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1699e25f5d33594be8ee808f6a770c24ebf08664 | # Dataset Card for "word_label_0.8_96_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_96_D | [
"region:us"
]
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| # Dataset Card for "word_label_0.8_96_D"
More Information needed | [
"# Dataset Card for \"word_label_0.8_96_D\"\n\nMore Information needed"
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|
9d242fa42aa0271b3705a3c6606ae3b1dd528f08 | # Dataset Card for "word_mask_Nf_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_Nf_64 | [
"region:us"
]
| 2023-03-04T00:15:05+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15802296.87628831, "num_examples": 105836}, {"name": "validation", "num_bytes": 1755877.1237116908, "num_examples": 11760}], "download_size": 12898256, "dataset_size": 17558174.0}} | 2023-03-04T00:38:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_Nf_64"
More Information needed | [
"# Dataset Card for \"word_mask_Nf_64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_Nf_64\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_Nf_64\"\n\nMore Information needed"
]
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|
be5862e5cb495891b765ae7dbbc4f2bb39f62d1f | # Dataset Card for "word_label_0.8_16_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_16_P | [
"region:us"
]
| 2023-03-04T00:15:06+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8537417.368139727, "num_examples": 47818}, {"name": "validation", "num_bytes": 948760.6318602725, "num_examples": 5314}], "download_size": 2471753, "dataset_size": 9486178.0}} | 2023-03-04T00:37:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_16_P"
More Information needed | [
"# Dataset Card for \"word_label_0.8_16_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_16_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_16_P\"\n\nMore Information needed"
]
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08e99cccdcc855835feeaf9db4f323455bc0900e | # Dataset Card for "word_label_0.5_32_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_32_P | [
"region:us"
]
| 2023-03-04T00:15:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21926189.645938054, "num_examples": 64099}, {"name": "validation", "num_bytes": 2436547.354061947, "num_examples": 7123}], "download_size": 5756106, "dataset_size": 24362737.0}} | 2023-03-04T00:37:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_32_P"
More Information needed | [
"# Dataset Card for \"word_label_0.5_32_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_32_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_32_P\"\n\nMore Information needed"
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|
e1b19da9f4d93b396c704913aceae1af143085ed | # Dataset Card for "word_label_0.8_64_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_64_D | [
"region:us"
]
| 2023-03-04T00:15:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 44508632.83413558, "num_examples": 71798}, {"name": "validation", "num_bytes": 4945679.16586442, "num_examples": 7978}], "download_size": 8657975, "dataset_size": 49454312.0}} | 2023-03-04T00:39:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_64_D"
More Information needed | [
"# Dataset Card for \"word_label_0.8_64_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_64_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_64_D\"\n\nMore Information needed"
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|
ed9de5f2dde584016c6195e82aa33de6acadb913 | # Dataset Card for "word_label_0.2_64_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_64_Nf | [
"region:us"
]
| 2023-03-04T00:15:26+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 44525516.432587914, "num_examples": 70712}, {"name": "validation", "num_bytes": 4947349.567412084, "num_examples": 7857}], "download_size": 8983283, "dataset_size": 49472866.0}} | 2023-03-04T00:39:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_64_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.2_64_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_64_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_64_Nf\"\n\nMore Information needed"
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c93051cd327cd3c84686c3772c3c3a70216a96cf | # Dataset Card for "word_label_0.2_16_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_16_P | [
"region:us"
]
| 2023-03-04T00:15:40+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8720066.7, "num_examples": 48906}, {"name": "validation", "num_bytes": 968896.3, "num_examples": 5434}], "download_size": 2525995, "dataset_size": 9688963.0}} | 2023-03-04T00:38:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_16_P"
More Information needed | [
"# Dataset Card for \"word_label_0.2_16_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_16_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_16_P\"\n\nMore Information needed"
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|
4caebd37f3764011d84d1f672a0ebfda61f620f8 | # Dataset Card for "word_label_0.2_32_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_32_D | [
"region:us"
]
| 2023-03-04T00:15:41+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 23006271.472732726, "num_examples": 69000}, {"name": "validation", "num_bytes": 2556363.5272672726, "num_examples": 7667}], "download_size": 5639857, "dataset_size": 25562635.0}} | 2023-03-04T00:40:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_32_D"
More Information needed | [
"# Dataset Card for \"word_label_0.2_32_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_32_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_32_D\"\n\nMore Information needed"
]
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|
6ea77bc309a3813b3e01c88db95814d1ba5dda30 | # Dataset Card for "word_label_0.5_96_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_96_Nf | [
"region:us"
]
| 2023-03-04T00:15:45+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}, {"name": "72", "dtype": "int64"}, {"name": "73", "dtype": "int64"}, {"name": "74", "dtype": "int64"}, {"name": "75", "dtype": "int64"}, {"name": "76", "dtype": "int64"}, {"name": "77", "dtype": "int64"}, {"name": "78", "dtype": "int64"}, {"name": "79", "dtype": "int64"}, {"name": "80", "dtype": "int64"}, {"name": "81", "dtype": "int64"}, {"name": "82", "dtype": "int64"}, {"name": "83", "dtype": "int64"}, {"name": "84", "dtype": "int64"}, {"name": "85", "dtype": "int64"}, {"name": "86", "dtype": "int64"}, {"name": "87", "dtype": "int64"}, {"name": "88", "dtype": "int64"}, {"name": "89", "dtype": "int64"}, {"name": "90", "dtype": "int64"}, {"name": "91", "dtype": "int64"}, {"name": "92", "dtype": "int64"}, {"name": "93", "dtype": "int64"}, {"name": "94", "dtype": "int64"}, {"name": "95", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 64474102.8, "num_examples": 71730}, {"name": "validation", "num_bytes": 7163789.2, "num_examples": 7970}], "download_size": 10691776, "dataset_size": 71637892.0}} | 2023-03-04T00:40:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_96_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.5_96_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_96_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_96_Nf\"\n\nMore Information needed"
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|
5e2075c3f6b1e5db4891b2d0b08a31cfc2933d85 | # Dataset Card for "word_mask_D_72"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_D_72 | [
"region:us"
]
| 2023-03-04T00:16:00+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32554740.882045604, "num_examples": 201540}, {"name": "validation", "num_bytes": 3617301.117954397, "num_examples": 22394}], "download_size": 26263345, "dataset_size": 36172042.0}} | 2023-03-04T00:40:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_D_72"
More Information needed | [
"# Dataset Card for \"word_mask_D_72\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_D_72\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_D_72\"\n\nMore Information needed"
]
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|
c566d3504997d011761d7b1df62bba14e4889d9e | # Dataset Card for "word_mask_P_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_P_16 | [
"region:us"
]
| 2023-03-04T00:16:02+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2918273.4578115, "num_examples": 49225}, {"name": "validation", "num_bytes": 324285.5421884999, "num_examples": 5470}], "download_size": 2455038, "dataset_size": 3242559.0}} | 2023-03-04T00:38:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_P_16"
More Information needed | [
"# Dataset Card for \"word_mask_P_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_P_16\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_P_16\"\n\nMore Information needed"
]
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6c2636436a82af89c44ee53b19534d25d8d40830 | # Dataset Card for "word_label_0.5_64_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_64_Nf | [
"region:us"
]
| 2023-03-04T00:16:04+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 44587747.16477193, "num_examples": 70695}, {"name": "validation", "num_bytes": 4954824.835228069, "num_examples": 7856}], "download_size": 9038324, "dataset_size": 49542572.0}} | 2023-03-04T00:41:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_64_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.5_64_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_64_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_64_Nf\"\n\nMore Information needed"
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|
cc36c8a18e912a084892972005d40147ed8d4a1c | # Dataset Card for "word_label_0.2_16_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_16_D | [
"region:us"
]
| 2023-03-04T00:16:24+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 10126017.389534974, "num_examples": 57500}, {"name": "validation", "num_bytes": 1125132.6104650253, "num_examples": 6389}], "download_size": 2846406, "dataset_size": 11251150.0}} | 2023-03-04T00:41:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_16_D"
More Information needed | [
"# Dataset Card for \"word_label_0.2_16_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_16_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_16_D\"\n\nMore Information needed"
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|
41349b66ed859975ba20cd7c637864c749b2ae1b | # Dataset Card for "word_label_0.5_64_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_64_P | [
"region:us"
]
| 2023-03-04T00:16:25+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 44942972.28061179, "num_examples": 70906}, {"name": "validation", "num_bytes": 4994015.719388208, "num_examples": 7879}], "download_size": 9294335, "dataset_size": 49936988.0}} | 2023-03-04T00:39:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_64_P"
More Information needed | [
"# Dataset Card for \"word_label_0.5_64_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_64_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_64_P\"\n\nMore Information needed"
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|
1d8f298f1ee688aabb4f1387956e3681a493e933 | # Dataset Card for "word_mask_Nf_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_Nf_96 | [
"region:us"
]
| 2023-03-04T00:16:33+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19890104.90254197, "num_examples": 113014}, {"name": "validation", "num_bytes": 2210168.097458032, "num_examples": 12558}], "download_size": 16204975, "dataset_size": 22100273.0}} | 2023-03-04T00:42:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_Nf_96"
More Information needed | [
"# Dataset Card for \"word_mask_Nf_96\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_Nf_96\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_Nf_96\"\n\nMore Information needed"
]
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|
f90720e05712f5e36032380f0417f9ee82067c02 | # Dataset Card for "word_label_0.2_96_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_96_D | [
"region:us"
]
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| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_96_D"
More Information needed | [
"# Dataset Card for \"word_label_0.2_96_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_96_D\"\n\nMore Information needed"
]
| [
6,
19
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_96_D\"\n\nMore Information needed"
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|
6b5d6a264c103c737755aa4a08c591d72e7b007d | # Dataset Card for "word_mask_P_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_P_96 | [
"region:us"
]
| 2023-03-04T00:16:43+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20759815.180721488, "num_examples": 115935}, {"name": "validation", "num_bytes": 2306705.819278511, "num_examples": 12882}], "download_size": 17151231, "dataset_size": 23066521.0}} | 2023-03-04T00:39:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_P_96"
More Information needed | [
"# Dataset Card for \"word_mask_P_96\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_P_96\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_P_96\"\n\nMore Information needed"
]
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|
45c2b58829598af5768ac38a24d566cd073924a6 | # Dataset Card for "word_label_0.5_32_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_32_Nf | [
"region:us"
]
| 2023-03-04T00:16:54+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21553433.626321174, "num_examples": 63352}, {"name": "validation", "num_bytes": 2395128.373678827, "num_examples": 7040}], "download_size": 5546282, "dataset_size": 23948562.0}} | 2023-03-04T00:42:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_32_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.5_32_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_32_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_32_Nf\"\n\nMore Information needed"
]
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|
e75660dd4904744a360a4ba9b7a5238c9571deec | # Dataset Card for "word_mask_D_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_D_16 | [
"region:us"
]
| 2023-03-04T00:17:00+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4642799.4, "num_examples": 78957}, {"name": "validation", "num_bytes": 515866.6, "num_examples": 8773}], "download_size": 3864497, "dataset_size": 5158666.0}} | 2023-03-04T00:43:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_D_16"
More Information needed | [
"# Dataset Card for \"word_mask_D_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_D_16\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_D_16\"\n\nMore Information needed"
]
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|
06bab678adc179e727fdf4a33dd49030f938d8eb | # Dataset Card for "word_mask_P_72"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_P_72 | [
"region:us"
]
| 2023-03-04T00:17:04+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17895491.331517126, "num_examples": 111330}, {"name": "validation", "num_bytes": 1988548.668482874, "num_examples": 12371}], "download_size": 14861144, "dataset_size": 19884040.0}} | 2023-03-04T00:40:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_P_72"
More Information needed | [
"# Dataset Card for \"word_mask_P_72\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_P_72\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_P_72\"\n\nMore Information needed"
]
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|
0bc3578165fa7370f4097e2dd6eb97804a29e108 | # Dataset Card for "word_label_0.5_16_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_16_Nf | [
"region:us"
]
| 2023-03-04T00:17:12+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8448928.23074444, "num_examples": 47475}, {"name": "validation", "num_bytes": 938947.7692555592, "num_examples": 5276}], "download_size": 2408819, "dataset_size": 9387876.0}} | 2023-03-04T00:43:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_16_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.5_16_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_16_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_16_Nf\"\n\nMore Information needed"
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|
67ed1c171867060f21d6dab245767ab83095f56a | # Dataset Card for "word_label_0.5_96_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_96_D | [
"region:us"
]
| 2023-03-04T00:17:19+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}, {"name": "72", "dtype": "int64"}, {"name": "73", "dtype": "int64"}, {"name": "74", "dtype": "int64"}, {"name": "75", "dtype": "int64"}, {"name": "76", "dtype": "int64"}, {"name": "77", "dtype": "int64"}, {"name": "78", "dtype": "int64"}, {"name": "79", "dtype": "int64"}, {"name": "80", "dtype": "int64"}, {"name": "81", "dtype": "int64"}, {"name": "82", "dtype": "int64"}, {"name": "83", "dtype": "int64"}, {"name": "84", "dtype": "int64"}, {"name": "85", "dtype": "int64"}, {"name": "86", "dtype": "int64"}, {"name": "87", "dtype": "int64"}, {"name": "88", "dtype": "int64"}, {"name": "89", "dtype": "int64"}, {"name": "90", "dtype": "int64"}, {"name": "91", "dtype": "int64"}, {"name": "92", "dtype": "int64"}, {"name": "93", "dtype": "int64"}, {"name": "94", "dtype": "int64"}, {"name": "95", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 63418468.38393638, "num_examples": 71983}, {"name": "validation", "num_bytes": 7047279.616063614, "num_examples": 7999}], "download_size": 9813776, "dataset_size": 70465748.0}} | 2023-03-04T00:44:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_96_D"
More Information needed | [
"# Dataset Card for \"word_label_0.5_96_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_96_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_96_D\"\n\nMore Information needed"
]
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1b1bb966cc7944263398074f4bdda417db25dc55 | # Dataset Card for "word_label_0.5_72_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_72_P | [
"region:us"
]
| 2023-03-04T00:17:26+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 50089342.41041301, "num_examples": 71322}, {"name": "validation", "num_bytes": 5565716.589586987, "num_examples": 7925}], "download_size": 9803275, "dataset_size": 55655059.0}} | 2023-03-04T00:41:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_72_P"
More Information needed | [
"# Dataset Card for \"word_label_0.5_72_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_72_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_72_P\"\n\nMore Information needed"
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|
66ad132b6410fc181d1610064af0ae881034d073 | # Dataset Card for "word_mask_Nf_72"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_Nf_72 | [
"region:us"
]
| 2023-03-04T00:17:30+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17106827.259037044, "num_examples": 108453}, {"name": "validation", "num_bytes": 1900863.7409629556, "num_examples": 12051}], "download_size": 13981369, "dataset_size": 19007691.0}} | 2023-03-04T00:43:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_Nf_72"
More Information needed | [
"# Dataset Card for \"word_mask_Nf_72\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_Nf_72\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_Nf_72\"\n\nMore Information needed"
]
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|
f6de0799f573118b7b88d60b685ff9888346ea29 | # Dataset Card for "word_mask_D_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_D_96 | [
"region:us"
]
| 2023-03-04T00:17:38+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38866820.726691455, "num_examples": 212542}, {"name": "validation", "num_bytes": 4318576.273308547, "num_examples": 23616}], "download_size": 31126765, "dataset_size": 43185397.0}} | 2023-03-04T00:45:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_D_96"
More Information needed | [
"# Dataset Card for \"word_mask_D_96\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_D_96\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_D_96\"\n\nMore Information needed"
]
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|
791652a09b7ed16eb1c718bf425f97ff7c8acf4d | # Dataset Card for "word_mask_P_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_P_32 | [
"region:us"
]
| 2023-03-04T00:17:44+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8229804.640037594, "num_examples": 82353}, {"name": "validation", "num_bytes": 914489.359962406, "num_examples": 9151}], "download_size": 6978927, "dataset_size": 9144294.0}} | 2023-03-04T00:41:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_P_32"
More Information needed | [
"# Dataset Card for \"word_mask_P_32\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_P_32\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_P_32\"\n\nMore Information needed"
]
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|
05501f7c84a44e3645adf0242864598453ada0f1 | # Dataset Card for "word_mask_Nf_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_Nf_16 | [
"region:us"
]
| 2023-03-04T00:17:51+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2787205.2620883104, "num_examples": 47165}, {"name": "validation", "num_bytes": 309715.7379116895, "num_examples": 5241}], "download_size": 2280457, "dataset_size": 3096921.0}} | 2023-03-04T00:44:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_Nf_16"
More Information needed | [
"# Dataset Card for \"word_mask_Nf_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_Nf_16\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_Nf_16\"\n\nMore Information needed"
]
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|
80b99a7f81b882fe773e852a63c7bc4290737ee8 | # Dataset Card for "word_label_0.2_32_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_32_P | [
"region:us"
]
| 2023-03-04T00:18:03+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21951104.611497793, "num_examples": 64264}, {"name": "validation", "num_bytes": 2439201.3885022057, "num_examples": 7141}], "download_size": 5729483, "dataset_size": 24390306.0}} | 2023-03-04T00:42:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_32_P"
More Information needed | [
"# Dataset Card for \"word_label_0.2_32_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_32_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_32_P\"\n\nMore Information needed"
]
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|
f11ac4879cac6dbb52172ed71fc6c1f8f1ef2c13 | # Dataset Card for "word_label_0.5_16_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_16_D | [
"region:us"
]
| 2023-03-04T00:18:04+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 10024350.095533932, "num_examples": 56788}, {"name": "validation", "num_bytes": 1113855.9044660686, "num_examples": 6310}], "download_size": 2835086, "dataset_size": 11138206.0}} | 2023-03-04T00:45:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_16_D"
More Information needed | [
"# Dataset Card for \"word_label_0.5_16_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_16_D\"\n\nMore Information needed"
]
| [
6,
19
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_16_D\"\n\nMore Information needed"
]
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|
d6594f12c191f301e18743f6849600c2838216dc | # Dataset Card for "word_label_0.2_16_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_16_Nf | [
"region:us"
]
| 2023-03-04T00:18:08+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8529329.417991105, "num_examples": 47963}, {"name": "validation", "num_bytes": 947841.5820088942, "num_examples": 5330}], "download_size": 2426819, "dataset_size": 9477171.0}} | 2023-03-04T00:44:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_16_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.2_16_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_16_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_16_Nf\"\n\nMore Information needed"
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|
5707c6bf18d6198ac835c827e357c18cef30bf63 | # Dataset Card for "word_label_0.5_96_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_96_P | [
"region:us"
]
| 2023-03-04T00:18:22+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}, {"name": "72", "dtype": "int64"}, {"name": "73", "dtype": "int64"}, {"name": "74", "dtype": "int64"}, {"name": "75", "dtype": "int64"}, {"name": "76", "dtype": "int64"}, {"name": "77", "dtype": "int64"}, {"name": "78", "dtype": "int64"}, {"name": "79", "dtype": "int64"}, {"name": "80", "dtype": "int64"}, {"name": "81", "dtype": "int64"}, {"name": "82", "dtype": "int64"}, {"name": "83", "dtype": "int64"}, {"name": "84", "dtype": "int64"}, {"name": "85", "dtype": "int64"}, {"name": "86", "dtype": "int64"}, {"name": "87", "dtype": "int64"}, {"name": "88", "dtype": "int64"}, {"name": "89", "dtype": "int64"}, {"name": "90", "dtype": "int64"}, {"name": "91", "dtype": "int64"}, {"name": "92", "dtype": "int64"}, {"name": "93", "dtype": "int64"}, {"name": "94", "dtype": "int64"}, {"name": "95", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 64790598.85112729, "num_examples": 71813}, {"name": "validation", "num_bytes": 7199657.148872708, "num_examples": 7980}], "download_size": 10959546, "dataset_size": 71990256.0}} | 2023-03-04T00:43:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_96_P"
More Information needed | [
"# Dataset Card for \"word_label_0.5_96_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_96_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_96_P\"\n\nMore Information needed"
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|
f34c3b7af680e4b419eacfe9d4883f832894eba6 | # Dataset Card for "word_label_0.5_72_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_72_Nf | [
"region:us"
]
| 2023-03-04T00:18:26+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 49714268.394184045, "num_examples": 71121}, {"name": "validation", "num_bytes": 5524273.6058159545, "num_examples": 7903}], "download_size": 9563013, "dataset_size": 55238542.0}} | 2023-03-04T00:45:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_72_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.5_72_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_72_Nf\"\n\nMore Information needed"
]
| [
6,
20
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_72_Nf\"\n\nMore Information needed"
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0345e5608687abcdbd51fc207202c33543044605 | # Dataset Card for "word_label_0.5_64_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_64_D | [
"region:us"
]
| 2023-03-04T00:18:28+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 44319033.9, "num_examples": 71802}, {"name": "validation", "num_bytes": 4924337.1, "num_examples": 7978}], "download_size": 8494767, "dataset_size": 49243371.0}} | 2023-03-04T00:46:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_64_D"
More Information needed | [
"# Dataset Card for \"word_label_0.5_64_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_64_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_64_D\"\n\nMore Information needed"
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4be940c5abb9fe7fca1f8293610d7fbe7f5cab73 | # Dataset Card for "word_label_0.8_64_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_64_P | [
"region:us"
]
| 2023-03-04T00:18:40+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 45010693.21886069, "num_examples": 70890}, {"name": "validation", "num_bytes": 5001399.7811393095, "num_examples": 7877}], "download_size": 9340990, "dataset_size": 50012093.0}} | 2023-03-04T00:44:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_64_P"
More Information needed | [
"# Dataset Card for \"word_label_0.8_64_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_64_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_64_P\"\n\nMore Information needed"
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2e8827821aa1609a17959579bcd8f7964fbdfc8b | # Dataset Card for "word_label_0.2_72_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_72_Nf | [
"region:us"
]
| 2023-03-04T00:18:45+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 49645913.488746345, "num_examples": 71136}, {"name": "validation", "num_bytes": 5516910.511253653, "num_examples": 7905}], "download_size": 9498163, "dataset_size": 55162824.0}} | 2023-03-04T00:46:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_72_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.2_72_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_72_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_72_Nf\"\n\nMore Information needed"
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|
6dcc280b258daf869aa0a95e56bf2c87eb93a6e6 | # Dataset Card for "word_label_0.2_64_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_64_D | [
"region:us"
]
| 2023-03-04T00:18:46+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 44130140.78143172, "num_examples": 71812}, {"name": "validation", "num_bytes": 4903895.218568278, "num_examples": 7980}], "download_size": 8294781, "dataset_size": 49034036.0}} | 2023-03-04T00:47:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_64_D"
More Information needed | [
"# Dataset Card for \"word_label_0.2_64_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_64_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_64_D\"\n\nMore Information needed"
]
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|
5970271a96d36ba1a29630be0664f23fffc6c943 | # Dataset Card for "word_label_0.2_96_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_96_P | [
"region:us"
]
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| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_96_P"
More Information needed | [
"# Dataset Card for \"word_label_0.2_96_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_96_P\"\n\nMore Information needed"
]
| [
6,
19
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_96_P\"\n\nMore Information needed"
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|
c45e9d7e8370c41c2ca3a2f6d6719bd7b1660eba | # Dataset Card for "word_label_0.8_64_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_64_Nf | [
"region:us"
]
| 2023-03-04T00:19:04+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 44644652.50552712, "num_examples": 70669}, {"name": "validation", "num_bytes": 4961078.494472886, "num_examples": 7853}], "download_size": 9082447, "dataset_size": 49605731.0}} | 2023-03-04T00:48:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_64_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.8_64_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_64_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_64_Nf\"\n\nMore Information needed"
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|
6aef908863db6a1b681b91a233958a083a1168ce | # Dataset Card for "word_mask_D_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_D_64 | [
"region:us"
]
| 2023-03-04T00:19:11+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29674460.920895755, "num_examples": 195361}, {"name": "validation", "num_bytes": 3297196.079104244, "num_examples": 21707}], "download_size": 23978359, "dataset_size": 32971657.0}} | 2023-03-04T00:48:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_D_64"
More Information needed | [
"# Dataset Card for \"word_mask_D_64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_D_64\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_D_64\"\n\nMore Information needed"
]
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d72c57e35752ad1b844b4c92e630ab331da67b6a | # Dataset Card for "word_label_0.2_72_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_72_P | [
"region:us"
]
| 2023-03-04T00:19:17+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 50006440.62244486, "num_examples": 71326}, {"name": "validation", "num_bytes": 5556894.37755514, "num_examples": 7926}], "download_size": 9720462, "dataset_size": 55563335.0}} | 2023-03-04T00:46:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_72_P"
More Information needed | [
"# Dataset Card for \"word_label_0.2_72_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_72_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_72_P\"\n\nMore Information needed"
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|
06112725860f6d87ae8d00d9be1bf1372e64d210 | # Dataset Card for "word_label_0.2_32_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_32_Nf | [
"region:us"
]
| 2023-03-04T00:19:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21579383.25483862, "num_examples": 63520}, {"name": "validation", "num_bytes": 2397784.7451613816, "num_examples": 7058}], "download_size": 5532335, "dataset_size": 23977168.0}} | 2023-03-04T00:48:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_32_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.2_32_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_32_Nf\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_32_Nf\"\n\nMore Information needed"
]
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|
3c735207ff1ce283e2460e12b005ae05e4604ef1 | # Dataset Card for "word_label_0.8_16_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_16_D | [
"region:us"
]
| 2023-03-04T00:19:34+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9930482.737176301, "num_examples": 56128}, {"name": "validation", "num_bytes": 1103485.2628236993, "num_examples": 6237}], "download_size": 2816557, "dataset_size": 11033968.0}} | 2023-03-04T00:48:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_16_D"
More Information needed | [
"# Dataset Card for \"word_label_0.8_16_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_16_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_16_D\"\n\nMore Information needed"
]
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ab16446a3e88f6423eb64c7148b8852a3733584a | # Dataset Card for "word_label_0.5_16_P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.5_16_P | [
"region:us"
]
| 2023-03-04T00:19:41+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8626248.157635102, "num_examples": 48347}, {"name": "validation", "num_bytes": 958491.8423648989, "num_examples": 5372}], "download_size": 2504451, "dataset_size": 9584740.0}} | 2023-03-04T00:47:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.5_16_P"
More Information needed | [
"# Dataset Card for \"word_label_0.5_16_P\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.5_16_P\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.5_16_P\"\n\nMore Information needed"
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|
c26557e38db2f60b04b24df7806760d506acceb4 | # Dataset Card for "word_label_0.2_96_Nf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.2_96_Nf | [
"region:us"
]
| 2023-03-04T00:19:41+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}, {"name": "72", "dtype": "int64"}, {"name": "73", "dtype": "int64"}, {"name": "74", "dtype": "int64"}, {"name": "75", "dtype": "int64"}, {"name": "76", "dtype": "int64"}, {"name": "77", "dtype": "int64"}, {"name": "78", "dtype": "int64"}, {"name": "79", "dtype": "int64"}, {"name": "80", "dtype": "int64"}, {"name": "81", "dtype": "int64"}, {"name": "82", "dtype": "int64"}, {"name": "83", "dtype": "int64"}, {"name": "84", "dtype": "int64"}, {"name": "85", "dtype": "int64"}, {"name": "86", "dtype": "int64"}, {"name": "87", "dtype": "int64"}, {"name": "88", "dtype": "int64"}, {"name": "89", "dtype": "int64"}, {"name": "90", "dtype": "int64"}, {"name": "91", "dtype": "int64"}, {"name": "92", "dtype": "int64"}, {"name": "93", "dtype": "int64"}, {"name": "94", "dtype": "int64"}, {"name": "95", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 64386035.41674926, "num_examples": 71734}, {"name": "validation", "num_bytes": 7154502.583250737, "num_examples": 7971}], "download_size": 10608080, "dataset_size": 71540538.0}} | 2023-03-04T00:50:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.2_96_Nf"
More Information needed | [
"# Dataset Card for \"word_label_0.2_96_Nf\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.2_96_Nf\"\n\nMore Information needed"
]
| [
6,
20
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.2_96_Nf\"\n\nMore Information needed"
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|
c46ce7ba82debc16a156965cdbf5793532fac1c3 | # Dataset Card for "word_mask_D_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_D_32 | [
"region:us"
]
| 2023-03-04T00:19:51+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13882115.11177655, "num_examples": 141711}, {"name": "validation", "num_bytes": 1542489.8882234516, "num_examples": 15746}], "download_size": 11544184, "dataset_size": 15424605.0}} | 2023-03-04T00:48:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_D_32"
More Information needed | [
"# Dataset Card for \"word_mask_D_32\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_D_32\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_D_32\"\n\nMore Information needed"
]
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|
e3335776b2d920562b13ba727e3068dc45c8447a | # Dataset Card for "word_mask_P_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_mask_P_64 | [
"region:us"
]
| 2023-03-04T00:20:03+00:00 | {"dataset_info": {"features": [{"name": "feature", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16510022.843358314, "num_examples": 108568}, {"name": "validation", "num_bytes": 1834582.156641687, "num_examples": 12064}], "download_size": 13707041, "dataset_size": 18344605.0}} | 2023-03-04T00:47:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_mask_P_64"
More Information needed | [
"# Dataset Card for \"word_mask_P_64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_mask_P_64\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_mask_P_64\"\n\nMore Information needed"
]
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|
d7b1d2a3c55ddddb694569e31bb69dcaf89c755a | # Dataset Card for "word_label_0.8_72_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuan-sf63/word_label_0.8_72_D | [
"region:us"
]
| 2023-03-04T00:20:12+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "0", "dtype": "int64"}, {"name": "1", "dtype": "int64"}, {"name": "2", "dtype": "int64"}, {"name": "3", "dtype": "int64"}, {"name": "4", "dtype": "int64"}, {"name": "5", "dtype": "int64"}, {"name": "6", "dtype": "int64"}, {"name": "7", "dtype": "int64"}, {"name": "8", "dtype": "int64"}, {"name": "9", "dtype": "int64"}, {"name": "10", "dtype": "int64"}, {"name": "11", "dtype": "int64"}, {"name": "12", "dtype": "int64"}, {"name": "13", "dtype": "int64"}, {"name": "14", "dtype": "int64"}, {"name": "15", "dtype": "int64"}, {"name": "16", "dtype": "int64"}, {"name": "17", "dtype": "int64"}, {"name": "18", "dtype": "int64"}, {"name": "19", "dtype": "int64"}, {"name": "20", "dtype": "int64"}, {"name": "21", "dtype": "int64"}, {"name": "22", "dtype": "int64"}, {"name": "23", "dtype": "int64"}, {"name": "24", "dtype": "int64"}, {"name": "25", "dtype": "int64"}, {"name": "26", "dtype": "int64"}, {"name": "27", "dtype": "int64"}, {"name": "28", "dtype": "int64"}, {"name": "29", "dtype": "int64"}, {"name": "30", "dtype": "int64"}, {"name": "31", "dtype": "int64"}, {"name": "32", "dtype": "int64"}, {"name": "33", "dtype": "int64"}, {"name": "34", "dtype": "int64"}, {"name": "35", "dtype": "int64"}, {"name": "36", "dtype": "int64"}, {"name": "37", "dtype": "int64"}, {"name": "38", "dtype": "int64"}, {"name": "39", "dtype": "int64"}, {"name": "40", "dtype": "int64"}, {"name": "41", "dtype": "int64"}, {"name": "42", "dtype": "int64"}, {"name": "43", "dtype": "int64"}, {"name": "44", "dtype": "int64"}, {"name": "45", "dtype": "int64"}, {"name": "46", "dtype": "int64"}, {"name": "47", "dtype": "int64"}, {"name": "48", "dtype": "int64"}, {"name": "49", "dtype": "int64"}, {"name": "50", "dtype": "int64"}, {"name": "51", "dtype": "int64"}, {"name": "52", "dtype": "int64"}, {"name": "53", "dtype": "int64"}, {"name": "54", "dtype": "int64"}, {"name": "55", "dtype": "int64"}, {"name": "56", "dtype": "int64"}, {"name": "57", "dtype": "int64"}, {"name": "58", "dtype": "int64"}, {"name": "59", "dtype": "int64"}, {"name": "60", "dtype": "int64"}, {"name": "61", "dtype": "int64"}, {"name": "62", "dtype": "int64"}, {"name": "63", "dtype": "int64"}, {"name": "64", "dtype": "int64"}, {"name": "65", "dtype": "int64"}, {"name": "66", "dtype": "int64"}, {"name": "67", "dtype": "int64"}, {"name": "68", "dtype": "int64"}, {"name": "69", "dtype": "int64"}, {"name": "70", "dtype": "int64"}, {"name": "71", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 49395397.74539947, "num_examples": 71893}, {"name": "validation", "num_bytes": 5488988.254600536, "num_examples": 7989}], "download_size": 9068599, "dataset_size": 54884386.0}} | 2023-03-04T00:50:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_label_0.8_72_D"
More Information needed | [
"# Dataset Card for \"word_label_0.8_72_D\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_label_0.8_72_D\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_label_0.8_72_D\"\n\nMore Information needed"
]
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|
007e1aefd91d1a39e293a8da45f7a3ef76a8a3a1 |
# Translations for Instruction dataset
Translations were generated by [M2M 12B](https://huggingface.co/facebook/m2m100-12B-avg-5-ckpt) and the output generations were limited at 512 tokens due to VRAM limit (40G).
| theblackcat102/instruction_translations | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"ChatGPT",
"SimpleAI",
"Detection",
"doi:10.57967/hf/0423",
"region:us"
]
| 2023-03-04T00:51:47+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "tags": ["ChatGPT", "SimpleAI", "Detection"]} | 2023-03-05T06:36:37+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-mit #ChatGPT #SimpleAI #Detection #doi-10.57967/hf/0423 #region-us
|
# Translations for Instruction dataset
Translations were generated by M2M 12B and the output generations were limited at 512 tokens due to VRAM limit (40G).
| [
"# Translations for Instruction dataset\n\nTranslations were generated by M2M 12B and the output generations were limited at 512 tokens due to VRAM limit (40G)."
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-mit #ChatGPT #SimpleAI #Detection #doi-10.57967/hf/0423 #region-us \n",
"# Translations for Instruction dataset\n\nTranslations were generated by M2M 12B and the output generations were limited at 512 tokens due to VRAM limit (40G)."
]
| [
61,
38
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-mit #ChatGPT #SimpleAI #Detection #doi-10.57967/hf/0423 #region-us \n# Translations for Instruction dataset\n\nTranslations were generated by M2M 12B and the output generations were limited at 512 tokens due to VRAM limit (40G)."
]
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|
09c85d1003aa3bb9e20314ebf11dbd2b037150c3 | # Dataset Card for "huggingface-test-model"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Djarnis/huggingface-test-model | [
"region:us"
]
| 2023-03-04T01:13:48+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"}, 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2000}], "download_size": 2886224, "dataset_size": 10676604}} | 2023-03-04T01:14:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "huggingface-test-model"
More Information needed | [
"# Dataset Card for \"huggingface-test-model\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"huggingface-test-model\"\n\nMore Information needed"
]
| [
6,
17
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"huggingface-test-model\"\n\nMore Information needed"
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|
61bd995b733ad5b4e5cb680a11f14363565dbb22 | # Dataset Card for "satellite-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Shavindra/satellite-512 | [
"region:us"
]
| 2023-03-04T01:34:57+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}, {"name": "pixel_values", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 332055030.0, "num_examples": 304}], "download_size": 0, "dataset_size": 332055030.0}} | 2023-03-05T14:59:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "satellite-512"
More Information needed | [
"# Dataset Card for \"satellite-512\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"satellite-512\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"satellite-512\"\n\nMore Information needed"
]
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|
02c89ce6b6817ee02a1223c2b6c9a3116e88857e |
# Dataset Card for Star Villain Marvel Comics LoRa
## Dataset Description
- **https://duskfallcrew.carrd.co/:**
- **https://civitai.com/models/14831/star-ryan-ripley:**
- **https://civitai.com/models/14831/star-ryan-ripley**
# Data set for Duskfallcrew/Star_Marvel_comics_LoRa
Trained with: https://colab.research.google.com/github/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb
# Where else can i find this ?
Both safetensors files are at: https://civitai.com/models/14831/star-ryan-ripley
# The outputs aren't comic format.
I haven't tested it in WEB UI yet, the scripted outputs largely rely on the actual model.
# YOU - YOU'RE A DATA SCRAPER!!!
And you're a booger picker! C'mon, work smarter, not harder. If you REALLY like this content, buy the comics. Seriously, or like find the artist, and buy their merch rather than supporting Disney.
# You do way too much is there a way to pay you in... anyway?
Yea, XD THROW ME PENNIES IN THE FOUNTAIN OF COFFEE!
https://ko-fi.com/DUSKFALLcrew
# What's the deal, is there a lisc on this?
Creative whatever it is, means that you basically just don't do this to do illegal crap. I'm not responsible for what you generate after you download this. I am not responsible for Disney coming after your ass for making Minnie kiss Deadpool. | EarthnDusk/Star_Marvel_comics | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"marvel",
"comics",
"duskfallcrew",
"text to image",
"stable diffusion",
"lora",
"region:us"
]
| 2023-03-04T03:23:47+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Star Villain Marvel Comics LoRa", "tags": ["marvel", "comics", "duskfallcrew", "text to image", "stable diffusion", "lora"]} | 2023-03-04T10:00:03+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #marvel #comics #duskfallcrew #text to image #stable diffusion #lora #region-us
|
# Dataset Card for Star Villain Marvel Comics LoRa
## Dataset Description
- URL
- URL
- URL
# Data set for Duskfallcrew/Star_Marvel_comics_LoRa
Trained with: URL
# Where else can i find this ?
Both safetensors files are at: URL
# The outputs aren't comic format.
I haven't tested it in WEB UI yet, the scripted outputs largely rely on the actual model.
# YOU - YOU'RE A DATA SCRAPER!!!
And you're a booger picker! C'mon, work smarter, not harder. If you REALLY like this content, buy the comics. Seriously, or like find the artist, and buy their merch rather than supporting Disney.
# You do way too much is there a way to pay you in... anyway?
Yea, XD THROW ME PENNIES IN THE FOUNTAIN OF COFFEE!
URL
# What's the deal, is there a lisc on this?
Creative whatever it is, means that you basically just don't do this to do illegal crap. I'm not responsible for what you generate after you download this. I am not responsible for Disney coming after your ass for making Minnie kiss Deadpool. | [
"# Dataset Card for Star Villain Marvel Comics LoRa",
"## Dataset Description\n\n- URL \n- URL \n- URL",
"# Data set for Duskfallcrew/Star_Marvel_comics_LoRa\nTrained with: URL",
"# Where else can i find this ?\nBoth safetensors files are at: URL",
"# The outputs aren't comic format.\nI haven't tested it in WEB UI yet, the scripted outputs largely rely on the actual model.",
"# YOU - YOU'RE A DATA SCRAPER!!!\nAnd you're a booger picker! C'mon, work smarter, not harder. If you REALLY like this content, buy the comics. Seriously, or like find the artist, and buy their merch rather than supporting Disney.",
"# You do way too much is there a way to pay you in... anyway?\nYea, XD THROW ME PENNIES IN THE FOUNTAIN OF COFFEE! \n\nURL",
"# What's the deal, is there a lisc on this?\nCreative whatever it is, means that you basically just don't do this to do illegal crap. I'm not responsible for what you generate after you download this. I am not responsible for Disney coming after your ass for making Minnie kiss Deadpool."
]
| [
"TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #marvel #comics #duskfallcrew #text to image #stable diffusion #lora #region-us \n",
"# Dataset Card for Star Villain Marvel Comics LoRa",
"## Dataset Description\n\n- URL \n- URL \n- URL",
"# Data set for Duskfallcrew/Star_Marvel_comics_LoRa\nTrained with: URL",
"# Where else can i find this ?\nBoth safetensors files are at: URL",
"# The outputs aren't comic format.\nI haven't tested it in WEB UI yet, the scripted outputs largely rely on the actual model.",
"# YOU - YOU'RE A DATA SCRAPER!!!\nAnd you're a booger picker! C'mon, work smarter, not harder. If you REALLY like this content, buy the comics. Seriously, or like find the artist, and buy their merch rather than supporting Disney.",
"# You do way too much is there a way to pay you in... anyway?\nYea, XD THROW ME PENNIES IN THE FOUNTAIN OF COFFEE! \n\nURL",
"# What's the deal, is there a lisc on this?\nCreative whatever it is, means that you basically just don't do this to do illegal crap. I'm not responsible for what you generate after you download this. I am not responsible for Disney coming after your ass for making Minnie kiss Deadpool."
]
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]
| [
"passage: TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #marvel #comics #duskfallcrew #text to image #stable diffusion #lora #region-us \n# Dataset Card for Star Villain Marvel Comics LoRa## Dataset Description\n\n- URL \n- URL \n- URL# Data set for Duskfallcrew/Star_Marvel_comics_LoRa\nTrained with: URL# Where else can i find this ?\nBoth safetensors files are at: URL# The outputs aren't comic format.\nI haven't tested it in WEB UI yet, the scripted outputs largely rely on the actual model.# YOU - YOU'RE A DATA SCRAPER!!!\nAnd you're a booger picker! C'mon, work smarter, not harder. If you REALLY like this content, buy the comics. Seriously, or like find the artist, and buy their merch rather than supporting Disney.# You do way too much is there a way to pay you in... anyway?\nYea, XD THROW ME PENNIES IN THE FOUNTAIN OF COFFEE! \n\nURL# What's the deal, is there a lisc on this?\nCreative whatever it is, means that you basically just don't do this to do illegal crap. I'm not responsible for what you generate after you download this. I am not responsible for Disney coming after your ass for making Minnie kiss Deadpool."
]
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|
13c02dd6766470b705a35b71f89986473ea94ab8 |
# Instruction-prompted low-level image processing dataset
To construct this dataset, we took different number of samples from the following datasets for each task and constructed
a single dataset with prompts added like so:
| **Task** | **Prompt** | **Dataset** | **Number of samples** |
|---|---|---|---|
| Deblurring | “deblur the blurry image” | REDS (train_blur and<br> train_sharp) | 1200 |
| Deraining | “derain the image” | Rain13k | 686 |
| Denoising | “denoise the noisy image” | SIDD | 8 |
| Low-light <br>image enhancement | "enhance the low-light image” | LOL | 23 |
To know more about how this sampling was performed, refer to [this notebook](https://huggingface.co/datasets/instruction-tuning-sd/low-level-image-proc/blob/main/data_preparation/sample_dataset.ipynb).This notebook outputs a CSV file which was then used for generating the final version of the dataset ([notebook](https://huggingface.co/datasets/instruction-tuning-sd/low-level-image-proc/blob/main/data_preparation/final_data_preparation.ipynb)).
## Known limitations and biases
Since this dataset was derived from various datasets, as mentioned above, it inherits their limitations and biases as well.
## Licensing
Since this dataset was derived from various datasets, as mentioned above, it inherits the licensing policies of those datasets. | instruction-tuning-sd/low-level-image-proc | [
"task_categories:image-to-image",
"size_categories:1K<n<10K",
"language:en",
"region:us"
]
| 2023-03-04T05:45:03+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["image-to-image"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input_image", "dtype": "image"}, {"name": "ground_truth_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3463030772.619, "num_examples": 1917}], "download_size": 3528678684, "dataset_size": 3463030772.619}} | 2023-05-11T14:22:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-image-to-image #size_categories-1K<n<10K #language-English #region-us
| Instruction-prompted low-level image processing dataset
=======================================================
To construct this dataset, we took different number of samples from the following datasets for each task and constructed
a single dataset with prompts added like so:
To know more about how this sampling was performed, refer to this notebook.This notebook outputs a CSV file which was then used for generating the final version of the dataset (notebook).
Known limitations and biases
----------------------------
Since this dataset was derived from various datasets, as mentioned above, it inherits their limitations and biases as well.
Licensing
---------
Since this dataset was derived from various datasets, as mentioned above, it inherits the licensing policies of those datasets.
| []
| [
"TAGS\n#task_categories-image-to-image #size_categories-1K<n<10K #language-English #region-us \n"
]
| [
34
]
| [
"passage: TAGS\n#task_categories-image-to-image #size_categories-1K<n<10K #language-English #region-us \n"
]
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|
9015589df09b0bf8750e721b7dff23c28ecad895 | # Dataset Card for "thvl_text_recognition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | napatswift/thvl_text_recognition | [
"region:us"
]
| 2023-03-04T07:12:34+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 734936377.786, "num_examples": 363922}], "download_size": 686919103, "dataset_size": 734936377.786}} | 2023-03-04T09:14:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "thvl_text_recognition"
More Information needed | [
"# Dataset Card for \"thvl_text_recognition\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"thvl_text_recognition\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"thvl_text_recognition\"\n\nMore Information needed"
]
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|
bc62bd58ab922a386236dde99c97d32fd88dd2d0 |
# NLLB-200
This is the model card of NLLB-200's 1.3B variant.
Here are the [metrics](https://tinyurl.com/nllb200dense1bmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues
## Intended Use
- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.
- Primary intended users: Primary users are researchers and machine translation research community.
- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.
## Metrics
• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.
## Evaluation Data
- Datasets: Flores-200 dataset is described in Section 4
- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200
- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The
SentencePiece model is released along with NLLB-200.
## Training Data
• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.
## Ethical Considerations
• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).
## Caveats and Recommendations
• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.
## Carbon Footprint Details
• The carbon dioxide (CO2e) estimate is reported in Section 8.8. | vpermilp/nllb-200-1.3B-rust | [
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"license:cc-by-nc-4.0",
"nllb",
"translation",
"region:us"
]
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| TAGS
#language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-nc-4.0 #nllb #translation #region-us
|
# NLLB-200
This is the model card of NLLB-200's 1.3B variant.
Here are the metrics for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: URL
## Intended Use
- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.
- Primary intended users: Primary users are researchers and machine translation research community.
- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.
## Metrics
• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.
## Evaluation Data
- Datasets: Flores-200 dataset is described in Section 4
- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200
- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The
SentencePiece model is released along with NLLB-200.
## Training Data
• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.
## Ethical Considerations
• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).
## Caveats and Recommendations
• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.
## Carbon Footprint Details
• The carbon dioxide (CO2e) estimate is reported in Section 8.8. | [
"# NLLB-200\n\nThis is the model card of NLLB-200's 1.3B variant.\n\nHere are the metrics for that particular checkpoint.\n\n- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.\n- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022\n- License: CC-BY-NC\n- Where to send questions or comments about the model: URL",
"## Intended Use\n- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.\n- Primary intended users: Primary users are researchers and machine translation research community.\n- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.",
"## Metrics\n• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.",
"## Evaluation Data\n- Datasets: Flores-200 dataset is described in Section 4\n- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200\n- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The\nSentencePiece model is released along with NLLB-200.",
"## Training Data\n• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.",
"## Ethical Considerations\n• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).",
"## Caveats and Recommendations\n• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.",
"## Carbon Footprint Details\n• The carbon dioxide (CO2e) estimate is reported in Section 8.8."
]
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"TAGS\n#language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-nc-4.0 #nllb #translation #region-us \n",
"# NLLB-200\n\nThis is the model card of NLLB-200's 1.3B variant.\n\nHere are the metrics for that particular checkpoint.\n\n- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.\n- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022\n- License: CC-BY-NC\n- Where to send questions or comments about the model: URL",
"## Intended Use\n- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.\n- Primary intended users: Primary users are researchers and machine translation research community.\n- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.",
"## Metrics\n• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.",
"## Evaluation Data\n- Datasets: Flores-200 dataset is described in Section 4\n- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200\n- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The\nSentencePiece model is released along with NLLB-200.",
"## Training Data\n• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.",
"## Ethical Considerations\n• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).",
"## Caveats and Recommendations\n• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.",
"## Carbon Footprint Details\n• The carbon dioxide (CO2e) estimate is reported in Section 8.8."
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"passage: TAGS\n#language-Achinese #language-Mesopotamian Arabic #language-Ta'izzi-Adeni Arabic #language-Tunisian Arabic #language-Afrikaans #language-South Levantine Arabic #language-Akan #language-Tosk Albanian #language-Amharic #language-Levantine Arabic #language-Arabic #language-Najdi Arabic #language-Moroccan Arabic #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Awadhi #language-Central Aymara #language-South Azerbaijani #language-North Azerbaijani #language-Bashkir #language-Bambara #language-Balinese #language-Belarusian #language-Bemba (Zambia) #language-Bengali #language-Bhojpuri #language-Banjar #language-Tibetan #language-Bosnian #language-Buginese #language-Bulgarian #language-Catalan #language-Cebuano #language-Czech #language-Chokwe #language-Central Kurdish #language-Crimean Tatar #language-Welsh #language-Danish #language-German #language-Southwestern Dinka #language-Dyula #language-Dzongkha #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Estonian #language-Basque #language-Ewe #language-Faroese #language-Fijian #language-Finnish #language-Fon #language-French #language-Friulian #language-Nigerian Fulfulde #language-West Central Oromo #language-Scottish Gaelic #language-Irish #language-Galician #language-Guarani #language-Gujarati #language-Haitian #language-Hausa #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hungarian #language-Armenian #language-Igbo #language-Iloko #language-Indonesian #language-Icelandic #language-Italian #language-Javanese #language-Japanese #language-Kabyle #language-Kachin #language-Kamba (Kenya) #language-Kannada #language-Kashmiri #language-Georgian #language-Kazakh #language-Kabiyè #language-Kabuverdianu #language-Halh Mongolian #language-Khmer #language-Kikuyu #language-Kinyarwanda #language-Kirghiz #language-Kimbundu #language-Northern Kurdish #language-Central Kanuri #language-Kongo #language-Korean #language-Lao #language-Ligurian #language-Limburgan #language-Lingala #language-Lithuanian #language-Lombard #language-Latgalian #language-Luxembourgish #language-Luba-Lulua #language-Ganda #language-Luo (Kenya and Tanzania) #language-Lushai #language-Standard Latvian #language-Magahi #language-Maithili #language-Malayalam #language-Marathi #language-Minangkabau #language-Macedonian #language-Maltese #language-Manipuri #language-Mossi #language-Maori #language-Burmese #language-Dutch #language-Norwegian Nynorsk #language-Norwegian Bokmål #language-Nepali (individual language) #language-Pedi #language-Nuer #language-Nyanja #language-Occitan (post 1500) #language-Odia #language-Pangasinan #language-Panjabi #language-Papiamento #language-Southern Pashto #language-Iranian Persian #language-Plateau Malagasy #language-Polish #language-Portuguese #language-Dari #language-Ayacucho Quechua #language-Romanian #language-Rundi #language-Russian #language-Sango #language-Sanskrit #language-Santali #language-Sicilian #language-Shan #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Sindhi #language-Somali #language-Southern Sotho #language-Spanish #language-Sardinian #language-Serbian #language-Swati #language-Sundanese #language-Swedish #language-Swahili (individual language) #language-Silesian #language-Tamil #language-Tamasheq #language-Tatar #language-Telugu #language-Tajik #language-Tagalog #language-Thai #language-Tigrinya #language-Tok Pisin #language-Tswana #language-Tsonga #language-Turkmen #language-Tumbuka #language-Turkish #language-Twi #language-Central Atlas Tamazight #language-Uighur #language-Ukrainian #language-Umbundu #language-Urdu #language-Northern Uzbek #language-Venetian #language-Vietnamese #language-Waray (Philippines) #language-Wolof #language-Xhosa #language-Eastern Yiddish #language-Yoruba #language-Yue Chinese #language-Chinese #language-Standard Malay #language-Zulu #license-cc-by-nc-4.0 #nllb #translation #region-us \n# NLLB-200\n\nThis is the model card of NLLB-200's 1.3B variant.\n\nHere are the metrics for that particular checkpoint.\n\n- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.\n- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022\n- License: CC-BY-NC\n- Where to send questions or comments about the model: URL## Intended Use\n- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.\n- Primary intended users: Primary users are researchers and machine translation research community.\n- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.## Metrics\n• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations."
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|
dbfdd59fbfd44b0067fc206dae2d10b030d5fa1e | # Dataset Card for "ema"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bhpardo/ema | [
"region:us"
]
| 2023-03-04T08:30:42+00:00 | {"dataset_info": {"features": [{"name": "ingl\u00e9s", "dtype": "string"}, {"name": "espa\u00f1ol", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1640138.8574291153, "num_examples": 7984}, {"name": "test", "num_bytes": 410240.14257088467, "num_examples": 1997}], "download_size": 848199, "dataset_size": 2050379.0}} | 2023-03-05T07:57:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ema"
More Information needed | [
"# Dataset Card for \"ema\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ema\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ema\"\n\nMore Information needed"
]
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|
1983a4c9334715df46d1b100af940ec4c00d9a25 | # Dataset Card for "gramvaani-reduplication-repetition-toy-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | theArif/gramvaani-reduplication-repetition-toy-dataset | [
"region:us"
]
| 2023-03-04T09:29:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2657637, "num_examples": 3978}, {"name": "validation", "num_bytes": 872848, "num_examples": 1326}, {"name": "test", "num_bytes": 700979, "num_examples": 1061}], "download_size": 664994, "dataset_size": 4231464}} | 2023-03-04T09:30:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "gramvaani-reduplication-repetition-toy-dataset"
More Information needed | [
"# Dataset Card for \"gramvaani-reduplication-repetition-toy-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"gramvaani-reduplication-repetition-toy-dataset\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"gramvaani-reduplication-repetition-toy-dataset\"\n\nMore Information needed"
]
| [
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|
2886b8dd6988547041bd2d751ed02e55dad30b10 | # Dataset Card for "wnut17_filtered_entities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ashwathjadhav23/wnut17_filtered_entities | [
"region:us"
]
| 2023-03-04T10:00:07+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-corporation", "2": "I-corporation", "3": "B-creative-work", "4": "I-creative-work", "5": "B-group", "6": "I-group", "7": "B-location", "8": "I-location", "9": "B-person", "10": "I-person", "11": "B-product", "12": "I-product"}}}}], "splits": [{"name": "train", "num_bytes": 390167.873895109, "num_examples": 1228}, {"name": "validation", "num_bytes": 259367, "num_examples": 1009}, {"name": "test", "num_bytes": 405520, "num_examples": 1287}], "download_size": 316469, "dataset_size": 1055054.873895109}} | 2023-03-04T10:00:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wnut17_filtered_entities"
More Information needed | [
"# Dataset Card for \"wnut17_filtered_entities\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wnut17_filtered_entities\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wnut17_filtered_entities\"\n\nMore Information needed"
]
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|
68c846a673ebe93d0c7fc643c7dd804f38743fe3 | # Dataset Card for "NSFW-reddit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jjmachan/NSFW-reddit | [
"region:us"
]
| 2023-03-04T10:22:32+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "post_id", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "link_flair_text", "dtype": "string"}, {"name": "is_self", "dtype": "bool"}, {"name": "over_18", "dtype": "bool"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "is_question", "dtype": "bool"}, {"name": "C1", "dtype": "string"}, {"name": "C2", "dtype": "string"}, {"name": "C3", "dtype": "string"}, {"name": "C4", "dtype": "string"}, {"name": "C5", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3178233, "num_examples": 23519}], "download_size": 1238046, "dataset_size": 3178233}} | 2023-03-04T10:22:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "NSFW-reddit"
More Information needed | [
"# Dataset Card for \"NSFW-reddit\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"NSFW-reddit\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"NSFW-reddit\"\n\nMore Information needed"
]
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|
a390557db09b8208236e98fb3c41234eba8ffaa0 |
# Dataset Card for DEArt: Dataset of European Art
## 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:** https://doi.org/10.5281/zenodo.6984525
- **Paper:** https://arxiv.org/abs/2211.01226
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
> DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural heritage specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art.
### Supported Tasks and Leaderboards
- `object-detection`: This dataset can be used to train or evaluate models for object-detection on historical document images.
- `image-classification`: This dataset can be used for image classification tasks by using only the labels and not the bounding box information
## Dataset Structure
This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.
- The first configuration, `raw, uses the data's original format.
- The second configuration converts the annotations into a format that is closer to the `COCO` annotation format. This is done to make it easier to work with the [`image_processors`](https://huggingface.co/docs/transformers/main_classes/image_processor) (formerly known as`feature_extractor`s) from the `Transformers` models for object detection, which expects data to be in a COCO-style format.
### Data Instances
An instance from the `raw` config:
```python
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>,
'source': 'Europeana Collection',
'width': 1019,
'height': 1680,
'dept': 3,
'segmented': None,
'objects': [{'name': 40,
'pose': 3,
'diffult': 0,
'xmin': 259.0,
'ymin': 166.0,
'xmax': 679.0,
'ymax': 479.0},
{'name': 19,
'pose': 2,
'diffult': 0,
'xmin': 115.0,
'ymin': 354.0,
'xmax': 882.0,
'ymax': 1168.0},
{'name': 15,
'pose': 3,
'diffult': 0,
'xmin': 445.0,
'ymin': 1170.0,
'xmax': 579.0,
'ymax': 1302.0},
{'name': 51,
'pose': 3,
'diffult': 0,
'xmin': 354.0,
'ymin': 1196.0,
'xmax': 445.0,
'ymax': 1330.0},
{'name': 51,
'pose': 3,
'diffult': 0,
'xmin': 580.0,
'ymin': 1203.0,
'xmax': 701.0,
'ymax': 1326.0},
{'name': 57,
'pose': 3,
'diffult': 0,
'xmin': 203.0,
'ymin': 642.0,
'xmax': 882.0,
'ymax': 1172.0}]}
```
An instance from the `coco` config:
```python
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1019x1680>,
'source': 'Europeana Collection',
'width': 1019,
'height': 1680,
'dept': 3,
'segmented': None,
'image_id': '0',
'annotations': [{'category_id': 40,
'image_id': '0',
'area': 131460,
'bbox': [259.0, 166.0, 420.0, 313.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 19,
'image_id': '0',
'area': 624338,
'bbox': [115.0, 354.0, 767.0, 814.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 15,
'image_id': '0',
'area': 17688,
'bbox': [445.0, 1170.0, 134.0, 132.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 51,
'image_id': '0',
'area': 12194,
'bbox': [354.0, 1196.0, 91.0, 134.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 51,
'image_id': '0',
'area': 14883,
'bbox': [580.0, 1203.0, 121.0, 123.0],
'segmentation': [],
'iscrowd': False},
{'category_id': 57,
'image_id': '0',
'area': 359870,
'bbox': [203.0, 642.0, 679.0, 530.0],
'segmentation': [],
'iscrowd': False}]}
```
### Data Fields
The fields for the COCO config:
- `image`: The Image being annotated
- `source`: source of the image i.e.'Europeana Collection'
- `width`: width of the image
- `height`: height of the image
- `dept`: number of channels in the image
- `segmented`: Whether the image has been segmented
- `image_id`: ID for the image
- `annotations`: annotations in coco format, consisting of a list containing dictionaries with the following keys:
- `bbox`: bounding boxes for the images
- `category_id`: a label for the image
- `image_id`: id for the image
- `iscrowd`: COCO `iscrowd` flag
- `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
### Data Splits
The dataset doesn't define set splits, so only a train split is provided. The paper associated with the dataset does discuss a train and validation split, but it doesn't appear this split was shared with the dataset or associated paper.
## Dataset Creation
### Curation Rationale
The creators of the dataset authors outline some of their motivations for creating the dataset in the abstract for their paper:
> Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations.
### Source Data
The source data comes from several cultural heritage institutions that have shared openly licenced images. The dictionary below shows the institutions and the frequency with which they are the provider of images in the dataset.
```python
{'National Museum in Warsaw': 2030,
'Europeana Collection': 1991,
'The Art Institute of Chicago': 1237,
'The Metropolitan Museum of Art': 1218,
'Rijksmuseum': 1066,
'National Gallery of Art': 871,
'Philadelphia Museum of Art': 774,
'WikiArt': 687,
'National museum in Krakow': 661,
'National Gallery of Denmark': 660,
'British Museum': 618,
'Victoria and Albert Museum': 561,
'Paul Mellon Centre': 391,
'National Gallery of Scotland': 384,
'Yale University Art Gallery': 376,
'Museo Nacional Thyssen-Bornemisza': 332,
'Harvard Art Museum': 279,
'The National Museum of Norvay': 270,
'LACMA': 262,
'The Cleveland Museum of Art': 242,
'The Leiden Collection': 159,
'The Clark Museum': 77,
'Pharos': 6,
'Wikimedia Commons': 2,
'Wikipedia': 1,
'Unknown': 1}
```
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | biglam/european_art | [
"task_categories:object-detection",
"task_categories:image-classification",
"size_categories:10K<n<100K",
"license:cc-by-nc-2.0",
"lam",
"art",
"historical",
"arxiv:2211.01226",
"region:us"
]
| 2023-03-04T15:05:33+00:00 | {"license": "cc-by-nc-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["object-detection", "image-classification"], "pretty_name": "DEArt: Dataset of European Art", "dataset_info": [{"config_name": "raw", "features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "objects", "list": [{"name": "name", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "pose", "dtype": {"class_label": {"names": {"0": "stand", "1": "sit", "2": "partial", "3": "Unspecified", "4": "squats", "5": "lie", "6": "bend", "7": "fall", "8": "walk", "9": "push", "10": "pray", "11": "undefined", "12": "kneel", "13": "unrecognize", "14": "unknown", "15": "other", "16": "ride"}}}}, {"name": "diffult", "dtype": "int32"}, {"name": "xmin", "dtype": "float64"}, {"name": "ymin", "dtype": "float64"}, {"name": "xmax", "dtype": "float64"}, {"name": "ymax", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 9046918, "num_examples": 15156}], "download_size": 18160510195, "dataset_size": 9046918}, {"config_name": "coco", "features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "objects", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}, {"name": "image_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8285204, "num_examples": 15156}], "download_size": 18160510195, "dataset_size": 8285204}], "tags": ["lam", "art", "historical"]} | 2023-08-03T08:39:40+00:00 | [
"2211.01226"
]
| []
| TAGS
#task_categories-object-detection #task_categories-image-classification #size_categories-10K<n<100K #license-cc-by-nc-2.0 #lam #art #historical #arxiv-2211.01226 #region-us
|
# Dataset Card for DEArt: Dataset of European Art
## 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: URL
- Paper: URL
- Leaderboard:
- Point of Contact:
### Dataset Summary
> DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural heritage specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art.
### Supported Tasks and Leaderboards
- 'object-detection': This dataset can be used to train or evaluate models for object-detection on historical document images.
- 'image-classification': This dataset can be used for image classification tasks by using only the labels and not the bounding box information
## Dataset Structure
This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.
- The first configuration, 'raw, uses the data's original format.
- The second configuration converts the annotations into a format that is closer to the 'COCO' annotation format. This is done to make it easier to work with the 'image_processors' (formerly known as'feature_extractor's) from the 'Transformers' models for object detection, which expects data to be in a COCO-style format.
### Data Instances
An instance from the 'raw' config:
An instance from the 'coco' config:
### Data Fields
The fields for the COCO config:
- 'image': The Image being annotated
- 'source': source of the image i.e.'Europeana Collection'
- 'width': width of the image
- 'height': height of the image
- 'dept': number of channels in the image
- 'segmented': Whether the image has been segmented
- 'image_id': ID for the image
- 'annotations': annotations in coco format, consisting of a list containing dictionaries with the following keys:
- 'bbox': bounding boxes for the images
- 'category_id': a label for the image
- 'image_id': id for the image
- 'iscrowd': COCO 'iscrowd' flag
- 'segmentation': COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
### Data Splits
The dataset doesn't define set splits, so only a train split is provided. The paper associated with the dataset does discuss a train and validation split, but it doesn't appear this split was shared with the dataset or associated paper.
## Dataset Creation
### Curation Rationale
The creators of the dataset authors outline some of their motivations for creating the dataset in the abstract for their paper:
> Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations.
### Source Data
The source data comes from several cultural heritage institutions that have shared openly licenced images. The dictionary below shows the institutions and the frequency with which they are the provider of images in the dataset.
#### 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
### Contributions
Thanks to @github-username for adding this dataset. | [
"# Dataset Card for DEArt: Dataset of European Art",
"## Table of Contents\n\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: URL\n- Paper: URL\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\n> DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural heritage specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art.",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': This dataset can be used to train or evaluate models for object-detection on historical document images.\n- 'image-classification': This dataset can be used for image classification tasks by using only the labels and not the bounding box information",
"## Dataset Structure\n\nThis dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.\n\n- The first configuration, 'raw, uses the data's original format.\n- The second configuration converts the annotations into a format that is closer to the 'COCO' annotation format. This is done to make it easier to work with the 'image_processors' (formerly known as'feature_extractor's) from the 'Transformers' models for object detection, which expects data to be in a COCO-style format.",
"### Data Instances\n\nAn instance from the 'raw' config:\n\n\n\nAn instance from the 'coco' config:",
"### Data Fields\n\nThe fields for the COCO config:\n\n- 'image': The Image being annotated\n- 'source': source of the image i.e.'Europeana Collection'\n- 'width': width of the image\n- 'height': height of the image\n- 'dept': number of channels in the image\n- 'segmented': Whether the image has been segmented\n- 'image_id': ID for the image\n- 'annotations': annotations in coco format, consisting of a list containing dictionaries with the following keys:\n - 'bbox': bounding boxes for the images\n - 'category_id': a label for the image\n - 'image_id': id for the image\n - 'iscrowd': COCO 'iscrowd' flag\n - 'segmentation': COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)",
"### Data Splits\n\nThe dataset doesn't define set splits, so only a train split is provided. The paper associated with the dataset does discuss a train and validation split, but it doesn't appear this split was shared with the dataset or associated paper.",
"## Dataset Creation",
"### Curation Rationale\n\nThe creators of the dataset authors outline some of their motivations for creating the dataset in the abstract for their paper:\n\n> Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations.",
"### Source Data\n\nThe source data comes from several cultural heritage institutions that have shared openly licenced images. The dictionary below shows the institutions and the frequency with which they are the provider of images in the dataset.",
"#### 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",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #task_categories-image-classification #size_categories-10K<n<100K #license-cc-by-nc-2.0 #lam #art #historical #arxiv-2211.01226 #region-us \n",
"# Dataset Card for DEArt: Dataset of European Art",
"## Table of Contents\n\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: URL\n- Paper: URL\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\n> DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural heritage specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art.",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': This dataset can be used to train or evaluate models for object-detection on historical document images.\n- 'image-classification': This dataset can be used for image classification tasks by using only the labels and not the bounding box information",
"## Dataset Structure\n\nThis dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.\n\n- The first configuration, 'raw, uses the data's original format.\n- The second configuration converts the annotations into a format that is closer to the 'COCO' annotation format. This is done to make it easier to work with the 'image_processors' (formerly known as'feature_extractor's) from the 'Transformers' models for object detection, which expects data to be in a COCO-style format.",
"### Data Instances\n\nAn instance from the 'raw' config:\n\n\n\nAn instance from the 'coco' config:",
"### Data Fields\n\nThe fields for the COCO config:\n\n- 'image': The Image being annotated\n- 'source': source of the image i.e.'Europeana Collection'\n- 'width': width of the image\n- 'height': height of the image\n- 'dept': number of channels in the image\n- 'segmented': Whether the image has been segmented\n- 'image_id': ID for the image\n- 'annotations': annotations in coco format, consisting of a list containing dictionaries with the following keys:\n - 'bbox': bounding boxes for the images\n - 'category_id': a label for the image\n - 'image_id': id for the image\n - 'iscrowd': COCO 'iscrowd' flag\n - 'segmentation': COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)",
"### Data Splits\n\nThe dataset doesn't define set splits, so only a train split is provided. The paper associated with the dataset does discuss a train and validation split, but it doesn't appear this split was shared with the dataset or associated paper.",
"## Dataset Creation",
"### Curation Rationale\n\nThe creators of the dataset authors outline some of their motivations for creating the dataset in the abstract for their paper:\n\n> Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations.",
"### Source Data\n\nThe source data comes from several cultural heritage institutions that have shared openly licenced images. The dictionary below shows the institutions and the frequency with which they are the provider of images in the dataset.",
"#### 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",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
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218,
58,
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412,
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8,
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| [
"passage: TAGS\n#task_categories-object-detection #task_categories-image-classification #size_categories-10K<n<100K #license-cc-by-nc-2.0 #lam #art #historical #arxiv-2211.01226 #region-us \n# Dataset Card for DEArt: Dataset of European Art## Table of Contents\n\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: URL\n- Paper: URL\n- Leaderboard:\n- Point of Contact:### Dataset Summary\n\n> DEArt is an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are cultural heritage specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art.### Supported Tasks and Leaderboards\n\n- 'object-detection': This dataset can be used to train or evaluate models for object-detection on historical document images.\n- 'image-classification': This dataset can be used for image classification tasks by using only the labels and not the bounding box information",
"passage: ## Dataset Structure\n\nThis dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.\n\n- The first configuration, 'raw, uses the data's original format.\n- The second configuration converts the annotations into a format that is closer to the 'COCO' annotation format. This is done to make it easier to work with the 'image_processors' (formerly known as'feature_extractor's) from the 'Transformers' models for object detection, which expects data to be in a COCO-style format.### Data Instances\n\nAn instance from the 'raw' config:\n\n\n\nAn instance from the 'coco' config:### Data Fields\n\nThe fields for the COCO config:\n\n- 'image': The Image being annotated\n- 'source': source of the image i.e.'Europeana Collection'\n- 'width': width of the image\n- 'height': height of the image\n- 'dept': number of channels in the image\n- 'segmented': Whether the image has been segmented\n- 'image_id': ID for the image\n- 'annotations': annotations in coco format, consisting of a list containing dictionaries with the following keys:\n - 'bbox': bounding boxes for the images\n - 'category_id': a label for the image\n - 'image_id': id for the image\n - 'iscrowd': COCO 'iscrowd' flag\n - 'segmentation': COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)### Data Splits\n\nThe dataset doesn't define set splits, so only a train split is provided. The paper associated with the dataset does discuss a train and validation split, but it doesn't appear this split was shared with the dataset or associated paper.## Dataset Creation"
]
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0.05092097073793411,
0.060233987867832184,
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|
b3d76ddfc30d715989e6613821087b65f2dbc3ef | # Dataset Card for "old"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tarteel-ai/tlog | [
"region:us"
]
| 2023-03-04T16:54:03+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "media", "1": "recordings", "2": "unidentified"}}}}], "splits": [{"name": "train", "num_bytes": 370981663502.674, "num_examples": 719853}], "download_size": 552670139289, "dataset_size": 370981663502.674}} | 2023-03-05T00:01:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "old"
More Information needed | [
"# Dataset Card for \"old\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"old\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"old\"\n\nMore Information needed"
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|
af5b1ee0f5a729856cf6e40906a2708c662b9d45 | # Dataset Card for "wnut_train_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liahchan/wnut_train_subset | [
"region:us"
]
| 2023-03-04T17:25:07+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 123526.0, "num_examples": 350}], "download_size": 40132, "dataset_size": 123526.0}} | 2023-03-04T17:48:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wnut_train_subset"
More Information needed | [
"# Dataset Card for \"wnut_train_subset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wnut_train_subset\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wnut_train_subset\"\n\nMore Information needed"
]
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|
8b6d88c26bf94e958014ac7a1aba49ab1b23675f | # Dataset Card for "wnut_test_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liahchan/wnut_test_subset | [
"region:us"
]
| 2023-03-04T17:48:48+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 23919.0, "num_examples": 70}], "download_size": 9876, "dataset_size": 23919.0}} | 2023-03-04T17:48:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wnut_test_subset"
More Information needed | [
"# Dataset Card for \"wnut_test_subset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wnut_test_subset\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wnut_test_subset\"\n\nMore Information needed"
]
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|
cf26515eb957460d269690ecc9c22ed07639cc03 | # Dataset Card for "wnut_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liahchan/wnut_subset | [
"region:us"
]
| 2023-03-04T18:06:33+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 121050.0, "num_examples": 350}, {"name": "test", "num_bytes": 20726.0, "num_examples": 70}], "download_size": 49301, "dataset_size": 141776.0}} | 2023-03-04T18:06:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wnut_subset"
More Information needed | [
"# Dataset Card for \"wnut_subset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wnut_subset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wnut_subset\"\n\nMore Information needed"
]
| [
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|
7880b53c71a821558f90b685c496a754526a31b8 | # Dataset Card for "tlcv2.0_oa"
Thai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts by Jitkapat Sawatphol (Faculty of Arts, Chulalongkorn University).
This project use [Thai Literature Corpora (TLC) v2.0](https://attapol.github.io/tlc.html). All text are from old Thai book that out of copyright (or public domain).
This dataset was build for [Open Assistant](https://github.com/LAION-AI/Open-Assistant/).
## Columns
The dataset was following columns:
1. **TEXT** (string)
2. **SOURCE** (string)
3. **METADATA** (JSON string, optional) | pythainlp/tlcv2.0_oa | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:th",
"license:mit",
"region:us"
]
| 2023-03-04T19:15:26+00:00 | {"language": ["th"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "TEXT", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}, {"name": "METADATA", "struct": [{"name": "ch_num", "dtype": "string"}, {"name": "title", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 27856275, "num_examples": 361}], "download_size": 11507610, "dataset_size": 27856275}} | 2023-03-04T19:36:15+00:00 | []
| [
"th"
]
| TAGS
#task_categories-text-generation #size_categories-n<1K #language-Thai #license-mit #region-us
| # Dataset Card for "tlcv2.0_oa"
Thai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts by Jitkapat Sawatphol (Faculty of Arts, Chulalongkorn University).
This project use Thai Literature Corpora (TLC) v2.0. All text are from old Thai book that out of copyright (or public domain).
This dataset was build for Open Assistant.
## Columns
The dataset was following columns:
1. TEXT (string)
2. SOURCE (string)
3. METADATA (JSON string, optional) | [
"# Dataset Card for \"tlcv2.0_oa\"\n\nThai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts by Jitkapat Sawatphol (Faculty of Arts, Chulalongkorn University).\n\nThis project use Thai Literature Corpora (TLC) v2.0. All text are from old Thai book that out of copyright (or public domain).\n\nThis dataset was build for Open Assistant.",
"## Columns\n\nThe dataset was following columns:\n\n1. TEXT (string)\n2. SOURCE (string)\n3. METADATA (JSON string, optional)"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-Thai #license-mit #region-us \n",
"# Dataset Card for \"tlcv2.0_oa\"\n\nThai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts by Jitkapat Sawatphol (Faculty of Arts, Chulalongkorn University).\n\nThis project use Thai Literature Corpora (TLC) v2.0. All text are from old Thai book that out of copyright (or public domain).\n\nThis dataset was build for Open Assistant.",
"## Columns\n\nThe dataset was following columns:\n\n1. TEXT (string)\n2. SOURCE (string)\n3. METADATA (JSON string, optional)"
]
| [
37,
96,
38
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-Thai #license-mit #region-us \n# Dataset Card for \"tlcv2.0_oa\"\n\nThai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts by Jitkapat Sawatphol (Faculty of Arts, Chulalongkorn University).\n\nThis project use Thai Literature Corpora (TLC) v2.0. All text are from old Thai book that out of copyright (or public domain).\n\nThis dataset was build for Open Assistant.## Columns\n\nThe dataset was following columns:\n\n1. TEXT (string)\n2. SOURCE (string)\n3. METADATA (JSON string, optional)"
]
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|
fb996682e4ad61e9fe409f1fe6a1ec63ec8294af | # Dataset Card for "dogdatasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dsupa/dogdatasets | [
"region:us"
]
| 2023-03-04T19:41:00+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "affenpinscher", "1": "afghan_hound", "2": "african_hunting_dog", "3": "airedale", "4": "american_staffordshire_terrier", "5": "appenzeller", "6": "australian_terrier", "7": "basenji", "8": "basset", "9": "beagle", "10": "bedlington_terrier", "11": "bernese_mountain_dog", "12": "black-and-tan_coonhound", "13": "blenheim_spaniel", "14": "bloodhound", "15": "bluetick", "16": "border_collie", "17": "border_terrier", "18": "borzoi", "19": "boston_bull", "20": "bouvier_des_flandres", "21": "boxer", "22": "brabancon_griffon", "23": "briard", "24": "brittany_spaniel", "25": "bull_mastiff", "26": "cairn", "27": "cardigan", "28": "chesapeake_bay_retriever", "29": "chihuahua", "30": "chow", "31": "clumber", "32": "cocker_spaniel", "33": "collie", "34": "curly-coated_retriever", "35": "dandie_dinmont", "36": "dhole", "37": "dingo", "38": "doberman", "39": "english_foxhound", "40": "english_setter", "41": "english_springer", "42": "entlebucher", "43": "eskimo_dog", "44": "flat-coated_retriever", "45": "french_bulldog", "46": "german_shepherd", "47": "german_short-haired_pointer", "48": "giant_schnauzer", "49": "golden_retriever", "50": "gordon_setter", "51": "great_dane", "52": "great_pyrenees", "53": "greater_swiss_mountain_dog", "54": "groenendael", "55": "ibizan_hound", "56": "irish_setter", "57": "irish_terrier", "58": "irish_water_spaniel", "59": "irish_wolfhound", "60": "italian_greyhound", "61": "japanese_spaniel", "62": "keeshond", "63": "kelpie", "64": "kerry_blue_terrier", "65": "komondor", "66": "kuvasz", "67": "labrador_retriever", "68": "lakeland_terrier", "69": "leonberg", "70": "lhasa", "71": "malamute", "72": "malinois", "73": "maltese_dog", "74": "mexican_hairless", "75": "miniature_pinscher", "76": "miniature_poodle", "77": "miniature_schnauzer", "78": "newfoundland", "79": "norfolk_terrier", "80": "norwegian_elkhound", "81": "norwich_terrier", "82": "old_english_sheepdog", "83": "otterhound", "84": "papillon", "85": "pekinese", "86": "pembroke", "87": "pomeranian", "88": "pug", "89": "redbone", "90": "rhodesian_ridgeback", "91": "rottweiler", "92": "saint_bernard", "93": "saluki", "94": "samoyed", "95": "schipperke", "96": "scotch_terrier", "97": "scottish_deerhound", "98": "sealyham_terrier", "99": "shetland_sheepdog", "100": "shih-tzu", "101": "siberian_husky", "102": "silky_terrier", "103": "soft-coated_wheaten_terrier", "104": "staffordshire_bullterrier", "105": "standard_poodle", "106": "standard_schnauzer", "107": "sussex_spaniel", "108": "tibetan_mastiff", "109": "tibetan_terrier", "110": "toy_poodle", "111": "toy_terrier", "112": "vizsla", "113": "walker_hound", "114": "weimaraner", "115": "welsh_springer_spaniel", "116": "west_highland_white_terrier", "117": "whippet", "118": "wire-haired_fox_terrier", "119": "yorkshire_terrier"}}}}], "splits": [{"name": "train", "num_bytes": 292133954.013, "num_examples": 8127}, {"name": "test", "num_bytes": 79266534.295, "num_examples": 2095}], "download_size": 361889607, "dataset_size": 371400488.308}} | 2023-03-04T19:41:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dogdatasets"
More Information needed | [
"# Dataset Card for \"dogdatasets\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dogdatasets\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dogdatasets\"\n\nMore Information needed"
]
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|
179a7d18c51a9ace44906eb6872b14c273f4d906 | # Dataset Card for "nilc-punctuation-binary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tiagoblima/tedtalk2012-punctuation-binary | [
"region:us"
]
| 2023-03-04T20:00:48+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 383989, "num_examples": 2442}], "download_size": 151918, "dataset_size": 383989}} | 2023-03-04T20:00:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nilc-punctuation-binary"
More Information needed | [
"# Dataset Card for \"nilc-punctuation-binary\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nilc-punctuation-binary\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nilc-punctuation-binary\"\n\nMore Information needed"
]
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|
4797c80e63a46f49fbe8e1bbee95885dfb12533a | # Dataset Card for "prosocial-nsfw-reddit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | shahules786/prosocial-nsfw-reddit | [
"region:us"
]
| 2023-03-04T21:53:19+00:00 | {"dataset_info": {"features": [{"name": "user", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "post_id", "dtype": "string"}, {"name": "link_flair_text", "dtype": "string"}, {"name": "over_18", "dtype": "bool"}, {"name": "is_question", "dtype": "bool"}, {"name": "rots", "sequence": "string"}, {"name": "safety_label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 167028.87151967647, "num_examples": 1502}], "download_size": 101362, "dataset_size": 167028.87151967647}} | 2023-03-04T21:53:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "prosocial-nsfw-reddit"
More Information needed | [
"# Dataset Card for \"prosocial-nsfw-reddit\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"prosocial-nsfw-reddit\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"prosocial-nsfw-reddit\"\n\nMore Information needed"
]
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|
15089f4eafda48e06f4ec23c413962e56bb6a46b | # Dataset Card for "wiki_20220301random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | wxh0718/wiki_20220301random | [
"region:us"
]
| 2023-03-04T22:44:13+00:00 | {"dataset_info": {"features": [{"name": "data.a", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2089104, "num_examples": 15247}], "download_size": 1292979, "dataset_size": 2089104}} | 2023-03-04T22:52:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki_20220301random"
More Information needed | [
"# Dataset Card for \"wiki_20220301random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_20220301random\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_20220301random\"\n\nMore Information needed"
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|
141baec299ce416bef7ce6e7be6dec8bfcbc463f | # Dataset Card for "flores200_incomplete"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bri25yu/flores200_incomplete | [
"region:us"
]
| 2023-03-04T23:46:46+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 15697219153, "num_examples": 20480000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 7817630008, "dataset_size": 15708717189}} | 2023-03-04T23:51:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flores200_incomplete"
More Information needed | [
"# Dataset Card for \"flores200_incomplete\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flores200_incomplete\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flores200_incomplete\"\n\nMore Information needed"
]
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|
82188edaa162cea4777d08b5e2bcb6e6ad03d19e |
# This is the Open Instruction Generalist Dataset
This is our attempt to create a large instruction dataset of medium quality along with a smaller high quality instruciton dataset (OIG-small-chip2).
The data is in the form of jsonl objects, with at least a 'text' field. Some datasets may also include a 'metadata' field. The 'text' field contains a string of the form of one or more of:
- \<human\>: instruction\n\<bot\>: response
- \<human\>: instruction\n\<bot\>: response .. \<human\>: instruction\n\<bot\>: response
The purpose of the larger dataset is to perform continued pre-training, followed by a finetune on the smaller high quality dataset.
The purpose of the smaller OIG-small-chip2 dataset is to make it easy to convert a language model pretrained on large amounts of text into an instruction following model using a small amount of additional compute via finetuning or softprompt tuning.
Many additional datasets are being prepared by various community members and will be incorporated into this dataset as we are able to verify the quality and formatting of the data. Our goal is to make helpful and non-toxic instruction tuned models available to everyone.
OIG is currently at 44M. We will continue to publish ever larger diverse instruction datasets with the goal of creating 1 trillion tokens of diverse instructions - enough to pretrain an LLM from scratch.
It is best to download the individual jsonl files directly that you wish to use instead of using HF load_datasets. https://huggingface.co/datasets/laion/OIG/tree/main
## unified_abstract_infill.jsonl (~232000)
dbpedia and wikipedia snippets combined with a small portion of https://github.com/google-research/dialog-inpainting
## unified_basic.jsonl (30)
## unified_conv_finqa.jsonl (~9000)
https://github.com/czyssrs/ConvFinQA
## unified_cuad.jsonl (~500)
https://www.atticusprojectai.org/cuad
## unified_essays.jsonl (~2000)
- essays available on the public web
## unified_grade_school_math_instructions.jsonl (~9000)
- https://github.com/openai/grade-school-math
## unified_hc3_human.jsonl (~58000)
## unified_image_prompts_instructions.jsonl (~15000)
- A very small subset of LAION-400M
## unified_joke_explanations.jsonl (356)
- Crawled from public internet.
## unified_mathqa_flanv2_kojma_cot.jsonl (~107000)
- https://huggingface.co/datasets/math_qa,
## unified_merged_code_xp3.jsonl (~67000)
- https://huggingface.co/datasets/bigscience/xP3
## unified_multi_news.jsonl (~90000)
- https://www.tensorflow.org/datasets/catalog/multi_news
## unified_multi_sum.jsonl (~1700000)
## unified_nq.jsonl (~307000)
## unified_openai_summarize_tldr.jsonl (~233000)
- https://github.com/openai/summarize-from-feedback
## unified_oscar_en_sample_dialog.jsonl (~2670000)
- https://oscar-project.org/
- https://huggingface.co/datasets/TurkuNLP/register_oscar
## unified_plot_screenplay_books_dialog.jsonl (~8000)
- https://github.com/markriedl/WikiPlots extracted from Wikipedia, snippets from the Pile’s https://huggingface.co/datasets/the_pile_books3, and snippets of screenplays available on the public web.
## unified_sqlv1.jsonl (~17000)
- public text 2 sql datasets.
## unified_sqlv2.jsonl(~24000)
- public text 2 sql datasets.
## unified_squad_v2.jsonl (~19000)
- https://rajpurkar.github.io/SQuAD-explorer/
## unified_squad_v2_more_neg.jsonl (~19000)
- https://rajpurkar.github.io/SQuAD-explorer/
## unified_ul2_plus_oscar_en_sample_dialog.jsonl (~2900000)
- https://oscar-project.org/
- https://huggingface.co/datasets/TurkuNLP/register_oscar
## unified_unifiedskg_instructions.jsonl (~223000)
- https://github.com/HKUNLP/UnifiedSKG
## unified_unnatural_instructions.jsonl (~238000)
- https://github.com/orhonovich/unnatural-instructions
## unified_xp3_sample.jsonl (~188000)
- https://huggingface.co/datasets/bigscience/xP3
## unified_canadian_parliament.jsonl(~301000)
- https://openparliament.ca/data-download/
## unified_poetry_2_song.jsonl (~12000)
- https://huggingface.co/datasets/merve/poetry
- https://huggingface.co/datasets/matthh/gutenberg-poetry-corpus
## unified_flan.jsonl (~2700000)
- https://github.com/google-research/FLAN/tree/main/flan/v2
## unified_ni.jsonl (~256000)
- https://github.com/allenai/natural-instructions
## unified_p3.jsonl (~31000000)
- https://huggingface.co/datasets/bigscience/P3
## unified_soda_dialog.jsonl (~1200000)
- https://huggingface.co/datasets/allenai/soda
## unified_rallio_soda_upgraded_2048.jsonl (~210000)
- https://huggingface.co/datasets/allenai/soda
- a newer version of the unified_soda_dialog dataset, with multiple dialogs on one line
- recommend to use either the unified_soda_dailog.jsonl or unified_rallio_soda_upgraded_2048, and not both.
## unified_rallio_safety_and_prosocial.jsonl (~319000)
- Generated from public datasets and generated from Wiki similar to the chip2 data
- Find a full list in the end of the document
- This dataset also includes https://huggingface.co/datasets/allenai/prosocial-dialog and https://huggingface.co/datasets/Anthropic/hh-rlhf
## unified-chip2.jsonl / OIG-small-chip2 (~210000):
This dataset was created as part of the LAION OA effort by @rallio67 and other members of the LAION contributors. It is a high quality dataset intended to be mixed into a large pre-train dataset and can be used for a final finetune. Chip2 contains:
### Python Code Examples (~6,000):
A set of instruction / response pairs where the User requests the agent to generate a python function. These examples were generated using a large language model and few shot prompting with python code verified to execute. There are also ~3000 examples of manually curated one line python code examples from the Conala publication (see: https://conala-corpus.github.io/)
### Natural Instruction Examples (~124,000):
A balanced set of diverse natural and factual questions and answers made using few shot prompted UL2 20B and an instruction tuned GPT-NeoX-20B model (Chip) and then rejection sampled using multiple automatic evaluations to remove low quality outputs and to filter out factually inaccurate answers. Also includes some filtered natural instructions from Anthropic Helpful instructions (see: https://github.com/anthropics/hh-rlhf).
### Generic Harmless Instruction Examples (~6,500):
A set of instruction / response pairs sourced from the Anthropic redteam paper github (see: https://github.com/anthropics/hh-rlhf). This dataset includes a lot of data regarding real humans trying to make the Anthropic language models say harmful/toxic/trolling things. For this dataset only examples that were rated lowly on the harmful scale (0,1,2 out of 4, where 4 is the most toxic) were included. Again, only the first lines of dialogue (instruction, first_agent_response) were retained.
### Instruction/Responses with Lists (~14,000):
A set of filtered and reformatted instruction / response pairs where the agent response contains a list. Sourced from the Anthropic github (see: https://github.com/anthropics/hh-rlhf). Sourced from wikihow text lists created by b-mc2 (https://huggingface.co/datasets/b-mc2/wikihow_lists). And rejection filtered instruction response pairs generated by Chip20B that contained lists. All lists are formatted in a similar style.
### Follow-up questions (~12,500):
Examples of instructions and responses where an appropriate response is to ask for more information from the prompter. These examples were generated from a combination of few shot prompted UL2 20B (to generate natural questions) and a large dialogue prompted language model to generate the responses containing follow-up questions.
### Wikipedia Toxic Adversarial Questions (~12,000):
Questions and answers generated from wikipedia articles that discuss potentially sensitive topics (flagged as potentially toxic by an early toxicity detection model).
### Grade School Math GSM8K (~9,000):
GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training problems and 1K test problems. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. It can be used for multi-step mathematical reasoning. (https://github.com/openai/grade-school-math)
### Reasoning Instructions (~4,500):
Examples from the Com2Sense and Strategy QA datasets that were reformatted into natural instructions using large language models with few shot prompting and additional quality filtering steps.
### Character and Scene Descriptions (~30,000):
Examples of instructions and responses for the generation of character or scene descriptions. Scenes were sourced from video game wikis and reformatted into instruction / response format using large language models or generated by few shot prompting with large language models.
## Support this project
Your contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can:
Submit Github issues, track issues and help create datasets that need improvement. https://github.com/LAION-AI/Open-Instruction-Generalist
Join our Discord to talk with other team members working on this! https://discord.gg/xBPBXfcFHd
## Update: March 20, 2023
- Added the metadata column to all datasets to alleviate issues with HF datasets loader.
- Broke some of the p3 dialogs into parts for ease of loading.
## Disclaimer
These datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets.
## License
The OIG dataset that is authored by LAION volunteers is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as Wikipedia data which is licensed under CC-BY-SA, or web-crawled data which is used under fair use principles.
## Acknowledgement
- We would like to thank all of our amazing LAION volunteers including: @Rallio, @Jue, @Ce Zhang, @Player-1, @Laurel, @danielpatrickhug, @Jjmachan, @Mylo, @Khalid, @Coco.han, @Jordiclive, @Pszemraj, all volunteers from the Open Assistant project who initially created synthetic data, and many others.
- We would like to thank Together for their tireless dedication to the open source and AI community and their contribution to many of the datasets.
- We would like to thank AI Horde and user @Db0 for their incredible contribution of filtered data that were flagged as unethical.
- Please check out our related project: https://github.com/LAION-AI/Open-Assistant for our work in human feedback gathering and RLHF.
- Lastly, Ontocord.ai’s founders are grateful to have the opportunity to create a portion of the data augmentation and safety-moderation code for this project.
| laion/OIG | [
"license:apache-2.0",
"region:us"
]
| 2023-03-05T00:34:58+00:00 | {"license": "apache-2.0"} | 2023-03-30T23:06:28+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
|
# This is the Open Instruction Generalist Dataset
This is our attempt to create a large instruction dataset of medium quality along with a smaller high quality instruciton dataset (OIG-small-chip2).
The data is in the form of jsonl objects, with at least a 'text' field. Some datasets may also include a 'metadata' field. The 'text' field contains a string of the form of one or more of:
- \<human\>: instruction\n\<bot\>: response
- \<human\>: instruction\n\<bot\>: response .. \<human\>: instruction\n\<bot\>: response
The purpose of the larger dataset is to perform continued pre-training, followed by a finetune on the smaller high quality dataset.
The purpose of the smaller OIG-small-chip2 dataset is to make it easy to convert a language model pretrained on large amounts of text into an instruction following model using a small amount of additional compute via finetuning or softprompt tuning.
Many additional datasets are being prepared by various community members and will be incorporated into this dataset as we are able to verify the quality and formatting of the data. Our goal is to make helpful and non-toxic instruction tuned models available to everyone.
OIG is currently at 44M. We will continue to publish ever larger diverse instruction datasets with the goal of creating 1 trillion tokens of diverse instructions - enough to pretrain an LLM from scratch.
It is best to download the individual jsonl files directly that you wish to use instead of using HF load_datasets. URL
## unified_abstract_infill.jsonl (~232000)
dbpedia and wikipedia snippets combined with a small portion of URL
## unified_basic.jsonl (30)
## unified_conv_finqa.jsonl (~9000)
URL
## unified_cuad.jsonl (~500)
URL
## unified_essays.jsonl (~2000)
- essays available on the public web
## unified_grade_school_math_instructions.jsonl (~9000)
- URL
## unified_hc3_human.jsonl (~58000)
## unified_image_prompts_instructions.jsonl (~15000)
- A very small subset of LAION-400M
## unified_joke_explanations.jsonl (356)
- Crawled from public internet.
## unified_mathqa_flanv2_kojma_cot.jsonl (~107000)
- URL
## unified_merged_code_xp3.jsonl (~67000)
- URL
## unified_multi_news.jsonl (~90000)
- URL
## unified_multi_sum.jsonl (~1700000)
## unified_nq.jsonl (~307000)
## unified_openai_summarize_tldr.jsonl (~233000)
- URL
## unified_oscar_en_sample_dialog.jsonl (~2670000)
- URL
- URL
## unified_plot_screenplay_books_dialog.jsonl (~8000)
- URL extracted from Wikipedia, snippets from the Pile’s URL and snippets of screenplays available on the public web.
## unified_sqlv1.jsonl (~17000)
- public text 2 sql datasets.
## unified_sqlv2.jsonl(~24000)
- public text 2 sql datasets.
## unified_squad_v2.jsonl (~19000)
- URL
## unified_squad_v2_more_neg.jsonl (~19000)
- URL
## unified_ul2_plus_oscar_en_sample_dialog.jsonl (~2900000)
- URL
- URL
## unified_unifiedskg_instructions.jsonl (~223000)
- URL
## unified_unnatural_instructions.jsonl (~238000)
- URL
## unified_xp3_sample.jsonl (~188000)
- URL
## unified_canadian_parliament.jsonl(~301000)
- URL
## unified_poetry_2_song.jsonl (~12000)
- URL
- URL
## unified_flan.jsonl (~2700000)
- URL
## unified_ni.jsonl (~256000)
- URL
## unified_p3.jsonl (~31000000)
- URL
## unified_soda_dialog.jsonl (~1200000)
- URL
## unified_rallio_soda_upgraded_2048.jsonl (~210000)
- URL
- a newer version of the unified_soda_dialog dataset, with multiple dialogs on one line
- recommend to use either the unified_soda_dailog.jsonl or unified_rallio_soda_upgraded_2048, and not both.
## unified_rallio_safety_and_prosocial.jsonl (~319000)
- Generated from public datasets and generated from Wiki similar to the chip2 data
- Find a full list in the end of the document
- This dataset also includes URL and URL
## URL / OIG-small-chip2 (~210000):
This dataset was created as part of the LAION OA effort by @rallio67 and other members of the LAION contributors. It is a high quality dataset intended to be mixed into a large pre-train dataset and can be used for a final finetune. Chip2 contains:
### Python Code Examples (~6,000):
A set of instruction / response pairs where the User requests the agent to generate a python function. These examples were generated using a large language model and few shot prompting with python code verified to execute. There are also ~3000 examples of manually curated one line python code examples from the Conala publication (see: URL
### Natural Instruction Examples (~124,000):
A balanced set of diverse natural and factual questions and answers made using few shot prompted UL2 20B and an instruction tuned GPT-NeoX-20B model (Chip) and then rejection sampled using multiple automatic evaluations to remove low quality outputs and to filter out factually inaccurate answers. Also includes some filtered natural instructions from Anthropic Helpful instructions (see: URL
### Generic Harmless Instruction Examples (~6,500):
A set of instruction / response pairs sourced from the Anthropic redteam paper github (see: URL This dataset includes a lot of data regarding real humans trying to make the Anthropic language models say harmful/toxic/trolling things. For this dataset only examples that were rated lowly on the harmful scale (0,1,2 out of 4, where 4 is the most toxic) were included. Again, only the first lines of dialogue (instruction, first_agent_response) were retained.
### Instruction/Responses with Lists (~14,000):
A set of filtered and reformatted instruction / response pairs where the agent response contains a list. Sourced from the Anthropic github (see: URL Sourced from wikihow text lists created by b-mc2 (URL And rejection filtered instruction response pairs generated by Chip20B that contained lists. All lists are formatted in a similar style.
### Follow-up questions (~12,500):
Examples of instructions and responses where an appropriate response is to ask for more information from the prompter. These examples were generated from a combination of few shot prompted UL2 20B (to generate natural questions) and a large dialogue prompted language model to generate the responses containing follow-up questions.
### Wikipedia Toxic Adversarial Questions (~12,000):
Questions and answers generated from wikipedia articles that discuss potentially sensitive topics (flagged as potentially toxic by an early toxicity detection model).
### Grade School Math GSM8K (~9,000):
GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training problems and 1K test problems. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. It can be used for multi-step mathematical reasoning. (URL
### Reasoning Instructions (~4,500):
Examples from the Com2Sense and Strategy QA datasets that were reformatted into natural instructions using large language models with few shot prompting and additional quality filtering steps.
### Character and Scene Descriptions (~30,000):
Examples of instructions and responses for the generation of character or scene descriptions. Scenes were sourced from video game wikis and reformatted into instruction / response format using large language models or generated by few shot prompting with large language models.
## Support this project
Your contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can:
Submit Github issues, track issues and help create datasets that need improvement. URL
Join our Discord to talk with other team members working on this! URL
## Update: March 20, 2023
- Added the metadata column to all datasets to alleviate issues with HF datasets loader.
- Broke some of the p3 dialogs into parts for ease of loading.
## Disclaimer
These datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets.
## License
The OIG dataset that is authored by LAION volunteers is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as Wikipedia data which is licensed under CC-BY-SA, or web-crawled data which is used under fair use principles.
## Acknowledgement
- We would like to thank all of our amazing LAION volunteers including: @Rallio, @Jue, @Ce Zhang, @Player-1, @Laurel, @danielpatrickhug, @Jjmachan, @Mylo, @Khalid, @URL, @Jordiclive, @Pszemraj, all volunteers from the Open Assistant project who initially created synthetic data, and many others.
- We would like to thank Together for their tireless dedication to the open source and AI community and their contribution to many of the datasets.
- We would like to thank AI Horde and user @Db0 for their incredible contribution of filtered data that were flagged as unethical.
- Please check out our related project: URL for our work in human feedback gathering and RLHF.
- Lastly, URL’s founders are grateful to have the opportunity to create a portion of the data augmentation and safety-moderation code for this project.
| [
"# This is the Open Instruction Generalist Dataset\n\nThis is our attempt to create a large instruction dataset of medium quality along with a smaller high quality instruciton dataset (OIG-small-chip2).\n\nThe data is in the form of jsonl objects, with at least a 'text' field. Some datasets may also include a 'metadata' field. The 'text' field contains a string of the form of one or more of:\n\n- \\<human\\>: instruction\\n\\<bot\\>: response\n- \\<human\\>: instruction\\n\\<bot\\>: response .. \\<human\\>: instruction\\n\\<bot\\>: response\n \nThe purpose of the larger dataset is to perform continued pre-training, followed by a finetune on the smaller high quality dataset.\n\nThe purpose of the smaller OIG-small-chip2 dataset is to make it easy to convert a language model pretrained on large amounts of text into an instruction following model using a small amount of additional compute via finetuning or softprompt tuning.\n\nMany additional datasets are being prepared by various community members and will be incorporated into this dataset as we are able to verify the quality and formatting of the data. Our goal is to make helpful and non-toxic instruction tuned models available to everyone.\n\nOIG is currently at 44M. We will continue to publish ever larger diverse instruction datasets with the goal of creating 1 trillion tokens of diverse instructions - enough to pretrain an LLM from scratch.\n\nIt is best to download the individual jsonl files directly that you wish to use instead of using HF load_datasets. URL",
"## unified_abstract_infill.jsonl (~232000)\ndbpedia and wikipedia snippets combined with a small portion of URL",
"## unified_basic.jsonl (30)",
"## unified_conv_finqa.jsonl (~9000)\nURL",
"## unified_cuad.jsonl (~500)\nURL",
"## unified_essays.jsonl (~2000)\n- essays available on the public web",
"## unified_grade_school_math_instructions.jsonl (~9000)\n- URL",
"## unified_hc3_human.jsonl (~58000)",
"## unified_image_prompts_instructions.jsonl (~15000)\n- A very small subset of LAION-400M",
"## unified_joke_explanations.jsonl (356)\n- Crawled from public internet.",
"## unified_mathqa_flanv2_kojma_cot.jsonl (~107000)\n- URL",
"## unified_merged_code_xp3.jsonl (~67000)\n- URL",
"## unified_multi_news.jsonl (~90000)\n- URL",
"## unified_multi_sum.jsonl (~1700000)",
"## unified_nq.jsonl (~307000)",
"## unified_openai_summarize_tldr.jsonl (~233000)\n- URL",
"## unified_oscar_en_sample_dialog.jsonl (~2670000)\n- URL\n- URL",
"## unified_plot_screenplay_books_dialog.jsonl (~8000)\n- URL extracted from Wikipedia, snippets from the Pile’s URL and snippets of screenplays available on the public web.",
"## unified_sqlv1.jsonl (~17000)\n- public text 2 sql datasets.",
"## unified_sqlv2.jsonl(~24000)\n- public text 2 sql datasets.",
"## unified_squad_v2.jsonl (~19000)\n- URL",
"## unified_squad_v2_more_neg.jsonl (~19000)\n- URL",
"## unified_ul2_plus_oscar_en_sample_dialog.jsonl (~2900000)\n- URL\n- URL",
"## unified_unifiedskg_instructions.jsonl (~223000)\n- URL",
"## unified_unnatural_instructions.jsonl (~238000)\n- URL",
"## unified_xp3_sample.jsonl (~188000)\n- URL",
"## unified_canadian_parliament.jsonl(~301000)\n- URL",
"## unified_poetry_2_song.jsonl (~12000)\n- URL\n- URL",
"## unified_flan.jsonl (~2700000)\n- URL",
"## unified_ni.jsonl (~256000)\n - URL",
"## unified_p3.jsonl (~31000000)\n- URL",
"## unified_soda_dialog.jsonl (~1200000)\n- URL",
"## unified_rallio_soda_upgraded_2048.jsonl (~210000)\n- URL \n- a newer version of the unified_soda_dialog dataset, with multiple dialogs on one line\n- recommend to use either the unified_soda_dailog.jsonl or unified_rallio_soda_upgraded_2048, and not both.",
"## unified_rallio_safety_and_prosocial.jsonl (~319000)\n- Generated from public datasets and generated from Wiki similar to the chip2 data\n- Find a full list in the end of the document\n- This dataset also includes URL and URL",
"## URL / OIG-small-chip2 (~210000):\nThis dataset was created as part of the LAION OA effort by @rallio67 and other members of the LAION contributors. It is a high quality dataset intended to be mixed into a large pre-train dataset and can be used for a final finetune. Chip2 contains:",
"### Python Code Examples (~6,000):\nA set of instruction / response pairs where the User requests the agent to generate a python function. These examples were generated using a large language model and few shot prompting with python code verified to execute. There are also ~3000 examples of manually curated one line python code examples from the Conala publication (see: URL",
"### Natural Instruction Examples (~124,000):\nA balanced set of diverse natural and factual questions and answers made using few shot prompted UL2 20B and an instruction tuned GPT-NeoX-20B model (Chip) and then rejection sampled using multiple automatic evaluations to remove low quality outputs and to filter out factually inaccurate answers. Also includes some filtered natural instructions from Anthropic Helpful instructions (see: URL",
"### Generic Harmless Instruction Examples (~6,500):\nA set of instruction / response pairs sourced from the Anthropic redteam paper github (see: URL This dataset includes a lot of data regarding real humans trying to make the Anthropic language models say harmful/toxic/trolling things. For this dataset only examples that were rated lowly on the harmful scale (0,1,2 out of 4, where 4 is the most toxic) were included. Again, only the first lines of dialogue (instruction, first_agent_response) were retained.",
"### Instruction/Responses with Lists (~14,000):\nA set of filtered and reformatted instruction / response pairs where the agent response contains a list. Sourced from the Anthropic github (see: URL Sourced from wikihow text lists created by b-mc2 (URL And rejection filtered instruction response pairs generated by Chip20B that contained lists. All lists are formatted in a similar style.",
"### Follow-up questions (~12,500):\nExamples of instructions and responses where an appropriate response is to ask for more information from the prompter. These examples were generated from a combination of few shot prompted UL2 20B (to generate natural questions) and a large dialogue prompted language model to generate the responses containing follow-up questions.",
"### Wikipedia Toxic Adversarial Questions (~12,000):\nQuestions and answers generated from wikipedia articles that discuss potentially sensitive topics (flagged as potentially toxic by an early toxicity detection model).",
"### Grade School Math GSM8K (~9,000):\nGSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training problems and 1K test problems. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. It can be used for multi-step mathematical reasoning. (URL",
"### Reasoning Instructions (~4,500):\nExamples from the Com2Sense and Strategy QA datasets that were reformatted into natural instructions using large language models with few shot prompting and additional quality filtering steps.",
"### Character and Scene Descriptions (~30,000):\nExamples of instructions and responses for the generation of character or scene descriptions. Scenes were sourced from video game wikis and reformatted into instruction / response format using large language models or generated by few shot prompting with large language models.",
"## Support this project\nYour contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can:\n\nSubmit Github issues, track issues and help create datasets that need improvement. URL \nJoin our Discord to talk with other team members working on this! URL",
"## Update: March 20, 2023\n- Added the metadata column to all datasets to alleviate issues with HF datasets loader.\n- Broke some of the p3 dialogs into parts for ease of loading.",
"## Disclaimer\nThese datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets.",
"## License\nThe OIG dataset that is authored by LAION volunteers is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as Wikipedia data which is licensed under CC-BY-SA, or web-crawled data which is used under fair use principles.",
"## Acknowledgement\n- We would like to thank all of our amazing LAION volunteers including: @Rallio, @Jue, @Ce Zhang, @Player-1, @Laurel, @danielpatrickhug, @Jjmachan, @Mylo, @Khalid, @URL, @Jordiclive, @Pszemraj, all volunteers from the Open Assistant project who initially created synthetic data, and many others.\n- We would like to thank Together for their tireless dedication to the open source and AI community and their contribution to many of the datasets.\n- We would like to thank AI Horde and user @Db0 for their incredible contribution of filtered data that were flagged as unethical.\n- Please check out our related project: URL for our work in human feedback gathering and RLHF.\n- Lastly, URL’s founders are grateful to have the opportunity to create a portion of the data augmentation and safety-moderation code for this project."
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"# This is the Open Instruction Generalist Dataset\n\nThis is our attempt to create a large instruction dataset of medium quality along with a smaller high quality instruciton dataset (OIG-small-chip2).\n\nThe data is in the form of jsonl objects, with at least a 'text' field. Some datasets may also include a 'metadata' field. The 'text' field contains a string of the form of one or more of:\n\n- \\<human\\>: instruction\\n\\<bot\\>: response\n- \\<human\\>: instruction\\n\\<bot\\>: response .. \\<human\\>: instruction\\n\\<bot\\>: response\n \nThe purpose of the larger dataset is to perform continued pre-training, followed by a finetune on the smaller high quality dataset.\n\nThe purpose of the smaller OIG-small-chip2 dataset is to make it easy to convert a language model pretrained on large amounts of text into an instruction following model using a small amount of additional compute via finetuning or softprompt tuning.\n\nMany additional datasets are being prepared by various community members and will be incorporated into this dataset as we are able to verify the quality and formatting of the data. Our goal is to make helpful and non-toxic instruction tuned models available to everyone.\n\nOIG is currently at 44M. We will continue to publish ever larger diverse instruction datasets with the goal of creating 1 trillion tokens of diverse instructions - enough to pretrain an LLM from scratch.\n\nIt is best to download the individual jsonl files directly that you wish to use instead of using HF load_datasets. URL",
"## unified_abstract_infill.jsonl (~232000)\ndbpedia and wikipedia snippets combined with a small portion of URL",
"## unified_basic.jsonl (30)",
"## unified_conv_finqa.jsonl (~9000)\nURL",
"## unified_cuad.jsonl (~500)\nURL",
"## unified_essays.jsonl (~2000)\n- essays available on the public web",
"## unified_grade_school_math_instructions.jsonl (~9000)\n- URL",
"## unified_hc3_human.jsonl (~58000)",
"## unified_image_prompts_instructions.jsonl (~15000)\n- A very small subset of LAION-400M",
"## unified_joke_explanations.jsonl (356)\n- Crawled from public internet.",
"## unified_mathqa_flanv2_kojma_cot.jsonl (~107000)\n- URL",
"## unified_merged_code_xp3.jsonl (~67000)\n- URL",
"## unified_multi_news.jsonl (~90000)\n- URL",
"## unified_multi_sum.jsonl (~1700000)",
"## unified_nq.jsonl (~307000)",
"## unified_openai_summarize_tldr.jsonl (~233000)\n- URL",
"## unified_oscar_en_sample_dialog.jsonl (~2670000)\n- URL\n- URL",
"## unified_plot_screenplay_books_dialog.jsonl (~8000)\n- URL extracted from Wikipedia, snippets from the Pile’s URL and snippets of screenplays available on the public web.",
"## unified_sqlv1.jsonl (~17000)\n- public text 2 sql datasets.",
"## unified_sqlv2.jsonl(~24000)\n- public text 2 sql datasets.",
"## unified_squad_v2.jsonl (~19000)\n- URL",
"## unified_squad_v2_more_neg.jsonl (~19000)\n- URL",
"## unified_ul2_plus_oscar_en_sample_dialog.jsonl (~2900000)\n- URL\n- URL",
"## unified_unifiedskg_instructions.jsonl (~223000)\n- URL",
"## unified_unnatural_instructions.jsonl (~238000)\n- URL",
"## unified_xp3_sample.jsonl (~188000)\n- URL",
"## unified_canadian_parliament.jsonl(~301000)\n- URL",
"## unified_poetry_2_song.jsonl (~12000)\n- URL\n- URL",
"## unified_flan.jsonl (~2700000)\n- URL",
"## unified_ni.jsonl (~256000)\n - URL",
"## unified_p3.jsonl (~31000000)\n- URL",
"## unified_soda_dialog.jsonl (~1200000)\n- URL",
"## unified_rallio_soda_upgraded_2048.jsonl (~210000)\n- URL \n- a newer version of the unified_soda_dialog dataset, with multiple dialogs on one line\n- recommend to use either the unified_soda_dailog.jsonl or unified_rallio_soda_upgraded_2048, and not both.",
"## unified_rallio_safety_and_prosocial.jsonl (~319000)\n- Generated from public datasets and generated from Wiki similar to the chip2 data\n- Find a full list in the end of the document\n- This dataset also includes URL and URL",
"## URL / OIG-small-chip2 (~210000):\nThis dataset was created as part of the LAION OA effort by @rallio67 and other members of the LAION contributors. It is a high quality dataset intended to be mixed into a large pre-train dataset and can be used for a final finetune. Chip2 contains:",
"### Python Code Examples (~6,000):\nA set of instruction / response pairs where the User requests the agent to generate a python function. These examples were generated using a large language model and few shot prompting with python code verified to execute. There are also ~3000 examples of manually curated one line python code examples from the Conala publication (see: URL",
"### Natural Instruction Examples (~124,000):\nA balanced set of diverse natural and factual questions and answers made using few shot prompted UL2 20B and an instruction tuned GPT-NeoX-20B model (Chip) and then rejection sampled using multiple automatic evaluations to remove low quality outputs and to filter out factually inaccurate answers. Also includes some filtered natural instructions from Anthropic Helpful instructions (see: URL",
"### Generic Harmless Instruction Examples (~6,500):\nA set of instruction / response pairs sourced from the Anthropic redteam paper github (see: URL This dataset includes a lot of data regarding real humans trying to make the Anthropic language models say harmful/toxic/trolling things. For this dataset only examples that were rated lowly on the harmful scale (0,1,2 out of 4, where 4 is the most toxic) were included. Again, only the first lines of dialogue (instruction, first_agent_response) were retained.",
"### Instruction/Responses with Lists (~14,000):\nA set of filtered and reformatted instruction / response pairs where the agent response contains a list. Sourced from the Anthropic github (see: URL Sourced from wikihow text lists created by b-mc2 (URL And rejection filtered instruction response pairs generated by Chip20B that contained lists. All lists are formatted in a similar style.",
"### Follow-up questions (~12,500):\nExamples of instructions and responses where an appropriate response is to ask for more information from the prompter. These examples were generated from a combination of few shot prompted UL2 20B (to generate natural questions) and a large dialogue prompted language model to generate the responses containing follow-up questions.",
"### Wikipedia Toxic Adversarial Questions (~12,000):\nQuestions and answers generated from wikipedia articles that discuss potentially sensitive topics (flagged as potentially toxic by an early toxicity detection model).",
"### Grade School Math GSM8K (~9,000):\nGSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training problems and 1K test problems. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. It can be used for multi-step mathematical reasoning. (URL",
"### Reasoning Instructions (~4,500):\nExamples from the Com2Sense and Strategy QA datasets that were reformatted into natural instructions using large language models with few shot prompting and additional quality filtering steps.",
"### Character and Scene Descriptions (~30,000):\nExamples of instructions and responses for the generation of character or scene descriptions. Scenes were sourced from video game wikis and reformatted into instruction / response format using large language models or generated by few shot prompting with large language models.",
"## Support this project\nYour contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can:\n\nSubmit Github issues, track issues and help create datasets that need improvement. URL \nJoin our Discord to talk with other team members working on this! URL",
"## Update: March 20, 2023\n- Added the metadata column to all datasets to alleviate issues with HF datasets loader.\n- Broke some of the p3 dialogs into parts for ease of loading.",
"## Disclaimer\nThese datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets.",
"## License\nThe OIG dataset that is authored by LAION volunteers is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as Wikipedia data which is licensed under CC-BY-SA, or web-crawled data which is used under fair use principles.",
"## Acknowledgement\n- We would like to thank all of our amazing LAION volunteers including: @Rallio, @Jue, @Ce Zhang, @Player-1, @Laurel, @danielpatrickhug, @Jjmachan, @Mylo, @Khalid, @URL, @Jordiclive, @Pszemraj, all volunteers from the Open Assistant project who initially created synthetic data, and many others.\n- We would like to thank Together for their tireless dedication to the open source and AI community and their contribution to many of the datasets.\n- We would like to thank AI Horde and user @Db0 for their incredible contribution of filtered data that were flagged as unethical.\n- Please check out our related project: URL for our work in human feedback gathering and RLHF.\n- Lastly, URL’s founders are grateful to have the opportunity to create a portion of the data augmentation and safety-moderation code for this project."
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"passage: TAGS\n#license-apache-2.0 #region-us \n# This is the Open Instruction Generalist Dataset\n\nThis is our attempt to create a large instruction dataset of medium quality along with a smaller high quality instruciton dataset (OIG-small-chip2).\n\nThe data is in the form of jsonl objects, with at least a 'text' field. Some datasets may also include a 'metadata' field. The 'text' field contains a string of the form of one or more of:\n\n- \\<human\\>: instruction\\n\\<bot\\>: response\n- \\<human\\>: instruction\\n\\<bot\\>: response .. \\<human\\>: instruction\\n\\<bot\\>: response\n \nThe purpose of the larger dataset is to perform continued pre-training, followed by a finetune on the smaller high quality dataset.\n\nThe purpose of the smaller OIG-small-chip2 dataset is to make it easy to convert a language model pretrained on large amounts of text into an instruction following model using a small amount of additional compute via finetuning or softprompt tuning.\n\nMany additional datasets are being prepared by various community members and will be incorporated into this dataset as we are able to verify the quality and formatting of the data. Our goal is to make helpful and non-toxic instruction tuned models available to everyone.\n\nOIG is currently at 44M. We will continue to publish ever larger diverse instruction datasets with the goal of creating 1 trillion tokens of diverse instructions - enough to pretrain an LLM from scratch.\n\nIt is best to download the individual jsonl files directly that you wish to use instead of using HF load_datasets. URL## unified_abstract_infill.jsonl (~232000)\ndbpedia and wikipedia snippets combined with a small portion of URL## unified_basic.jsonl (30)## unified_conv_finqa.jsonl (~9000)\nURL## unified_cuad.jsonl (~500)\nURL## unified_essays.jsonl (~2000)\n- essays available on the public web",
"passage: ## unified_grade_school_math_instructions.jsonl (~9000)\n- URL## unified_hc3_human.jsonl (~58000)## unified_image_prompts_instructions.jsonl (~15000)\n- A very small subset of LAION-400M## unified_joke_explanations.jsonl (356)\n- Crawled from public internet.## unified_mathqa_flanv2_kojma_cot.jsonl (~107000)\n- URL## unified_merged_code_xp3.jsonl (~67000)\n- URL## unified_multi_news.jsonl (~90000)\n- URL## unified_multi_sum.jsonl (~1700000)## unified_nq.jsonl (~307000)## unified_openai_summarize_tldr.jsonl (~233000)\n- URL## unified_oscar_en_sample_dialog.jsonl (~2670000)\n- URL\n- URL## unified_plot_screenplay_books_dialog.jsonl (~8000)\n- URL extracted from Wikipedia, snippets from the Pile’s URL and snippets of screenplays available on the public web.## unified_sqlv1.jsonl (~17000)\n- public text 2 sql datasets.## unified_sqlv2.jsonl(~24000)\n- public text 2 sql datasets.## unified_squad_v2.jsonl (~19000)\n- URL## unified_squad_v2_more_neg.jsonl (~19000)\n- URL## unified_ul2_plus_oscar_en_sample_dialog.jsonl (~2900000)\n- URL\n- URL## unified_unifiedskg_instructions.jsonl (~223000)\n- URL## unified_unnatural_instructions.jsonl (~238000)\n- URL## unified_xp3_sample.jsonl (~188000)\n- URL## unified_canadian_parliament.jsonl(~301000)\n- URL",
"passage: ## unified_poetry_2_song.jsonl (~12000)\n- URL\n- URL## unified_flan.jsonl (~2700000)\n- URL## unified_ni.jsonl (~256000)\n - URL## unified_p3.jsonl (~31000000)\n- URL## unified_soda_dialog.jsonl (~1200000)\n- URL## unified_rallio_soda_upgraded_2048.jsonl (~210000)\n- URL \n- a newer version of the unified_soda_dialog dataset, with multiple dialogs on one line\n- recommend to use either the unified_soda_dailog.jsonl or unified_rallio_soda_upgraded_2048, and not both.## unified_rallio_safety_and_prosocial.jsonl (~319000)\n- Generated from public datasets and generated from Wiki similar to the chip2 data\n- Find a full list in the end of the document\n- This dataset also includes URL and URL## URL / OIG-small-chip2 (~210000):\nThis dataset was created as part of the LAION OA effort by @rallio67 and other members of the LAION contributors. It is a high quality dataset intended to be mixed into a large pre-train dataset and can be used for a final finetune. Chip2 contains:### Python Code Examples (~6,000):\nA set of instruction / response pairs where the User requests the agent to generate a python function. These examples were generated using a large language model and few shot prompting with python code verified to execute. There are also ~3000 examples of manually curated one line python code examples from the Conala publication (see: URL### Natural Instruction Examples (~124,000):\nA balanced set of diverse natural and factual questions and answers made using few shot prompted UL2 20B and an instruction tuned GPT-NeoX-20B model (Chip) and then rejection sampled using multiple automatic evaluations to remove low quality outputs and to filter out factually inaccurate answers. Also includes some filtered natural instructions from Anthropic Helpful instructions (see: URL",
"passage: ### Generic Harmless Instruction Examples (~6,500):\nA set of instruction / response pairs sourced from the Anthropic redteam paper github (see: URL This dataset includes a lot of data regarding real humans trying to make the Anthropic language models say harmful/toxic/trolling things. For this dataset only examples that were rated lowly on the harmful scale (0,1,2 out of 4, where 4 is the most toxic) were included. Again, only the first lines of dialogue (instruction, first_agent_response) were retained.### Instruction/Responses with Lists (~14,000):\nA set of filtered and reformatted instruction / response pairs where the agent response contains a list. Sourced from the Anthropic github (see: URL Sourced from wikihow text lists created by b-mc2 (URL And rejection filtered instruction response pairs generated by Chip20B that contained lists. All lists are formatted in a similar style.### Follow-up questions (~12,500):\nExamples of instructions and responses where an appropriate response is to ask for more information from the prompter. These examples were generated from a combination of few shot prompted UL2 20B (to generate natural questions) and a large dialogue prompted language model to generate the responses containing follow-up questions.### Wikipedia Toxic Adversarial Questions (~12,000):\nQuestions and answers generated from wikipedia articles that discuss potentially sensitive topics (flagged as potentially toxic by an early toxicity detection model).### Grade School Math GSM8K (~9,000):\nGSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training problems and 1K test problems. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. It can be used for multi-step mathematical reasoning. (URL### Reasoning Instructions (~4,500):\nExamples from the Com2Sense and Strategy QA datasets that were reformatted into natural instructions using large language models with few shot prompting and additional quality filtering steps.### Character and Scene Descriptions (~30,000):\nExamples of instructions and responses for the generation of character or scene descriptions. Scenes were sourced from video game wikis and reformatted into instruction / response format using large language models or generated by few shot prompting with large language models."
]
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|
417a6eb9912ab8d2cfcc6a326c0b240cb5d32aa8 | Some voices i got from Youtube for Tortoise TTS usage. Fair Use. | etherealxx/tortoise_voices | [
"license:unknown",
"region:us"
]
| 2023-03-05T00:59:30+00:00 | {"license": "unknown"} | 2023-03-05T07:56:25+00:00 | []
| []
| TAGS
#license-unknown #region-us
| Some voices i got from Youtube for Tortoise TTS usage. Fair Use. | []
| [
"TAGS\n#license-unknown #region-us \n"
]
| [
13
]
| [
"passage: TAGS\n#license-unknown #region-us \n"
]
| [
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|
673bb062ae740ff9e1837ddd098a863c8f01e9f5 | # Dataset Card for "undone-frames-1ep1-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | meric/undone-frames-1ep1-3 | [
"region:us"
]
| 2023-03-05T01:03:48+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 97969396.0, "num_examples": 982}], "download_size": 97384233, "dataset_size": 97969396.0}} | 2023-03-05T08:58:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "undone-frames-1ep1-3"
More Information needed | [
"# Dataset Card for \"undone-frames-1ep1-3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"undone-frames-1ep1-3\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"undone-frames-1ep1-3\"\n\nMore Information needed"
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|
6199042c2be03ae679d558b6757a2d2a2596998f | # 作者 https://space.bilibili.com/317820045 | Junity/UmaMusume-TokaiTeio-Dataset | [
"license:openrail",
"region:us"
]
| 2023-03-05T01:39:23+00:00 | {"license": "openrail"} | 2023-03-17T15:35:09+00:00 | []
| []
| TAGS
#license-openrail #region-us
| # 作者 URL | [
"# 作者 URL"
]
| [
"TAGS\n#license-openrail #region-us \n",
"# 作者 URL"
]
| [
12,
4
]
| [
"passage: TAGS\n#license-openrail #region-us \n# 作者 URL"
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|
ac448762e40d24a5da644a5d2f0669bcbf1b6896 | # Dataset Card for "wiki_20220301flatten"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | whu9/wiki_20220301flatten | [
"region:us"
]
| 2023-03-05T02:40:06+00:00 | {"dataset_info": {"features": [{"name": "sentences", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1224908657, "num_examples": 22874571}], "download_size": 778259253, "dataset_size": 1224908657}} | 2023-03-05T02:40:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki_20220301flatten"
More Information needed | [
"# Dataset Card for \"wiki_20220301flatten\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_20220301flatten\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_20220301flatten\"\n\nMore Information needed"
]
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|
5b839a3d22eb6cd0c3d93b2b242a93912766d23b | # Dataset Card for "en_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yip-i/en_corpora_parliament_processed | [
"region:us"
]
| 2023-03-05T03:34:44+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 281100121, "num_examples": 1892723}], "download_size": 155904108, "dataset_size": 281100121}} | 2023-03-05T03:34:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "en_corpora_parliament_processed"
More Information needed | [
"# Dataset Card for \"en_corpora_parliament_processed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"en_corpora_parliament_processed\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"en_corpora_parliament_processed\"\n\nMore Information needed"
]
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|
70a551c6f28aff0d47f25e0fbe21e45c1bbea041 | # Dataset Card for "NSFW-questions-inter-cleaned_df"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jjmachan/NSFW-questions-inter-cleaned_df | [
"region:us"
]
| 2023-03-05T04:20:22+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "post_id", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "link_flair_text", "dtype": "string"}, {"name": "is_self", "dtype": "bool"}, {"name": "over_18", "dtype": "bool"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "is_question", "dtype": "bool"}, {"name": "C1", "dtype": "string"}, {"name": "C2", "dtype": "string"}, {"name": "C3", "dtype": "string"}, {"name": "C4", "dtype": "string"}, {"name": "C5", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1974116, "num_examples": 12858}], "download_size": 885500, "dataset_size": 1974116}} | 2023-03-05T04:20:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "NSFW-questions-inter-cleaned_df"
More Information needed | [
"# Dataset Card for \"NSFW-questions-inter-cleaned_df\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"NSFW-questions-inter-cleaned_df\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"NSFW-questions-inter-cleaned_df\"\n\nMore Information needed"
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|
273646caa6725ed469008e31d7b66a0cdbc9c2dc | # Dataset Card for "prosocial-nsfw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | shahules786/prosocial-nsfw | [
"region:us"
]
| 2023-03-05T05:55:13+00:00 | {"dataset_info": {"features": [{"name": "user", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "post_id", "dtype": "string"}, {"name": "is_self", "dtype": "bool"}, {"name": "over_18", "dtype": "bool"}, {"name": "is_question", "dtype": "bool"}, {"name": "rots", "sequence": "string"}, {"name": "safety_label", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "episode_done", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 341584, "num_examples": 1502}], "download_size": 166054, "dataset_size": 341584}} | 2023-03-05T05:56:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "prosocial-nsfw"
More Information needed | [
"# Dataset Card for \"prosocial-nsfw\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"prosocial-nsfw\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"prosocial-nsfw\"\n\nMore Information needed"
]
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|
985d1fb9cf4f666d4109961dbbb01a2c7b883da8 |
# MEDIQA-Chat 2023 Training/Validation Data
# Task A
The training set consists of 1,201 pairs of conversations and associated section headers and contents.
The validation set consists of 100 pairs of conversations and their summaries.
The full list of normalized section headers:
1. fam/sochx [FAMILY HISTORY/SOCIAL HISTORY]
2. genhx [HISTORY of PRESENT ILLNESS]
3. pastmedicalhx [PAST MEDICAL HISTORY]
4. cc [CHIEF COMPLAINT]
5. pastsurgical [PAST SURGICAL HISTORY]
6. allergy
7. ros [REVIEW OF SYSTEMS]
8. medications
9. assessment
10. exam
11. diagnosis
12. disposition
13. plan
14. edcourse [EMERGENCY DEPARTMENT COURSE]
15. immunizations
16. imaging
17. gynhx [GYNECOLOGIC HISTORY]
18. procedures
19. other_history
20. labs
# Task B
The training set consists of 67 pairs of conversations and full notes. The validation set includes 20 pairs of conversations and clinical notes.
Full encounter notes are expected to have at least one of four overall section divisions demarked by the first-occuring of its related section headers:
> | note_division | section_headers
> | subjective | chief complaint, history of present illness, hpi, subjective
> | objective_exam | physical exam, exam
> | objective_results | results, findings
> | assessment_and_plan | assessment, plan
Depending on the encounter, objective_exam and objective_results may not be relevant.
We encourage review the sample data as well as the evaluation script to understand the best demarkation headers for your generated note.
# Task C
The training set consists of 67 pairs of full doctor-patient conversations and notes and the validation set includes 20 pairs of full conversations and clinical notes (same as Task-B datasets). The Task-A training and validation sets (1,301 pairs) could be used as additional training data.
| Elfsong/ClinicalDataset | [
"task_categories:summarization",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:en",
"region:us"
]
| 2023-03-05T06:15:46+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["summarization", "conversational"], "pretty_name": "MediQA"} | 2023-03-05T06:43:13+00:00 | []
| [
"en"
]
| TAGS
#task_categories-summarization #task_categories-conversational #size_categories-1K<n<10K #language-English #region-us
|
# MEDIQA-Chat 2023 Training/Validation Data
# Task A
The training set consists of 1,201 pairs of conversations and associated section headers and contents.
The validation set consists of 100 pairs of conversations and their summaries.
The full list of normalized section headers:
1. fam/sochx [FAMILY HISTORY/SOCIAL HISTORY]
2. genhx [HISTORY of PRESENT ILLNESS]
3. pastmedicalhx [PAST MEDICAL HISTORY]
4. cc [CHIEF COMPLAINT]
5. pastsurgical [PAST SURGICAL HISTORY]
6. allergy
7. ros [REVIEW OF SYSTEMS]
8. medications
9. assessment
10. exam
11. diagnosis
12. disposition
13. plan
14. edcourse [EMERGENCY DEPARTMENT COURSE]
15. immunizations
16. imaging
17. gynhx [GYNECOLOGIC HISTORY]
18. procedures
19. other_history
20. labs
# Task B
The training set consists of 67 pairs of conversations and full notes. The validation set includes 20 pairs of conversations and clinical notes.
Full encounter notes are expected to have at least one of four overall section divisions demarked by the first-occuring of its related section headers:
> | note_division | section_headers
> | subjective | chief complaint, history of present illness, hpi, subjective
> | objective_exam | physical exam, exam
> | objective_results | results, findings
> | assessment_and_plan | assessment, plan
Depending on the encounter, objective_exam and objective_results may not be relevant.
We encourage review the sample data as well as the evaluation script to understand the best demarkation headers for your generated note.
# Task C
The training set consists of 67 pairs of full doctor-patient conversations and notes and the validation set includes 20 pairs of full conversations and clinical notes (same as Task-B datasets). The Task-A training and validation sets (1,301 pairs) could be used as additional training data.
| [
"# MEDIQA-Chat 2023 Training/Validation Data",
"# Task A\nThe training set consists of 1,201 pairs of conversations and associated section headers and contents. \nThe validation set consists of 100 pairs of conversations and their summaries. \n\nThe full list of normalized section headers: \n 1. fam/sochx [FAMILY HISTORY/SOCIAL HISTORY]\n 2. genhx [HISTORY of PRESENT ILLNESS]\n 3. pastmedicalhx [PAST MEDICAL HISTORY]\n 4. cc [CHIEF COMPLAINT]\n 5. pastsurgical [PAST SURGICAL HISTORY]\n 6. allergy\n 7. ros [REVIEW OF SYSTEMS]\n 8. medications\n 9. assessment\n 10. exam\n 11. diagnosis\n 12. disposition\n 13. plan\n 14. edcourse [EMERGENCY DEPARTMENT COURSE]\n 15. immunizations\n 16. imaging\n 17. gynhx [GYNECOLOGIC HISTORY]\n 18. procedures\n 19. other_history\n 20. labs",
"# Task B\nThe training set consists of 67 pairs of conversations and full notes. The validation set includes 20 pairs of conversations and clinical notes. \nFull encounter notes are expected to have at least one of four overall section divisions demarked by the first-occuring of its related section headers:\n\n> | note_division | section_headers\n> | subjective | chief complaint, history of present illness, hpi, subjective\n> | objective_exam | physical exam, exam\n> | objective_results | results, findings\n> | assessment_and_plan | assessment, plan\n\nDepending on the encounter, objective_exam and objective_results may not be relevant.\nWe encourage review the sample data as well as the evaluation script to understand the best demarkation headers for your generated note.",
"# Task C\nThe training set consists of 67 pairs of full doctor-patient conversations and notes and the validation set includes 20 pairs of full conversations and clinical notes (same as Task-B datasets). The Task-A training and validation sets (1,301 pairs) could be used as additional training data."
]
| [
"TAGS\n#task_categories-summarization #task_categories-conversational #size_categories-1K<n<10K #language-English #region-us \n",
"# MEDIQA-Chat 2023 Training/Validation Data",
"# Task A\nThe training set consists of 1,201 pairs of conversations and associated section headers and contents. \nThe validation set consists of 100 pairs of conversations and their summaries. \n\nThe full list of normalized section headers: \n 1. fam/sochx [FAMILY HISTORY/SOCIAL HISTORY]\n 2. genhx [HISTORY of PRESENT ILLNESS]\n 3. pastmedicalhx [PAST MEDICAL HISTORY]\n 4. cc [CHIEF COMPLAINT]\n 5. pastsurgical [PAST SURGICAL HISTORY]\n 6. allergy\n 7. ros [REVIEW OF SYSTEMS]\n 8. medications\n 9. assessment\n 10. exam\n 11. diagnosis\n 12. disposition\n 13. plan\n 14. edcourse [EMERGENCY DEPARTMENT COURSE]\n 15. immunizations\n 16. imaging\n 17. gynhx [GYNECOLOGIC HISTORY]\n 18. procedures\n 19. other_history\n 20. labs",
"# Task B\nThe training set consists of 67 pairs of conversations and full notes. The validation set includes 20 pairs of conversations and clinical notes. \nFull encounter notes are expected to have at least one of four overall section divisions demarked by the first-occuring of its related section headers:\n\n> | note_division | section_headers\n> | subjective | chief complaint, history of present illness, hpi, subjective\n> | objective_exam | physical exam, exam\n> | objective_results | results, findings\n> | assessment_and_plan | assessment, plan\n\nDepending on the encounter, objective_exam and objective_results may not be relevant.\nWe encourage review the sample data as well as the evaluation script to understand the best demarkation headers for your generated note.",
"# Task C\nThe training set consists of 67 pairs of full doctor-patient conversations and notes and the validation set includes 20 pairs of full conversations and clinical notes (same as Task-B datasets). The Task-A training and validation sets (1,301 pairs) could be used as additional training data."
]
| [
42,
12,
202,
189,
75
]
| [
"passage: TAGS\n#task_categories-summarization #task_categories-conversational #size_categories-1K<n<10K #language-English #region-us \n# MEDIQA-Chat 2023 Training/Validation Data# Task A\nThe training set consists of 1,201 pairs of conversations and associated section headers and contents. \nThe validation set consists of 100 pairs of conversations and their summaries. \n\nThe full list of normalized section headers: \n 1. fam/sochx [FAMILY HISTORY/SOCIAL HISTORY]\n 2. genhx [HISTORY of PRESENT ILLNESS]\n 3. pastmedicalhx [PAST MEDICAL HISTORY]\n 4. cc [CHIEF COMPLAINT]\n 5. pastsurgical [PAST SURGICAL HISTORY]\n 6. allergy\n 7. ros [REVIEW OF SYSTEMS]\n 8. medications\n 9. assessment\n 10. exam\n 11. diagnosis\n 12. disposition\n 13. plan\n 14. edcourse [EMERGENCY DEPARTMENT COURSE]\n 15. immunizations\n 16. imaging\n 17. gynhx [GYNECOLOGIC HISTORY]\n 18. procedures\n 19. other_history\n 20. labs# Task B\nThe training set consists of 67 pairs of conversations and full notes. The validation set includes 20 pairs of conversations and clinical notes. \nFull encounter notes are expected to have at least one of four overall section divisions demarked by the first-occuring of its related section headers:\n\n> | note_division | section_headers\n> | subjective | chief complaint, history of present illness, hpi, subjective\n> | objective_exam | physical exam, exam\n> | objective_results | results, findings\n> | assessment_and_plan | assessment, plan\n\nDepending on the encounter, objective_exam and objective_results may not be relevant.\nWe encourage review the sample data as well as the evaluation script to understand the best demarkation headers for your generated note."
]
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|
0cd38cbd80dec3b7f93bec1baf57fc7f773f7fcf | 这是Kaggle的云端部署文件，不定期更新，
# 下载kaggle开头的文件，版本号选择最新的版本
# 作者正在加载....，感谢Christina在开发中的帮助，以及yiyiooo在早期的开发
# 群632428790，有问题艾特Yiyiooo或者Christina或者群主
# 更新日志：
2022年11月18日，Yiyiooo在Kaggle发布了第一个云端部署stable diffusion的笔记本
期间发布过100多个版本
2023年3月3日， Kaggle大规模封禁用户，包括yiyiooo，连脚本也一同被封了。所以现在下载的笔记本基本都是基于yiyiooo原笔记本的修复版
~~~~
2023年3月5日更新：现在支持通过下载链接上传模型了，省去了下载模型后再上传后的麻烦.
2023年4月14日更新：更新了python环境至3.10.10
2023年5月1日更新：在christina帮助下实现内存优化，支持下载链接上传所有模型
2023年5月15日更新：现在可以双开webui了，可以双线程跑图（GPU请选择 T4 x2 ， 将use2设置为True）
2023年5月15日更新：更新了多线程启动，启动速度更快一些
2023年6月6日更新：更新了xformers版本，生成速度更快一些
2023年6月16日更新：修复了apt版本过低导致不能安装aria2包的bug
| ACCA225/Kaggle-Stable-Diffusion | [
"region:us"
]
| 2023-03-05T07:16:05+00:00 | {} | 2024-02-12T12:27:21+00:00 | []
| []
| TAGS
#region-us
| 这是Kaggle的云端部署文件，不定期更新，
# 下载kaggle开头的文件，版本号选择最新的版本
# 作者正在加载....，感谢Christina在开发中的帮助，以及yiyiooo在早期的开发
# 群632428790，有问题艾特Yiyiooo或者Christina或者群主
# 更新日志：
2022年11月18日，Yiyiooo在Kaggle发布了第一个云端部署stable diffusion的笔记本
期间发布过100多个版本
2023年3月3日， Kaggle大规模封禁用户，包括yiyiooo，连脚本也一同被封了。所以现在下载的笔记本基本都是基于yiyiooo原笔记本的修复版
~~~~
2023年3月5日更新：现在支持通过下载链接上传模型了，省去了下载模型后再上传后的麻烦.
2023年4月14日更新：更新了python环境至3.10.10
2023年5月1日更新：在christina帮助下实现内存优化，支持下载链接上传所有模型
2023年5月15日更新：现在可以双开webui了，可以双线程跑图（GPU请选择 T4 x2 ， 将use2设置为True）
2023年5月15日更新：更新了多线程启动，启动速度更快一些
2023年6月6日更新：更新了xformers版本，生成速度更快一些
2023年6月16日更新：修复了apt版本过低导致不能安装aria2包的bug
| [
"# 下载kaggle开头的文件，版本号选择最新的版本",
"# 作者正在加载....，感谢Christina在开发中的帮助，以及yiyiooo在早期的开发",
"# 群632428790，有问题艾特Yiyiooo或者Christina或者群主",
"# 更新日志：\n2022年11月18日，Yiyiooo在Kaggle发布了第一个云端部署stable diffusion的笔记本\n\n期间发布过100多个版本\n\n2023年3月3日， Kaggle大规模封禁用户，包括yiyiooo，连脚本也一同被封了。所以现在下载的笔记本基本都是基于yiyiooo原笔记本的修复版\n\n~~~~\n\n2023年3月5日更新：现在支持通过下载链接上传模型了，省去了下载模型后再上传后的麻烦.\n\n2023年4月14日更新：更新了python环境至3.10.10\n\n2023年5月1日更新：在christina帮助下实现内存优化，支持下载链接上传所有模型\n\n2023年5月15日更新：现在可以双开webui了，可以双线程跑图（GPU请选择 T4 x2 ， 将use2设置为True）\n\n2023年5月15日更新：更新了多线程启动，启动速度更快一些\n\n2023年6月6日更新：更新了xformers版本，生成速度更快一些\n\n2023年6月16日更新：修复了apt版本过低导致不能安装aria2包的bug"
]
| [
"TAGS\n#region-us \n",
"# 下载kaggle开头的文件，版本号选择最新的版本",
"# 作者正在加载....，感谢Christina在开发中的帮助，以及yiyiooo在早期的开发",
"# 群632428790，有问题艾特Yiyiooo或者Christina或者群主",
"# 更新日志：\n2022年11月18日，Yiyiooo在Kaggle发布了第一个云端部署stable diffusion的笔记本\n\n期间发布过100多个版本\n\n2023年3月3日， Kaggle大规模封禁用户，包括yiyiooo，连脚本也一同被封了。所以现在下载的笔记本基本都是基于yiyiooo原笔记本的修复版\n\n~~~~\n\n2023年3月5日更新：现在支持通过下载链接上传模型了，省去了下载模型后再上传后的麻烦.\n\n2023年4月14日更新：更新了python环境至3.10.10\n\n2023年5月1日更新：在christina帮助下实现内存优化，支持下载链接上传所有模型\n\n2023年5月15日更新：现在可以双开webui了，可以双线程跑图（GPU请选择 T4 x2 ， 将use2设置为True）\n\n2023年5月15日更新：更新了多线程启动，启动速度更快一些\n\n2023年6月6日更新：更新了xformers版本，生成速度更快一些\n\n2023年6月16日更新：修复了apt版本过低导致不能安装aria2包的bug"
]
| [
6,
15,
25,
21,
260
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| [
"passage: TAGS\n#region-us \n# 下载kaggle开头的文件，版本号选择最新的版本# 作者正在加载....，感谢Christina在开发中的帮助，以及yiyiooo在早期的开发# 群632428790，有问题艾特Yiyiooo或者Christina或者群主# 更新日志：\n2022年11月18日，Yiyiooo在Kaggle发布了第一个云端部署stable diffusion的笔记本\n\n期间发布过100多个版本\n\n2023年3月3日， Kaggle大规模封禁用户，包括yiyiooo，连脚本也一同被封了。所以现在下载的笔记本基本都是基于yiyiooo原笔记本的修复版\n\n~~~~\n\n2023年3月5日更新：现在支持通过下载链接上传模型了，省去了下载模型后再上传后的麻烦.\n\n2023年4月14日更新：更新了python环境至3.10.10\n\n2023年5月1日更新：在christina帮助下实现内存优化，支持下载链接上传所有模型\n\n2023年5月15日更新：现在可以双开webui了，可以双线程跑图（GPU请选择 T4 x2 ， 将use2设置为True）\n\n2023年5月15日更新：更新了多线程启动，启动速度更快一些\n\n2023年6月6日更新：更新了xformers版本，生成速度更快一些\n\n2023年6月16日更新：修复了apt版本过低导致不能安装aria2包的bug"
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|
65f3a4a2f644fb5cd27255aef06ea71ba20c443d | # Dataset Card for "conill2003_filtered_entities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ashwathjadhav23/conill2003_filtered_entities | [
"region:us"
]
| 2023-03-05T07:20:17+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC", "7": "B-MISC", "8": "I-MISC"}}}}], "splits": [{"name": "train", "num_bytes": 680716.5731785485, "num_examples": 2684}, {"name": "validation", "num_bytes": 891431, "num_examples": 3250}, {"name": "test", "num_bytes": 811470, "num_examples": 3453}], "download_size": 643826, "dataset_size": 2383617.5731785484}} | 2023-03-05T07:24:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "conill2003_filtered_entities"
More Information needed | [
"# Dataset Card for \"conill2003_filtered_entities\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"conill2003_filtered_entities\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"conill2003_filtered_entities\"\n\nMore Information needed"
]
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|
f6db64e33c5358279721afbba2acdf5fdc864820 | # Dataset Card for "selected_4373HME"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zhixiaoni/selected_4373HME | [
"region:us"
]
| 2023-03-05T08:05:42+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 41601610.932, "num_examples": 4373}], "download_size": 40031352, "dataset_size": 41601610.932}} | 2023-03-05T08:06:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "selected_4373HME"
More Information needed | [
"# Dataset Card for \"selected_4373HME\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"selected_4373HME\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"selected_4373HME\"\n\nMore Information needed"
]
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|
ebf0fda6e42b597b3b301baeba8da9f97e9aef03 | dataset_info:
features:
- name: id
dtype: string
- name: post_text
sequence: string
- name: pre_text
sequence: string
- name: question
dtype: string
- name: answers
dtype: string
- name: table
sequence:
sequence: string
splits:
- name: train
num_bytes: 26984130
num_examples: 6251
- name: validation
num_bytes: 3757103
num_examples: 883
- name: test
num_bytes: 4838430
num_examples: 1147
download_size: 21240722
dataset_size: 35579663
| dreamerdeo/finqa | [
"region:us"
]
| 2023-03-05T08:38:40+00:00 | {} | 2023-03-06T08:29:39+00:00 | []
| []
| TAGS
#region-us
| dataset_info:
features:
- name: id
dtype: string
- name: post_text
sequence: string
- name: pre_text
sequence: string
- name: question
dtype: string
- name: answers
dtype: string
- name: table
sequence:
sequence: string
splits:
- name: train
num_bytes: 26984130
num_examples: 6251
- name: validation
num_bytes: 3757103
num_examples: 883
- name: test
num_bytes: 4838430
num_examples: 1147
download_size: 21240722
dataset_size: 35579663
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
c34c878e2ff2d7f42ae4628967d9a8ca70f63350 | # AutoTrain Dataset for project: fake_news_fine_tuned_v4
## Dataset Description
This dataset has been automatically processed by AutoTrain for project fake_news_fine_tuned_v4.
### 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
[
{
"text": "Many of us do not share Donald Trump s past values but we do love America In my opinion it is not about him It is about Who will preserve and a protect the constitution Who will protect the second amendment Who will not send your son or daughter into harms way unless there is a good reason Who does not support partial birth abortion Who wants to fix our inner cities and our infrastructure Who will improve our economy and bring jobs back to the USA Who is a nationalist and not a globalist Who will protect our vets from dying in the streets and on park benches and get them help when they need it Who supports our police firemen military men and women Who will protect our borders from criminal elements and terrorist Who will not leave Americans to die in foreign countries under the hands of terrorist Who is now a professed Christian and changed man according to many Christian leaders and pastors These are just a few reasons to vote for Donald Trump ",
"target": 1
},
{
"text": "Good morning Here s what you need to know President Trump is expected to order the construction of a Mexican border wall today and slash immigration of refugees from terror prone nations White House officials said He pressed automakers to increase jobs in the U S but a new forecast of a federal budget deficit that could expand to almost trillion over years could complicate his agenda to cut taxes and ramp up spending Mr Trump also revived two pipelines blocked under President Obama the Keystone XL pipeline the focus of years of debate over energy needs and climate change and the Dakota Access pipeline the target of Native American protests And tensions are rising between the U S and China Beijing after a challenge from a Trump spokesman insisted it had irrefutable sovereignty over disputed islands in the South China Sea China sees opportunities to extend its global reach with Mr Trump in power but it also sees a threat to its prized goal stability Israel discarding diplomatic restraint just a few days into the Trump administration approved the construction of housing units for Jews in West Bank settlements A month ago the Obama administration declined to veto a Security Council resolution condemning the settlements and last week a gathering of world leaders warned Israel to stop expanding them The office of Prime Minister Theresa May pledged to stay on its timetable for taking Britain out of the European Union despite a top court ruling requiring the approval of Parliament before the process can begin Political observers said it was unlikely that legislators despite being largely opposed to Brexit would try to block the will of the voters Three months after Prime Minister Narendra Modi abruptly banned percent of India s currency in a bid to fight black money hidden reserves that feed corruption unemployment is spreading and some people are going without fruit vegetables and milk This has actually hurt the poor enormously one business leader said Above a protest over cash shortages last week And this year s Oscar nominations are out Voters gave La La Land nominations a tie for the most in Academy Award history The academy also honored six black actors a record Here is the full list of nominees The Alibaba Group beat estimates with strong earnings The results showed how much the giant despite a growing global profile and its record I P O on Wall Street is still dependent on China Silver Lake an investment firm is leading a billion investment in Koubei an business that is part of what is increasingly known as the sector or O O President Trump s pick for the Federal Communications Commission Ajit Pai an from Kansas is expected to roll back rules that ban internet service providers from favoring certain websites and apps Nearly million Chinese citizens used their mobile phones to make payments last year an increase of percent from new government data shows Japan releases data on its imports and exports for December The country s economy has been struggling despite government efforts at stimulus South Korea and Taiwan release G D P figures Wall Street was up Here s a snapshot of global markets China s nuclear plant begun in the s was one of the country s most ambitious military projects Today the mountain has been reborn as a tourist attraction The New York Times Peter Thiel the American billionaire has taken New Zealand citizenship and quietly acquired a sprawling estate on Lake Wanaka New Zealand Herald Afghanistan ordered the arrest of nine bodyguards of Gen Abdul Rashid Dostum the more senior of the country s two vice presidents on accusations of the rape and torture of an Uzbek elder The New York Times A wave of smog is forecast to engulf four northern Chinese provinces as the Lunar New Year approaches Reuters Talks in Astana Kazakhstan may have done more to to firm up Russia s growing role in diplomacy over Syria than to create any progress toward peace The New York Times A proposal to ban the display of Vietnam s flag on city poles has divided the Vietnamese community of San Jose Calif The Mercury News Gambia s former president Yahya Jammeh didn t go into exile in Equatorial Guinea He left with two a and a plane full of luxury items The New York Times China s government has shut down of the country s golf courses since citing illegal land or water use Xinhua Scientists found the fossils of a huge nearly otter that roamed rivers and lakes in southwestern China about million years ago Reuters Doing just minutes of yoga can improve your bone health Vacations don t have to be all about relaxing Here s how to plan one with some good deeds along the way Recipe of the day Try this grilled cheese sandwich featuring mayonnaise for some midweek comfort food In China members of the Tanka people have survived on southern coastal waterways and on the margins of society since ancient times As big cities spread their floating way of life is disappearing Genetic research is taking some truly turns Ant researchers have identified the molecular and neural cues that help explain social behaviors including caring for young breeding even capturing and upstarts like tiny police officers And a pioneering scientist Maria Sibylla Merian captivated Europeans years ago with her studies of insects Now her findings are being celebrated again Grandstands filled with cheering fans perfectly plated vegetables and your country s name on the line this isn t your typical cooking competition This is the Bocuse d Or or the culinary version of the Olympics The competition which takes place this week in Lyon France was founded by the master French chef Paul Bocuse He was influential in establishing nouvelle cuisine which emphasizes ingredients and presentation At Mr Bocuse remains a king among chefs Long before became a trendy concept a Bocuse was learning how to butcher a cow for his first restaurant job He eventually joined his father at their family restaurant L Auberge du Pont de Collonges One year after he took over the restaurant won a Michelin star He recalled how early in his career he wowed a prominent food critic with fresh ingredients Mr Bocuse served haricots verts picked that morning lightly boiled and served with olive oil shallots and salt In The Times s Craig Claiborne described the restaurant outside of Lyon as one of the most elegant and proudest restaurants outside of Paris Mr Bocuse though once offered a less refined assessment of his skills Some men have mistresses Mr Bocuse once said I run a luxury restaurant Remy Tumin contributed reporting Your Morning Briefing is published weekday mornings What would you like to see here Contact us at asiabriefing nytimes com ",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['0', '1'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 7998 |
| valid | 2000 |
| systash/autotrain-data-fake_news_fine_tuned_v4 | [
"task_categories:text-classification",
"region:us"
]
| 2023-03-05T08:54:05+00:00 | {"task_categories": ["text-classification"]} | 2023-03-05T08:55:49+00:00 | []
| []
| TAGS
#task_categories-text-classification #region-us
| AutoTrain Dataset for project: fake\_news\_fine\_tuned\_v4
==========================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project fake\_news\_fine\_tuned\_v4.
### 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-text-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-text-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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|
5ea952852cfa4a65f5d4bf136306f1d4a39f2bae | # Dataset Card for "wikisqlQandA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | RAHULssastae/wikisqlqanda | [
"region:us"
]
| 2023-03-05T08:54:53+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 9802871, "num_examples": 56355}, {"name": "validation", "num_bytes": 2755625, "num_examples": 15878}], "download_size": 3795526, "dataset_size": 12558496}} | 2023-03-05T12:24:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikisqlQandA"
More Information needed | [
"# Dataset Card for \"wikisqlQandA\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikisqlQandA\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikisqlQandA\"\n\nMore Information needed"
]
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|
0eaee2ae808cecc3facf04d7bed7d8db6075d5bf | # Dataset Card for "spenish_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ashwathjadhav23/spenish_dataset | [
"region:us"
]
| 2023-03-05T10:46:24+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3948364, "num_examples": 25295}], "download_size": 2458416, "dataset_size": 3948364}} | 2023-03-05T10:54:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "spenish_dataset"
More Information needed | [
"# Dataset Card for \"spenish_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"spenish_dataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"spenish_dataset\"\n\nMore Information needed"
]
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|
f5db2780f712ad57b022391ff1f3b3358eadb11c | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Jelly/github-issues | [
"region:us"
]
| 2023-03-05T12:15:09+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", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "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": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "due_on", "dtype": "null"}, {"name": "closed_at", "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": 13846886, "num_examples": 2797}], "download_size": 3821819, "dataset_size": 13846886}} | 2023-03-05T12:15:11+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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|
927d28a4ab07069026e71971fda5316c52963a59 | # Dataset Card for "test_push_dataset_update_dataset_info"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/test_push_dataset_update_dataset_info | [
"region:us"
]
| 2023-03-05T12:59:49+00:00 | {"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1120, "num_examples": 70}], "download_size": 1983, "dataset_size": 1120}, {"config_name": "v1", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 800, "num_examples": 50}], "download_size": 1763, "dataset_size": 800}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v1", "data_dir": "v1"}]} | 2023-03-05T12:59:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_push_dataset_update_dataset_info"
More Information needed | [
"# Dataset Card for \"test_push_dataset_update_dataset_info\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_push_dataset_update_dataset_info\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_push_dataset_update_dataset_info\"\n\nMore Information needed"
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|
528f241b02d9033cf1f7edece8841b04b4c98ced | # Dataset Card for "test_push_dataset_dict_update_dataset_info"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/test_push_dataset_dict_update_dataset_info | [
"region:us"
]
| 2023-03-05T13:02:31+00:00 | {"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1120, "num_examples": 70}, {"name": "random", "num_bytes": 800, "num_examples": 50}], "download_size": 3746, "dataset_size": 1920}, {"config_name": "v1", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1280, "num_examples": 80}, {"name": "random", "num_bytes": 960, "num_examples": 60}], "download_size": 3932, "dataset_size": 2240}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v1", "data_dir": "v1"}]} | 2023-03-05T13:03:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_push_dataset_dict_update_dataset_info"
More Information needed | [
"# Dataset Card for \"test_push_dataset_dict_update_dataset_info\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_push_dataset_dict_update_dataset_info\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_push_dataset_dict_update_dataset_info\"\n\nMore Information needed"
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|
54a045a593ef11b6fde47df29a56a74f0133adc0 | # Dataset Card for "scenario_instructor"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | diwank/scenario_instructor | [
"region:us"
]
| 2023-03-05T13:02:49+00:00 | {"dataset_info": {"features": [{"name": "query", "sequence": "string"}, {"name": "pos", "sequence": "string"}, {"name": "neg", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 14083675, "num_examples": 14732}, {"name": "test", "num_bytes": 788355, "num_examples": 819}, {"name": "validation", "num_bytes": 769580, "num_examples": 818}], "download_size": 7159274, "dataset_size": 15641610}} | 2023-03-05T13:04:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "scenario_instructor"
More Information needed | [
"# Dataset Card for \"scenario_instructor\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"scenario_instructor\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"scenario_instructor\"\n\nMore Information needed"
]
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|
ae4e12e5dcce4974dd943bca177db96698014edb | # Dataset Card for "sci_summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 0x70DA/sci_summ | [
"region:us"
]
| 2023-03-05T13:56:55+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 23361937.97759879, "num_examples": 4631}, {"name": "test", "num_bytes": 23487172.952651516, "num_examples": 4665}, {"name": "train", "num_bytes": 176474272.610434, "num_examples": 34083}], "download_size": 120216439, "dataset_size": 223323383.54068428}} | 2023-03-05T18:12:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sci_summ"
More Information needed | [
"# Dataset Card for \"sci_summ\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sci_summ\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sci_summ\"\n\nMore Information needed"
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|
0fda022559b4e821f3071948ec45bccd51374b1a | Nothing.... | Ojimi/Hifu-Lora-Example | [
"license:creativeml-openrail-m",
"region:us"
]
| 2023-03-05T14:22:33+00:00 | {"license": "creativeml-openrail-m"} | 2023-03-05T14:26:43+00:00 | []
| []
| TAGS
#license-creativeml-openrail-m #region-us
| Nothing.... | []
| [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#license-creativeml-openrail-m #region-us \n"
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|
4b1c1ccf212702db4b68c10b8ecc1b68ea88ad7d | # Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Volodymyr/dreambooth-hackathon-images | [
"region:us"
]
| 2023-03-05T14:38:11+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "pixel_values", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 62824895.0, "num_examples": 84}], "download_size": 62830151, "dataset_size": 62824895.0}} | 2023-03-05T14:38:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dreambooth-hackathon-images"
More Information needed | [
"# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth-hackathon-images\"\n\nMore Information needed"
]
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|
188e1346c7e106df573a8fa9d7fc3931db491435 | # Dataset Card for "test_push_dataset_split_exists_main"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/test_push_dataset_split_exists_main | [
"region:us"
]
| 2023-03-05T14:48:01+00:00 | {"dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 320, "num_examples": 20}], "download_size": 1469, "dataset_size": 320}} | 2023-03-05T14:48:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_push_dataset_split_exists_main"
More Information needed | [
"# Dataset Card for \"test_push_dataset_split_exists_main\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_push_dataset_split_exists_main\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_push_dataset_split_exists_main\"\n\nMore Information needed"
]
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|
a9f15ac8585d29a36d5b5d851e9556519cc33d33 | # Dataset Card for "test_push_dataset_split_exists_main2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/test_push_dataset_split_exists_main2 | [
"region:us"
]
| 2023-03-05T14:54:09+00:00 | {"dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 320, "num_examples": 20}, {"name": "random", "num_bytes": 640, "num_examples": 40}], "download_size": 3128, "dataset_size": 960}} | 2023-03-05T14:55:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_push_dataset_split_exists_main2"
More Information needed | [
"# Dataset Card for \"test_push_dataset_split_exists_main2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_push_dataset_split_exists_main2\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_push_dataset_split_exists_main2\"\n\nMore Information needed"
]
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146ec91014c0b47957a2b158a042ab6d99cd3b29 | # Dataset Card for "test_push_dataset_infos_json"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/test_push_dataset_infos_json | [
"region:us"
]
| 2023-03-05T15:29:38+00:00 | {"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1600, "num_examples": 100}, {"name": "random", "num_bytes": 3200, "num_examples": 200}], "download_size": 3299, "dataset_size": 4800}, {"config_name": "v2", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3200, "num_examples": 200}], "download_size": 0, "dataset_size": 3200}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v2", "data_dir": "v2"}]} | 2023-03-05T16:10:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_push_dataset_infos_json"
More Information needed | [
"# Dataset Card for \"test_push_dataset_infos_json\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_push_dataset_infos_json\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_push_dataset_infos_json\"\n\nMore Information needed"
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|
95f9412e874a061da3f1b4ff9cf8ec74ccdf9e21 | # Dataset Card for "TreeImageDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ru3ll/TreeImageDataset | [
"region:us"
]
| 2023-03-05T15:44:59+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "train", "2": "val"}}}}], "splits": [{"name": "train", "num_bytes": 1882655120.0, "num_examples": 922}], "download_size": 1882708375, "dataset_size": 1882655120.0}} | 2023-03-05T15:54:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "TreeImageDataset"
More Information needed | [
"# Dataset Card for \"TreeImageDataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"TreeImageDataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"TreeImageDataset\"\n\nMore Information needed"
]
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|
2d0e061031c3a701bf2418e347c94d7931287a80 |
# Bengali Abstractive News Summarization (BANS)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:** [BANS PAPER](https://doi.org/10.1007/978-981-33-4673-4_4)
- **Leaderboard:**
- **Point of Contact:** [Prithwiraj Bhattacharjee]([email protected])
### Dataset Summary
Nowadays news or text summarization becomes very popular in the NLP field. Both the extractive and abstractive approaches of summarization are implemented in different languages. A significant amount of data is a primary need for any summarization. For the Bengali language, there are only a few datasets are available. Our dataset is made for Bengali Abstractive News Summarization (BANS) purposes. As abstractive summarization is basically neural network-based it needs more and more data to perform well. So we made a standard Bengali abstractive summarization data crawling from online Bengali news portal bangla.bdnews24.com. We crawled more than 19k articles and summaries and standardized the data.
### Downloading the data
```
from datasets import load_dataset
train = load_dataset("sustcsenlp/bn_news_summarization",split="train")
```
### Dataset Description
| Description | Data Info. |
| ----------- | ----------- |
| Total no of articles | 19096 |
| Total no of summaries | 19096 |
| Maximum no of words in an article | 76 |
| Maximum no of words in a summary | 12 |
| Minimum no of words in an article | 5 |
| Minimum no of words in a summary | 3 |
### Languages
This dataset contains Bangla Text Data.
## Acknowledgement
We would like to thank Shahjalal University of Science and Technology (SUST) research center and SUST NLP research group for their support.
### Citation Information
```
@InProceedings{10.1007/978-981-33-4673-4_4,
author="Bhattacharjee, Prithwiraj
and Mallick, Avi
and Saiful Islam, Md.
and Marium-E-Jannat",
editor="Kaiser, M. Shamim
and Bandyopadhyay, Anirban
and Mahmud, Mufti
and Ray, Kanad",
title="Bengali Abstractive News Summarization (BANS): A Neural Attention Approach",
booktitle="Proceedings of International Conference on Trends in Computational and Cognitive Engineering",
year="2021",
publisher="Springer Singapore",
address="Singapore",
pages="41--51",
abstract="Bhattacharjee, PrithwirajMallick, AviSaiful Islam, Md.Marium-E-JannatAbstractive summarization is the process of generating novel sentences based on the information extracted from the original text document while retaining the context. Due to abstractive summarization's underlying complexities, most of the past research work has been done on the extractive summarization approach. Nevertheless, with the triumph of the sequence-to-sequence (seq2seq) model, abstractive summarization becomes more viable. Although a significant number of notable research has been done in the English language based on abstractive summarization, only a couple of works have been done on Bengali abstractive news summarization (BANS). In this article, we presented a seq2seq based Long Short-Term Memory (LSTM) network model with attention at encoder-decoder. Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences with noteworthy information of the original document. We also prepared a dataset of more than 19 k articles and corresponding human-written summaries collected from bangla.bdnews24.com (https://bangla.bdnews24.com/) which is till now the most extensive dataset for Bengali news document summarization and publicly published in Kaggle (https://www.kaggle.com/prithwirajsust/bengali-news-summarization-dataset) We evaluated our model qualitatively and quantitatively and compared it with other published results. It showed significant improvement in terms of human evaluation scores with state-of-the-art approaches for BANS.",
isbn="978-981-33-4673-4"
}
```
### Contributors
| Name | University |
| ----------- | ----------- |
| Prithwiraj Bhattacharjee | Shahjalal University of Science and Technology |
| Avi Mallick | Shahjalal University of Science and Technology |
| Md. Saiful Islam | Shahjalal University of Science and Technology |
| Marium-E-Jannat | Shahjalal University of Science and Technology | | sustcsenlp/bn_news_summarization | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:bn",
"license:cc0-1.0",
"region:us"
]
| 2023-03-05T15:47:29+00:00 | {"language": ["bn"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "task_categories": ["summarization"]} | 2023-03-05T16:41:32+00:00 | []
| [
"bn"
]
| TAGS
#task_categories-summarization #size_categories-1K<n<10K #language-Bengali #license-cc0-1.0 #region-us
| Bengali Abstractive News Summarization (BANS)
=============================================
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper: BANS PAPER
* Leaderboard:
* Point of Contact: Prithwiraj Bhattacharjee
### Dataset Summary
Nowadays news or text summarization becomes very popular in the NLP field. Both the extractive and abstractive approaches of summarization are implemented in different languages. A significant amount of data is a primary need for any summarization. For the Bengali language, there are only a few datasets are available. Our dataset is made for Bengali Abstractive News Summarization (BANS) purposes. As abstractive summarization is basically neural network-based it needs more and more data to perform well. So we made a standard Bengali abstractive summarization data crawling from online Bengali news portal URL. We crawled more than 19k articles and summaries and standardized the data.
### Downloading the data
### Dataset Description
### Languages
This dataset contains Bangla Text Data.
Acknowledgement
---------------
We would like to thank Shahjalal University of Science and Technology (SUST) research center and SUST NLP research group for their support.
### Contributors
| [
"### Dataset Summary\n\n\nNowadays news or text summarization becomes very popular in the NLP field. Both the extractive and abstractive approaches of summarization are implemented in different languages. A significant amount of data is a primary need for any summarization. For the Bengali language, there are only a few datasets are available. Our dataset is made for Bengali Abstractive News Summarization (BANS) purposes. As abstractive summarization is basically neural network-based it needs more and more data to perform well. So we made a standard Bengali abstractive summarization data crawling from online Bengali news portal URL. We crawled more than 19k articles and summaries and standardized the data.",
"### Downloading the data",
"### Dataset Description",
"### Languages\n\n\nThis dataset contains Bangla Text Data.\n\n\nAcknowledgement\n---------------\n\n\nWe would like to thank Shahjalal University of Science and Technology (SUST) research center and SUST NLP research group for their support.",
"### Contributors"
]
| [
"TAGS\n#task_categories-summarization #size_categories-1K<n<10K #language-Bengali #license-cc0-1.0 #region-us \n",
"### Dataset Summary\n\n\nNowadays news or text summarization becomes very popular in the NLP field. Both the extractive and abstractive approaches of summarization are implemented in different languages. A significant amount of data is a primary need for any summarization. For the Bengali language, there are only a few datasets are available. Our dataset is made for Bengali Abstractive News Summarization (BANS) purposes. As abstractive summarization is basically neural network-based it needs more and more data to perform well. So we made a standard Bengali abstractive summarization data crawling from online Bengali news portal URL. We crawled more than 19k articles and summaries and standardized the data.",
"### Downloading the data",
"### Dataset Description",
"### Languages\n\n\nThis dataset contains Bangla Text Data.\n\n\nAcknowledgement\n---------------\n\n\nWe would like to thank Shahjalal University of Science and Technology (SUST) research center and SUST NLP research group for their support.",
"### Contributors"
]
| [
41,
158,
6,
5,
50,
5
]
| [
"passage: TAGS\n#task_categories-summarization #size_categories-1K<n<10K #language-Bengali #license-cc0-1.0 #region-us \n### Dataset Summary\n\n\nNowadays news or text summarization becomes very popular in the NLP field. Both the extractive and abstractive approaches of summarization are implemented in different languages. A significant amount of data is a primary need for any summarization. For the Bengali language, there are only a few datasets are available. Our dataset is made for Bengali Abstractive News Summarization (BANS) purposes. As abstractive summarization is basically neural network-based it needs more and more data to perform well. So we made a standard Bengali abstractive summarization data crawling from online Bengali news portal URL. We crawled more than 19k articles and summaries and standardized the data.### Downloading the data### Dataset Description### Languages\n\n\nThis dataset contains Bangla Text Data.\n\n\nAcknowledgement\n---------------\n\n\nWe would like to thank Shahjalal University of Science and Technology (SUST) research center and SUST NLP research group for their support.### Contributors"
]
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|
22d8bd29f65dd4589a646d2b8d2ce59f5ad1d063 | # Dataset Card for "ema_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bhpardo/ema_en | [
"region:us"
]
| 2023-03-05T15:58:10+00:00 | {"dataset_info": {"features": [{"name": "english", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 436742.4575850489, "num_examples": 5479}, {"name": "test", "num_bytes": 109205.5424149511, "num_examples": 1370}], "download_size": 340686, "dataset_size": 545948.0}} | 2023-03-05T16:26:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ema_en"
More Information needed | [
"# Dataset Card for \"ema_en\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ema_en\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ema_en\"\n\nMore Information needed"
]
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|
f4c030a88bd1d54b0572db80a4709821a0159dfb | # Dataset Card for "test_push_dataset_dict_infos_json"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polinaeterna/test_push_dataset_dict_infos_json | [
"region:us"
]
| 2023-03-05T16:09:45+00:00 | {"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1600, "num_examples": 100}, {"name": "random", "num_bytes": 3200, "num_examples": 200}], "download_size": 5578, "dataset_size": 4800}, {"config_name": "v2", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3200, "num_examples": 200}, {"name": "random", "num_bytes": 4800, "num_examples": 300}], "download_size": 0, "dataset_size": 8000}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v2", "data_dir": "v2"}]} | 2023-03-05T17:25:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_push_dataset_dict_infos_json"
More Information needed | [
"# Dataset Card for \"test_push_dataset_dict_infos_json\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_push_dataset_dict_infos_json\"\n\nMore Information needed"
]
| [
6,
25
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_push_dataset_dict_infos_json\"\n\nMore Information needed"
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|
619b827ee4c0275c9eda1d65c751b6b62a33dc21 | # Dataset Card for "satellite-512-alt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Shavindra/satellite-512-alt | [
"region:us"
]
| 2023-03-05T16:10:19+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}, {"name": "mask_rgb", "dtype": "image"}, {"name": "pixel_values", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 333395732.0, "num_examples": 304}], "download_size": 333272045, "dataset_size": 333395732.0}} | 2023-03-05T17:08:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "satellite-512-alt"
More Information needed | [
"# Dataset Card for \"satellite-512-alt\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"satellite-512-alt\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"satellite-512-alt\"\n\nMore Information needed"
]
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|
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