Data Splits\n\n\nThis dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nXStoryCloze is opensourced under CC BY-SA 4.0, the same license as the original English StoryCloze.", "### Contributions\n\n\nThanks to @juletx." ]
[ 155, 51, 14, 43, 52, 167, 81, 7, 4, 10, 10, 5, 5, 9, 18, 7, 8, 14, 6, 32, 12 ]
[ "passage: TAGS\n#task_categories-other #annotations_creators-found #language_creators-found #language_creators-expert-generated #multilinguality-multilingual #size_categories-1K<n<10K #source_datasets-extended|story_cloze #language-English #language-Russian #language-Chinese #language-Spanish #language-Arabic #language-Hindi #language-Indonesian #language-Telugu #language-Swahili (macrolanguage) #language-Basque #language-Burmese #license-cc-by-sa-4.0 #arxiv-2112.10668 #region-us \n### Dataset Summary\n\n\nXStoryCloze consists of the professionally translated version of the English StoryCloze dataset (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI.### Supported Tasks and Leaderboards\n\n\ncommonsense reasoning### Languages\n\n\nen, ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\n* Size of downloaded dataset files: 2.03 MB\n* Size of the generated dataset: 2.03 MB\n* Total amount of disk used: 2.05 MB\n\n\nAn example of 'train' looks as follows.### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'input\\_sentence\\_1': The first statement in the story.\n* 'input\\_sentence\\_2': The second statement in the story.\n* 'input\\_sentence\\_3': The third statement in the story.\n* 'input\\_sentence\\_4': The forth statement in the story.\n* 'sentence\\_quiz1': first possible continuation of the story.\n* 'sentence\\_quiz2': second possible continuation of the story.\n* 'answer\\_right\\_ending': correct possible ending; either 1 or 2.\n* 'story\\_id': story id." ]
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7a17c80179d6448ae30d68738d2b3e6109262475
### Dataset Summary Keyword spotting refers to the task of learning to detect spoken keywords. It interfaces all modern voice-based virtual assistants on the market: Amazon’s Alexa, Apple’s Siri, and the Google Home device. Contrarily to speech recognition models, keyword spotting doesn’t run on the cloud, but directly on the device. The motivation of this paper is to extend the Speech commands dataset (Warden 2018) with African languages. In particular, we are going to focus on 4 Senegalese languages: Wolof, Pulaar, Serer, Diola. The choice of these languages is guided, on the one hand, by their status as languages considered to be the languages of the first generation, that is to say, the first codified languages (endowed with a writing system and considered by the state of Senegal as national languages) with decree n ° 68-871 of July 24, 1968. On the other hand, they represent the languages that are most spoken in Senegal. ### Languages The ID of the languages are the following: - Wolof: `7` - Pulaar: `5` - Serer: `6` - Diola: `3` ## Dataset Structure ```python from datasets import load_dataset dataset = load_dataset("galsenai/waxal_dataset") DatasetDict({ train: Dataset({ features: ['audio', 'label', 'translation', 'locale_id'], num_rows: 26387 }) }) ``` ### Data Fields - `audio`: Audio file in MP3 format - `label`: label of the audio file - `translation` : Translation of the keyword in french - `locale_id`: ID of the language
galsenai/waxal_dataset
[ "license:creativeml-openrail-m", "region:us" ]
2023-01-28T17:00:34+00:00
{"license": "creativeml-openrail-m", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "A canoon", "1": "A cinj", "2": "A keen", "3": "A lanq", "4": "A \u00f1aa\u01b4", "5": "A \u00f1amaak", "6": "Alaa", "7": "Bacaac", "8": "Benn", "9": "B\u00e1lamuk", "10": "B\u00fabaar", "11": "Caggal", "12": "Ceme", "13": "Ci ginnaaw", "14": "Ci kanam", "15": "Ci kow", "16": "Ci suuf", "17": "C\u00e0mmo\u00f1", "18": "Darnde", "19": "Dow", "20": "Doxal", "21": "D\u00e9edet", "22": "Eey", "23": "Esuwa", "24": "Eyen", "25": "E\u00e9", "26": "Fatiya", "27": "Fukk", "28": "Funoom", "29": "Futok", "30": "Futok di sibaakiir", "31": "Futok di sigaba", "32": "Futok di s\u00edfeejir", "33": "Futok di y\u00e1kon", "34": "F\u00e1cul", "35": "Garab", "36": "Goo", "37": "Hani", "38": "Jaay", "39": "Jeegom", "40": "Jeenay", "41": "Jeetati", "42": "Jee\u0257i\u0257i", "43": "Jik", "44": "Jiku", "45": "Joy", "46": "Juni", "47": "Junne", "48": "Juroom", "49": "Juroom-benn", "50": "Juroom-\u00f1aar", "51": "Juroom-\u00f1eent", "52": "Juroom-\u00f1ett", "53": "J\u00ebnd", "54": "Kakamben", "55": "Kamay", "56": "Kanoomen", "57": "K\u00e1kambul", "58": "K\u00e1rir", "59": "Lal", "60": "Lees", "61": "Leng", "62": "Le\u0257ki", "63": "Li", "64": "Mbaamir", "65": "Mbalndi", "66": "Nano", "67": "Naxik", "68": "Nay", "69": "Ndaxar", "70": "Ndeyjoor", "71": "Ndiga", "72": "Ndii\u01ad", "73": "Njong", "74": "O \u0253ox", "75": "Picc", "76": "Rawaandu", "77": "Sappo", "78": "Sibaakiir", "79": "Sigaba", "80": "Solndu", "81": "Soodde", "82": "S\u00edfeejir", "83": "Tadik", "84": "Tati", "85": "Taxawal", "86": "Teemedere", "87": "Teemeed", "88": "Tentaam", "89": "Tik", "90": "Took", "91": "Tus", "92": "T\u00e9emeer", "93": "Ub /T\u00ebj", "94": "Ub/T\u00ebj", "95": "Ubbi /Tijji", "96": "Udditde", "97": "Uddude", "98": "Ujaw", "99": "Ujunere", "100": "Ujuum", "101": "U\u00f1en", "102": "Waafulet", "103": "Waaw", "104": "Weg", "105": "Wet", "106": "W\u00fali", "107": "Xa-aa", "108": "Xaj", "109": "Xar\u0253axay", "110": "Yahdu", "111": "Yeeso", "112": "Yeeyde", "113": "Y\u00e1kon", "114": "\u00d1aamo", "115": "\u00d1aar", "116": "\u00d1eent", "117": "\u00d1ett", "118": "\u018ai\u0257i", "119": "\u01a4etaa-fo-leng", "120": "\u01a4etaa-naxak", "121": "\u01a4etaa-tadak", "122": "\u01a4etaa-\u01adaq", "123": "\u01a4etik"}}}}, {"name": "translation", "dtype": "string"}, {"name": "locale_id", "dtype": "int64"}, {"name": "transcript", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 567773923.639, "num_examples": 26387}], "download_size": 546144081, "dataset_size": 567773923.639}}
2023-02-06T12:32:12+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
### Dataset Summary Keyword spotting refers to the task of learning to detect spoken keywords. It interfaces all modern voice-based virtual assistants on the market: Amazon’s Alexa, Apple’s Siri, and the Google Home device. Contrarily to speech recognition models, keyword spotting doesn’t run on the cloud, but directly on the device. The motivation of this paper is to extend the Speech commands dataset (Warden 2018) with African languages. In particular, we are going to focus on 4 Senegalese languages: Wolof, Pulaar, Serer, Diola. The choice of these languages is guided, on the one hand, by their status as languages considered to be the languages of the first generation, that is to say, the first codified languages (endowed with a writing system and considered by the state of Senegal as national languages) with decree n ° 68-871 of July 24, 1968. On the other hand, they represent the languages that are most spoken in Senegal. ### Languages The ID of the languages are the following: - Wolof: '7' - Pulaar: '5' - Serer: '6' - Diola: '3' ## Dataset Structure ### Data Fields - 'audio': Audio file in MP3 format - 'label': label of the audio file - 'translation' : Translation of the keyword in french - 'locale_id': ID of the language
[ "### Dataset Summary\n\nKeyword spotting refers to the task of learning to detect spoken keywords. It interfaces all modern voice-based virtual assistants on the market: Amazon’s Alexa, Apple’s Siri, and the Google Home device. Contrarily to speech recognition models, keyword spotting doesn’t run on the cloud, but directly on the device. \n\nThe motivation of this paper is to extend the Speech commands dataset (Warden 2018) with African languages. In particular, we are going to focus on 4 Senegalese languages: Wolof, Pulaar, Serer, Diola. \n\nThe choice of these languages is guided, on the one hand, by their status as languages considered to be the languages of the first generation, that is to say, the first codified languages (endowed with a writing system and considered by the state of Senegal as national languages) with decree n ° 68-871 of July 24, 1968. On the other hand, they represent the languages that are most spoken in Senegal.", "### Languages\nThe ID of the languages are the following:\n- Wolof: '7'\n- Pulaar: '5'\n- Serer: '6'\n- Diola: '3'", "## Dataset Structure", "### Data Fields\n\n- 'audio': Audio file in MP3 format\n- 'label': label of the audio file\n- 'translation' : Translation of the keyword in french\n- 'locale_id': ID of the language" ]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "### Dataset Summary\n\nKeyword spotting refers to the task of learning to detect spoken keywords. It interfaces all modern voice-based virtual assistants on the market: Amazon’s Alexa, Apple’s Siri, and the Google Home device. Contrarily to speech recognition models, keyword spotting doesn’t run on the cloud, but directly on the device. \n\nThe motivation of this paper is to extend the Speech commands dataset (Warden 2018) with African languages. In particular, we are going to focus on 4 Senegalese languages: Wolof, Pulaar, Serer, Diola. \n\nThe choice of these languages is guided, on the one hand, by their status as languages considered to be the languages of the first generation, that is to say, the first codified languages (endowed with a writing system and considered by the state of Senegal as national languages) with decree n ° 68-871 of July 24, 1968. On the other hand, they represent the languages that are most spoken in Senegal.", "### Languages\nThe ID of the languages are the following:\n- Wolof: '7'\n- Pulaar: '5'\n- Serer: '6'\n- Diola: '3'", "## Dataset Structure", "### Data Fields\n\n- 'audio': Audio file in MP3 format\n- 'label': label of the audio file\n- 'translation' : Translation of the keyword in french\n- 'locale_id': ID of the language" ]
[ 18, 225, 42, 6, 52 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n### Dataset Summary\n\nKeyword spotting refers to the task of learning to detect spoken keywords. It interfaces all modern voice-based virtual assistants on the market: Amazon’s Alexa, Apple’s Siri, and the Google Home device. Contrarily to speech recognition models, keyword spotting doesn’t run on the cloud, but directly on the device. \n\nThe motivation of this paper is to extend the Speech commands dataset (Warden 2018) with African languages. In particular, we are going to focus on 4 Senegalese languages: Wolof, Pulaar, Serer, Diola. \n\nThe choice of these languages is guided, on the one hand, by their status as languages considered to be the languages of the first generation, that is to say, the first codified languages (endowed with a writing system and considered by the state of Senegal as national languages) with decree n ° 68-871 of July 24, 1968. On the other hand, they represent the languages that are most spoken in Senegal.### Languages\nThe ID of the languages are the following:\n- Wolof: '7'\n- Pulaar: '5'\n- Serer: '6'\n- Diola: '3'## Dataset Structure### Data Fields\n\n- 'audio': Audio file in MP3 format\n- 'label': label of the audio file\n- 'translation' : Translation of the keyword in french\n- 'locale_id': ID of the language" ]
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55e53e58cd302aabf6612a5502e516e5be91f9fa
# Dataset Card for "50-pythonclean-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
martino-canavate/50-pythonclean-dataset
[ "region:us" ]
2023-01-28T17:39:47+00:00
{"dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "copies", "dtype": "string"}, {"name": "size", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "hash", "dtype": "int64"}, {"name": "line_mean", "dtype": "float64"}, {"name": "line_max", "dtype": "int64"}, {"name": "alpha_frac", "dtype": "float64"}, {"name": "autogenerated", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 54587956838, "num_examples": 5361373}], "download_size": 19727904629, "dataset_size": 54587956838}}
2023-01-28T18:08:04+00:00
[]
[]
TAGS #region-us
# Dataset Card for "50-pythonclean-dataset" More Information needed
[ "# Dataset Card for \"50-pythonclean-dataset\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"50-pythonclean-dataset\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"50-pythonclean-dataset\"\n\nMore Information needed" ]
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b111d3d6428a85c608b30aaa6ff00ce57516c355
# Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mholi/github-issues
[ "region:us" ]
2023-01-28T18:01:46+00:00
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "dtype": "null"}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 11054130, "num_examples": 2500}], "download_size": 3121241, "dataset_size": 11054130}}
2023-01-28T18:01:59+00:00
[]
[]
TAGS #region-us
# Dataset Card for "github-issues" More Information needed
[ "# Dataset Card for \"github-issues\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"github-issues\"\n\nMore Information needed" ]
[ 6, 15 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"github-issues\"\n\nMore Information needed" ]
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e839825f9ec5c6cfa585c654a59610969ec13993
# Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/hendrycks/math - **Repository:** https://github.com/hendrycks/math - **Paper:** https://arxiv.org/pdf/2103.03874.pdf - **Leaderboard:** N/A - **Point of Contact:** Dan Hendrycks ### Dataset Summary The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's `\boxed` tag. An example from the dataset is: ``` {'problem': 'A board game spinner is divided into three parts labeled $A$, $B$ and $C$. The probability of the spinner landing on $A$ is $\\frac{1}{3}$ and the probability of the spinner landing on $B$ is $\\frac{5}{12}$. What is the probability of the spinner landing on $C$? Express your answer as a common fraction.', 'level': 'Level 1', 'type': 'Counting & Probability', 'solution': 'The spinner is guaranteed to land on exactly one of the three regions, so we know that the sum of the probabilities of it landing in each region will be 1. If we let the probability of it landing in region $C$ be $x$, we then have the equation $1 = \\frac{5}{12}+\\frac{1}{3}+x$, from which we have $x=\\boxed{\\frac{1}{4}}$.'} ``` ### Data Fields * `problem`: The competition math problem. * `solution`: The step-by-step solution. * `level`: The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'. * `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus. ## 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 https://github.com/hendrycks/math/blob/main/LICENSE ### Citation Information ```bibtex @article{hendrycksmath2021, title={Measuring Mathematical Problem Solving With the MATH Dataset}, author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, journal={arXiv preprint arXiv:2103.03874}, year={2021} } ```
qwedsacf/competition_math
[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "explanation-generation", "arxiv:2103.03874", "region:us" ]
2023-01-28T18:44:57+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "Mathematics Aptitude Test of Heuristics (MATH)", "tags": ["explanation-generation"]}
2023-01-28T20:28:01+00:00
[ "2103.03874" ]
[ "en" ]
TAGS #task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #explanation-generation #arxiv-2103.03874 #region-us
# Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: N/A - Point of Contact: Dan Hendrycks ### Dataset Summary The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems from mathematics competitions, including the AMC 10, AMC 12, AIME, and more. Each problem in MATH has a full step-by-step solution, which can be used to teach models to generate answer derivations and explanations. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's '\boxed' tag. An example from the dataset is: ### Data Fields * 'problem': The competition math problem. * 'solution': The step-by-step solution. * 'level': The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'. * 'type': The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information URL
[ "# Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: N/A\n- Point of Contact: Dan Hendrycks", "### Dataset Summary\n\nThe Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems\nfrom mathematics competitions, including the AMC 10, AMC 12, AIME, and more. \nEach problem in MATH has a full step-by-step solution, which can be used to teach\nmodels to generate answer derivations and explanations.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances\n\nA data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's '\\boxed' tag.\n\nAn example from the dataset is:", "### Data Fields\n\n* 'problem': The competition math problem.\n* 'solution': The step-by-step solution.\n* 'level': The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'.\n* 'type': The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nURL" ]
[ "TAGS\n#task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #explanation-generation #arxiv-2103.03874 #region-us \n", "# Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset", "## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: N/A\n- Point of Contact: Dan Hendrycks", "### Dataset Summary\n\nThe Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems\nfrom mathematics competitions, including the AMC 10, AMC 12, AIME, and more. \nEach problem in MATH has a full step-by-step solution, which can be used to teach\nmodels to generate answer derivations and explanations.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances\n\nA data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's '\\boxed' tag.\n\nAn example from the dataset is:", "### Data Fields\n\n* 'problem': The competition math problem.\n* 'solution': The step-by-step solution.\n* 'level': The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'.\n* 'type': The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nURL" ]
[ 96, 21, 125, 34, 81, 10, 4, 6, 69, 136, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 7 ]
[ "passage: TAGS\n#task_categories-text2text-generation #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-mit #explanation-generation #arxiv-2103.03874 #region-us \n# Dataset Card for Mathematics Aptitude Test of Heuristics (MATH) dataset## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: N/A\n- Point of Contact: Dan Hendrycks### Dataset Summary\n\nThe Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems\nfrom mathematics competitions, including the AMC 10, AMC 12, AIME, and more. \nEach problem in MATH has a full step-by-step solution, which can be used to teach\nmodels to generate answer derivations and explanations.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances\n\nA data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's '\\boxed' tag.\n\nAn example from the dataset is:" ]
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66e214674f096d5a11e9d031dc6f139c26e17201
# Dataset Card for Active/Passive/Logical Transforms ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Dataset Subsets (Tasks)](#data-tasks) - [Dataset Splits](#data-splits) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [Roland Fernandez](mailto:[email protected]) ### Dataset Summary This dataset is a synthetic dataset containing structure-to-structure transformation tasks between English sentences in 3 forms: active, passive, and logical. The dataset also includes several tree-transformation diagnostic/warm-up tasks. ### Supported Tasks and Leaderboards [TBD] ### Languages All data is in English. ## Dataset Structure The dataset consists of several subsets, or tasks. Each task contains a train split, a validation split, and a test split, with most tasks also containing two out-of-distruction splits (one for new adjectives and one for longer adjective phrases). Each sample in a split contains a source string, a target string, and 0-2 annotation strings. ### Dataset Subsets (Tasks) The dataset consists of diagnostic/warm-up tasks and core tasks. The core tasks represent the translation of English sentences between the active, passive, and logical forms. The 12 diagnostic/warm-up tasks are: ``` - car_cdr_cons (small phrase translation tasks that require only: CAR, CDR, or CAR+CDR+CONS operations) - car_cdr_cons_tuc (same task as car_cdr_cons, but requires mapping lowercase fillers to their uppercase tokens) - car_cdr_rcons (same task as car_cdr_cons, but the CONS samples have their left/right children swapped) - car_cdr_rcons_tuc (same task as car_cdr_rcons, but requires mapping lowercase fillers to their uppercase tokens) - car_cdr_seq (each samples requires 1-4 combinations of CAR and CDR, as identified by the root filler oken) - car_cdr_seq_40k (same task as car_cdr_seq, but train samples increased from 10K to 40K) - car_cdr_seq_tuc (same task as car_cdr_seq, but requires mapping lowercase fillers to their uppercase tokens) - car_cdr_seq_40k_tuc (same task as car_cdr_seq_tuc, but train samples increased from 10K to 40K) - car_cdr_seq_path (similiar to car_cdr_seq, but each needed operation in represented as a node in the left child of the root) - car_cdr_seq_path_40k (same task as car_cdr_seq_path, but train samples increased from 10K to 40K) - car_cdr_seq_path_40k_tuc (same task as car_cdr_seq_path_40k, but requires mapping lowercase fillers to their uppercase tokens) - car_cdr_seq_path_tuc (same task as car_cdr_seq_path, but requires mapping lowercase fillers to their uppercase tokens) ``` There are 22 core tasks are: ``` - active_active_stb (active sentence translation, from sentence to parenthesized tree form, both directions) - active_active_stb_40k (same task as active_active_stb, but train samples increased from 10K to 40K) - active_logical_ssb (active to logical sentence translation, in both directions) - active_logical_ssb_40k (same task as active_logical_ssb, but train samples increased from 10K to 40K) - active_logical_ttb (active to logical tree translation, in both directions) - active_logical_ttb_40k (same task as active_logical_ttb, but train samples increased from 10K to 40K) - active_passive_ssb (active to passive sentence translation, in both directions) - active_passive_ssb_40k (same task as active_passive_ssb, but train samples increased from 10K to 40K) - active_passive_ttb (active to passive tree translation, in both directions) - active_passive_ttb_40k (same task as active_passive_ttb, but train samples increased from 10K to 40K) - actpass_logical_ss (mixture of active to logical and passive to logical sentence translations, single direction) - actpass_logical_ss_40k (same task as actpass_logical_ss, but train samples increased from 10K to 40K) - actpass_logical_tt (mixture of active to logical and passive to logical tree translations, single direction) - actpass_logical_tt_40k (same task as actpass_logical_tt, but train samples increased from 10K to 40K) - logical_logical_stb (logical form sentence translation, from sentence to parenthesized tree form, both directions) - logical_logical_stb_40k (same task as logical_logical_stb, but train samples increased from 10K to 40K) - passive_logical_ssb (passive to logical sentence translation, in both directions) - passive_logical_ssb_40k (same task as passive_logical_ssb, but train samples increased from 10K to 40K) - passive_logical_ttb (passive to logical tree translation, in both directions) - passive_logical_ttb_40k (same task as passive_logical_ttb, but train samples increased from 10K to 40K) - passive_passive_stb (passive sentence translation, from sentence to parenthesized tree form, both directions) - passive_passive_stb_40k (same task as passive_passive_stb, but train samples increased from 10K to 40K) ``` ### Data Splits Most tasks have the following splits: - train - validation - test - ood_new - ood_long - ood_all Here is a table showing how the number of examples varies by split (for most tasks): | Dataset Split | Number of Instances in Split | | ------------- | ------------------------------------------- | | train | 10,000 | | validation | 1,250 | | test | 1,250 | | ood_new | 1,250 | | ood_long | 1,250 | | ood_all | 1,250 | ### Data Instances For each sample, there is source and target string. Source and target string are either plain text, or a parenthesized version of a tree, depending on the task. Here is an example from the *train* split of the *active_passive_ttb* task: ``` { 'source': '( S ( NP ( DET his ) ( AP ( N cat ) ) ) ( VP ( V discovered ) ( NP ( DET the ) ( AP ( ADJ blue ) ( AP ( N priest ) ) ) ) ) )', 'target': '( S ( NP ( DET the ) ( AP ( ADJ blue ) ( AP ( N priest ) ) ) ) ( VP ( AUXPS was ) ( VPPS ( V discovered ) ( PPPS ( PPS by ) ( NP ( DET his ) ( AP ( N cat ) ) ) ) ) ) )', 'direction': 'forward' } ``` ### Data Fields - `source`: the string denoting the sequence or tree structure to be translated - `target`: the string denoting the gold (aka label) sequence or tree structure Optional annotation fields (their presence varies by task): - `direction`: describes the direction of the translation (forward, backward), relative to the task name - `count` : a string denoting the count of symbolic operations needed (e.g., "s3") to translate the source to the target - `class` : a string denoting the type of translation needed ## Dataset Creation ### Curation Rationale We wanted a dataset comprised of relatively simple English active/passive/logical form translations, where we could focus on two types of out of distribution generalization: longer source sequences and new adjectives. ### Source Data [N/A] #### Initial Data Collection and Normalization [N/A] #### Who are the source language producers? The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez. ### Annotations Besides the source and target structured sequences, some of the subsets (tasks) contain 1-2 additional columns that describe the category and tree depth of each sample. #### Annotation process The annotation columns were generated from the each sample template and source sequence. #### Who are the annotators? [N/A] ### Personal and Sensitive Information No names or other sensitive information are included in the data. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop models that can translated structured data from one form to another, in a way that generalizes to out of distribution adjective values and lengths. ### Discussion of Biases [TBD] ### Other Known Limitations [TBD] ## Additional Information The internal name of this dataset is nc_pat. ### Dataset Curators The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez. ### Licensing Information This dataset is released under the [Permissive 2.0 license](https://cdla.dev/permissive-2-0/). ### Citation Information [TBD] ### Contributions Thanks to [The Neurocompositional AI group at Microsoft Research](https://www.microsoft.com/en-us/research/project/neurocompositional-ai/) for creating and adding this dataset.
rfernand/basic_sentence_transforms
[ "task_categories:text2text-generation", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "size_categories:n<1K", "source_datasets:original", "language:en", "license:other", "struct2struct", "tree2tree", "region:us" ]
2023-01-28T18:45:06+00:00
{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K", "1K<n<10K", "n<1K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "Active/Passive/Logical Transforms", "tags": ["struct2struct", "tree2tree"]}
2023-05-17T17:33:56+00:00
[]
[ "en" ]
TAGS #task_categories-text2text-generation #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-English #license-other #struct2struct #tree2tree #region-us
Dataset Card for Active/Passive/Logical Transforms ================================================== Table of Contents ----------------- * Dataset Description + Dataset Summary + Supported Tasks and Leaderboards + Languages * Dataset Structure + Dataset Subsets (Tasks) + Dataset Splits + Data Instances + Data Fields * Dataset Creation + Curation Rationale + Source Data + Annotations + Personal and Sensitive Information * Considerations for Using the Data + Social Impact of Dataset + Discussion of Biases + Other Known Limitations * Additional Information + Dataset Curators + Licensing Information + Citation Information + Contributions Dataset Description ------------------- * Homepage: * Repository: * Paper: * Leaderboard: * Point of Contact: Roland Fernandez ### Dataset Summary This dataset is a synthetic dataset containing structure-to-structure transformation tasks between English sentences in 3 forms: active, passive, and logical. The dataset also includes several tree-transformation diagnostic/warm-up tasks. ### Supported Tasks and Leaderboards [TBD] ### Languages All data is in English. Dataset Structure ----------------- The dataset consists of several subsets, or tasks. Each task contains a train split, a validation split, and a test split, with most tasks also containing two out-of-distruction splits (one for new adjectives and one for longer adjective phrases). Each sample in a split contains a source string, a target string, and 0-2 annotation strings. ### Dataset Subsets (Tasks) The dataset consists of diagnostic/warm-up tasks and core tasks. The core tasks represent the translation of English sentences between the active, passive, and logical forms. The 12 diagnostic/warm-up tasks are: There are 22 core tasks are: ### Data Splits Most tasks have the following splits: * train * validation * test * ood\_new * ood\_long * ood\_all Here is a table showing how the number of examples varies by split (for most tasks): ### Data Instances For each sample, there is source and target string. Source and target string are either plain text, or a parenthesized version of a tree, depending on the task. Here is an example from the *train* split of the *active\_passive\_ttb* task: ### Data Fields * 'source': the string denoting the sequence or tree structure to be translated * 'target': the string denoting the gold (aka label) sequence or tree structure Optional annotation fields (their presence varies by task): * 'direction': describes the direction of the translation (forward, backward), relative to the task name * 'count' : a string denoting the count of symbolic operations needed (e.g., "s3") to translate the source to the target * 'class' : a string denoting the type of translation needed Dataset Creation ---------------- ### Curation Rationale We wanted a dataset comprised of relatively simple English active/passive/logical form translations, where we could focus on two types of out of distribution generalization: longer source sequences and new adjectives. ### Source Data [N/A] #### Initial Data Collection and Normalization [N/A] #### Who are the source language producers? The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez. ### Annotations Besides the source and target structured sequences, some of the subsets (tasks) contain 1-2 additional columns that describe the category and tree depth of each sample. #### Annotation process The annotation columns were generated from the each sample template and source sequence. #### Who are the annotators? [N/A] ### Personal and Sensitive Information No names or other sensitive information are included in the data. Considerations for Using the Data --------------------------------- ### Social Impact of Dataset The purpose of this dataset is to help develop models that can translated structured data from one form to another, in a way that generalizes to out of distribution adjective values and lengths. ### Discussion of Biases [TBD] ### Other Known Limitations [TBD] Additional Information ---------------------- The internal name of this dataset is nc\_pat. ### Dataset Curators The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez. ### Licensing Information This dataset is released under the Permissive 2.0 license. [TBD] ### Contributions Thanks to The Neurocompositional AI group at Microsoft Research for creating and adding this dataset.
[ "### Dataset Summary\n\n\nThis dataset is a synthetic dataset containing structure-to-structure transformation tasks between\nEnglish sentences in 3 forms: active, passive, and logical. The dataset also includes several\ntree-transformation diagnostic/warm-up tasks.", "### Supported Tasks and Leaderboards\n\n\n[TBD]", "### Languages\n\n\nAll data is in English.\n\n\nDataset Structure\n-----------------\n\n\nThe dataset consists of several subsets, or tasks. Each task contains a train split, a validation split, and a\ntest split, with most tasks also containing two out-of-distruction splits (one for new adjectives and one for longer adjective phrases).\n\n\nEach sample in a split contains a source string, a target string, and 0-2 annotation strings.", "### Dataset Subsets (Tasks)\n\n\nThe dataset consists of diagnostic/warm-up tasks and core tasks. The core tasks represent the translation of English sentences between the active, passive, and logical forms.\n\n\nThe 12 diagnostic/warm-up tasks are:\n\n\nThere are 22 core tasks are:", "### Data Splits\n\n\nMost tasks have the following splits:\n\n\n* train\n* validation\n* test\n* ood\\_new\n* ood\\_long\n* ood\\_all\n\n\nHere is a table showing how the number of examples varies by split (for most tasks):", "### Data Instances\n\n\nFor each sample, there is source and target string. Source and target string are either plain text, or a parenthesized\nversion of a tree, depending on the task.\n\n\nHere is an example from the *train* split of the *active\\_passive\\_ttb* task:", "### Data Fields\n\n\n* 'source': the string denoting the sequence or tree structure to be translated\n* 'target': the string denoting the gold (aka label) sequence or tree structure\n\n\nOptional annotation fields (their presence varies by task):\n\n\n* 'direction': describes the direction of the translation (forward, backward), relative to the task name\n* 'count' : a string denoting the count of symbolic operations needed (e.g., \"s3\") to translate the source to the target\n* 'class' : a string denoting the type of translation needed\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWe wanted a dataset comprised of relatively simple English active/passive/logical form translations, where we could focus\non two types of out of distribution generalization: longer source sequences and new adjectives.", "### Source Data\n\n\n[N/A]", "#### Initial Data Collection and Normalization\n\n\n[N/A]", "#### Who are the source language producers?\n\n\nThe dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.", "### Annotations\n\n\nBesides the source and target structured sequences, some of the subsets (tasks) contain 1-2 additional columns that\ndescribe the category and tree depth of each sample.", "#### Annotation process\n\n\nThe annotation columns were generated from the each sample template and source sequence.", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nNo names or other sensitive information are included in the data.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to help develop models that can translated structured data from one form to another, in a\nway that generalizes to out of distribution adjective values and lengths.", "### Discussion of Biases\n\n\n[TBD]", "### Other Known Limitations\n\n\n[TBD]\n\n\nAdditional Information\n----------------------\n\n\nThe internal name of this dataset is nc\\_pat.", "### Dataset Curators\n\n\nThe dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.", "### Licensing Information\n\n\nThis dataset is released under the Permissive 2.0 license.\n\n\n[TBD]", "### Contributions\n\n\nThanks to The Neurocompositional AI group at Microsoft Research for creating and adding this dataset." ]
[ "TAGS\n#task_categories-text2text-generation #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-English #license-other #struct2struct #tree2tree #region-us \n", "### Dataset Summary\n\n\nThis dataset is a synthetic dataset containing structure-to-structure transformation tasks between\nEnglish sentences in 3 forms: active, passive, and logical. The dataset also includes several\ntree-transformation diagnostic/warm-up tasks.", "### Supported Tasks and Leaderboards\n\n\n[TBD]", "### Languages\n\n\nAll data is in English.\n\n\nDataset Structure\n-----------------\n\n\nThe dataset consists of several subsets, or tasks. Each task contains a train split, a validation split, and a\ntest split, with most tasks also containing two out-of-distruction splits (one for new adjectives and one for longer adjective phrases).\n\n\nEach sample in a split contains a source string, a target string, and 0-2 annotation strings.", "### Dataset Subsets (Tasks)\n\n\nThe dataset consists of diagnostic/warm-up tasks and core tasks. The core tasks represent the translation of English sentences between the active, passive, and logical forms.\n\n\nThe 12 diagnostic/warm-up tasks are:\n\n\nThere are 22 core tasks are:", "### Data Splits\n\n\nMost tasks have the following splits:\n\n\n* train\n* validation\n* test\n* ood\\_new\n* ood\\_long\n* ood\\_all\n\n\nHere is a table showing how the number of examples varies by split (for most tasks):", "### Data Instances\n\n\nFor each sample, there is source and target string. Source and target string are either plain text, or a parenthesized\nversion of a tree, depending on the task.\n\n\nHere is an example from the *train* split of the *active\\_passive\\_ttb* task:", "### Data Fields\n\n\n* 'source': the string denoting the sequence or tree structure to be translated\n* 'target': the string denoting the gold (aka label) sequence or tree structure\n\n\nOptional annotation fields (their presence varies by task):\n\n\n* 'direction': describes the direction of the translation (forward, backward), relative to the task name\n* 'count' : a string denoting the count of symbolic operations needed (e.g., \"s3\") to translate the source to the target\n* 'class' : a string denoting the type of translation needed\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\nWe wanted a dataset comprised of relatively simple English active/passive/logical form translations, where we could focus\non two types of out of distribution generalization: longer source sequences and new adjectives.", "### Source Data\n\n\n[N/A]", "#### Initial Data Collection and Normalization\n\n\n[N/A]", "#### Who are the source language producers?\n\n\nThe dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.", "### Annotations\n\n\nBesides the source and target structured sequences, some of the subsets (tasks) contain 1-2 additional columns that\ndescribe the category and tree depth of each sample.", "#### Annotation process\n\n\nThe annotation columns were generated from the each sample template and source sequence.", "#### Who are the annotators?\n\n\n[N/A]", "### Personal and Sensitive Information\n\n\nNo names or other sensitive information are included in the data.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nThe purpose of this dataset is to help develop models that can translated structured data from one form to another, in a\nway that generalizes to out of distribution adjective values and lengths.", "### Discussion of Biases\n\n\n[TBD]", "### Other Known Limitations\n\n\n[TBD]\n\n\nAdditional Information\n----------------------\n\n\nThe internal name of this dataset is nc\\_pat.", "### Dataset Curators\n\n\nThe dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.", "### Licensing Information\n\n\nThis dataset is released under the Permissive 2.0 license.\n\n\n[TBD]", "### Contributions\n\n\nThanks to The Neurocompositional AI group at Microsoft Research for creating and adding this dataset." ]
[ 110, 62, 14, 106, 73, 60, 68, 147, 55, 9, 15, 29, 45, 25, 14, 30, 51, 12, 32, 25, 23, 25 ]
[ "passage: TAGS\n#task_categories-text2text-generation #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-English #license-other #struct2struct #tree2tree #region-us \n### Dataset Summary\n\n\nThis dataset is a synthetic dataset containing structure-to-structure transformation tasks between\nEnglish sentences in 3 forms: active, passive, and logical. The dataset also includes several\ntree-transformation diagnostic/warm-up tasks.### Supported Tasks and Leaderboards\n\n\n[TBD]### Languages\n\n\nAll data is in English.\n\n\nDataset Structure\n-----------------\n\n\nThe dataset consists of several subsets, or tasks. Each task contains a train split, a validation split, and a\ntest split, with most tasks also containing two out-of-distruction splits (one for new adjectives and one for longer adjective phrases).\n\n\nEach sample in a split contains a source string, a target string, and 0-2 annotation strings.### Dataset Subsets (Tasks)\n\n\nThe dataset consists of diagnostic/warm-up tasks and core tasks. The core tasks represent the translation of English sentences between the active, passive, and logical forms.\n\n\nThe 12 diagnostic/warm-up tasks are:\n\n\nThere are 22 core tasks are:### Data Splits\n\n\nMost tasks have the following splits:\n\n\n* train\n* validation\n* test\n* ood\\_new\n* ood\\_long\n* ood\\_all\n\n\nHere is a table showing how the number of examples varies by split (for most tasks):### Data Instances\n\n\nFor each sample, there is source and target string. Source and target string are either plain text, or a parenthesized\nversion of a tree, depending on the task.\n\n\nHere is an example from the *train* split of the *active\\_passive\\_ttb* task:" ]
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dab97ed8568c29eab1ac936cdddb2dcd6c36fb90
# AutoTrain Dataset for project: ex-and-pt ## Dataset Description This dataset has been automatically processed by AutoTrain for project ex-and-pt. ### 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 [ { "image": "<3840x2160 RGB PIL image>", "target": 2 }, { "image": "<3840x2160 RGBA PIL image>", "target": 5 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['EX and PT', 'EX and PT Logo', 'EX and PT Mutant', 'EX and PT Mutants', 'EX and PT TCG', 'Vagitron'], 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 | 15 | | valid | 7 |
Lloviant/autotrain-data-ex-and-pt
[ "task_categories:image-classification", "region:us" ]
2023-01-28T19:40:27+00:00
{"task_categories": ["image-classification"]}
2023-01-28T20:39:11+00:00
[]
[]
TAGS #task_categories-image-classification #region-us
AutoTrain Dataset for project: ex-and-pt ======================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project ex-and-pt. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-image-classification #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 17, 27, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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690ba1e6cba66ae91db4d4b97db713cad4057bb7
# Dataset Card for "test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
will33am/test
[ "region:us" ]
2023-01-28T19:51:30+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "not-hateful", "1": "hateful"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 361720695.0, "num_examples": 1000}], "download_size": 361543046, "dataset_size": 361720695.0}}
2023-01-28T19:51:55+00:00
[]
[]
TAGS #region-us
# Dataset Card for "test" More Information needed
[ "# Dataset Card for \"test\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"test\"\n\nMore Information needed" ]
[ 6, 11 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"test\"\n\nMore Information needed" ]
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8a2e5a1e67624e57d0e8eb30603ee7a6ab68ee27
# 概要 [DeDeDe](https://huggingface.co/nakayama/DeDeDe)の学習過程において利用された、 画像生成サービスやモデルを用いて作成された画像のセットのうち、 nsfwに相当すると判断したものを除いたものです。 # ライセンス CreativeML OpenRAIL-M
nakayama/DeDeDeDataset
[ "license:creativeml-openrail-m", "region:us" ]
2023-01-28T21:11:29+00:00
{"license": "creativeml-openrail-m"}
2023-02-26T11:40:03+00:00
[]
[]
TAGS #license-creativeml-openrail-m #region-us
# 概要 DeDeDeの学習過程において利用された、 画像生成サービスやモデルを用いて作成された画像のセットのうち、 nsfwに相当すると判断したものを除いたものです。 # ライセンス CreativeML OpenRAIL-M
[ "# 概要\n DeDeDeの学習過程において利用された、 \n画像生成サービスやモデルを用いて作成された画像のセットのうち、 \nnsfwに相当すると判断したものを除いたものです。", "# ライセンス\nCreativeML OpenRAIL-M" ]
[ "TAGS\n#license-creativeml-openrail-m #region-us \n", "# 概要\n DeDeDeの学習過程において利用された、 \n画像生成サービスやモデルを用いて作成された画像のセットのうち、 \nnsfwに相当すると判断したものを除いたものです。", "# ライセンス\nCreativeML OpenRAIL-M" ]
[ 18, 44, 12 ]
[ "passage: TAGS\n#license-creativeml-openrail-m #region-us \n# 概要\n DeDeDeの学習過程において利用された、 \n画像生成サービスやモデルを用いて作成された画像のセットのうち、 \nnsfwに相当すると判断したものを除いたものです。# ライセンス\nCreativeML OpenRAIL-M" ]
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e71920f420a7b89953fcee1f4c5db3556d7947fa
Simple English Wikipedia it has only about 170k articles. We split these articles into paragraphs. wikipedia_filepath = 'simplewiki-2020-11-01.jsonl.gz' if not os.path.exists(wikipedia_filepath): util.http_get('http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz', wikipedia_filepath) embedded into vectors using SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
gfhayworth/wiki_mini_embed
[ "region:us" ]
2023-01-28T21:22:26+00:00
{}
2023-01-28T23:40:40+00:00
[]
[]
TAGS #region-us
Simple English Wikipedia it has only about 170k articles. We split these articles into paragraphs. wikipedia_filepath = 'URL' if not URL(wikipedia_filepath): util.http_get('URL wikipedia_filepath) embedded into vectors using SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
[]
[ "TAGS\n#region-us \n" ]
[ 6 ]
[ "passage: TAGS\n#region-us \n" ]
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2dc9e9592f61badbf33bfd6db4f541f3390c8925
# Dataset Card for WHOOPS! - [Dataset Description](#dataset-description) - [Contribute Images to Extend WHOOPS!](#contribute-images-to-extend-whoops) - [Languages](#languages) - [Dataset](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Data Loading](#data-loading) - [Licensing Information](#licensing-information) - [Annotations](#annotations) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Citation Information](#citation-information) ## Dataset Description WHOOPS! is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains commonsense-defying image from a wide range of reasons, deviations from expected social norms and everyday knowledge. The WHOOPS! benchmark includes four tasks: 1. A novel task of explanation-of-violation: generating a detailed explanation for what makes the image weird. 2. Generating a literal caption 3. Distinguishing between detailed and underspecified captions 4. Answering questions that test compositional understanding The results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!. * Homepage: https://whoops-benchmark.github.io/ * Paper: https://arxiv.org/pdf/2303.07274.pdf * WHOOPS! Explorer: https://huggingface.co/spaces/nlphuji/whoops-explorer-full * Normal vs. Wired Explorer: https://huggingface.co/spaces/nlphuji/whoops-explorer-analysis * Point of Contact: [email protected] [//]: # (Colab notebook code for WHOOPS evaluation ) ## Contribute Images to Extend WHOOPS! Would you like to add a commonsense-defying image to our database? Please send candidate images to [email protected]. Thanks! ### Languages English. ## Dataset ### Data Fields image (image) - The weird image. designer_explanation (string) - Detailed single-sentence explanation given by the designer, explaining why the image is weird. selected_caption (string) - The caption that was selected from the crowed collected captions. crowd_captions (list) - Crowd collected captions, depicting whats been seen in the image. crowd_explanations (list) - Crowd collected single-sentence explanations, explaining why the image is weird. crowd_underspecified_captions (list) - Crowd collected under-specified captions, depicting what is seen in the image, without depicting the commonsense-violation. question_answering_pairs (list) - Automatically generated Q-A pairs. FlanT5 XL was used to answer the questions and filter out instances where the BEM metric is above 0.1. commonsense_category (string) - The commonsense category the images related to (Full categories list can be found in [paper](https://arxiv.org/pdf/2303.07274.pdf)). image_id (string)- The unique id of the image in the dataset image_designer (string) - The name of the image designer. ### Data Splits There is a single TEST split. Although primarily intended as a challenging test set, we trained on the WHOOPS! dataset to demonstrate the value of the data and to create a better model. We will provide the splits in the future. ### Data Loading You can load the data as follows (credit to [Winoground](https://huggingface.co/datasets/facebook/winoground)): ``` from datasets import load_dataset examples = load_dataset('nlphuji/whoops', use_auth_token=<YOUR USER ACCESS TOKEN>) ``` You can get `<YOUR USER ACCESS TOKEN>` by following these steps: 1) log into your Hugging Face account 2) click on your profile picture 3) click "Settings" 4) click "Access Tokens" 5) generate an access token ## Licensing Information [CC-By 4.0](https://creativecommons.org/licenses/by/4.0/) Additional license information: [license_agreement.txt](https://huggingface.co/datasets/nlphuji/whoops/blob/main/license_agreement.txt) You may also view all instances through the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and consult the accompanying [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md). 1. **Purpose:** The dataset was primarily designed for use as a test set. 2. **Commercial Use:** Commercially, the dataset may be used as a test set, but it's prohibited to use it as a training set. 3. **Rights on Images:** All rights to the images within the dataset are retained by the WHOOPS! authors. If you are unsure about your specific case - do not hesitate to reach out: [email protected]. [//]: # (To evaluate WHOOPS! with a fine-tune BLIP2, we split the images in WHOOPS! into 5 cross- validation splits. For these 5 splits independently, we train supervised models using 60% of the data as training, 20% as validation, and 20% for test.) ## Annotations We paid designers to create images, and supply explanations for what is making the image wierd. We paid Amazon Mechanical Turk Workers to supply explanations, captions and under-specified captions for each image in our dataset. ## Considerations for Using the Data We took measures to filter out potentially harmful or offensive images and texts in WHOOPS!, but it is still possible that some individuals may find certain content objectionable. If you come across any instances of harm, please report them to our point of contact. We will review and eliminate any images from the dataset that are deemed harmful. [//]: # (All images, explanations, captions and under-specified captions were obtained with human annotators.) ### Citation Information @article{bitton2023breaking, title={Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images}, author={Bitton-Guetta, Nitzan and Bitton, Yonatan and Hessel, Jack and Schmidt, Ludwig and Elovici, Yuval and Stanovsky, Gabriel and Schwartz, Roy}, journal={arXiv preprint arXiv:2303.07274}, year={2023} }
nlphuji/whoops
[ "annotations_creators:crowdsourced", "language_creators:found", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "commonsense-reasoning", "explanation-generation", "visual-commonsense-reasoning", "compositionality", "image-generation", "visual-question-answering(VQA)", "question-answering", "image-captioning", "arxiv:2303.07274", "region:us" ]
2023-01-28T22:04:03+00:00
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_ids": [], "paperswithcode_id": "whoops", "pretty_name": "WHOOPS!", "tags": ["commonsense-reasoning", "explanation-generation", "visual-commonsense-reasoning", "compositionality", "image-generation", "visual-question-answering(VQA)", "question-answering", "image-captioning"], "extra_gated_prompt": "# By clicking \u201cAccess repository\u201c below, you assert your intention to exclusively use this resource for research, not for commercial chatbot development, and agree to abide by the terms detailed in the [WHOOPS! license](https://whoops-benchmark.github.io/static/pdfs/whoops_license_agreement.txt). You may also view all instances through the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and consult the accompanying [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md) prior to acceptance. If you are unsure about your specific case - do not hesitate to reach out: [email protected]. By clicking \u201cAccess repository\u201d below, you confirm your understanding that for commercial models, this resource is permitted for use as a test set, but not as a training set. Please ensure adherence to the terms detailed in the [WHOOPS! license](https://whoops-benchmark.github.io/static/pdfs/whoops_license_agreement.txt). You may view all instances via the [WHOOPS! Explorer](https://huggingface.co/spaces/nlphuji/whoops-explorer-full) and refer to the [WHOOPS! Dataset card](https://huggingface.co/spaces/nlphuji/whoops-explorer-full/blob/main/README.md) prior to acceptance. If you are unsure about your specific case, don't hesitate to contact: [email protected]."}
2023-08-18T22:06:45+00:00
[ "2303.07274" ]
[ "en" ]
TAGS #annotations_creators-crowdsourced #language_creators-found #size_categories-10K<n<100K #source_datasets-original #language-English #commonsense-reasoning #explanation-generation #visual-commonsense-reasoning #compositionality #image-generation #visual-question-answering(VQA) #question-answering #image-captioning #arxiv-2303.07274 #region-us
# Dataset Card for WHOOPS! - Dataset Description - Contribute Images to Extend WHOOPS! - Languages - Dataset - Data Fields - Data Splits - Data Loading - Licensing Information - Annotations - Considerations for Using the Data - Citation Information ## Dataset Description WHOOPS! is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains commonsense-defying image from a wide range of reasons, deviations from expected social norms and everyday knowledge. The WHOOPS! benchmark includes four tasks: 1. A novel task of explanation-of-violation: generating a detailed explanation for what makes the image weird. 2. Generating a literal caption 3. Distinguishing between detailed and underspecified captions 4. Answering questions that test compositional understanding The results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!. * Homepage: URL * Paper: URL * WHOOPS! Explorer: URL * Normal vs. Wired Explorer: URL * Point of Contact: yonatanbitton1@URL [//]: # (Colab notebook code for WHOOPS evaluation ) ## Contribute Images to Extend WHOOPS! Would you like to add a commonsense-defying image to our database? Please send candidate images to yonatanbitton1@URL. Thanks! ### Languages English. ## Dataset ### Data Fields image (image) - The weird image. designer_explanation (string) - Detailed single-sentence explanation given by the designer, explaining why the image is weird. selected_caption (string) - The caption that was selected from the crowed collected captions. crowd_captions (list) - Crowd collected captions, depicting whats been seen in the image. crowd_explanations (list) - Crowd collected single-sentence explanations, explaining why the image is weird. crowd_underspecified_captions (list) - Crowd collected under-specified captions, depicting what is seen in the image, without depicting the commonsense-violation. question_answering_pairs (list) - Automatically generated Q-A pairs. FlanT5 XL was used to answer the questions and filter out instances where the BEM metric is above 0.1. commonsense_category (string) - The commonsense category the images related to (Full categories list can be found in paper). image_id (string)- The unique id of the image in the dataset image_designer (string) - The name of the image designer. ### Data Splits There is a single TEST split. Although primarily intended as a challenging test set, we trained on the WHOOPS! dataset to demonstrate the value of the data and to create a better model. We will provide the splits in the future. ### Data Loading You can load the data as follows (credit to Winoground): You can get '<YOUR USER ACCESS TOKEN>' by following these steps: 1) log into your Hugging Face account 2) click on your profile picture 3) click "Settings" 4) click "Access Tokens" 5) generate an access token ## Licensing Information CC-By 4.0 Additional license information: license_agreement.txt You may also view all instances through the WHOOPS! Explorer and consult the accompanying WHOOPS! Dataset card. 1. Purpose: The dataset was primarily designed for use as a test set. 2. Commercial Use: Commercially, the dataset may be used as a test set, but it's prohibited to use it as a training set. 3. Rights on Images: All rights to the images within the dataset are retained by the WHOOPS! authors. If you are unsure about your specific case - do not hesitate to reach out: yonatanbitton1@URL. [//]: # (To evaluate WHOOPS! with a fine-tune BLIP2, we split the images in WHOOPS! into 5 cross- validation splits. For these 5 splits independently, we train supervised models using 60% of the data as training, 20% as validation, and 20% for test.) ## Annotations We paid designers to create images, and supply explanations for what is making the image wierd. We paid Amazon Mechanical Turk Workers to supply explanations, captions and under-specified captions for each image in our dataset. ## Considerations for Using the Data We took measures to filter out potentially harmful or offensive images and texts in WHOOPS!, but it is still possible that some individuals may find certain content objectionable. If you come across any instances of harm, please report them to our point of contact. We will review and eliminate any images from the dataset that are deemed harmful. [//]: # (All images, explanations, captions and under-specified captions were obtained with human annotators.) @article{bitton2023breaking, title={Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images}, author={Bitton-Guetta, Nitzan and Bitton, Yonatan and Hessel, Jack and Schmidt, Ludwig and Elovici, Yuval and Stanovsky, Gabriel and Schwartz, Roy}, journal={arXiv preprint arXiv:2303.07274}, year={2023} }
[ "# Dataset Card for WHOOPS!\n\n- Dataset Description\n- Contribute Images to Extend WHOOPS!\n - Languages\n- Dataset\n - Data Fields\n - Data Splits\n - Data Loading\n- Licensing Information\n- Annotations\n- Considerations for Using the Data\n- Citation Information", "## Dataset Description\nWHOOPS! is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains commonsense-defying image from a wide range of reasons, deviations from expected social norms and everyday knowledge.\n\nThe WHOOPS! benchmark includes four tasks:\n1. A novel task of explanation-of-violation: generating a detailed explanation for what makes the image weird.\n2. Generating a literal caption\n3. Distinguishing between detailed and underspecified captions\n4. Answering questions that test compositional understanding\n\nThe results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!.\n\n* Homepage: URL\n* Paper: URL\n* WHOOPS! Explorer: URL\n* Normal vs. Wired Explorer: URL\n* Point of Contact: yonatanbitton1@URL\n\n[//]: # (Colab notebook code for WHOOPS evaluation )", "## Contribute Images to Extend WHOOPS!\nWould you like to add a commonsense-defying image to our database? Please send candidate images to yonatanbitton1@URL. Thanks!", "### Languages\nEnglish.", "## Dataset", "### Data Fields\n image (image) - The weird image.\n designer_explanation (string) - Detailed single-sentence explanation given by the designer, explaining why the image is weird.\n selected_caption (string) - The caption that was selected from the crowed collected captions.\n crowd_captions (list) - Crowd collected captions, depicting whats been seen in the image.\n crowd_explanations (list) - Crowd collected single-sentence explanations, explaining why the image is weird.\n crowd_underspecified_captions (list) - Crowd collected under-specified captions, depicting what is seen in the image, without depicting the commonsense-violation.\n question_answering_pairs (list) - Automatically generated Q-A pairs. FlanT5 XL was used to answer the questions and filter out instances where the BEM metric is above 0.1.\n commonsense_category (string) - The commonsense category the images related to (Full categories list can be found in paper).\n image_id (string)- The unique id of the image in the dataset\n image_designer (string) - The name of the image designer.", "### Data Splits\nThere is a single TEST split. \nAlthough primarily intended as a challenging test set, we trained on the WHOOPS! dataset to demonstrate the value of the data and to create a better model. \nWe will provide the splits in the future.", "### Data Loading\nYou can load the data as follows (credit to Winoground):\n\nYou can get '<YOUR USER ACCESS TOKEN>' by following these steps:\n1) log into your Hugging Face account\n2) click on your profile picture\n3) click \"Settings\"\n4) click \"Access Tokens\"\n5) generate an access token", "## Licensing Information\nCC-By 4.0 \nAdditional license information: license_agreement.txt \nYou may also view all instances through the WHOOPS! Explorer and consult the accompanying WHOOPS! Dataset card. \n\n1. Purpose: The dataset was primarily designed for use as a test set.\n2. Commercial Use: Commercially, the dataset may be used as a test set, but it's prohibited to use it as a training set.\n3. Rights on Images: All rights to the images within the dataset are retained by the WHOOPS! authors.\n\nIf you are unsure about your specific case - do not hesitate to reach out: yonatanbitton1@URL.\n\n[//]: # (To evaluate WHOOPS! with a fine-tune BLIP2, we split the images in WHOOPS! into 5 cross- validation splits. For these 5 splits independently, we train supervised models using 60% of the data as training, 20% as validation, and 20% for test.)", "## Annotations\nWe paid designers to create images, and supply explanations for what is making the image wierd.\nWe paid Amazon Mechanical Turk Workers to supply explanations, captions and under-specified captions for each image in our dataset.", "## Considerations for Using the Data\nWe took measures to filter out potentially harmful or offensive images and texts in WHOOPS!, but it is still possible that some individuals may find certain content objectionable. \nIf you come across any instances of harm, please report them to our point of contact. We will review and eliminate any images from the dataset that are deemed harmful.\n\n[//]: # (All images, explanations, captions and under-specified captions were obtained with human annotators.)\n\n\n\n @article{bitton2023breaking,\n title={Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images},\n author={Bitton-Guetta, Nitzan and Bitton, Yonatan and Hessel, Jack and Schmidt, Ludwig and Elovici, Yuval and Stanovsky, Gabriel and Schwartz, Roy},\n journal={arXiv preprint arXiv:2303.07274},\n year={2023}\n }" ]
[ "TAGS\n#annotations_creators-crowdsourced #language_creators-found #size_categories-10K<n<100K #source_datasets-original #language-English #commonsense-reasoning #explanation-generation #visual-commonsense-reasoning #compositionality #image-generation #visual-question-answering(VQA) #question-answering #image-captioning #arxiv-2303.07274 #region-us \n", "# Dataset Card for WHOOPS!\n\n- Dataset Description\n- Contribute Images to Extend WHOOPS!\n - Languages\n- Dataset\n - Data Fields\n - Data Splits\n - Data Loading\n- Licensing Information\n- Annotations\n- Considerations for Using the Data\n- Citation Information", "## Dataset Description\nWHOOPS! is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains commonsense-defying image from a wide range of reasons, deviations from expected social norms and everyday knowledge.\n\nThe WHOOPS! benchmark includes four tasks:\n1. A novel task of explanation-of-violation: generating a detailed explanation for what makes the image weird.\n2. Generating a literal caption\n3. Distinguishing between detailed and underspecified captions\n4. Answering questions that test compositional understanding\n\nThe results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!.\n\n* Homepage: URL\n* Paper: URL\n* WHOOPS! Explorer: URL\n* Normal vs. Wired Explorer: URL\n* Point of Contact: yonatanbitton1@URL\n\n[//]: # (Colab notebook code for WHOOPS evaluation )", "## Contribute Images to Extend WHOOPS!\nWould you like to add a commonsense-defying image to our database? Please send candidate images to yonatanbitton1@URL. Thanks!", "### Languages\nEnglish.", "## Dataset", "### Data Fields\n image (image) - The weird image.\n designer_explanation (string) - Detailed single-sentence explanation given by the designer, explaining why the image is weird.\n selected_caption (string) - The caption that was selected from the crowed collected captions.\n crowd_captions (list) - Crowd collected captions, depicting whats been seen in the image.\n crowd_explanations (list) - Crowd collected single-sentence explanations, explaining why the image is weird.\n crowd_underspecified_captions (list) - Crowd collected under-specified captions, depicting what is seen in the image, without depicting the commonsense-violation.\n question_answering_pairs (list) - Automatically generated Q-A pairs. FlanT5 XL was used to answer the questions and filter out instances where the BEM metric is above 0.1.\n commonsense_category (string) - The commonsense category the images related to (Full categories list can be found in paper).\n image_id (string)- The unique id of the image in the dataset\n image_designer (string) - The name of the image designer.", "### Data Splits\nThere is a single TEST split. \nAlthough primarily intended as a challenging test set, we trained on the WHOOPS! dataset to demonstrate the value of the data and to create a better model. \nWe will provide the splits in the future.", "### Data Loading\nYou can load the data as follows (credit to Winoground):\n\nYou can get '<YOUR USER ACCESS TOKEN>' by following these steps:\n1) log into your Hugging Face account\n2) click on your profile picture\n3) click \"Settings\"\n4) click \"Access Tokens\"\n5) generate an access token", "## Licensing Information\nCC-By 4.0 \nAdditional license information: license_agreement.txt \nYou may also view all instances through the WHOOPS! Explorer and consult the accompanying WHOOPS! Dataset card. \n\n1. Purpose: The dataset was primarily designed for use as a test set.\n2. Commercial Use: Commercially, the dataset may be used as a test set, but it's prohibited to use it as a training set.\n3. Rights on Images: All rights to the images within the dataset are retained by the WHOOPS! authors.\n\nIf you are unsure about your specific case - do not hesitate to reach out: yonatanbitton1@URL.\n\n[//]: # (To evaluate WHOOPS! with a fine-tune BLIP2, we split the images in WHOOPS! into 5 cross- validation splits. For these 5 splits independently, we train supervised models using 60% of the data as training, 20% as validation, and 20% for test.)", "## Annotations\nWe paid designers to create images, and supply explanations for what is making the image wierd.\nWe paid Amazon Mechanical Turk Workers to supply explanations, captions and under-specified captions for each image in our dataset.", "## Considerations for Using the Data\nWe took measures to filter out potentially harmful or offensive images and texts in WHOOPS!, but it is still possible that some individuals may find certain content objectionable. \nIf you come across any instances of harm, please report them to our point of contact. We will review and eliminate any images from the dataset that are deemed harmful.\n\n[//]: # (All images, explanations, captions and under-specified captions were obtained with human annotators.)\n\n\n\n @article{bitton2023breaking,\n title={Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images},\n author={Bitton-Guetta, Nitzan and Bitton, Yonatan and Hessel, Jack and Schmidt, Ludwig and Elovici, Yuval and Stanovsky, Gabriel and Schwartz, Roy},\n journal={arXiv preprint arXiv:2303.07274},\n year={2023}\n }" ]
[ 120, 61, 226, 44, 6, 3, 271, 58, 72, 224, 55, 239 ]
[ "passage: TAGS\n#annotations_creators-crowdsourced #language_creators-found #size_categories-10K<n<100K #source_datasets-original #language-English #commonsense-reasoning #explanation-generation #visual-commonsense-reasoning #compositionality #image-generation #visual-question-answering(VQA) #question-answering #image-captioning #arxiv-2303.07274 #region-us \n# Dataset Card for WHOOPS!\n\n- Dataset Description\n- Contribute Images to Extend WHOOPS!\n - Languages\n- Dataset\n - Data Fields\n - Data Splits\n - Data Loading\n- Licensing Information\n- Annotations\n- Considerations for Using the Data\n- Citation Information## Dataset Description\nWHOOPS! is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains commonsense-defying image from a wide range of reasons, deviations from expected social norms and everyday knowledge.\n\nThe WHOOPS! benchmark includes four tasks:\n1. A novel task of explanation-of-violation: generating a detailed explanation for what makes the image weird.\n2. Generating a literal caption\n3. Distinguishing between detailed and underspecified captions\n4. Answering questions that test compositional understanding\n\nThe results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!.\n\n* Homepage: URL\n* Paper: URL\n* WHOOPS! Explorer: URL\n* Normal vs. Wired Explorer: URL\n* Point of Contact: yonatanbitton1@URL\n\n[//]: # (Colab notebook code for WHOOPS evaluation )## Contribute Images to Extend WHOOPS!\nWould you like to add a commonsense-defying image to our database? Please send candidate images to yonatanbitton1@URL. Thanks!### Languages\nEnglish.## Dataset", "passage: ### Data Fields\n image (image) - The weird image.\n designer_explanation (string) - Detailed single-sentence explanation given by the designer, explaining why the image is weird.\n selected_caption (string) - The caption that was selected from the crowed collected captions.\n crowd_captions (list) - Crowd collected captions, depicting whats been seen in the image.\n crowd_explanations (list) - Crowd collected single-sentence explanations, explaining why the image is weird.\n crowd_underspecified_captions (list) - Crowd collected under-specified captions, depicting what is seen in the image, without depicting the commonsense-violation.\n question_answering_pairs (list) - Automatically generated Q-A pairs. FlanT5 XL was used to answer the questions and filter out instances where the BEM metric is above 0.1.\n commonsense_category (string) - The commonsense category the images related to (Full categories list can be found in paper).\n image_id (string)- The unique id of the image in the dataset\n image_designer (string) - The name of the image designer.### Data Splits\nThere is a single TEST split. \nAlthough primarily intended as a challenging test set, we trained on the WHOOPS! dataset to demonstrate the value of the data and to create a better model. \nWe will provide the splits in the future.### Data Loading\nYou can load the data as follows (credit to Winoground):\n\nYou can get '<YOUR USER ACCESS TOKEN>' by following these steps:\n1) log into your Hugging Face account\n2) click on your profile picture\n3) click \"Settings\"\n4) click \"Access Tokens\"\n5) generate an access token## Licensing Information\nCC-By 4.0 \nAdditional license information: license_agreement.txt \nYou may also view all instances through the WHOOPS! Explorer and consult the accompanying WHOOPS! Dataset card. \n\n1. Purpose: The dataset was primarily designed for use as a test set.\n2. Commercial Use: Commercially, the dataset may be used as a test set, but it's prohibited to use it as a training set.\n3. Rights on Images: All rights to the images within the dataset are retained by the WHOOPS! authors.\n\nIf you are unsure about your specific case - do not hesitate to reach out: yonatanbitton1@URL.\n\n[//]: # (To evaluate WHOOPS! with a fine-tune BLIP2, we split the images in WHOOPS! into 5 cross- validation splits. For these 5 splits independently, we train supervised models using 60% of the data as training, 20% as validation, and 20% for test.)## Annotations\nWe paid designers to create images, and supply explanations for what is making the image wierd.\nWe paid Amazon Mechanical Turk Workers to supply explanations, captions and under-specified captions for each image in our dataset." ]
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c958756763d9015f7ccc2555b3b0ec08418c21f9
# Dataset Card for Common Voice Corpus 11.0 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://arxiv.org/abs/1912.06670 - **Leaderboard:** https://paperswithcode.com/dataset/common-voice - **Point of Contact:** [Anton Lozhkov](mailto:[email protected]) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 24210 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 16413 validated hours in 100 languages, but more voices and languages are always added. Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the [🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) ### Languages ``` Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh ``` ## Dataset Structure ### Data Instances A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. `sentence` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ```python from datasets import load_dataset ds = load_dataset("mozilla-foundation/common_voice_11_0", "en", use_auth_token=True) def prepare_dataset(batch): """Function to preprocess the dataset with the .map method""" transcription = batch["sentence"] if transcription.startswith('"') and transcription.endswith('"'): # we can remove trailing quotation marks as they do not affect the transcription transcription = transcription[1:-1] if transcription[-1] not in [".", "?", "!"]: # append a full-stop to sentences that do not end in punctuation transcription = transcription + "." batch["sentence"] = transcription return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```
taqwa92/cm.trial
[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:multilingual", "source_datasets:extended|common_voice", "license:cc0-1.0", "arxiv:1912.06670", "region:us" ]
2023-01-28T23:05:40+00:00
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": {"ab": ["10K<n<100K"], "ar": ["100K<n<1M"], "as": ["1K<n<10K"], "ast": ["n<1K"], "az": ["n<1K"], "ba": ["100K<n<1M"], "bas": ["1K<n<10K"], "be": ["100K<n<1M"], "bg": ["1K<n<10K"], "bn": ["100K<n<1M"], "br": ["10K<n<100K"], "ca": ["1M<n<10M"], "ckb": ["100K<n<1M"], "cnh": ["1K<n<10K"], "cs": ["10K<n<100K"], "cv": ["10K<n<100K"], "cy": ["100K<n<1M"], "da": ["1K<n<10K"], "de": ["100K<n<1M"], "dv": ["10K<n<100K"], "el": ["10K<n<100K"], "en": ["1M<n<10M"], "eo": ["1M<n<10M"], "es": ["1M<n<10M"], "et": ["10K<n<100K"], "eu": ["100K<n<1M"], "fa": ["100K<n<1M"], "fi": ["10K<n<100K"], "fr": ["100K<n<1M"], "fy-NL": ["10K<n<100K"], "ga-IE": ["1K<n<10K"], "gl": ["10K<n<100K"], "gn": ["1K<n<10K"], "ha": ["1K<n<10K"], "hi": ["10K<n<100K"], "hsb": ["1K<n<10K"], "hu": ["10K<n<100K"], "hy-AM": ["1K<n<10K"], "ia": ["10K<n<100K"], "id": ["10K<n<100K"], "ig": ["1K<n<10K"], "it": ["100K<n<1M"], "ja": ["10K<n<100K"], "ka": ["10K<n<100K"], "kab": ["100K<n<1M"], "kk": ["1K<n<10K"], "kmr": ["10K<n<100K"], "ky": ["10K<n<100K"], "lg": ["100K<n<1M"], "lt": ["10K<n<100K"], "lv": ["1K<n<10K"], "mdf": ["n<1K"], "mhr": ["100K<n<1M"], "mk": ["n<1K"], "ml": ["1K<n<10K"], "mn": ["10K<n<100K"], "mr": ["10K<n<100K"], "mrj": ["10K<n<100K"], "mt": ["10K<n<100K"], "myv": ["1K<n<10K"], "nan-tw": ["10K<n<100K"], "ne-NP": ["n<1K"], "nl": ["10K<n<100K"], "nn-NO": ["n<1K"], "or": ["1K<n<10K"], "pa-IN": ["1K<n<10K"], "pl": ["100K<n<1M"], "pt": ["100K<n<1M"], "rm-sursilv": ["1K<n<10K"], "rm-vallader": ["1K<n<10K"], "ro": ["10K<n<100K"], "ru": ["100K<n<1M"], "rw": ["1M<n<10M"], "sah": ["1K<n<10K"], "sat": ["n<1K"], "sc": ["1K<n<10K"], "sk": ["10K<n<100K"], "skr": ["1K<n<10K"], "sl": ["10K<n<100K"], "sr": ["1K<n<10K"], "sv-SE": ["10K<n<100K"], "sw": ["100K<n<1M"], "ta": ["100K<n<1M"], "th": ["100K<n<1M"], "ti": ["n<1K"], "tig": ["n<1K"], "tok": ["1K<n<10K"], "tr": ["10K<n<100K"], "tt": ["10K<n<100K"], "tw": ["n<1K"], "ug": ["10K<n<100K"], "uk": ["10K<n<100K"], "ur": ["100K<n<1M"], "uz": ["100K<n<1M"], "vi": ["10K<n<100K"], "vot": ["n<1K"], "yue": ["10K<n<100K"], "zh-CN": ["100K<n<1M"], "zh-HK": ["100K<n<1M"], "zh-TW": ["100K<n<1M"]}, "source_datasets": ["extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "paperswithcode_id": "common-voice", "pretty_name": "Common Voice Corpus 11.0", "language_bcp47": ["ab", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy-NL", "ga-IE", "gl", "gn", "ha", "hi", "hsb", "hu", "hy-AM", "ia", "id", "ig", "it", "ja", "ka", "kab", "kk", "kmr", "ky", "lg", "lt", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan-tw", "ne-NP", "nl", "nn-NO", "or", "pa-IN", "pl", "pt", "rm-sursilv", "rm-vallader", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sr", "sv-SE", "sw", "ta", "th", "ti", "tig", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yue", "zh-CN", "zh-HK", "zh-TW"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."}
2023-01-28T23:33:24+00:00
[ "1912.06670" ]
[]
TAGS #task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us
# Dataset Card for Common Voice Corpus 11.0 ## Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - Personal and Sensitive Information - Considerations for Using the Data - Social Impact of Dataset - Discussion of Biases - Other Known Limitations - Additional Information - Dataset Curators - Licensing Information - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: URL - Point of Contact: Anton Lozhkov ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 24210 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 16413 validated hours in 100 languages, but more voices and languages are always added. Take a look at the Languages page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the Speech Bench ### Languages ## Dataset Structure ### Data Instances A typical data point comprises the 'path' to the audio file and its 'sentence'. Additional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'. ### Data Fields 'client_id' ('string'): An id for which client (voice) made the recording 'path' ('string'): The path to the audio file 'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '"audio"' column, *i.e.* 'dataset[0]["audio"]' should always be preferred over 'dataset["audio"][0]'. 'sentence' ('string'): The sentence the user was prompted to speak 'up_votes' ('int64'): How many upvotes the audio file has received from reviewers 'down_votes' ('int64'): How many downvotes the audio file has received from reviewers 'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties') 'gender' ('string'): The gender of the speaker 'accent' ('string'): Accent of the speaker 'locale' ('string'): The locale of the speaker 'segment' ('string'): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information Public Domain, CC-0
[ "# Dataset Card for Common Voice Corpus 11.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 24210 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 16413 validated hours in 100 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]
[ "TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n", "# Dataset Card for Common Voice Corpus 11.0", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 24210 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 16413 validated hours in 100 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench", "### Languages", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field", "### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.", "## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nPublic Domain, CC-0" ]
[ 87, 10, 120, 32, 108, 30, 4, 6, 77, 378, 145, 233, 5, 7, 4, 10, 10, 5, 5, 9, 42, 8, 41, 8, 7, 5, 6, 11 ]
[ "passage: TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n# Dataset Card for Common Voice Corpus 11.0## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Anton Lozhkov### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 24210 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 16413 validated hours in 100 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Speech Bench### Languages## Dataset Structure### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.", "passage: ### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.## Considerations for Using the Data" ]
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7afb1b05ae0ac2b980046a7220befcaf53b63f2a
# Dataset for the project: reviews-sentiment-analysis ## Dataset Description This dataset is for project reviews-sentiment-analysis. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "Now, I won't deny that when I purchased this off eBay, I had high expectations. This was an incredible out-of-print work from the master of comedy that I so enjoy. However, I was soon to be disappointed. Apologies to those who enjoyed it, but I just found the Compleat Al to be very difficult to watch. I got a few smiles, sure, but the majority of the funny came from the music videos (which I've got on DVD) and the rest was basically filler. You could tell that this was not Al's greatest video achievement (that honor goes to UHF). Honestly, I doubt if this will ever make the jump to DVD, so if you're an ultra-hardcore Al fan and just HAVE to own everything, buy the tape off eBay. Just don't pay too much for it.", "target": 0 }, { "text": "The saddest thing about this \"tribute\" is that almost all the singers (including the otherwise incredibly talented Nick Cave) seem to have missed the whole point where Cohen's intensity lies: by delivering his lines in an almost tuneless poise, Cohen transmits the full extent of his poetry, his irony, his all-round humanity, laughter and tears in one.<br /><br />To see some of these singer upstarts make convoluted suffering faces, launch their pathetic squeals in the patent effort to scream \"I'm a singer!,\" is a true pain. It's the same feeling many of you probably had listening in to some horrendous operatic versions of simple songs such as Lennon's \"Imagine.\" Nothing, simply nothing gets close to the simplicity and directness of the original. If there is a form of art that doesn't need embellishments, it's Cohen's art. Embellishments cast it in the street looking like the tasteless make-up of sex for sale.<br /><br />In this Cohen's tribute I found myself suffering and suffering through pitiful tributes and awful reinterpretations, all of them entirely lacking the original irony of the master and, if truth be told, several of these singers sounded as if they had been recruited at some asylum talent show. It's Cohen doing a tribute to them by letting them sing his material, really, not the other way around: they may have been friends, or his daughter's, he could have become very tender-hearted and in the mood for a gift. Too bad it didn't stay in the family.<br /><br />Fortunately, but only at the very end, Cohen himself performed his majestic \"Tower of Song,\" but even that flower was spoiled by the totally incongruous background of the U2, all of them carrying the expression that bored kids have when they visit their poor grandpa at the nursing home.<br /><br />A sad show, really, and sadder if you truly love Cohen as I do.", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['Negative', 'Positive'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follows: | Split name | Num samples | | ------------ | ------------------- | | train | 7499 | | valid | 2497 |
Kaludi/data-reviews-sentiment-analysis
[ "task_categories:text-classification", "language:en", "region:us" ]
2023-01-28T23:13:59+00:00
{"language": ["en"], "task_categories": ["text-classification"]}
2023-01-29T08:21:39+00:00
[]
[ "en" ]
TAGS #task_categories-text-classification #language-English #region-us
Dataset for the project: reviews-sentiment-analysis =================================================== Dataset Description ------------------- This dataset is for project reviews-sentiment-analysis. ### Languages The BCP-47 code for the dataset's language is en. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follows:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follows:" ]
[ "TAGS\n#task_categories-text-classification #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follows:" ]
[ 21, 26, 17, 23, 28 ]
[ "passage: TAGS\n#task_categories-text-classification #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follows:" ]
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fd3de8c7249a5ab97a1a8c45a0402abf961e4846
# Dataset Card for "Caltech101_with_background_test_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_with_background_test_embeddings
[ "region:us" ]
2023-01-29T01:34:32+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": 113162423.0, "num_examples": 6084}], "download_size": 116470550, "dataset_size": 113162423.0}}
2023-01-29T01:34:37+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_with_background_test_embeddings" More Information needed
[ "# Dataset Card for \"Caltech101_with_background_test_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_with_background_test_embeddings\"\n\nMore Information needed" ]
[ 6, 24 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_embeddings\"\n\nMore Information needed" ]
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f83d4fd1d8e9ade9e9acfd9c0d331d5127cd1061
# Dataset Card for "StanfordCars_test_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/StanfordCars_test_embeddings
[ "region:us" ]
2023-01-29T01:36:11+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": 1019088043.0, "num_examples": 8041}], "download_size": 1017679318, "dataset_size": 1019088043.0}}
2023-01-29T01:37:22+00:00
[]
[]
TAGS #region-us
# Dataset Card for "StanfordCars_test_embeddings" More Information needed
[ "# Dataset Card for \"StanfordCars_test_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"StanfordCars_test_embeddings\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"StanfordCars_test_embeddings\"\n\nMore Information needed" ]
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c2a41513721801698d7ef759e41d5ab99214d8ad
# Dataset Card for "FGVC_Aircraft_test_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/FGVC_Aircraft_test_embeddings
[ "region:us" ]
2023-01-29T01:38:31+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": 933154950.0, "num_examples": 3333}], "download_size": 935494333, "dataset_size": 933154950.0}}
2023-01-29T01:39:23+00:00
[]
[]
TAGS #region-us
# Dataset Card for "FGVC_Aircraft_test_embeddings" More Information needed
[ "# Dataset Card for \"FGVC_Aircraft_test_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"FGVC_Aircraft_test_embeddings\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_embeddings\"\n\nMore Information needed" ]
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5d216dc4020ec0738fc13c0b7d70def83e9f5d73
# Dataset Card for "Caltech101_with_background_train_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_with_background_train_embeddings
[ "region:us" ]
2023-01-29T01:43:28+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": 53675646.0, "num_examples": 3060}], "download_size": 55501321, "dataset_size": 53675646.0}}
2023-01-29T01:43:36+00:00
[]
[]
TAGS #region-us
# Dataset Card for "Caltech101_with_background_train_embeddings" More Information needed
[ "# Dataset Card for \"Caltech101_with_background_train_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"Caltech101_with_background_train_embeddings\"\n\nMore Information needed" ]
[ 6, 25 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_train_embeddings\"\n\nMore Information needed" ]
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941038fdcaf87bb03d1284bb238224ae1790619d
# Dataset Card for "StanfordCars_train_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/StanfordCars_train_embeddings
[ "region:us" ]
2023-01-29T01:44:51+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": 1020546140.0, "num_examples": 8144}], "download_size": 1019546810, "dataset_size": 1020546140.0}}
2023-01-29T01:45:29+00:00
[]
[]
TAGS #region-us
# Dataset Card for "StanfordCars_train_embeddings" More Information needed
[ "# Dataset Card for \"StanfordCars_train_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"StanfordCars_train_embeddings\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"StanfordCars_train_embeddings\"\n\nMore Information needed" ]
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5230e1a8fb200412b928312050c47e5f2ea35f0a
# Dataset Card for "FGVC_Aircraft_train_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/FGVC_Aircraft_train_embeddings
[ "region:us" ]
2023-01-29T01:46:39+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": 935571469.0, "num_examples": 3334}], "download_size": 937894510, "dataset_size": 935571469.0}}
2023-01-29T01:47:37+00:00
[]
[]
TAGS #region-us
# Dataset Card for "FGVC_Aircraft_train_embeddings" More Information needed
[ "# Dataset Card for \"FGVC_Aircraft_train_embeddings\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"FGVC_Aircraft_train_embeddings\"\n\nMore Information needed" ]
[ 6, 22 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_train_embeddings\"\n\nMore Information needed" ]
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2640b264e75badcdf998ed832acb96d1d0cb723d
111122223334444444444
Xieyiyiyi/ceshi0129
[ "license:bsl-1.0", "doi:10.57967/hf/0323", "region:us" ]
2023-01-29T02:23:25+00:00
{"license": "bsl-1.0"}
2023-02-14T01:13:47+00:00
[]
[]
TAGS #license-bsl-1.0 #doi-10.57967/hf/0323 #region-us
111122223334444444444
[]
[ "TAGS\n#license-bsl-1.0 #doi-10.57967/hf/0323 #region-us \n" ]
[ 26 ]
[ "passage: TAGS\n#license-bsl-1.0 #doi-10.57967/hf/0323 #region-us \n" ]
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6c876ee657072e677c8b95d5d4fdbbae37e3037f
# Dataset Card for "nllb-eng-tgl-600k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ramos-Ramos/nllb-eng-tgl-600k
[ "region:us" ]
2023-01-29T04:19:44+00:00
{"dataset_info": {"features": [{"name": "translation", "struct": [{"name": "eng_Latn", "dtype": "string"}, {"name": "tgl_Latn", "dtype": "string"}]}, {"name": "laser_score", "dtype": "float64"}, {"name": "source_sentence_lid", "dtype": "float64"}, {"name": "target_sentence_lid", "dtype": "float64"}, {"name": "source_sentence_source", "dtype": "string"}, {"name": "source_sentence_url", "dtype": "string"}, {"name": "target_sentence_source", "dtype": "string"}, {"name": "target_sentence_url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 331474940, "num_examples": 600000}], "download_size": 173139480, "dataset_size": 331474940}}
2023-01-29T04:19:52+00:00
[]
[]
TAGS #region-us
# Dataset Card for "nllb-eng-tgl-600k" More Information needed
[ "# Dataset Card for \"nllb-eng-tgl-600k\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"nllb-eng-tgl-600k\"\n\nMore Information needed" ]
[ 6, 21 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"nllb-eng-tgl-600k\"\n\nMore Information needed" ]
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e2559de8fa5637ae7092813d231fe3f753ecff7e
# Dataset Card for "DTD_parition1_test_facebook_opt_350m_Attributes_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_350m_Attributes_ns_1880
[ "region:us" ]
2023-01-29T05:58:25+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91667024.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92070924.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 92895981.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 93723680.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 94963882.0, "num_examples": 1880}], "download_size": 454688624, "dataset_size": 465321491.0}}
2023-01-29T08:46:25+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_350m_Attributes_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Attributes_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Attributes_ns_1880\"\n\nMore Information needed" ]
[ 6, 34 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Attributes_ns_1880\"\n\nMore Information needed" ]
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6f2e19004241a38b3d85c5f20dacd68ab86c1755
# Dataset Card for "DTD_parition1_test_facebook_opt_1.3b_Attributes_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_1.3b_Attributes_ns_1880
[ "region:us" ]
2023-01-29T06:01:03+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91664886.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92070929.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 92896001.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 93723698.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 94963960.0, "num_examples": 1880}], "download_size": 454688813, "dataset_size": 465319474.0}}
2023-01-29T08:52:44+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_1.3b_Attributes_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Attributes_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Attributes_ns_1880\"\n\nMore Information needed" ]
[ 6, 34 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Attributes_ns_1880\"\n\nMore Information needed" ]
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3e27cb37817ff8f69a03067c3b92c2c4a6498afa
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Attributes_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_2.7b_Attributes_ns_1880
[ "region:us" ]
2023-01-29T06:04:35+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91664330.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92070939.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 92895920.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 93723597.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 94963765.0, "num_examples": 1880}], "download_size": 454689738, "dataset_size": 465318551.0}}
2023-01-29T09:02:21+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Attributes_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Attributes_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Attributes_ns_1880\"\n\nMore Information needed" ]
[ 6, 35 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Attributes_ns_1880\"\n\nMore Information needed" ]
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44f790f77c414c939b323beb9df2ec8b5d00328e
# Dataset Card for "DTD_parition1_test_facebook_opt_350m_Attributes_Caption_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_350m_Attributes_Caption_ns_1880
[ "region:us" ]
2023-01-29T06:07:11+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91760302.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92256072.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93264952.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 94274000.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 95791819.0, "num_examples": 1880}], "download_size": 455213233, "dataset_size": 467347145.0}}
2023-01-29T09:06:53+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_350m_Attributes_Caption_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
[ 6, 37 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
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49f336e5ee867c46308840973d4c7dd2f7a12c4f
# Dataset Card for "DTD_parition1_test_facebook_opt_1.3b_Attributes_Caption_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_1.3b_Attributes_Caption_ns_1880
[ "region:us" ]
2023-01-29T06:10:05+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91757391.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92256063.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93264935.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 94273956.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 95791838.0, "num_examples": 1880}], "download_size": 455214193, "dataset_size": 467344183.0}}
2023-01-29T09:14:31+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_1.3b_Attributes_Caption_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
[ 6, 37 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
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a8e6748e5c67fc89a56afe537ebd7c9a591601c7
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Attributes_Caption_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_2.7b_Attributes_Caption_ns_1880
[ "region:us" ]
2023-01-29T06:14:01+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91756737.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92256089.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93264842.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 94273945.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 95791825.0, "num_examples": 1880}], "download_size": 455214836, "dataset_size": 467343438.0}}
2023-01-29T09:26:36+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Attributes_Caption_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
[ 6, 38 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Attributes_Caption_ns_1880\"\n\nMore Information needed" ]
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826cc5fb4c20896caee803130d7d79d227df002e
# Dataset Card for "DTD_parition1_test_facebook_opt_350m_Visclues_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_350m_Visclues_ns_1880
[ "region:us" ]
2023-01-29T06:16:33+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91911247.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92558193.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93868398.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 95179894.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 97151446.0, "num_examples": 1880}], "download_size": 456395457, "dataset_size": 470669178.0}}
2023-01-29T09:32:26+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_350m_Visclues_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Visclues_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Visclues_ns_1880\"\n\nMore Information needed" ]
[ 6, 34 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_350m_Visclues_ns_1880\"\n\nMore Information needed" ]
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d87aeb75a712c4e619c4c8ac9a8ec5662b61adda
# Dataset Card for "DTD_parition1_test_facebook_opt_1.3b_Visclues_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_1.3b_Visclues_ns_1880
[ "region:us" ]
2023-01-29T06:20:10+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91908245.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92558181.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93868412.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 95179877.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 97151294.0, "num_examples": 1880}], "download_size": 456395586, "dataset_size": 470666009.0}}
2023-01-29T09:42:57+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_1.3b_Visclues_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Visclues_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Visclues_ns_1880\"\n\nMore Information needed" ]
[ 6, 34 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_1.3b_Visclues_ns_1880\"\n\nMore Information needed" ]
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7f157596a6147d0f1227fdfd43b0b098fe93f93b
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880
[ "region:us" ]
2023-01-29T06:24:45+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 91908427.0, "num_examples": 1880}, {"name": "fewshot_1_bs_16", "num_bytes": 92558233.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93868356.0, "num_examples": 1880}, {"name": "fewshot_5_bs_16", "num_bytes": 95179914.0, "num_examples": 1880}, {"name": "fewshot_8_bs_16", "num_bytes": 97151425.0, "num_examples": 1880}], "download_size": 456396207, "dataset_size": 470666355.0}}
2023-02-18T00:16:16+00:00
[]
[]
TAGS #region-us
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880" More Information needed
[ "# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880\"\n\nMore Information needed" ]
[ 6, 35 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880\"\n\nMore Information needed" ]
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f3f61a87e0d4fe50b50dcbdc937b015aecda04a6
# Dataset for project: food-classification ## Dataset Description This dataset has been processed for project food-classification. ### 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 [ { "image": "<308x512 RGB PIL image>", "target": 0 }, { "image": "<512x512 RGB PIL image>", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['apple_pie', 'falafel', 'french_toast', 'ice_cream', 'ramen', 'sushi', 'tiramisu'], 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 | 1050 | | valid | 350 |
Kaludi/data-food-classification
[ "task_categories:image-classification", "region:us" ]
2023-01-29T06:49:56+00:00
{"task_categories": ["image-classification"]}
2023-01-29T19:34:43+00:00
[]
[]
TAGS #task_categories-image-classification #region-us
Dataset for project: food-classification ======================================== Dataset Description ------------------- This dataset has been processed for project food-classification. ### Languages The BCP-47 code for the dataset's language is unk. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:
[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ "TAGS\n#task_categories-image-classification #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
[ 17, 27, 17, 23, 27 ]
[ "passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
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55f5fb02401251e2783b0646f6aa276bb8ddd7c7
PNGからプロンプトとシードをとってきてグリッド作成
muchkanensys/PNG_Model_Grid_Script
[ "license:unlicense", "region:us" ]
2023-01-29T09:00:00+00:00
{"license": "unlicense"}
2023-01-29T09:04:44+00:00
[]
[]
TAGS #license-unlicense #region-us
PNGからプロンプトとシードをとってきてグリッド作成
[]
[ "TAGS\n#license-unlicense #region-us \n" ]
[ 13 ]
[ "passage: TAGS\n#license-unlicense #region-us \n" ]
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f6b8406c123b081967415266424e57d76d9f2a47
The Metafont-Glyph dataset (aka "Meggy" dataset) is a collection of glyph images generated from random metafont scripts. The main focus of this dataset is to help in construction the mappings between glyph images and metafont scripts by data-driven method. The ultimate goal is to develop models that can generate accurate geometric descriptions of glyphs from ancient, non-standardized characters, which can then be used in typesetting systems such as Metafont and Tex. The target is a different problem from OCR, which is focused on recognizing and transcribing written text from images.
mingli/metafont-glyph
[ "size_categories:10K<n<100K", "license:cc0-1.0", "metafont", "glyph", "region:us" ]
2023-01-29T09:00:45+00:00
{"license": "cc0-1.0", "size_categories": ["10K<n<100K"], "pretty_name": "Meggy", "tags": ["metafont", "glyph"]}
2023-01-29T09:40:29+00:00
[]
[]
TAGS #size_categories-10K<n<100K #license-cc0-1.0 #metafont #glyph #region-us
The Metafont-Glyph dataset (aka "Meggy" dataset) is a collection of glyph images generated from random metafont scripts. The main focus of this dataset is to help in construction the mappings between glyph images and metafont scripts by data-driven method. The ultimate goal is to develop models that can generate accurate geometric descriptions of glyphs from ancient, non-standardized characters, which can then be used in typesetting systems such as Metafont and Tex. The target is a different problem from OCR, which is focused on recognizing and transcribing written text from images.
[]
[ "TAGS\n#size_categories-10K<n<100K #license-cc0-1.0 #metafont #glyph #region-us \n" ]
[ 32 ]
[ "passage: TAGS\n#size_categories-10K<n<100K #license-cc0-1.0 #metafont #glyph #region-us \n" ]
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9d5fc5b53bae7dc142bd27ae6724d12a4e1078bb
# b0o
b0o/b0o
[ "license:mit", "region:us" ]
2023-01-29T09:18:21+00:00
{"license": "mit"}
2023-01-29T09:19:37+00:00
[]
[]
TAGS #license-mit #region-us
# b0o
[ "# b0o" ]
[ "TAGS\n#license-mit #region-us \n", "# b0o" ]
[ 11, 4 ]
[ "passage: TAGS\n#license-mit #region-us \n# b0o" ]
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2d5d08eb9132d7964a733bcb43d2f4a3a48947c1
# Dataset Card for "dreambooth-nilesh-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nileshpp/dreambooth-nilesh-images
[ "region:us" ]
2023-01-29T10:30:36+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1566455.0, "num_examples": 3}], "download_size": 1567537, "dataset_size": 1566455.0}}
2023-01-29T10:30:40+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dreambooth-nilesh-images" More Information needed
[ "# Dataset Card for \"dreambooth-nilesh-images\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-nilesh-images\"\n\nMore Information needed" ]
[ 6, 19 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth-nilesh-images\"\n\nMore Information needed" ]
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5f14fcee2256643e046fcca4680adb3806740e2c
# Dataset Card for "dreambooth-nilesh-images-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nileshpp/dreambooth-nilesh-images-1
[ "region:us" ]
2023-01-29T10:34:08+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 426098.0, "num_examples": 3}], "download_size": 427109, "dataset_size": 426098.0}}
2023-01-29T10:34:11+00:00
[]
[]
TAGS #region-us
# Dataset Card for "dreambooth-nilesh-images-1" More Information needed
[ "# Dataset Card for \"dreambooth-nilesh-images-1\"\n\nMore Information needed" ]
[ "TAGS\n#region-us \n", "# Dataset Card for \"dreambooth-nilesh-images-1\"\n\nMore Information needed" ]
[ 6, 20 ]
[ "passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth-nilesh-images-1\"\n\nMore Information needed" ]
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