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1876ee7c596916d65f2abf6eb3d23a42223ddc32 | # Dataset Card for "cstnews_data-xlsumm_cstnews_1024_results"
rouge= {'rouge1': 0.5573278177294199, 'rouge2': 0.37453934272536266, 'rougeL': 0.4198626248383525, 'rougeLsum': 0.4198626248383525}
bert= {'precision': 0.8057523816823959, 'recall': 0.7992453724145889, 'f1': 0.8017213456332684}
mover = 0.6422802882004404 | arthurmluz/cstnews_data-xlsum_cstnews_1024_results | [
"region:us"
]
| 2023-11-03T01:13:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 56397, "num_examples": 16}], "download_size": 54256, "dataset_size": 56397}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-15T03:53:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cstnews_data-xlsumm_cstnews_1024_results"
rouge= {'rouge1': 0.5573278177294199, 'rouge2': 0.37453934272536266, 'rougeL': 0.4198626248383525, 'rougeLsum': 0.4198626248383525}
bert= {'precision': 0.8057523816823959, 'recall': 0.7992453724145889, 'f1': 0.8017213456332684}
mover = 0.6422802882004404 | [
"# Dataset Card for \"cstnews_data-xlsumm_cstnews_1024_results\"\n\nrouge= {'rouge1': 0.5573278177294199, 'rouge2': 0.37453934272536266, 'rougeL': 0.4198626248383525, 'rougeLsum': 0.4198626248383525}\n\nbert= {'precision': 0.8057523816823959, 'recall': 0.7992453724145889, 'f1': 0.8017213456332684}\n\nmover = 0.6422802882004404"
]
| [
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"# Dataset Card for \"cstnews_data-xlsumm_cstnews_1024_results\"\n\nrouge= {'rouge1': 0.5573278177294199, 'rouge2': 0.37453934272536266, 'rougeL': 0.4198626248383525, 'rougeLsum': 0.4198626248383525}\n\nbert= {'precision': 0.8057523816823959, 'recall': 0.7992453724145889, 'f1': 0.8017213456332684}\n\nmover = 0.6422802882004404"
]
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"passage: TAGS\n#region-us \n# Dataset Card for \"cstnews_data-xlsumm_cstnews_1024_results\"\n\nrouge= {'rouge1': 0.5573278177294199, 'rouge2': 0.37453934272536266, 'rougeL': 0.4198626248383525, 'rougeLsum': 0.4198626248383525}\n\nbert= {'precision': 0.8057523816823959, 'recall': 0.7992453724145889, 'f1': 0.8017213456332684}\n\nmover = 0.6422802882004404"
]
|
3aeca21a65f8128da7a31cb66881b298e47e1441 | # Dataset Card for "SDv2-GPT4Spatial-200-T5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Doub7e/SDv2-GPT4Spatial-200-T5 | [
"region:us"
]
| 2023-11-03T01:29:52+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "T5_last_hidden_states", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 203072791.0, "num_examples": 200}], "download_size": 204322556, "dataset_size": 203072791.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T01:30:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "SDv2-GPT4Spatial-200-T5"
More Information needed | [
"# Dataset Card for \"SDv2-GPT4Spatial-200-T5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"SDv2-GPT4Spatial-200-T5\"\n\nMore Information needed"
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| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"SDv2-GPT4Spatial-200-T5\"\n\nMore Information needed"
]
|
0b74a110ef07204ecf2e0fc0b8e3ce1f4f46a9f4 | # Dataset Card for "click_bate_article_train_val"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | re2panda/click_bate_article_train_val | [
"region:us"
]
| 2023-11-03T01:35:53+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 603257228.0105588, "num_examples": 195362}, {"name": "validation", "num_bytes": 67028923.98944115, "num_examples": 21707}], "download_size": 387271308, "dataset_size": 670286152.0}} | 2023-11-03T01:42:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "click_bate_article_train_val"
More Information needed | [
"# Dataset Card for \"click_bate_article_train_val\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"click_bate_article_train_val\"\n\nMore Information needed"
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| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"click_bate_article_train_val\"\n\nMore Information needed"
]
|
3b1cd493b26ba92d87d970b8edaed262fde1ae82 | # Dataset Card for "cstnews_data-cstnews_results"
rouge= {'rouge1': 0.523455778306692, 'rouge2': 0.33030442094138346, 'rougeL': 0.36319070128497594, 'rougeLsum': 0.36319070128497594}
bert= {'precision': 0.7488009743392467, 'recall': 0.8160672560334206, 'f1': 0.7805671989917755}
mover = 0.6279614063989508 | arthurmluz/cstnews_data-cstnews_results | [
"region:us"
]
| 2023-11-03T01:36:10+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 64561, "num_examples": 16}], "download_size": 0, "dataset_size": 64561}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-15T04:01:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cstnews_data-cstnews_results"
rouge= {'rouge1': 0.523455778306692, 'rouge2': 0.33030442094138346, 'rougeL': 0.36319070128497594, 'rougeLsum': 0.36319070128497594}
bert= {'precision': 0.7488009743392467, 'recall': 0.8160672560334206, 'f1': 0.7805671989917755}
mover = 0.6279614063989508 | [
"# Dataset Card for \"cstnews_data-cstnews_results\"\n\nrouge= {'rouge1': 0.523455778306692, 'rouge2': 0.33030442094138346, 'rougeL': 0.36319070128497594, 'rougeLsum': 0.36319070128497594}\n\nbert= {'precision': 0.7488009743392467, 'recall': 0.8160672560334206, 'f1': 0.7805671989917755}\n\nmover = 0.6279614063989508"
]
| [
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"# Dataset Card for \"cstnews_data-cstnews_results\"\n\nrouge= {'rouge1': 0.523455778306692, 'rouge2': 0.33030442094138346, 'rougeL': 0.36319070128497594, 'rougeLsum': 0.36319070128497594}\n\nbert= {'precision': 0.7488009743392467, 'recall': 0.8160672560334206, 'f1': 0.7805671989917755}\n\nmover = 0.6279614063989508"
]
| [
6,
137
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cstnews_data-cstnews_results\"\n\nrouge= {'rouge1': 0.523455778306692, 'rouge2': 0.33030442094138346, 'rougeL': 0.36319070128497594, 'rougeLsum': 0.36319070128497594}\n\nbert= {'precision': 0.7488009743392467, 'recall': 0.8160672560334206, 'f1': 0.7805671989917755}\n\nmover = 0.6279614063989508"
]
|
180a813cb58b1b45edd0ecfcbddf57184e7be430 | # Dataset Card for "GPTextSum_data-cstnews_results"
rouge= {'rouge1': 0.3955081290524632, 'rouge2': 0.20128951815252058, 'rougeL': 0.30747537835802, 'rougeLsum': 0.30747537835802}
bert= {'precision': 0.7137928068637848, 'recall': 0.7949426531791687, 'f1': 0.7516956090927124} | arthurmluz/GPTextSum_data-cstnews_results | [
"region:us"
]
| 2023-11-03T01:37:26+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 35556, "num_examples": 20}], "download_size": 43970, "dataset_size": 35556}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-15T04:01:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "GPTextSum_data-cstnews_results"
rouge= {'rouge1': 0.3955081290524632, 'rouge2': 0.20128951815252058, 'rougeL': 0.30747537835802, 'rougeLsum': 0.30747537835802}
bert= {'precision': 0.7137928068637848, 'recall': 0.7949426531791687, 'f1': 0.7516956090927124} | [
"# Dataset Card for \"GPTextSum_data-cstnews_results\"\n\nrouge= {'rouge1': 0.3955081290524632, 'rouge2': 0.20128951815252058, 'rougeL': 0.30747537835802, 'rougeLsum': 0.30747537835802}\n\nbert= {'precision': 0.7137928068637848, 'recall': 0.7949426531791687, 'f1': 0.7516956090927124}"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"GPTextSum_data-cstnews_results\"\n\nrouge= {'rouge1': 0.3955081290524632, 'rouge2': 0.20128951815252058, 'rougeL': 0.30747537835802, 'rougeLsum': 0.30747537835802}\n\nbert= {'precision': 0.7137928068637848, 'recall': 0.7949426531791687, 'f1': 0.7516956090927124}"
]
| [
6,
130
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"passage: TAGS\n#region-us \n# Dataset Card for \"GPTextSum_data-cstnews_results\"\n\nrouge= {'rouge1': 0.3955081290524632, 'rouge2': 0.20128951815252058, 'rougeL': 0.30747537835802, 'rougeLsum': 0.30747537835802}\n\nbert= {'precision': 0.7137928068637848, 'recall': 0.7949426531791687, 'f1': 0.7516956090927124}"
]
|
443f9ab72658798b37a9151960d852c966d65376 | # Dataset Card for "temario_data-cstnews_results"
rouge= {'rouge1': 0.4625552716828615, 'rouge2': 0.19128215243444158, 'rougeL': 0.2812235162681903, 'rougeLsum': 0.2812235162681903}
bert= {'precision': 0.731306676864624, 'recall': 0.7333952784538269, 'f1': 0.7321366620063782}
moverscore = 0.632796284594281 | arthurmluz/temario_data-cstnews_results | [
"region:us"
]
| 2023-11-03T01:47:16+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 235004, "num_examples": 25}], "download_size": 187245, "dataset_size": 235004}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-15T03:17:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "temario_data-cstnews_results"
rouge= {'rouge1': 0.4625552716828615, 'rouge2': 0.19128215243444158, 'rougeL': 0.2812235162681903, 'rougeLsum': 0.2812235162681903}
bert= {'precision': 0.731306676864624, 'recall': 0.7333952784538269, 'f1': 0.7321366620063782}
moverscore = 0.632796284594281 | [
"# Dataset Card for \"temario_data-cstnews_results\"\n\nrouge= {'rouge1': 0.4625552716828615, 'rouge2': 0.19128215243444158, 'rougeL': 0.2812235162681903, 'rougeLsum': 0.2812235162681903}\n\nbert= {'precision': 0.731306676864624, 'recall': 0.7333952784538269, 'f1': 0.7321366620063782}\n\nmoverscore = 0.632796284594281"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"temario_data-cstnews_results\"\n\nrouge= {'rouge1': 0.4625552716828615, 'rouge2': 0.19128215243444158, 'rougeL': 0.2812235162681903, 'rougeLsum': 0.2812235162681903}\n\nbert= {'precision': 0.731306676864624, 'recall': 0.7333952784538269, 'f1': 0.7321366620063782}\n\nmoverscore = 0.632796284594281"
]
| [
6,
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"passage: TAGS\n#region-us \n# Dataset Card for \"temario_data-cstnews_results\"\n\nrouge= {'rouge1': 0.4625552716828615, 'rouge2': 0.19128215243444158, 'rougeL': 0.2812235162681903, 'rougeLsum': 0.2812235162681903}\n\nbert= {'precision': 0.731306676864624, 'recall': 0.7333952784538269, 'f1': 0.7321366620063782}\n\nmoverscore = 0.632796284594281"
]
|
3d229690f10c71877d43c73c305ee9721a854d34 | # Dataset Card for "summary-text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nlplabtdtu/summary-text | [
"region:us"
]
| 2023-11-03T02:09:32+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "prompt_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 183756861, "num_examples": 65361}, {"name": "test", "num_bytes": 2786318, "num_examples": 1000}], "download_size": 99910723, "dataset_size": 186543179}} | 2023-11-03T02:09:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "summary-text"
More Information needed | [
"# Dataset Card for \"summary-text\"\n\nMore Information needed"
]
| [
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"# Dataset Card for \"summary-text\"\n\nMore Information needed"
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|
75ccbf8858fa0c11a8134348302490e077b1cd91 | # Dataset Card for "ct_scans_90pct_2000_cutoff_llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/ct_scans_90pct_2000_cutoff_llama | [
"region:us"
]
| 2023-11-03T02:17:34+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 821780150.4561067, "num_examples": 164551}], "download_size": 148829636, "dataset_size": 821780150.4561067}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T02:17:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ct_scans_90pct_2000_cutoff_llama"
More Information needed | [
"# Dataset Card for \"ct_scans_90pct_2000_cutoff_llama\"\n\nMore Information needed"
]
| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"ct_scans_90pct_2000_cutoff_llama\"\n\nMore Information needed"
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|
f3e230e9478ba25a2b6af97a12843245c5dcf339 | # Dataset Card for "trainset1_2000_cutoff_llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/trainset1_2000_cutoff_llama | [
"region:us"
]
| 2023-11-03T02:25:48+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 249703784.98341143, "num_examples": 50000}], "download_size": 45211692, "dataset_size": 249703784.98341143}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T02:25:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trainset1_2000_cutoff_llama"
More Information needed | [
"# Dataset Card for \"trainset1_2000_cutoff_llama\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"trainset1_2000_cutoff_llama\"\n\nMore Information needed"
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| [
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|
46873235a764fc4cc7068bbd5f70d5e167aa1393 | # Dataset Card for "trainset2_2000_cutoff_llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/trainset2_2000_cutoff_llama | [
"region:us"
]
| 2023-11-03T02:25:54+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 249703784.98341143, "num_examples": 50000}], "download_size": 45154240, "dataset_size": 249703784.98341143}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T02:25:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trainset2_2000_cutoff_llama"
More Information needed | [
"# Dataset Card for \"trainset2_2000_cutoff_llama\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
762230b410289b6b07da279282dd7212e60a2d4b | # Dataset Card for "trainset3_2000_cutoff_llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/trainset3_2000_cutoff_llama | [
"region:us"
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| 2023-11-03T02:25:59+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 249703784.98341143, "num_examples": 50000}], "download_size": 45234048, "dataset_size": 249703784.98341143}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T02:26:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trainset3_2000_cutoff_llama"
More Information needed | [
"# Dataset Card for \"trainset3_2000_cutoff_llama\"\n\nMore Information needed"
]
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|
127d960d4056a53b46c6d936b697a03ba7776d73 | # Dataset Card for "validation_2000_cutoff_llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rewcifer/validation_2000_cutoff_llama | [
"region:us"
]
| 2023-11-03T02:26:02+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72668795.5058724, "num_examples": 14551}], "download_size": 13175560, "dataset_size": 72668795.5058724}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T02:26:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "validation_2000_cutoff_llama"
More Information needed | [
"# Dataset Card for \"validation_2000_cutoff_llama\"\n\nMore Information needed"
]
| [
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|
7c9927be1eb445d3ab35bf2c22feddc66a13f09b | # Dataset Card for "wikilingua_data-wiki_results"
rouge= {'rouge1': 0.34956276279808024, 'rouge2': 0.14816108637651773, 'rougeL': 0.27501599207153526, 'rougeLsum': 0.27501599207153526}
bert= {'precision': 0.7906100700329362, 'recall': 0.7631471133823419, 'f1': 0.7758486540348195}
moverscore: 0.6231903700836654 | arthurmluz/wikilingua_data-wiki_results | [
"region:us"
]
| 2023-11-03T03:10:05+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 21826073, "num_examples": 8165}], "download_size": 12802622, "dataset_size": 21826073}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-13T19:27:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikilingua_data-wiki_results"
rouge= {'rouge1': 0.34956276279808024, 'rouge2': 0.14816108637651773, 'rougeL': 0.27501599207153526, 'rougeLsum': 0.27501599207153526}
bert= {'precision': 0.7906100700329362, 'recall': 0.7631471133823419, 'f1': 0.7758486540348195}
moverscore: 0.6231903700836654 | [
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]
|
b74f53f3cc2e0f16d58abcfec087ab8e3407edb0 | # Dataset Card for "lsc_acrosplit_LDA_topics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tomashs/lsc_acrosplit_LDA_topics | [
"region:us"
]
| 2023-11-03T03:11:40+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "short_form", "dtype": "string"}, {"name": "long_form", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "text_prep", "dtype": "string"}, {"name": "topic_vector", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 384321369, "num_examples": 381714}, {"name": "val", "num_bytes": 70977757, "num_examples": 69424}, {"name": "test", "num_bytes": 51528242, "num_examples": 52461}], "download_size": 95761661, "dataset_size": 506827368}} | 2023-11-03T03:12:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "lsc_acrosplit_LDA_topics"
More Information needed | [
"# Dataset Card for \"lsc_acrosplit_LDA_topics\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
e7f84a34fbccd77011719cb0ad7cbf31bb610d01 | # Dataset Card for "WikiDoc_Dutch_translated_with_MariaNMT"
Translation of the **English** corpus [WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc),
to **Dutch** using an [Maria NMT model](https://marian-nmt.github.io/), trained by [Helsinki NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).
Note, for reference: Maria NMT is based on [BART](https://huggingface.co/docs/transformers/model_doc/bart), described [here](https://arxiv.org/abs/1910.13461).
# Attribution
If you use this dataset please use the following to credit the creators of the OPUS-MT models:
```
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
```
and
```
@misc {van_es_2023,
author = { {Bram van Es} },
title = { WikiDoc_Dutch_translated_with_MariaNMT (Revision 0db6106) },
year = 2023,
url = { https://huggingface.co/datasets/UMCU/WikiDoc_Dutch_translated_with_MariaNMT },
doi = { 10.57967/hf/1483 },
publisher = { Hugging Face }
}
```
# License
For both the Maria NMT model and the original [Helsinki NLP](https://twitter.com/HelsinkiNLP) [Opus MT model](https://huggingface.co/Helsinki-NLP)
we did **not** find a license, if this was in error please let us know and we will add the appropriate licensing promptly.
We adopt the licensing of the WikiDoc corpus: a [Creative Commons License](http://creativecommons.org/licenses/by-sa/4.0/). | UMCU/WikiDoc_Dutch_translated_with_MariaNMT | [
"license:cc",
"arxiv:1910.13461",
"doi:10.57967/hf/1483",
"region:us"
]
| 2023-11-03T03:23:39+00:00 | {"license": "cc", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5594660, "num_examples": 9696}], "download_size": 3051082, "dataset_size": 5594660}} | 2023-12-14T14:40:39+00:00 | [
"1910.13461"
]
| []
| TAGS
#license-cc #arxiv-1910.13461 #doi-10.57967/hf/1483 #region-us
| # Dataset Card for "WikiDoc_Dutch_translated_with_MariaNMT"
Translation of the English corpus WikiDoc,
to Dutch using an Maria NMT model, trained by Helsinki NLP.
Note, for reference: Maria NMT is based on BART, described here.
# Attribution
If you use this dataset please use the following to credit the creators of the OPUS-MT models:
and
# License
For both the Maria NMT model and the original Helsinki NLP Opus MT model
we did not find a license, if this was in error please let us know and we will add the appropriate licensing promptly.
We adopt the licensing of the WikiDoc corpus: a Creative Commons License. | [
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"# Attribution\n\nIf you use this dataset please use the following to credit the creators of the OPUS-MT models:\n\n\nand",
"# License\n\nFor both the Maria NMT model and the original Helsinki NLP Opus MT model \nwe did not find a license, if this was in error please let us know and we will add the appropriate licensing promptly.\n\nWe adopt the licensing of the WikiDoc corpus: a Creative Commons License."
]
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"# Attribution\n\nIf you use this dataset please use the following to credit the creators of the OPUS-MT models:\n\n\nand",
"# License\n\nFor both the Maria NMT model and the original Helsinki NLP Opus MT model \nwe did not find a license, if this was in error please let us know and we will add the appropriate licensing promptly.\n\nWe adopt the licensing of the WikiDoc corpus: a Creative Commons License."
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"passage: TAGS\n#license-cc #arxiv-1910.13461 #doi-10.57967/hf/1483 #region-us \n# Dataset Card for \"WikiDoc_Dutch_translated_with_MariaNMT\"\n\nTranslation of the English corpus WikiDoc,\nto Dutch using an Maria NMT model, trained by Helsinki NLP.\nNote, for reference: Maria NMT is based on BART, described here.# Attribution\n\nIf you use this dataset please use the following to credit the creators of the OPUS-MT models:\n\n\nand# License\n\nFor both the Maria NMT model and the original Helsinki NLP Opus MT model \nwe did not find a license, if this was in error please let us know and we will add the appropriate licensing promptly.\n\nWe adopt the licensing of the WikiDoc corpus: a Creative Commons License."
]
|
a263d2bde237a0193e6b7029199769ebd9e4c4cc | # Dataset Card for "new_dataset_finetune1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Varun1808/new_dataset_finetune1 | [
"region:us"
]
| 2023-11-03T03:33:06+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13245, "num_examples": 55}], "download_size": 4687, "dataset_size": 13245}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T03:33:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "new_dataset_finetune1"
More Information needed | [
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7509bd7873dda592263875ddc8a1407863549978 | # Dataset Card for "1b9c0c11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | result-kand2-sdxl-wuerst-karlo/1b9c0c11 | [
"region:us"
]
| 2023-11-03T03:47:36+00:00 | {"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 159, "num_examples": 10}], "download_size": 1316, "dataset_size": 159}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T03:47:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "1b9c0c11"
More Information needed | [
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|
0d856a4d79808efd21ba338ff4f9408753648a79 | # Dataset Card for "guarani"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | michaelginn/guarani | [
"region:us"
]
| 2023-11-03T03:59:35+00:00 | {"dataset_info": {"features": [{"name": "glottocode", "dtype": "string"}, {"name": "metalang_glottocode", "dtype": "string"}, {"name": "is_segmented", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "ID", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "glosses", "dtype": "string"}, {"name": "translation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 412977, "num_examples": 1606}], "download_size": 116518, "dataset_size": 412977}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T03:59:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "guarani"
More Information needed | [
"# Dataset Card for \"guarani\"\n\nMore Information needed"
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|
6542016503b4da9dea670b22445f6137efd818f8 | # Dataset Card for "guarani"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lecslab/guarani | [
"region:us"
]
| 2023-11-03T04:03:43+00:00 | {"dataset_info": {"features": [{"name": "glottocode", "dtype": "string"}, {"name": "metalang_glottocode", "dtype": "string"}, {"name": "is_segmented", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "glosses", "dtype": "string"}, {"name": "translation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 395887, "num_examples": 1606}], "download_size": 116792, "dataset_size": 395887}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-23T18:21:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "guarani"
More Information needed | [
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|
195a67212b2882cb733e90858d4c96c43ccd8bdb | # Dataset Card for "wikilingua_data-wiki_1024_results"
rouge= {'rouge1': 0.3547652574772463, 'rouge2': 0.1505956971978055, 'rougeL': 0.2785170891387953, 'rougeLsum': 0.2785170891387953}
bert= {'precision': 0.7906573472691691, 'recall': 0.7655439093188866, 'f1': 0.7771048831560097}
mover = 0.6245790568121278 | arthurmluz/wikilingua_data-wiki_1024_results | [
"region:us"
]
| 2023-11-03T04:28:06+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 21885909, "num_examples": 8165}], "download_size": 12842290, "dataset_size": 21885909}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-13T19:28:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikilingua_data-wiki_1024_results"
rouge= {'rouge1': 0.3547652574772463, 'rouge2': 0.1505956971978055, 'rougeL': 0.2785170891387953, 'rougeLsum': 0.2785170891387953}
bert= {'precision': 0.7906573472691691, 'recall': 0.7655439093188866, 'f1': 0.7771048831560097}
mover = 0.6245790568121278 | [
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|
b9eb7decb4d5bb788a96fa577e2e3d32751ac1c4 |
These texts are used in our "Hour of code" activity on computing and using ngrams. | Toxicode/various_texts | [
"task_categories:text-generation",
"license:unknown",
"region:us"
]
| 2023-11-03T05:04:05+00:00 | {"license": "unknown", "task_categories": ["text-generation"]} | 2023-11-06T23:57:23+00:00 | []
| []
| TAGS
#task_categories-text-generation #license-unknown #region-us
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These texts are used in our "Hour of code" activity on computing and using ngrams. | []
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16c97827bd911b96d6cf073daf1a397a74c3bbf2 | # Dataset Card for Dataset Name
<!-- AI XDR playbook -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- AI xdr paper
XDR (Extended Detection and Response) is a security solution that combines multiple detection and response technologies to provide a more comprehensive view of an organization's security posture, making it easier to recognize and respond to potential threats[1]. AI/ML (Artificial Intelligence/Machine Learning) is a key component of XDR, as it enables advanced analytics techniques to identify potential threats and automate response actions[1][2]. Here are some ways in which AI enhances XDR platforms:
- **Advanced analytics**: XDR solutions use advanced analytics techniques supported by machine learning (ML) models to identify potential threats and automate response actions[1][5].
- **Automated response**: XDR solutions can automatically block or quarantine malicious files and alert security teams to potential incidents[1].
- **Single pane of glass view**: XDR solutions provide a unified view of all security events and incidents, making it easier for security teams to investigate and respond to threats[1].
- **Detecting unknown or zero-day threats**: AI-powered XDR solutions can detect unknown or zero-day threats, making them more effective than traditional detection and response technologies that rely on rule-based or signature-based detection methods[1][5].
- **Predicting future cyberattacks**: AI is able to predict future cyberattacks and identify their mechanisms to determine their origin, accelerating responses to attacks[5].
XDR platforms with AI can perform analyses on every layer of an organization's infrastructure, including those that were previously inaccessible to analysts[5]. AI analyzes logs and compares current activities on an organization's infrastructure to detect any unusual action on all its infrastructures, including servers, workstations, and networks[5]. Additionally, an AI-powered XDR with Next Generation Antivirus (NGAV) can detect unknown malicious files[5]. If an anomaly is detected, the sensors immediately send the information back to the XDR, which can automatically prioritize alerts so that security teams can immediately respond to potential threats[5].
Citations:
[1] Machine Learning and Artificial Intelligence (AI/ML): The Secret Sauce Behind XDR https://www.computer.org/publications/tech-news/trends/the-secret-sauce-behind-xdr/
[2] AI-Driven XDR: Defeating the Most Complex Attack Sequences - Cybereason https://www.cybereason.com/blog/ai-driven-xdr-defeating-the-most-complex-attack-sequences
[3] Harnessing the Power of AI-Driven XDR - Cybereason https://www.cybereason.com/blog/harnessing-the-power-of-ai-driven-xdr
[4] Explainable dimensionality reduction (XDR) to unbox AI 'black box' models: A study of AI perspectives on the ethnic styles of village dwellings - Nature https://www.nature.com/articles/s41599-023-01505-4
[5] How does AI enhance XDR platforms? - TEHTRIS https://tehtris.com/en/blog/how-does-ai-enhance-xdr-platforms
[6] XDR Should Be Viewed as An Open Architecture - Vectra AI https://www.vectra.ai/resources/research-reports/esg-xdr-open-architecture
By Perplexity at https://www.perplexity.ai/search/fd37ce22-dccf-4aa9-8478-d24cf6db23c4?s=m -->
- **Curated by:** [Edward Nyameri ]
- **Funded by [optional]:** [Nil funding but any interested POC is welcome]
- **Shared by [optional]:** [Edward Nyameri ]
- **Language(s) (NLP):** [LLM]
- **License:** [MIT]
### Dataset Sources [optional]
<!-- schooly-Computer Breaches -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Threat Hunting for AI cyber Security Tool Kit -->
### Direct Use
<!-- application platform analysis for Threat Hunters-->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Advancement of the Threat Hunt using Computational Intelligence to curb & contain comprising of information -->
[More Information Needed]
### Source Data
<!-- 🏫 Computer Breaches-->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | Nyameri/AIXDR | [
"task_categories:summarization",
"task_categories:feature-extraction",
"size_categories:1K<n<10K",
"license:mit",
"region:us"
]
| 2023-11-03T05:19:15+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["summarization", "feature-extraction"], "pretty_name": "AI threat Hunter's playbook"} | 2023-11-03T05:40:01+00:00 | []
| []
| TAGS
#task_categories-summarization #task_categories-feature-extraction #size_categories-1K<n<10K #license-mit #region-us
| # Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by: [Edward Nyameri ]
- Funded by [optional]: [Nil funding but any interested POC is welcome]
- Shared by [optional]: [Edward Nyameri ]
- Language(s) (NLP): [LLM]
- License: [MIT]
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
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"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
]
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"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: [Edward Nyameri ]\n- Funded by [optional]: [Nil funding but any interested POC is welcome]\n- Shared by [optional]: [Edward Nyameri ]\n- Language(s) (NLP): [LLM]\n- License: [MIT]",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Out-of-Scope Use",
"## Dataset Structure",
"## Dataset Creation",
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"#### Data Collection and Processing",
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"passage: TAGS\n#task_categories-summarization #task_categories-feature-extraction #size_categories-1K<n<10K #license-mit #region-us \n# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: [Edward Nyameri ]\n- Funded by [optional]: [Nil funding but any interested POC is welcome]\n- Shared by [optional]: [Edward Nyameri ]\n- Language(s) (NLP): [LLM]\n- License: [MIT]### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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|
1d45cda6b6da886d7f99e10b98516b9a317c0b62 | # Dataset Card for "airoboros-3.1-no-mathjson"
This is a copy of "jondurbin/airoboros-3.1" excluding mathjson instances. | habanoz/airoboros-3.1-no-mathjson | [
"region:us"
]
| 2023-11-03T06:02:09+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 76681303.64476947, "num_examples": 42129}], "download_size": 47376788, "dataset_size": 76681303.64476947}} | 2023-11-03T06:04:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "airoboros-3.1-no-mathjson"
This is a copy of "jondurbin/airoboros-3.1" excluding mathjson instances. | [
"# Dataset Card for \"airoboros-3.1-no-mathjson\"\n\nThis is a copy of \"jondurbin/airoboros-3.1\" excluding mathjson instances."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"airoboros-3.1-no-mathjson\"\n\nThis is a copy of \"jondurbin/airoboros-3.1\" excluding mathjson instances."
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"airoboros-3.1-no-mathjson\"\n\nThis is a copy of \"jondurbin/airoboros-3.1\" excluding mathjson instances."
]
|
ac212b8d887efdcf9f6ac9c852f0f620b6aeda0c | ```python
import pandas as pd
# Load the dataset
df = pd.read_csv('cyber_security_breaches.csv')
# Print the first 14 rows of the dataset
print(df.head())
# Get the number of rows and columns in the dataset
print(df.shape)
# Get the summary statistics of the dataset
print(df.describe())
# Get the unique values of a column
print(df['Year'].unique())
# Filter the dataset based on a condition
print(df[df['Year'] == 2023])
```
language: English
"isensor XDR for Threat Hunt in Computational Intelligence as explained in depth by Edward Nyameri"
license: "MIT"
license: "MIT"
AI xdr paper
XDR (Extended Detection and Response) is a security solution that combines multiple detection and response technologies to provide a more comprehensive view of an organization's security posture, making it easier to recognize and respond to potential threats[1]. AI/ML (Artificial Intelligence/Machine Learning) is a key component of XDR, as it enables advanced analytics techniques to identify potential threats and automate response actions[1][2]. Here are some ways in which AI enhances XDR platforms:
- **Advanced analytics**: XDR solutions use advanced analytics techniques supported by machine learning (ML) models to identify potential threats and automate response actions[1][5].
- **Automated response**: XDR solutions can automatically block or quarantine malicious files and alert security teams to potential incidents[1].
- **Single pane of glass view**: XDR solutions provide a unified view of all security events and incidents, making it easier for security teams to investigate and respond to threats[1].
- **Detecting unknown or zero-day threats**: AI-powered XDR solutions can detect unknown or zero-day threats, making them more effective than traditional detection and response technologies that rely on rule-based or signature-based detection methods[1][5].
- **Predicting future cyberattacks**: AI is able to predict future cyberattacks and identify their mechanisms to determine their origin, accelerating responses to attacks[5].
XDR platforms with AI can perform analyses on every layer of an organization's infrastructure, including those that were previously inaccessible to analysts[5]. AI analyzes logs and compares current activities on an organization's infrastructure to detect any unusual action on all its infrastructures, including servers, workstations, and networks[5]. Additionally, an AI-powered XDR with Next Generation Antivirus (NGAV) can detect unknown malicious files[5]. If an anomaly is detected, the sensors immediately send the information back to the XDR, which can automatically prioritize alerts so that security teams can immediately respond to potential threats[5].
Citations:
[1] Machine Learning and Artificial Intelligence (AI/ML): The Secret Sauce Behind XDR https://www.computer.org/publications/tech-news/trends/the-secret-sauce-behind-xdr/
[2] AI-Driven XDR: Defeating the Most Complex Attack Sequences - Cybereason https://www.cybereason.com/blog/ai-driven-xdr-defeating-the-most-complex-attack-sequences
[3] Harnessing the Power of AI-Driven XDR - Cybereason https://www.cybereason.com/blog/harnessing-the-power-of-ai-driven-xdr
[4] Explainable dimensionality reduction (XDR) to unbox AI 'black box' models: A study of AI perspectives on the ethnic styles of village dwellings - Nature https://www.nature.com/articles/s41599-023-01505-4
[5] How does AI enhance XDR platforms? - TEHTRIS https://tehtris.com/en/blog/how-does-ai-enhance-xdr-platforms
[6] XDR Should Be Viewed as An Open Architecture - Vectra AI https://www.vectra.ai/resources/research-reports/esg-xdr-open-architecture
By Perplexity at https://www.perplexity.ai/search/fd37ce22-dccf-4aa9-8478-d24cf6db23c4?s=m | Nyameri/autotrain-data-isensor-on-xdr-playbook-for-threat-hunt | [
"region:us"
]
| 2023-11-03T06:04:18+00:00 | {} | 2023-11-03T06:48:55+00:00 | []
| []
| TAGS
#region-us
|
language: English
"isensor XDR for Threat Hunt in Computational Intelligence as explained in depth by Edward Nyameri"
license: "MIT"
license: "MIT"
AI xdr paper
XDR (Extended Detection and Response) is a security solution that combines multiple detection and response technologies to provide a more comprehensive view of an organization's security posture, making it easier to recognize and respond to potential threats[1]. AI/ML (Artificial Intelligence/Machine Learning) is a key component of XDR, as it enables advanced analytics techniques to identify potential threats and automate response actions[1][2]. Here are some ways in which AI enhances XDR platforms:
- Advanced analytics: XDR solutions use advanced analytics techniques supported by machine learning (ML) models to identify potential threats and automate response actions[1][5].
- Automated response: XDR solutions can automatically block or quarantine malicious files and alert security teams to potential incidents[1].
- Single pane of glass view: XDR solutions provide a unified view of all security events and incidents, making it easier for security teams to investigate and respond to threats[1].
- Detecting unknown or zero-day threats: AI-powered XDR solutions can detect unknown or zero-day threats, making them more effective than traditional detection and response technologies that rely on rule-based or signature-based detection methods[1][5].
- Predicting future cyberattacks: AI is able to predict future cyberattacks and identify their mechanisms to determine their origin, accelerating responses to attacks[5].
XDR platforms with AI can perform analyses on every layer of an organization's infrastructure, including those that were previously inaccessible to analysts[5]. AI analyzes logs and compares current activities on an organization's infrastructure to detect any unusual action on all its infrastructures, including servers, workstations, and networks[5]. Additionally, an AI-powered XDR with Next Generation Antivirus (NGAV) can detect unknown malicious files[5]. If an anomaly is detected, the sensors immediately send the information back to the XDR, which can automatically prioritize alerts so that security teams can immediately respond to potential threats[5].
Citations:
[1] Machine Learning and Artificial Intelligence (AI/ML): The Secret Sauce Behind XDR URL
[2] AI-Driven XDR: Defeating the Most Complex Attack Sequences - Cybereason URL
[3] Harnessing the Power of AI-Driven XDR - Cybereason URL
[4] Explainable dimensionality reduction (XDR) to unbox AI 'black box' models: A study of AI perspectives on the ethnic styles of village dwellings - Nature URL
[5] How does AI enhance XDR platforms? - TEHTRIS URL
[6] XDR Should Be Viewed as An Open Architecture - Vectra AI URL
By Perplexity at URL | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
|
3b6c822a06932519d4966d820b4cd0378af41904 | # Dataset Card for "92580e64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | result-kand2-sdxl-wuerst-karlo/92580e64 | [
"region:us"
]
| 2023-11-03T06:17:01+00:00 | {"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 168, "num_examples": 10}], "download_size": 1306, "dataset_size": 168}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T06:17:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "92580e64"
More Information needed | [
"# Dataset Card for \"92580e64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"92580e64\"\n\nMore Information needed"
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| [
6,
15
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"passage: TAGS\n#region-us \n# Dataset Card for \"92580e64\"\n\nMore Information needed"
]
|
a8cdaa818910969e4ac26d52982c4506ca03f607 |
# Dataset Card for amazon_polarity
## Licensing Information
The data is distributed under the [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) license.
## Source Data Citation Information
McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)
| KETI-AIR/kor_amazon_polarity | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ko",
"license:cc0-1.0",
"region:us"
]
| 2023-11-03T06:33:37+00:00 | {"language": ["ko"], "license": "cc0-1.0", "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}, {"name": "title", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "data_index_by_user", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 2059069183, "num_examples": 3600000}, {"name": "test", "num_bytes": 228905323, "num_examples": 400000}], "download_size": 1298504656, "dataset_size": 2287974506}} | 2023-11-15T01:14:28+00:00 | []
| [
"ko"
]
| TAGS
#task_categories-text-classification #task_ids-sentiment-classification #size_categories-1M<n<10M #source_datasets-original #language-Korean #license-cc0-1.0 #region-us
|
# Dataset Card for amazon_polarity
## Licensing Information
The data is distributed under the CC0 1.0 license.
## Source Data Citation Information
McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)
| [
"# Dataset Card for amazon_polarity",
"## Licensing Information\n\nThe data is distributed under the CC0 1.0 license.",
"## Source Data Citation Information\n\nMcAuley, Julian, and Jure Leskovec. \"Hidden factors and hidden topics: understanding rating dimensions with review text.\" In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.\n\nXiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)"
]
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"TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #size_categories-1M<n<10M #source_datasets-original #language-Korean #license-cc0-1.0 #region-us \n",
"# Dataset Card for amazon_polarity",
"## Licensing Information\n\nThe data is distributed under the CC0 1.0 license.",
"## Source Data Citation Information\n\nMcAuley, Julian, and Jure Leskovec. \"Hidden factors and hidden topics: understanding rating dimensions with review text.\" In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.\n\nXiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)"
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"passage: TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #size_categories-1M<n<10M #source_datasets-original #language-Korean #license-cc0-1.0 #region-us \n# Dataset Card for amazon_polarity## Licensing Information\n\nThe data is distributed under the CC0 1.0 license.## Source Data Citation Information\n\nMcAuley, Julian, and Jure Leskovec. \"Hidden factors and hidden topics: understanding rating dimensions with review text.\" In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.\n\nXiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)"
]
|
7725656f60a8c21ae501c16bf56b17cb9f56c974 |
# **Indonesian Wikipedia Data Repository**
---
license: cc-by-sa-3.0
---
Welcome to Indonesian Wikipedia Data Repository. The datasets are extracted from [Wikipedia HF](https://huggingface.co/datasets/wikipedia) and processed using the scripts available in this repository for reproducibility purpose.
# **FAQS**
### What are the available languages provided in dataset?
Please check the following table.
| Lang Code | Lang Desc | Wiki Info | Total Data | Total Size (bytes) |
| :---: | :----: | :--- | ---: | ---: |
| ace | Acehnese | [Wiki Link](https://en.wikipedia.org/wiki/Acehnese_language) | 12904 | 4867838 |
| ban | Balinese | [Wiki Link](https://en.wikipedia.org/wiki/Balinese_language) | 19837 | 17366080 |
| bjn | Acehnese | [Wiki Link](https://en.wikipedia.org/wiki/Banjarese_language) | 10437 | 6655378 |
| bug | Buginese | [Wiki Link](https://en.wikipedia.org/wiki/Buginese_language) | 9793 | 2072609 |
| gor | Gorontalo | [Wiki Link](https://en.wikipedia.org/wiki/Gorontalo_language) | 14514 | 5989252 |
| id | Indonesian | [Wiki Link](https://en.wikipedia.org/wiki/Indonesian_language) | 654287 | 1100932403 |
| jv | Javanese | [Wiki Link](https://en.wikipedia.org/wiki/Javanese_language) | 72667 | 69774853 |
| map_bms | Banyumasan <br />(Dialect of Javanese) | [Wiki Link](https://en.wikipedia.org/wiki/Banyumasan_dialect) | 11832 | 5060989 |
| min | Minangkabau | [Wiki Link](https://en.wikipedia.org/wiki/Minangkabau_language) | 225858 | 116376870 |
| ms | Malay | [Wiki Link](https://en.wikipedia.org/wiki/Malay_language) | 346186 | 410443550 |
| nia | Nias | [Wiki Link](https://en.wikipedia.org/wiki/Nias_language) | 1650 | 1938121 |
| su | Sundanese | [Wiki Link](https://en.wikipedia.org/wiki/Sundanese_language) | 61494 | 47410439 |
| tet | Tetum | [Wiki Link](https://en.wikipedia.org/wiki/Tetum_language) | 1465 | 1452716 |
### How do I extract new Wikipedia Dataset of Indonesian languages?
You may check to the script [_```extract_raw_wiki_data.py```_](https://huggingface.co/datasets/sabilmakbar/indo_wiki/blob/main/extract_raw_wiki_data.py) to understand its implementations, or you can adjust the bash provided in [_```extract_raw_wiki_data_indo.sh```_](https://huggingface.co/datasets/sabilmakbar/indo_wiki/blob/main/extract_raw_wiki_data_indo.sh) to extract it on your own. Please note that this dataset is extensible to any languages of your choice.
### How do I extract new Wikipedia Dataset of Indonesian languages?
You may visit this [Wikipedia Dump Index](https://dumps.wikimedia.org/backup-index.html) to check any latest available data and this link [Wikipedia Language Coverage](https://meta.wikimedia.org/wiki/List_of_Wikipedias#All_Wikipedias_ordered_by_number_of_articles) to map into any languages that you're wanting to extract.
### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?
The data available in here are processed with following flows:
1. Raw data is being deduplicated on ```title``` and ```text``` (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for no-available informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.
2. Furthermore, the ```title``` and ```text``` data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars validated). You may check this [ ```cleanse_wiki_data.py```](https://huggingface.co/datasets/sabilmakbar/indo_wiki/blob/main/cleanse_wiki_data.py) script to understand its implementation.
# Getting Started #
### To read the datasets directly ###
Use one of the following code chunks to load it from HuggingFace Hub:
You can refer to the 2nd args of ```config name``` using the following script
```
dataset = load_dataset(
"sabilmakbar/indo_wiki",
"indo_wiki_dedup_data" # a config name, can be "indo_wiki_raw_data" or "indowiki_dedup_id_only", defaults to "indo_wiki_dedup_data"
)
```
Or you can provide both ```lang``` and ```date_stamp``` (providing only one will thrown an error)
```
dataset = load_dataset(
"sabilmakbar/indo_wiki",
lang = "id", # see the splits for complete lang choices
date_stamp="20230901"
)
```
### To replicate the whole dataset generation process ###
1. Set-up a new Python/Conda Environment (recommended Python version: 3.9.6 to 3.9.18 or 3.10.0 to 3.10.13) and install the requirements on ```requirements.txt``` use this codebase via ```pip install -r requirements.txt```.
2. Activate the chosen Python/Conda environment which the requirements are being installed.
3. Run this ```sh``` script for extractions from Wikimedia Dump:
```sh extract_raw_wiki_data_indo.sh```.
4. Run this ```sh``` script of deduplication:
```sh dedup_raw_wiki_data_indo.sh```.
## Citation Info:
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"}
@ONLINE{wikipedia-hf,
title = "Huggingface Wikipedia Dataset",
url = "https://huggingface.co/datasets/wikipedia"}
``` | sabilmakbar/indo_wiki | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:Wikipedia-HF",
"language:ace",
"language:ban",
"language:bjn",
"language:bug",
"language:gor",
"language:id",
"language:jv",
"language:mis",
"language:min",
"language:ms",
"language:nia",
"language:su",
"language:tet",
"license:cc-by-sa-3.0",
"license:gfdl",
"Wikipedia",
"Indonesian",
"Sundanese",
"Javanese",
"Malay",
"Dialect",
"Javanese Dialect (Banyumase/Ngapak)",
"Indonesian Language",
"Malay Language",
"Indonesia-related Languages",
"Indonesian Local Languages",
"region:us"
]
| 2023-11-03T06:49:33+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["ace", "ban", "bjn", "bug", "gor", "id", "jv", "mis", "min", "ms", "nia", "su", "tet"], "license": ["cc-by-sa-3.0", "gfdl"], "multilinguality": ["multilingual"], "source_datasets": ["Wikipedia-HF"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "Wikipedia Archive for Indonesian Languages & Local Languages", "tags": ["Wikipedia", "Indonesian", "Sundanese", "Javanese", "Malay", "Dialect", "Javanese Dialect (Banyumase/Ngapak)", "Indonesian Language", "Malay Language", "Indonesia-related Languages", "Indonesian Local Languages"], "dataset_info": [{"config_name": "indowiki_all", "features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "ace", "num_bytes": 4875688, "num_examples": 12932}, {"name": "ban", "num_bytes": 17561379, "num_examples": 20243}, {"name": "bjn", "num_bytes": 6669628, "num_examples": 10460}, {"name": "bug", "num_bytes": 3297641, "num_examples": 15877}, {"name": "gor", "num_bytes": 6007726, "num_examples": 14572}, {"name": "id", "num_bytes": 1103106307, "num_examples": 657990}, {"name": "jv", "num_bytes": 70335030, "num_examples": 73150}, {"name": "map_bms", "num_bytes": 5215803, "num_examples": 13574}, {"name": "min", "num_bytes": 116481049, "num_examples": 227024}, {"name": "ms", "num_bytes": 416001194, "num_examples": 367463}, {"name": "nia", "num_bytes": 1938378, "num_examples": 1651}, {"name": "su", "num_bytes": 47489084, "num_examples": 61557}, {"name": "tet", "num_bytes": 1452716, "num_examples": 1465}], "download_size": 1803193334, "dataset_size": 1800431623}, {"config_name": "indowiki_dedup_all", "features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "ace", "num_bytes": 4867838, "num_examples": 12904}, {"name": "ban", "num_bytes": 17366080, "num_examples": 19837}, {"name": "bjn", "num_bytes": 6655378, "num_examples": 10437}, {"name": "bug", "num_bytes": 2072609, "num_examples": 9793}, {"name": "gor", "num_bytes": 5989252, "num_examples": 14514}, {"name": "id", "num_bytes": 1100932403, "num_examples": 654287}, {"name": "jv", "num_bytes": 69774853, "num_examples": 72667}, {"name": "map_bms", "num_bytes": 5060989, "num_examples": 11832}, {"name": "min", "num_bytes": 116376870, "num_examples": 225858}, {"name": "ms", "num_bytes": 410443550, "num_examples": 346186}, {"name": "nia", "num_bytes": 1938121, "num_examples": 1650}, {"name": "su", "num_bytes": 47410439, "num_examples": 61494}, {"name": "tet", "num_bytes": 1447926, "num_examples": 1460}], "download_size": 1793103024, "dataset_size": 1790336308}, {"config_name": "indowiki_dedup_id_only", "features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1100932403, "num_examples": 654287}], "download_size": 1103131493, "dataset_size": 1100932403}]} | 2023-11-03T07:59:24+00:00 | []
| [
"ace",
"ban",
"bjn",
"bug",
"gor",
"id",
"jv",
"mis",
"min",
"ms",
"nia",
"su",
"tet"
]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-Wikipedia-HF #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-Gorontalo #language-Indonesian #language-Javanese #language-Uncoded languages #language-Minangkabau #language-Malay (macrolanguage) #language-Nias #language-Sundanese #language-Tetum #license-cc-by-sa-3.0 #license-gfdl #Wikipedia #Indonesian #Sundanese #Javanese #Malay #Dialect #Javanese Dialect (Banyumase/Ngapak) #Indonesian Language #Malay Language #Indonesia-related Languages #Indonesian Local Languages #region-us
| Indonesian Wikipedia Data Repository
====================================
---
license: cc-by-sa-3.0
---------------------
Welcome to Indonesian Wikipedia Data Repository. The datasets are extracted from Wikipedia HF and processed using the scripts available in this repository for reproducibility purpose.
FAQS
====
### What are the available languages provided in dataset?
Please check the following table.
### How do I extract new Wikipedia Dataset of Indonesian languages?
You may check to the script \_\_ to understand its implementations, or you can adjust the bash provided in \_\_ to extract it on your own. Please note that this dataset is extensible to any languages of your choice.
### How do I extract new Wikipedia Dataset of Indonesian languages?
You may visit this Wikipedia Dump Index to check any latest available data and this link Wikipedia Language Coverage to map into any languages that you're wanting to extract.
### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?
The data available in here are processed with following flows:
1. Raw data is being deduplicated on and (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for no-available informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.
2. Furthermore, the and data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars validated). You may check this script to understand its implementation.
Getting Started
===============
### To read the datasets directly
Use one of the following code chunks to load it from HuggingFace Hub:
You can refer to the 2nd args of using the following script
Or you can provide both and (providing only one will thrown an error)
### To replicate the whole dataset generation process
1. Set-up a new Python/Conda Environment (recommended Python version: 3.9.6 to 3.9.18 or 3.10.0 to 3.10.13) and install the requirements on use this codebase via .
2. Activate the chosen Python/Conda environment which the requirements are being installed.
3. Run this script for extractions from Wikimedia Dump:
.
4. Run this script of deduplication:
.
Info:
| [
"### What are the available languages provided in dataset?\n\n\nPlease check the following table.",
"### How do I extract new Wikipedia Dataset of Indonesian languages?\n\n\nYou may check to the script \\_\\_ to understand its implementations, or you can adjust the bash provided in \\_\\_ to extract it on your own. Please note that this dataset is extensible to any languages of your choice.",
"### How do I extract new Wikipedia Dataset of Indonesian languages?\n\n\nYou may visit this Wikipedia Dump Index to check any latest available data and this link Wikipedia Language Coverage to map into any languages that you're wanting to extract.",
"### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?\n\n\nThe data available in here are processed with following flows:\n\n\n1. Raw data is being deduplicated on and (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for no-available informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.\n2. Furthermore, the and data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars validated). You may check this script to understand its implementation.\n\n\nGetting Started\n===============",
"### To read the datasets directly\n\n\nUse one of the following code chunks to load it from HuggingFace Hub:\nYou can refer to the 2nd args of using the following script\n\n\nOr you can provide both and (providing only one will thrown an error)",
"### To replicate the whole dataset generation process\n\n\n1. Set-up a new Python/Conda Environment (recommended Python version: 3.9.6 to 3.9.18 or 3.10.0 to 3.10.13) and install the requirements on use this codebase via .\n2. Activate the chosen Python/Conda environment which the requirements are being installed.\n3. Run this script for extractions from Wikimedia Dump:\n.\n4. Run this script of deduplication:\n.\n\n\nInfo:"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-Wikipedia-HF #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-Gorontalo #language-Indonesian #language-Javanese #language-Uncoded languages #language-Minangkabau #language-Malay (macrolanguage) #language-Nias #language-Sundanese #language-Tetum #license-cc-by-sa-3.0 #license-gfdl #Wikipedia #Indonesian #Sundanese #Javanese #Malay #Dialect #Javanese Dialect (Banyumase/Ngapak) #Indonesian Language #Malay Language #Indonesia-related Languages #Indonesian Local Languages #region-us \n",
"### What are the available languages provided in dataset?\n\n\nPlease check the following table.",
"### How do I extract new Wikipedia Dataset of Indonesian languages?\n\n\nYou may check to the script \\_\\_ to understand its implementations, or you can adjust the bash provided in \\_\\_ to extract it on your own. Please note that this dataset is extensible to any languages of your choice.",
"### How do I extract new Wikipedia Dataset of Indonesian languages?\n\n\nYou may visit this Wikipedia Dump Index to check any latest available data and this link Wikipedia Language Coverage to map into any languages that you're wanting to extract.",
"### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?\n\n\nThe data available in here are processed with following flows:\n\n\n1. Raw data is being deduplicated on and (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for no-available informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.\n2. Furthermore, the and data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars validated). You may check this script to understand its implementation.\n\n\nGetting Started\n===============",
"### To read the datasets directly\n\n\nUse one of the following code chunks to load it from HuggingFace Hub:\nYou can refer to the 2nd args of using the following script\n\n\nOr you can provide both and (providing only one will thrown an error)",
"### To replicate the whole dataset generation process\n\n\n1. Set-up a new Python/Conda Environment (recommended Python version: 3.9.6 to 3.9.18 or 3.10.0 to 3.10.13) and install the requirements on use this codebase via .\n2. Activate the chosen Python/Conda environment which the requirements are being installed.\n3. Run this script for extractions from Wikimedia Dump:\n.\n4. Run this script of deduplication:\n.\n\n\nInfo:"
]
| [
244,
19,
72,
52,
178,
58,
104
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-Wikipedia-HF #language-Achinese #language-Balinese #language-Banjar #language-Buginese #language-Gorontalo #language-Indonesian #language-Javanese #language-Uncoded languages #language-Minangkabau #language-Malay (macrolanguage) #language-Nias #language-Sundanese #language-Tetum #license-cc-by-sa-3.0 #license-gfdl #Wikipedia #Indonesian #Sundanese #Javanese #Malay #Dialect #Javanese Dialect (Banyumase/Ngapak) #Indonesian Language #Malay Language #Indonesia-related Languages #Indonesian Local Languages #region-us \n### What are the available languages provided in dataset?\n\n\nPlease check the following table.### How do I extract new Wikipedia Dataset of Indonesian languages?\n\n\nYou may check to the script \\_\\_ to understand its implementations, or you can adjust the bash provided in \\_\\_ to extract it on your own. Please note that this dataset is extensible to any languages of your choice.### How do I extract new Wikipedia Dataset of Indonesian languages?\n\n\nYou may visit this Wikipedia Dump Index to check any latest available data and this link Wikipedia Language Coverage to map into any languages that you're wanting to extract."
]
|
a3549b3c8cf83114193c89cd2a297f30bb33ec2f | # Dataset Card for "en17272_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Jonglee/en17272_dataset | [
"region:us"
]
| 2023-11-03T07:06:36+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59360, "num_examples": 30}], "download_size": 38240, "dataset_size": 59360}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T07:06:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "en17272_dataset"
More Information needed | [
"# Dataset Card for \"en17272_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"en17272_dataset\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"en17272_dataset\"\n\nMore Information needed"
]
|
b001356f0f40debaed8ff3287658b7a2999cc486 | # Dataset Card for "click_bate_1000_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | re2panda/click_bate_1000_final | [
"region:us"
]
| 2023-11-03T07:08:04+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35358262.8, "num_examples": 11400}, {"name": "test", "num_bytes": 1860961.2, "num_examples": 600}], "download_size": 20825026, "dataset_size": 37219224.0}} | 2023-11-03T07:31:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "click_bate_1000_final"
More Information needed | [
"# Dataset Card for \"click_bate_1000_final\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"click_bate_1000_final\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"click_bate_1000_final\"\n\nMore Information needed"
]
|
96f8f145d8190922b6718cd0fdd9621dd5131f7a | # Dataset Card for "indian_food_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Krithiga/indian_food_images | [
"region:us"
]
| 2023-11-03T07:15:48+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "burger", "1": "butter_naan", "2": "chai", "3": "chapati", "4": "chole_bhature", "5": "dal_makhani", "6": "dhokla", "7": "fried_rice", "8": "idli", "9": "jalebi", "10": "kaathi_rolls", "11": "kadai_paneer", "12": "kulfi", "13": "masala_dosa", "14": "momos", "15": "paani_puri", "16": "pakode", "17": "pav_bhaji", "18": "pizza", "19": "samosa"}}}}], "splits": [{"name": "train", "num_bytes": 1180551063.6474335, "num_examples": 5328}, {"name": "test", "num_bytes": 252662054.3925666, "num_examples": 941}], "download_size": 1601645630, "dataset_size": 1433213118.0400002}} | 2023-11-13T14:43:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "indian_food_images"
More Information needed | [
"# Dataset Card for \"indian_food_images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"indian_food_images\"\n\nMore Information needed"
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| [
6,
17
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"passage: TAGS\n#region-us \n# Dataset Card for \"indian_food_images\"\n\nMore Information needed"
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|
e0d586e4ed039eeb7d4481b79fb6357f19d92329 |
# 📘 BookMIA Datasets
The **BookMIA datasets** serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003).
The dataset contains non-member and member data:
- non-member data consists of text excerpts from books first published in 2023
- member data includes text excerpts from older books, as categorized by Chang et al. in 2023.
### 📌 Applicability
The datasets can be applied to various OpenAI models released before **2023**:
- text-davinci-001
- text-davinci-002
- ... and more.
## Loading the datasets
To load the dataset:
```python
from datasets import load_dataset
dataset = load_dataset("swj0419/BookMIA")
```
* Text Lengths: `512`.
* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.
## 🛠️ Codebase
For evaluating MIA methods on our datasets, visit our [GitHub repository](https://github.com/swj0419/detect-pretrain-code).
## ⭐ Citing our Work
If you find our codebase and datasets beneficial, kindly cite our work:
```bibtex
@misc{shi2023detecting,
title={Detecting Pretraining Data from Large Language Models},
author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer},
year={2023},
eprint={2310.16789},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
[1] Kent K Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman. Speak, memory: An archaeology of books known to chatgpt/gpt-4. arXiv preprint arXiv:2305.00118, 2023. | swj0419/BookMIA | [
"license:mit",
"arxiv:2310.16789",
"region:us"
]
| 2023-11-03T07:54:57+00:00 | {"license": "mit"} | 2023-11-03T16:39:56+00:00 | [
"2310.16789"
]
| []
| TAGS
#license-mit #arxiv-2310.16789 #region-us
|
# BookMIA Datasets
The BookMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003).
The dataset contains non-member and member data:
- non-member data consists of text excerpts from books first published in 2023
- member data includes text excerpts from older books, as categorized by Chang et al. in 2023.
### Applicability
The datasets can be applied to various OpenAI models released before 2023:
- text-davinci-001
- text-davinci-002
- ... and more.
## Loading the datasets
To load the dataset:
* Text Lengths: '512'.
* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.
## ️ Codebase
For evaluating MIA methods on our datasets, visit our GitHub repository.
## ⭐ Citing our Work
If you find our codebase and datasets beneficial, kindly cite our work:
[1] Kent K Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman. Speak, memory: An archaeology of books known to chatgpt/gpt-4. arXiv preprint arXiv:2305.00118, 2023. | [
"# BookMIA Datasets\n\nThe BookMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003). \n\nThe dataset contains non-member and member data: \n- non-member data consists of text excerpts from books first published in 2023\n- member data includes text excerpts from older books, as categorized by Chang et al. in 2023.",
"### Applicability\n\nThe datasets can be applied to various OpenAI models released before 2023:\n\n- text-davinci-001\n- text-davinci-002\n- ... and more.",
"## Loading the datasets\n\nTo load the dataset:\n\n\n* Text Lengths: '512'.\n* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.",
"## ️ Codebase\n\nFor evaluating MIA methods on our datasets, visit our GitHub repository.",
"## ⭐ Citing our Work\n\nIf you find our codebase and datasets beneficial, kindly cite our work:\n\n\n\n[1] Kent K Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman. Speak, memory: An archaeology of books known to chatgpt/gpt-4. arXiv preprint arXiv:2305.00118, 2023."
]
| [
"TAGS\n#license-mit #arxiv-2310.16789 #region-us \n",
"# BookMIA Datasets\n\nThe BookMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003). \n\nThe dataset contains non-member and member data: \n- non-member data consists of text excerpts from books first published in 2023\n- member data includes text excerpts from older books, as categorized by Chang et al. in 2023.",
"### Applicability\n\nThe datasets can be applied to various OpenAI models released before 2023:\n\n- text-davinci-001\n- text-davinci-002\n- ... and more.",
"## Loading the datasets\n\nTo load the dataset:\n\n\n* Text Lengths: '512'.\n* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.",
"## ️ Codebase\n\nFor evaluating MIA methods on our datasets, visit our GitHub repository.",
"## ⭐ Citing our Work\n\nIf you find our codebase and datasets beneficial, kindly cite our work:\n\n\n\n[1] Kent K Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman. Speak, memory: An archaeology of books known to chatgpt/gpt-4. arXiv preprint arXiv:2305.00118, 2023."
]
| [
20,
113,
40,
54,
26,
83
]
| [
"passage: TAGS\n#license-mit #arxiv-2310.16789 #region-us \n# BookMIA Datasets\n\nThe BookMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003). \n\nThe dataset contains non-member and member data: \n- non-member data consists of text excerpts from books first published in 2023\n- member data includes text excerpts from older books, as categorized by Chang et al. in 2023.### Applicability\n\nThe datasets can be applied to various OpenAI models released before 2023:\n\n- text-davinci-001\n- text-davinci-002\n- ... and more.## Loading the datasets\n\nTo load the dataset:\n\n\n* Text Lengths: '512'.\n* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.## ️ Codebase\n\nFor evaluating MIA methods on our datasets, visit our GitHub repository.## ⭐ Citing our Work\n\nIf you find our codebase and datasets beneficial, kindly cite our work:\n\n\n\n[1] Kent K Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman. Speak, memory: An archaeology of books known to chatgpt/gpt-4. arXiv preprint arXiv:2305.00118, 2023."
]
|
587d951c108118e899220de13f67e2dc3024dd82 | # Dataset Card for "en17272_diaglogue"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Jonglee/en17272_diaglogue | [
"region:us"
]
| 2023-11-03T08:04:46+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59360, "num_examples": 30}], "download_size": 38240, "dataset_size": 59360}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T08:04:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "en17272_diaglogue"
More Information needed | [
"# Dataset Card for \"en17272_diaglogue\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"en17272_diaglogue\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"en17272_diaglogue\"\n\nMore Information needed"
]
|
33220ff59a6acb9c1855f20c1a977bc798399428 |
# Dataset Card for duorc
## Licensing Information
[MIT License](https://github.com/duorc/duorc/blob/master/LICENSE)
## Source Data Citation Information
```
@inproceedings{DuoRC,
author = { Amrita Saha and Rahul Aralikatte and Mitesh M. Khapra and Karthik Sankaranarayanan},
title = {{DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension}},
booktitle = {Meeting of the Association for Computational Linguistics (ACL)},
year = {2018}
}
``` | KETI-AIR/kor_duorc | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:extractive-qa",
"multilinguality:monolingual",
"size_categories:10K<n<1M",
"source_datasets:original",
"language:ko",
"license:mit",
"region:us"
]
| 2023-11-03T08:19:16+00:00 | {"language": ["ko"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering", "text2text-generation"], "task_ids": ["abstractive-qa", "extractive-qa"], "paperswithcode_id": "duorc", "pretty_name": "DuoRC", "dataset_info": {"features": [{"name": "plot_id", "dtype": "string"}, {"name": "plot", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "data_index_by_user", "dtype": "int32"}, {"name": "no_answer", "dtype": "bool"}], "splits": [{"name": "ParaphraseRC_train", "num_bytes": 602587402, "num_examples": 69524}, {"name": "ParaphraseRC_validation", "num_bytes": 128698408, "num_examples": 15591}, {"name": "ParaphraseRC_test", "num_bytes": 140358453, "num_examples": 15857}, {"name": "SelfRC_train", "num_bytes": 288548501, "num_examples": 60721}, {"name": "SelfRC_validation", "num_bytes": 62171721, "num_examples": 12961}, {"name": "SelfRC_test", "num_bytes": 59196683, "num_examples": 12559}], "download_size": 67341464, "dataset_size": 1281561168}} | 2023-11-15T01:15:36+00:00 | []
| [
"ko"
]
| TAGS
#task_categories-question-answering #task_categories-text2text-generation #task_ids-abstractive-qa #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<1M #source_datasets-original #language-Korean #license-mit #region-us
|
# Dataset Card for duorc
## Licensing Information
MIT License
## Source Data Citation Information
| [
"# Dataset Card for duorc",
"## Licensing Information\n\nMIT License",
"## Source Data Citation Information"
]
| [
"TAGS\n#task_categories-question-answering #task_categories-text2text-generation #task_ids-abstractive-qa #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<1M #source_datasets-original #language-Korean #license-mit #region-us \n",
"# Dataset Card for duorc",
"## Licensing Information\n\nMIT License",
"## Source Data Citation Information"
]
| [
91,
7,
7,
6
]
| [
"passage: TAGS\n#task_categories-question-answering #task_categories-text2text-generation #task_ids-abstractive-qa #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<1M #source_datasets-original #language-Korean #license-mit #region-us \n# Dataset Card for duorc## Licensing Information\n\nMIT License## Source Data Citation Information"
]
|
4ace83e02178ed8ac47223e724f3710111b48518 | copy of [data-of-multimodal-sarcasm-detection](https://github.com/headacheboy/data-of-multimodal-sarcasm-detection)
```python
# usage
from datasets import load_dataset
from transformers import CLIPImageProcessor, CLIPTokenizer
from torch.utils.data import DataLoader
image_processor = CLIPImageProcessor.from_pretrained(clip_path)
tokenizer = CLIPTokenizer.from_pretrained(clip_path)
def tokenization(example):
text_inputs = tokenizer(example["text"], truncation=True, padding=True, return_tensors="pt")
image_inputs = image_processor(example["image"], return_tensors="pt")
return {'pixel_values': image_inputs['pixel_values'],
'input_ids': text_inputs['input_ids'],
'attention_mask': text_inputs['attention_mask'],
"label": example["label"]}
dataset = load_dataset('quaeast/multimodal_sarcasm_detection')
dataset.set_transform(tokenization)
# get torch dataloader
train_dl = DataLoader(dataset['train'], batch_size=256, shuffle=True)
test_dl = DataLoader(dataset['test'], batch_size=256, shuffle=True)
val_dl = DataLoader(dataset['validation'], batch_size=256, shuffle=True)
```
| quaeast/multimodal_sarcasm_detection | [
"language:en",
"region:us"
]
| 2023-11-03T08:32:05+00:00 | {"language": ["en"]} | 2023-11-17T06:37:37+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
| copy of data-of-multimodal-sarcasm-detection
| []
| [
"TAGS\n#language-English #region-us \n"
]
| [
10
]
| [
"passage: TAGS\n#language-English #region-us \n"
]
|
17799a2a70d8b394744a0e19c14f3c4b728fb490 |
# Dataset Card for ag_news
## Licensing Information & Dataset Summary
AG is a collection of more than 1 million news articles. News articles have been
gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
activity. ComeToMyHead is an academic news search engine which has been running
since July, 2004. The dataset is provided by the academic comunity for research
purposes in data mining (clustering, classification, etc), information retrieval
(ranking, search, etc), xml, data compression, data streaming, and any other
non-commercial activity. For more information, please refer to the link
http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
The AG's news topic classification dataset is constructed by Xiang Zhang
([email protected]) from the dataset above. It is used as a text
classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann
LeCun. Character-level Convolutional Networks for Text Classification. Advances
in Neural Information Processing Systems 28 (NIPS 2015).
## Source Data Citation INformation
```
@inproceedings{Zhang2015CharacterlevelCN,
title={Character-level Convolutional Networks for Text Classification},
author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
booktitle={NIPS},
year={2015}
}
``` | KETI-AIR/kor_ag_news | [
"task_categories:text-classification",
"task_ids:topic-classification",
"size_categories:100K<n<1M",
"language:ko",
"license:unknown",
"region:us"
]
| 2023-11-03T08:42:19+00:00 | {"language": ["ko"], "license": "unknown", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "task_ids": ["topic-classification"], "paperswithcode_id": "ag-news", "pretty_name": "AG\u2019s News Corpus", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "World", "1": "Sports", "2": "Business", "3": "Sci/Tech"}}}}, {"name": "data_index_by_user", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 35075728, "num_examples": 120000}, {"name": "test", "num_bytes": 2195191, "num_examples": 7600}], "download_size": 22724153, "dataset_size": 37270919}} | 2023-11-15T01:14:56+00:00 | []
| [
"ko"
]
| TAGS
#task_categories-text-classification #task_ids-topic-classification #size_categories-100K<n<1M #language-Korean #license-unknown #region-us
|
# Dataset Card for ag_news
## Licensing Information & Dataset Summary
AG is a collection of more than 1 million news articles. News articles have been
gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
activity. ComeToMyHead is an academic news search engine which has been running
since July, 2004. The dataset is provided by the academic comunity for research
purposes in data mining (clustering, classification, etc), information retrieval
(ranking, search, etc), xml, data compression, data streaming, and any other
non-commercial activity. For more information, please refer to the link
URL .
The AG's news topic classification dataset is constructed by Xiang Zhang
(URL@URL) from the dataset above. It is used as a text
classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann
LeCun. Character-level Convolutional Networks for Text Classification. Advances
in Neural Information Processing Systems 28 (NIPS 2015).
## Source Data Citation INformation
| [
"# Dataset Card for ag_news",
"## Licensing Information & Dataset Summary\nAG is a collection of more than 1 million news articles. News articles have been\ngathered from more than 2000 news sources by ComeToMyHead in more than 1 year of\nactivity. ComeToMyHead is an academic news search engine which has been running\nsince July, 2004. The dataset is provided by the academic comunity for research\npurposes in data mining (clustering, classification, etc), information retrieval\n(ranking, search, etc), xml, data compression, data streaming, and any other\nnon-commercial activity. For more information, please refer to the link\nURL .\nThe AG's news topic classification dataset is constructed by Xiang Zhang\n(URL@URL) from the dataset above. It is used as a text\nclassification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann\nLeCun. Character-level Convolutional Networks for Text Classification. Advances\nin Neural Information Processing Systems 28 (NIPS 2015).",
"## Source Data Citation INformation"
]
| [
"TAGS\n#task_categories-text-classification #task_ids-topic-classification #size_categories-100K<n<1M #language-Korean #license-unknown #region-us \n",
"# Dataset Card for ag_news",
"## Licensing Information & Dataset Summary\nAG is a collection of more than 1 million news articles. News articles have been\ngathered from more than 2000 news sources by ComeToMyHead in more than 1 year of\nactivity. ComeToMyHead is an academic news search engine which has been running\nsince July, 2004. The dataset is provided by the academic comunity for research\npurposes in data mining (clustering, classification, etc), information retrieval\n(ranking, search, etc), xml, data compression, data streaming, and any other\nnon-commercial activity. For more information, please refer to the link\nURL .\nThe AG's news topic classification dataset is constructed by Xiang Zhang\n(URL@URL) from the dataset above. It is used as a text\nclassification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann\nLeCun. Character-level Convolutional Networks for Text Classification. Advances\nin Neural Information Processing Systems 28 (NIPS 2015).",
"## Source Data Citation INformation"
]
| [
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]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-topic-classification #size_categories-100K<n<1M #language-Korean #license-unknown #region-us \n# Dataset Card for ag_news## Licensing Information & Dataset Summary\nAG is a collection of more than 1 million news articles. News articles have been\ngathered from more than 2000 news sources by ComeToMyHead in more than 1 year of\nactivity. ComeToMyHead is an academic news search engine which has been running\nsince July, 2004. The dataset is provided by the academic comunity for research\npurposes in data mining (clustering, classification, etc), information retrieval\n(ranking, search, etc), xml, data compression, data streaming, and any other\nnon-commercial activity. For more information, please refer to the link\nURL .\nThe AG's news topic classification dataset is constructed by Xiang Zhang\n(URL@URL) from the dataset above. It is used as a text\nclassification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann\nLeCun. Character-level Convolutional Networks for Text Classification. Advances\nin Neural Information Processing Systems 28 (NIPS 2015).## Source Data Citation INformation"
]
|
4c0eb33645de476ae774dccf42a709c30ab990c6 | # Dataset Card for "AIPD_nlp_sbert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | patent/AIPD_nlp_sbert_dataset | [
"region:us"
]
| 2023-11-03T09:25:48+00:00 | {"dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "label", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1130851138.7014475, "num_examples": 453043}, {"name": "test", "num_bytes": 62827420.71087167, "num_examples": 25170}, {"name": "valid", "num_bytes": 62824924.58768093, "num_examples": 25169}], "download_size": 476485691, "dataset_size": 1256503484.0}} | 2023-11-03T23:57:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "AIPD_nlp_sbert_dataset"
More Information needed | [
"# Dataset Card for \"AIPD_nlp_sbert_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"AIPD_nlp_sbert_dataset\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"AIPD_nlp_sbert_dataset\"\n\nMore Information needed"
]
|
9721989b2c1c743fb3c409b524b43ab653a1c988 | # Dataset Card for "EXTRADATA_ecc_crackdetector_dataset_exhaustive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rishitunu/EXTRADATA_ecc_crackdetector_dataset_exhaustive | [
"region:us"
]
| 2023-11-03T09:31:01+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 508679985.419, "num_examples": 23267}, {"name": "test", "num_bytes": 344446810.09, "num_examples": 5817}], "download_size": 269873364, "dataset_size": 853126795.5090001}} | 2023-11-03T09:38:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "EXTRADATA_ecc_crackdetector_dataset_exhaustive"
More Information needed | [
"# Dataset Card for \"EXTRADATA_ecc_crackdetector_dataset_exhaustive\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"EXTRADATA_ecc_crackdetector_dataset_exhaustive\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"EXTRADATA_ecc_crackdetector_dataset_exhaustive\"\n\nMore Information needed"
]
|
1b7698f40ef04e006c8efa607dfecf4a84263aa7 |
# Bangumi Image Base of Musaigen No Phantom World
This is the image base of bangumi Musaigen no Phantom World, we detected 27 characters, 2442 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 237 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 26 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 538 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 23 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 11 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 25 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 13 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 18 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 14 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 307 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 12 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 31 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 10 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 61 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 16 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 221 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 268 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 49 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 7 | [Download](18/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 19 | 12 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 190 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 10 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 8 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 13 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 5 | [Download](24/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 25 | 11 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 306 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| BangumiBase/musaigennophantomworld | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| 2023-11-03T09:40:46+00:00 | {"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]} | 2023-11-03T10:56:18+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
| Bangumi Image Base of Musaigen No Phantom World
===============================================
This is the image base of bangumi Musaigen no Phantom World, we detected 27 characters, 2442 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| []
| [
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
]
| [
25
]
| [
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
]
|
8d8d15b181090142121aa76233308701b4a2bcc9 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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[More Information Needed] | Shivam22182/train | [
"region:us"
]
| 2023-11-03T10:06:29+00:00 | {} | 2023-11-03T11:49:47+00:00 | []
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# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
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42f5a9e5a2de9e61622eac0ef6fe4de4d95d1b89 | # Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
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- **Curated by:** [More Information Needed]
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## Citation [optional]
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[More Information Needed] | Shivam22182/model | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"license:unknown",
"code",
"region:us"
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| 2023-11-03T10:23:08+00:00 | {"license": "unknown", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "tags": ["code"]} | 2023-11-03T10:40:39+00:00 | []
| []
| TAGS
#task_categories-question-answering #size_categories-10K<n<100K #license-unknown #code #region-us
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This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
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- Funded by [optional]:
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c0ecc9fc38d936fe076d90d9a0c0693161567789 | # Dataset Card for "ultrachat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vietgpt/ultrachat | [
"region:us"
]
| 2023-11-03T10:28:43+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "prompt_id", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train_1024", "num_bytes": 1395429967.5845509, "num_examples": 270812}, {"name": "train_2048", "num_bytes": 2779271371.8960648, "num_examples": 539375}, {"name": "train_4096", "num_bytes": 3360683349.1806855, "num_examples": 652210}], "download_size": 3454050489, "dataset_size": 7535384688.661301}, "configs": [{"config_name": "default", "data_files": [{"split": "train_1024", "path": "data/train_1024-*"}, {"split": "train_2048", "path": "data/train_2048-*"}, {"split": "train_4096", "path": "data/train_4096-*"}]}]} | 2023-11-03T10:35:43+00:00 | []
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c6417b5a43d86932121ba98cbd4dc59290d40482 | # Dataset Card for "tuned_prompt_ig_db_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | toilaluan/tuned_prompt_ig_db_v2 | [
"region:us"
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| 2023-11-03T10:31:47+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "topic", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "request_id", "dtype": "int64"}, {"name": "model_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 915770478.0, "num_examples": 18000}], "download_size": 1373541683, "dataset_size": 915770478.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-04T03:54:33+00:00 | []
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3838022278a3e29fff979123babf364033e08978 | # Dataset Card for "updates_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cfierro/updates_data | [
"region:us"
]
| 2023-11-03T10:41:31+00:00 | {"dataset_info": {"features": [{"name": "query", "struct": [{"name": "label", "dtype": "string"}, {"name": "objects", "list": [{"name": "label", "dtype": "string"}, {"name": "qid", "dtype": "string"}]}, {"name": "qid", "dtype": "string"}, {"name": "rel_id", "dtype": "string"}, {"name": "relation", "dtype": "string"}]}, {"name": "prediction", "struct": [{"name": "predictions", "list": [{"name": "answer", "dtype": "string"}, {"name": "first_token_probability", "dtype": "float64"}, {"name": "per_token_probability", "sequence": "float64"}, {"name": "perplexity", "dtype": "float64"}]}, {"name": "query", "dtype": "string"}]}, {"name": "relation", "dtype": "string"}, {"name": "updates", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1456957, "num_examples": 5081}], "download_size": 602132, "dataset_size": 1456957}} | 2023-11-06T13:52:25+00:00 | []
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242bb93f9caab3fd947fa48bdd361d7feb19eb62 | # General information
This dataset contains 12 Mio Token of Literary French prose 1751-1800 in plain text format, built within the project 'Mining and Modeling Text' (2019-2023) at Trier University.
For the dataset in XML/TEI see the [GitHub repository of the project](https://github.com/MiMoText/roman18/blob/master/README.md).
# Collection de romans français du dix-huitième siècle (1751-1800) / Collection of Eighteenth-Century French Novels (1751-1800)
This collection of Eighteenth-Century French Novels contains 200 digital French texts of novels created or first published between 1751 and 1800. The collection is created in the context of [Mining and Modeling Text](https://www.mimotext.uni-trier.de/en) (2019-2023), a project which is located at the Trier Center for Digital Humanities ([TCDH](https://tcdh.uni-trier.de/en)) at Trier University.
## Corpus building
In the first step, about 40 novels have been carefully created by double keying. Using this first group of novels, an OCR-model has been trained in cooperation with Christian Reul (University of Würzburg), who is one of the developers of OCR4all.
Applying this OCR-model to additional scans provided by for instance by Gallica (bnf.fr) and other sources (see metadata for details), a second group of novels which are not yet available in full text (or only in low quality) was produced.
A third group of texts, based on existing full texts (from Wikisource and other sources) helped us reach 200 volumes.
## Balancing criteria
At the beginning, corpus composition depended primarily on pragmatic criteria. We then proceeded and used additional metadata on the literary production to balance the corpus of full texts. A bibliography documenting the overall production of novels in the period is Angus Martin, Vivienne G. Mylne and Richard Frautschi, *Bibliographie du genre romanesque français 1751-1800*, 1977. We used this metadata to balance our corpus of texts regarding the parameters gender, year of first publication and narrative form in approaching the historical distribution of these parameters in our full text metadata.
BGRF = abreviation for *Bibliographie du genre romanesque français 1751-1800*, a source of bibliographic metadata we mined for extracting publication years, narrative form, authors and more.
### Year of first publication per decades
The year of first publication according to BGRF data. We compared the overall novel publication with the corpus data and added novels per year according to the known historical publication proportions. Shown here is an overview per decade. Please note that the last bar ('1800') contains only data for one year.
### Gender balance
Concerning gender, we used statements from Wikidata as well as a python script filtering for gender specific titles (Abbé, Marquis etc.). In cases where names lacked a Wikidata match or a specific title, we employed the gender guesser Python package to make gender predictions.
### Narrative form
Information on narrative form was extracted from the BGRF data (Mylne et al., 1977) supplemented by human evaluations conducted on the full texts.
For a more detailed documentation of our sampling and balancing strategy, see our [Jupyter Notebook](https://github.com/MiMoText/balance_novels/blob/main/balance_analysis_newStructure.ipynb).
## Metadata
There is a metadata file on the level of the full texts. The column names are explained in the next paragraph.
# Data Fields
* filename: file name
* au-name: author name
* au-birth: birth date of author
* au-death: death date of author
* title: title of literary work
* au-gender: gender of author
* firsted-yr: first year of publication
* printSource-yr: year of publication of print source
* form: narrative form
* spelling: information in historical spelling
* data-capture: information on data capture
* token count: token count of text file
* vols_count: count of volumes ('tome')
* size: size according to Eltec scheme https://distantreading.github.io/Schema/eltec-1.html#TEI.size
* bgrf: unique identifier in 'Bibliographie du genre romanesque français, 1751-1800 (Martin / Mylne / Frautschi 1977)'
* author_wikidata: unique identifier of author on Wikidata
* author_MiMoText-ID: unique identifier of author on MiMoText: https://data.mimotext.uni-trier.de
* title_wikidata: unique identifier of title on Wikidata
* title_MiMoText-ID: unique identifier of title on MiMoText: https://data.mimotext.uni-trier.de
* lang: language of text file
* publisher: information on publisher
* distributor: information on distributor of file
* distribution_date: information on distribuation date
* copyright_status: information on copyrights status of text file
* digitalSource_Title: title of digital text source
* digitalSource_Ref: reference of digital source
* digitalSource_Publisher: publisher of digital source
* digitalSource_Date: date of digital source
* printSource_title: title of print source
* printSource_author: author according to print source
* printSource_pubPlace: place of publication according to print source
* printSource_publisher: publisher of print source
* printSource_date: date of publication of print source
* resp_datacapture: person responsible for data capture
* resp_encoding: person responsible for encoding | roettger/eighteenth_century_french_novels | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:fr",
"license:cc-by-4.0",
"region:us"
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| 2023-11-03T10:49:39+00:00 | {"language": ["fr"], "license": "cc-by-4.0", "size_categories": ["10M<n<100M"], "task_categories": ["text-generation"], "pretty_name": "Collection of Eighteenth-Century French Novels (1751-1800)"} | 2023-11-07T10:43:16+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-text-generation #size_categories-10M<n<100M #language-French #license-cc-by-4.0 #region-us
| # General information
This dataset contains 12 Mio Token of Literary French prose 1751-1800 in plain text format, built within the project 'Mining and Modeling Text' (2019-2023) at Trier University.
For the dataset in XML/TEI see the GitHub repository of the project.
# Collection de romans français du dix-huitième siècle (1751-1800) / Collection of Eighteenth-Century French Novels (1751-1800)
This collection of Eighteenth-Century French Novels contains 200 digital French texts of novels created or first published between 1751 and 1800. The collection is created in the context of Mining and Modeling Text (2019-2023), a project which is located at the Trier Center for Digital Humanities (TCDH) at Trier University.
## Corpus building
In the first step, about 40 novels have been carefully created by double keying. Using this first group of novels, an OCR-model has been trained in cooperation with Christian Reul (University of Würzburg), who is one of the developers of OCR4all.
Applying this OCR-model to additional scans provided by for instance by Gallica (URL) and other sources (see metadata for details), a second group of novels which are not yet available in full text (or only in low quality) was produced.
A third group of texts, based on existing full texts (from Wikisource and other sources) helped us reach 200 volumes.
## Balancing criteria
At the beginning, corpus composition depended primarily on pragmatic criteria. We then proceeded and used additional metadata on the literary production to balance the corpus of full texts. A bibliography documenting the overall production of novels in the period is Angus Martin, Vivienne G. Mylne and Richard Frautschi, *Bibliographie du genre romanesque français 1751-1800*, 1977. We used this metadata to balance our corpus of texts regarding the parameters gender, year of first publication and narrative form in approaching the historical distribution of these parameters in our full text metadata.
BGRF = abreviation for *Bibliographie du genre romanesque français 1751-1800*, a source of bibliographic metadata we mined for extracting publication years, narrative form, authors and more.
### Year of first publication per decades
The year of first publication according to BGRF data. We compared the overall novel publication with the corpus data and added novels per year according to the known historical publication proportions. Shown here is an overview per decade. Please note that the last bar ('1800') contains only data for one year.
!Balancing of the collection
### Gender balance
Concerning gender, we used statements from Wikidata as well as a python script filtering for gender specific titles (Abbé, Marquis etc.). In cases where names lacked a Wikidata match or a specific title, we employed the gender guesser Python package to make gender predictions.
!Balancing of the collection
### Narrative form
Information on narrative form was extracted from the BGRF data (Mylne et al., 1977) supplemented by human evaluations conducted on the full texts.
!Balancing of the collection
For a more detailed documentation of our sampling and balancing strategy, see our Jupyter Notebook.
## Metadata
There is a metadata file on the level of the full texts. The column names are explained in the next paragraph.
# Data Fields
* filename: file name
* au-name: author name
* au-birth: birth date of author
* au-death: death date of author
* title: title of literary work
* au-gender: gender of author
* firsted-yr: first year of publication
* printSource-yr: year of publication of print source
* form: narrative form
* spelling: information in historical spelling
* data-capture: information on data capture
* token count: token count of text file
* vols_count: count of volumes ('tome')
* size: size according to Eltec scheme URL
* bgrf: unique identifier in 'Bibliographie du genre romanesque français, 1751-1800 (Martin / Mylne / Frautschi 1977)'
* author_wikidata: unique identifier of author on Wikidata
* author_MiMoText-ID: unique identifier of author on MiMoText: URL
* title_wikidata: unique identifier of title on Wikidata
* title_MiMoText-ID: unique identifier of title on MiMoText: URL
* lang: language of text file
* publisher: information on publisher
* distributor: information on distributor of file
* distribution_date: information on distribuation date
* copyright_status: information on copyrights status of text file
* digitalSource_Title: title of digital text source
* digitalSource_Ref: reference of digital source
* digitalSource_Publisher: publisher of digital source
* digitalSource_Date: date of digital source
* printSource_title: title of print source
* printSource_author: author according to print source
* printSource_pubPlace: place of publication according to print source
* printSource_publisher: publisher of print source
* printSource_date: date of publication of print source
* resp_datacapture: person responsible for data capture
* resp_encoding: person responsible for encoding | [
"# General information\n\nThis dataset contains 12 Mio Token of Literary French prose 1751-1800 in plain text format, built within the project 'Mining and Modeling Text' (2019-2023) at Trier University. \nFor the dataset in XML/TEI see the GitHub repository of the project.",
"# Collection de romans français du dix-huitième siècle (1751-1800) / Collection of Eighteenth-Century French Novels (1751-1800)\n\nThis collection of Eighteenth-Century French Novels contains 200 digital French texts of novels created or first published between 1751 and 1800. The collection is created in the context of Mining and Modeling Text (2019-2023), a project which is located at the Trier Center for Digital Humanities (TCDH) at Trier University.",
"## Corpus building\n\nIn the first step, about 40 novels have been carefully created by double keying. Using this first group of novels, an OCR-model has been trained in cooperation with Christian Reul (University of Würzburg), who is one of the developers of OCR4all. \n\nApplying this OCR-model to additional scans provided by for instance by Gallica (URL) and other sources (see metadata for details), a second group of novels which are not yet available in full text (or only in low quality) was produced.\n\nA third group of texts, based on existing full texts (from Wikisource and other sources) helped us reach 200 volumes.",
"## Balancing criteria\nAt the beginning, corpus composition depended primarily on pragmatic criteria. We then proceeded and used additional metadata on the literary production to balance the corpus of full texts. A bibliography documenting the overall production of novels in the period is Angus Martin, Vivienne G. Mylne and Richard Frautschi, *Bibliographie du genre romanesque français 1751-1800*, 1977. We used this metadata to balance our corpus of texts regarding the parameters gender, year of first publication and narrative form in approaching the historical distribution of these parameters in our full text metadata. \n\nBGRF = abreviation for *Bibliographie du genre romanesque français 1751-1800*, a source of bibliographic metadata we mined for extracting publication years, narrative form, authors and more.",
"### Year of first publication per decades\nThe year of first publication according to BGRF data. We compared the overall novel publication with the corpus data and added novels per year according to the known historical publication proportions. Shown here is an overview per decade. Please note that the last bar ('1800') contains only data for one year. \n!Balancing of the collection",
"### Gender balance\nConcerning gender, we used statements from Wikidata as well as a python script filtering for gender specific titles (Abbé, Marquis etc.). In cases where names lacked a Wikidata match or a specific title, we employed the gender guesser Python package to make gender predictions.\n!Balancing of the collection",
"### Narrative form \nInformation on narrative form was extracted from the BGRF data (Mylne et al., 1977) supplemented by human evaluations conducted on the full texts.\n!Balancing of the collection\n\nFor a more detailed documentation of our sampling and balancing strategy, see our Jupyter Notebook.",
"## Metadata \nThere is a metadata file on the level of the full texts. The column names are explained in the next paragraph.",
"# Data Fields\n\n* filename: file name \n* au-name: author name\n* au-birth: birth date of author\n* au-death: death date of author\n* title: title of literary work\n* au-gender: gender of author\n* firsted-yr: first year of publication\n* printSource-yr: year of publication of print source\n* form: narrative form\n* spelling: information in historical spelling\n* data-capture: information on data capture\n* token count: token count of text file\n* vols_count: count of volumes ('tome')\n* size: size according to Eltec scheme URL\n* bgrf: unique identifier in 'Bibliographie du genre romanesque français, 1751-1800 (Martin / Mylne / Frautschi 1977)'\n* author_wikidata: unique identifier of author on Wikidata\n* author_MiMoText-ID: unique identifier of author on MiMoText: URL\n* title_wikidata: unique identifier of title on Wikidata\n* title_MiMoText-ID: \tunique identifier of title on MiMoText: URL\n* lang: language of text file\n* publisher: information on publisher\n* distributor: information on distributor of file\n* distribution_date: information on distribuation date\n* copyright_status: information on copyrights status of text file\n* digitalSource_Title: title of digital text source\n* digitalSource_Ref: reference of digital source\n* digitalSource_Publisher: publisher of digital source\n* digitalSource_Date: date of digital source\n* printSource_title: title of print source\n* printSource_author: author according to print source\n* printSource_pubPlace: place of publication according to print source\n* printSource_publisher: publisher of print source\n* printSource_date: date of publication of print source\n* resp_datacapture: person responsible for data capture\n* resp_encoding: person responsible for encoding"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10M<n<100M #language-French #license-cc-by-4.0 #region-us \n",
"# General information\n\nThis dataset contains 12 Mio Token of Literary French prose 1751-1800 in plain text format, built within the project 'Mining and Modeling Text' (2019-2023) at Trier University. \nFor the dataset in XML/TEI see the GitHub repository of the project.",
"# Collection de romans français du dix-huitième siècle (1751-1800) / Collection of Eighteenth-Century French Novels (1751-1800)\n\nThis collection of Eighteenth-Century French Novels contains 200 digital French texts of novels created or first published between 1751 and 1800. The collection is created in the context of Mining and Modeling Text (2019-2023), a project which is located at the Trier Center for Digital Humanities (TCDH) at Trier University.",
"## Corpus building\n\nIn the first step, about 40 novels have been carefully created by double keying. Using this first group of novels, an OCR-model has been trained in cooperation with Christian Reul (University of Würzburg), who is one of the developers of OCR4all. \n\nApplying this OCR-model to additional scans provided by for instance by Gallica (URL) and other sources (see metadata for details), a second group of novels which are not yet available in full text (or only in low quality) was produced.\n\nA third group of texts, based on existing full texts (from Wikisource and other sources) helped us reach 200 volumes.",
"## Balancing criteria\nAt the beginning, corpus composition depended primarily on pragmatic criteria. We then proceeded and used additional metadata on the literary production to balance the corpus of full texts. A bibliography documenting the overall production of novels in the period is Angus Martin, Vivienne G. Mylne and Richard Frautschi, *Bibliographie du genre romanesque français 1751-1800*, 1977. We used this metadata to balance our corpus of texts regarding the parameters gender, year of first publication and narrative form in approaching the historical distribution of these parameters in our full text metadata. \n\nBGRF = abreviation for *Bibliographie du genre romanesque français 1751-1800*, a source of bibliographic metadata we mined for extracting publication years, narrative form, authors and more.",
"### Year of first publication per decades\nThe year of first publication according to BGRF data. We compared the overall novel publication with the corpus data and added novels per year according to the known historical publication proportions. Shown here is an overview per decade. Please note that the last bar ('1800') contains only data for one year. \n!Balancing of the collection",
"### Gender balance\nConcerning gender, we used statements from Wikidata as well as a python script filtering for gender specific titles (Abbé, Marquis etc.). In cases where names lacked a Wikidata match or a specific title, we employed the gender guesser Python package to make gender predictions.\n!Balancing of the collection",
"### Narrative form \nInformation on narrative form was extracted from the BGRF data (Mylne et al., 1977) supplemented by human evaluations conducted on the full texts.\n!Balancing of the collection\n\nFor a more detailed documentation of our sampling and balancing strategy, see our Jupyter Notebook.",
"## Metadata \nThere is a metadata file on the level of the full texts. The column names are explained in the next paragraph.",
"# Data Fields\n\n* filename: file name \n* au-name: author name\n* au-birth: birth date of author\n* au-death: death date of author\n* title: title of literary work\n* au-gender: gender of author\n* firsted-yr: first year of publication\n* printSource-yr: year of publication of print source\n* form: narrative form\n* spelling: information in historical spelling\n* data-capture: information on data capture\n* token count: token count of text file\n* vols_count: count of volumes ('tome')\n* size: size according to Eltec scheme URL\n* bgrf: unique identifier in 'Bibliographie du genre romanesque français, 1751-1800 (Martin / Mylne / Frautschi 1977)'\n* author_wikidata: unique identifier of author on Wikidata\n* author_MiMoText-ID: unique identifier of author on MiMoText: URL\n* title_wikidata: unique identifier of title on Wikidata\n* title_MiMoText-ID: \tunique identifier of title on MiMoText: URL\n* lang: language of text file\n* publisher: information on publisher\n* distributor: information on distributor of file\n* distribution_date: information on distribuation date\n* copyright_status: information on copyrights status of text file\n* digitalSource_Title: title of digital text source\n* digitalSource_Ref: reference of digital source\n* digitalSource_Publisher: publisher of digital source\n* digitalSource_Date: date of digital source\n* printSource_title: title of print source\n* printSource_author: author according to print source\n* printSource_pubPlace: place of publication according to print source\n* printSource_publisher: publisher of print source\n* printSource_date: date of publication of print source\n* resp_datacapture: person responsible for data capture\n* resp_encoding: person responsible for encoding"
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"passage: TAGS\n#task_categories-text-generation #size_categories-10M<n<100M #language-French #license-cc-by-4.0 #region-us \n# General information\n\nThis dataset contains 12 Mio Token of Literary French prose 1751-1800 in plain text format, built within the project 'Mining and Modeling Text' (2019-2023) at Trier University. \nFor the dataset in XML/TEI see the GitHub repository of the project.# Collection de romans français du dix-huitième siècle (1751-1800) / Collection of Eighteenth-Century French Novels (1751-1800)\n\nThis collection of Eighteenth-Century French Novels contains 200 digital French texts of novels created or first published between 1751 and 1800. The collection is created in the context of Mining and Modeling Text (2019-2023), a project which is located at the Trier Center for Digital Humanities (TCDH) at Trier University.## Corpus building\n\nIn the first step, about 40 novels have been carefully created by double keying. Using this first group of novels, an OCR-model has been trained in cooperation with Christian Reul (University of Würzburg), who is one of the developers of OCR4all. \n\nApplying this OCR-model to additional scans provided by for instance by Gallica (URL) and other sources (see metadata for details), a second group of novels which are not yet available in full text (or only in low quality) was produced.\n\nA third group of texts, based on existing full texts (from Wikisource and other sources) helped us reach 200 volumes.",
"passage: ## Balancing criteria\nAt the beginning, corpus composition depended primarily on pragmatic criteria. We then proceeded and used additional metadata on the literary production to balance the corpus of full texts. A bibliography documenting the overall production of novels in the period is Angus Martin, Vivienne G. Mylne and Richard Frautschi, *Bibliographie du genre romanesque français 1751-1800*, 1977. We used this metadata to balance our corpus of texts regarding the parameters gender, year of first publication and narrative form in approaching the historical distribution of these parameters in our full text metadata. \n\nBGRF = abreviation for *Bibliographie du genre romanesque français 1751-1800*, a source of bibliographic metadata we mined for extracting publication years, narrative form, authors and more.### Year of first publication per decades\nThe year of first publication according to BGRF data. We compared the overall novel publication with the corpus data and added novels per year according to the known historical publication proportions. Shown here is an overview per decade. Please note that the last bar ('1800') contains only data for one year. \n!Balancing of the collection### Gender balance\nConcerning gender, we used statements from Wikidata as well as a python script filtering for gender specific titles (Abbé, Marquis etc.). In cases where names lacked a Wikidata match or a specific title, we employed the gender guesser Python package to make gender predictions.\n!Balancing of the collection### Narrative form \nInformation on narrative form was extracted from the BGRF data (Mylne et al., 1977) supplemented by human evaluations conducted on the full texts.\n!Balancing of the collection\n\nFor a more detailed documentation of our sampling and balancing strategy, see our Jupyter Notebook.## Metadata \nThere is a metadata file on the level of the full texts. The column names are explained in the next paragraph."
]
|
12bcb38a8a43e1805f7db7e155181f753b1db295 | # Dataset Card for "bumbabert-v2-lener-br"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fabricioalmeida/bumbabert-v2-lener-br | [
"region:us"
]
| 2023-11-03T10:52:03+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1417620, "num_examples": 2659}, {"name": "validation", "num_bytes": 411025, "num_examples": 665}], "download_size": 1021467, "dataset_size": 1828645}} | 2023-11-03T10:52:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bumbabert-v2-lener-br"
More Information needed | [
"# Dataset Card for \"bumbabert-v2-lener-br\"\n\nMore Information needed"
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|
94b57b4a66d1aaa691015d53d786dd983b5c8fac | # Dataset Card for "v0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Aassemtkt/v0.1 | [
"region:us"
]
| 2023-11-03T10:55:14+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1916970702.0, "num_examples": 519}], "download_size": 125913722, "dataset_size": 1916970702.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T10:56:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "v0.1"
More Information needed | [
"# Dataset Card for \"v0.1\"\n\nMore Information needed"
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|
2336818db4c06570fcdf263e1bcb9993b786f67a | # XMarketDE Category to Product Retrieval Dataset
An ecommerce category to product retrieval dataset in German.
The data comes from:
https://xmrec.github.io/ | jinaai/xmarket_de | [
"region:us"
]
| 2023-11-03T11:19:50+00:00 | {} | 2023-11-03T11:21:32+00:00 | []
| []
| TAGS
#region-us
| # XMarketDE Category to Product Retrieval Dataset
An ecommerce category to product retrieval dataset in German.
The data comes from:
URL | [
"# XMarketDE Category to Product Retrieval Dataset\n\nAn ecommerce category to product retrieval dataset in German.\nThe data comes from:\nURL"
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|
88a01fff18b6b9934fd51c1a56bdbb831d740a9f | # Dataset Card for "orca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vietgpt/orca | [
"region:us"
]
| 2023-11-03T11:23:25+00:00 | {"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train_2048", "num_bytes": 569938554.1909978, "num_examples": 343944}, {"name": "train_1024", "num_bytes": 467379309.0929899, "num_examples": 282052}], "download_size": 643797649, "dataset_size": 1630738726.3724823}, "configs": [{"config_name": "default", "data_files": [{"split": "train_2048", "path": "data/train_2048-*"}, {"split": "train_1024", "path": "data/train_1024-*"}]}]} | 2023-11-07T09:25:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "orca"
More Information needed | [
"# Dataset Card for \"orca\"\n\nMore Information needed"
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|
a44ca37d33c178abbc9c99d20fe2343b3968838b | # Dataset Card for "wikilingua_data-wiki_cstnews_results"
rouge= {'rouge1': 0.33585461725953336, 'rouge2': 0.12560781595736892, 'rougeL': 0.24430651764891076, 'rougeLsum': 0.24430651764891076}
bert= {'precision': 0.7478412784394207, 'recall': 0.764229743763923, 'f1': 0.7549921488586702}
mover = 0.6170112649348396 | arthurmluz/wikilingua_data-wiki_cstnews_results | [
"region:us"
]
| 2023-11-03T11:28:47+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 22734398, "num_examples": 8165}], "download_size": 13435795, "dataset_size": 22734398}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-13T19:37:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikilingua_data-wiki_cstnews_results"
rouge= {'rouge1': 0.33585461725953336, 'rouge2': 0.12560781595736892, 'rougeL': 0.24430651764891076, 'rougeLsum': 0.24430651764891076}
bert= {'precision': 0.7478412784394207, 'recall': 0.764229743763923, 'f1': 0.7549921488586702}
mover = 0.6170112649348396 | [
"# Dataset Card for \"wikilingua_data-wiki_cstnews_results\"\n\nrouge= {'rouge1': 0.33585461725953336, 'rouge2': 0.12560781595736892, 'rougeL': 0.24430651764891076, 'rougeLsum': 0.24430651764891076}\n\nbert= {'precision': 0.7478412784394207, 'recall': 0.764229743763923, 'f1': 0.7549921488586702}\n\nmover = 0.6170112649348396"
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]
|
0e4b704aa153eb53a271cfed53765453b2765822 |
# Dataset Card for "PubMedCausal_Dutch_translated_with_MariaNMT"
Translation of the **English** version of [PubMedCausal](https://huggingface.co/datasets/medalpaca/medical_meadow_pubmed_causal),
to **Dutch** using an [Maria NMT model](https://marian-nmt.github.io/), trained by [Helsinki NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl).
Note, for reference: Maria NMT is based on [BART](https://huggingface.co/docs/transformers/model_doc/bart), described [here](https://arxiv.org/abs/1910.13461).
# Attribution
If you use this dataset please use the following to credit the creators of the Health Advice corpus:
```citation
@inproceedings{yu-etal-2019-detecting,
title = "Detecting Causal Language Use in Science Findings",
author = "Yu, Bei and
Li, Yingya and
Wang, Jun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1473",
doi = "10.18653/v1/D19-1473",
pages = "4664--4674",
}
```
The creators of the OPUS-MT models:
```
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
```
and
```
@misc{van_es_2023,
author = { {Bram van Es} },
title = { PubMedCausal_Dutch_translated_with_MariaNMT (Revision 14bfca1) },
year = 2023,
url = { https://huggingface.co/datasets/UMCU/PubMedCausal_Dutch_translated_with_MariaNMT },
doi = { 10.57967/hf/1482 },
publisher = { Hugging Face }
}
```
# License
For both the Maria NMT model and the original [Helsinki NLP](https://twitter.com/HelsinkiNLP) [Opus MT model](https://huggingface.co/Helsinki-NLP)
we did **not** find a license. We also did not find a license for the HealthAdvice corpus. For these reasons we use an academic free license v3.
license. If this was in error please let us know and we will add the appropriate licensing promptly. | UMCU/PubMedCausal_Dutch_translated_with_MariaNMT | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"size_categories:1K<n<10K",
"language:nl",
"license:afl-3.0",
"healthcare",
"arxiv:1910.13461",
"doi:10.57967/hf/1482",
"region:us"
]
| 2023-11-03T11:33:39+00:00 | {"language": ["nl"], "license": "afl-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "question-answering", "sentence-similarity"], "pretty_name": "PubMedCausal_Dutch", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 847311, "num_examples": 2368}], "download_size": 224775, "dataset_size": 847311}, "tags": ["healthcare"]} | 2023-12-14T14:33:44+00:00 | [
"1910.13461"
]
| [
"nl"
]
| TAGS
#task_categories-text-classification #task_categories-question-answering #task_categories-sentence-similarity #size_categories-1K<n<10K #language-Dutch #license-afl-3.0 #healthcare #arxiv-1910.13461 #doi-10.57967/hf/1482 #region-us
|
# Dataset Card for "PubMedCausal_Dutch_translated_with_MariaNMT"
Translation of the English version of PubMedCausal,
to Dutch using an Maria NMT model, trained by Helsinki NLP.
Note, for reference: Maria NMT is based on BART, described here.
# Attribution
If you use this dataset please use the following to credit the creators of the Health Advice corpus:
The creators of the OPUS-MT models:
and
# License
For both the Maria NMT model and the original Helsinki NLP Opus MT model
we did not find a license. We also did not find a license for the HealthAdvice corpus. For these reasons we use an academic free license v3.
license. If this was in error please let us know and we will add the appropriate licensing promptly. | [
"# Dataset Card for \"PubMedCausal_Dutch_translated_with_MariaNMT\"\n\n\nTranslation of the English version of PubMedCausal,\nto Dutch using an Maria NMT model, trained by Helsinki NLP.\nNote, for reference: Maria NMT is based on BART, described here.",
"# Attribution\n\nIf you use this dataset please use the following to credit the creators of the Health Advice corpus:\n\n\n\nThe creators of the OPUS-MT models:\n\n\nand",
"# License\n\nFor both the Maria NMT model and the original Helsinki NLP Opus MT model \nwe did not find a license. We also did not find a license for the HealthAdvice corpus. For these reasons we use an academic free license v3. \nlicense. If this was in error please let us know and we will add the appropriate licensing promptly."
]
| [
"TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-sentence-similarity #size_categories-1K<n<10K #language-Dutch #license-afl-3.0 #healthcare #arxiv-1910.13461 #doi-10.57967/hf/1482 #region-us \n",
"# Dataset Card for \"PubMedCausal_Dutch_translated_with_MariaNMT\"\n\n\nTranslation of the English version of PubMedCausal,\nto Dutch using an Maria NMT model, trained by Helsinki NLP.\nNote, for reference: Maria NMT is based on BART, described here.",
"# Attribution\n\nIf you use this dataset please use the following to credit the creators of the Health Advice corpus:\n\n\n\nThe creators of the OPUS-MT models:\n\n\nand",
"# License\n\nFor both the Maria NMT model and the original Helsinki NLP Opus MT model \nwe did not find a license. We also did not find a license for the HealthAdvice corpus. For these reasons we use an academic free license v3. \nlicense. If this was in error please let us know and we will add the appropriate licensing promptly."
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"passage: TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-sentence-similarity #size_categories-1K<n<10K #language-Dutch #license-afl-3.0 #healthcare #arxiv-1910.13461 #doi-10.57967/hf/1482 #region-us \n# Dataset Card for \"PubMedCausal_Dutch_translated_with_MariaNMT\"\n\n\nTranslation of the English version of PubMedCausal,\nto Dutch using an Maria NMT model, trained by Helsinki NLP.\nNote, for reference: Maria NMT is based on BART, described here.# Attribution\n\nIf you use this dataset please use the following to credit the creators of the Health Advice corpus:\n\n\n\nThe creators of the OPUS-MT models:\n\n\nand# License\n\nFor both the Maria NMT model and the original Helsinki NLP Opus MT model \nwe did not find a license. We also did not find a license for the HealthAdvice corpus. For these reasons we use an academic free license v3. \nlicense. If this was in error please let us know and we will add the appropriate licensing promptly."
]
|
ea477dd36aa70df6e62d1198e224b4ec2951cf69 | # Dataset Card for "judgelm-instruction-dataset-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | alvarobartt/judgelm-instruction-dataset-mini | [
"region:us"
]
| 2023-11-03T11:33:46+00:00 | {"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "generations", "sequence": "string"}, {"name": "raw_generation_response", "sequence": "string"}, {"name": "ratings", "sequence": "int64"}, {"name": "rationale", "dtype": "string"}, {"name": "raw_labelling_response", "struct": [{"name": "choices", "list": [{"name": "finish_reason", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "message", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}]}, {"name": "created", "dtype": "int64"}, {"name": "id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "object", "dtype": "string"}, {"name": "usage", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "total_tokens", "dtype": "int64"}]}]}], "splits": [{"name": "train", "num_bytes": 44153, "num_examples": 10}], "download_size": 52931, "dataset_size": 44153}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T11:48:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "judgelm-instruction-dataset-mini"
More Information needed | [
"# Dataset Card for \"judgelm-instruction-dataset-mini\"\n\nMore Information needed"
]
| [
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|
40e18852cf197d5db1573dca205b7bb9a46c0abb | # Dataset Card for "dataset_cards_with_long_context_embeddins"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/dataset_cards_with_long_context_embeddins | [
"region:us"
]
| 2023-11-03T11:47:59+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "lastModified", "dtype": "string"}, {"name": "tags", "sequence": "string"}, {"name": "author", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "citation", "dtype": "string"}, {"name": "likes", "dtype": "int64"}, {"name": "downloads", "dtype": "int64"}, {"name": "created", "dtype": "timestamp[us]"}, {"name": "card", "dtype": "string"}, {"name": "card_len", "dtype": "int64"}, {"name": "embeddings", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 334799613.62829113, "num_examples": 64462}], "download_size": 128564352, "dataset_size": 334799613.62829113}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T11:48:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_cards_with_long_context_embeddins"
More Information needed | [
"# Dataset Card for \"dataset_cards_with_long_context_embeddins\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dataset_cards_with_long_context_embeddins\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"dataset_cards_with_long_context_embeddins\"\n\nMore Information needed"
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|
b5bfcea951546a32874c2cbc287d5d9ef818b840 |
# Turkish Instructions
## Apache 2.0
This dataset is a cleaned and organized version (for Mistral) of [afkfatih/turkishdataset](https://huggingface.co/datasets/afkfatih/turkishdataset)
| SoAp9035/turkish_instructions | [
"language:tr",
"license:apache-2.0",
"region:us"
]
| 2023-11-03T12:11:25+00:00 | {"language": ["tr"], "license": "apache-2.0"} | 2023-11-03T13:45:02+00:00 | []
| [
"tr"
]
| TAGS
#language-Turkish #license-apache-2.0 #region-us
|
# Turkish Instructions
## Apache 2.0
This dataset is a cleaned and organized version (for Mistral) of afkfatih/turkishdataset
| [
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]
|
b8885d4b928d5184faa29d4e0bd58db762235d62 | # Dataset Card for "holiday"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Eitanli/holiday | [
"region:us"
]
| 2023-11-03T12:15:56+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "recipe", "dtype": "string"}, {"name": "holiday", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 107496782, "num_examples": 74465}], "download_size": 54257690, "dataset_size": 107496782}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-08T10:51:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "holiday"
More Information needed | [
"# Dataset Card for \"holiday\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"holiday\"\n\nMore Information needed"
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|
00ab29445d18fda2391b2f659cdb5acda61ed77f | # Dataset Card for "hakkadict"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ouob/hakkadict_210430_sixian | [
"region:us"
]
| 2023-11-03T12:31:09+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "text_thrs", "dtype": "string"}, {"name": "path", "dtype": {"audio": {"sampling_rate": 22000}}}], "splits": [{"name": "train", "num_bytes": 340801105.128, "num_examples": 15263}], "download_size": 335215646, "dataset_size": 340801105.128}} | 2023-11-07T17:10:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hakkadict"
More Information needed | [
"# Dataset Card for \"hakkadict\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hakkadict\"\n\nMore Information needed"
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|
0003c3e8217bccc5c7c969ee1e266753a1860f7a | # dolly-14k-ines
### Description
The **dolly-14k-ines** dataset is derived from the `databricks-dolly-15k-ja`, which is a machine-translated version of the `databricks/dolly-15k`. The entries have been further converted to reflect the speech pattern of Ines Fujin, a character from "Umamusume Pretty Derby." The conversion process utilized a model specifically developed for this task, and entries that did not successfully emulate the character's textual speech style were omitted, resulting in a slightly smaller dataset. The foundational `databricks-dolly-15k` dataset includes instruction-following records generated by Databricks employees and spans several behavioral categories, such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization, as defined in the InstructGPT paper.
This dataset is created and provided based on Umamusume Pretty Derby's derivative creation guidelines.
### License
This dataset is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License.
Additionally, to use this dataset, you must comply with the 'Umamusume Pretty Derby' derivative creation guidelines. The full text of the guidelines can be found at the link below.
[Derivative creation guidelines for Umamusume Pretty Derby.](https://umamusume.jp/sp/derivativework_guidelines/)
### Included Tasks
The tasks included in the **dolly-14k-ines** dataset mirror those from the original `databricks-dolly-15k` dataset and encompass a variety of natural language processing scenarios:
- **Creative Writing**: Devise prompts that elicit a creative, open-ended written response, suitable for someone with general knowledge, and detailed enough to avoid the need for external research.
- **Closed QA**: Create complex questions that rely on factual accuracy, based on provided Wikipedia text excerpts, that involve human-level reasoning without necessitating specialized expertise.
- **Open QA**: Frame questions that can be answered with general world knowledge or minimal research, soliciting both factual and opinion-based responses without the need for reference material.
- **Summarization**: Condense information from a Wikipedia paragraph, ensuring the summarization task can be reasonably completed within a brief timeframe.
- **Information Extraction**: Extract specific information from a Wikipedia paragraph, where the text contains all the necessary details for formulating a response.
- **Classification**: Classify entities from given lists or categories, such as movie reviews or products, where the classification criteria are contained within the prompt itself.
- **Brainstorming**: Generate a diverse array of ideas in response to a given question, fostering creativity and a wide range of suggestions.
These tasks are originally defined in the `databricks-dolly-15k` dataset and are retained in the **dolly-14k-ines** dataset to facilitate research and application in natural language processing, particularly for those interested in style-specific text adaptation or character-specific speech emulation.
### Updates
2023-11-04: Added description regarding license. When using this dataset, you must follow Umamusume Pretty Derby's derivative creation guidelines. | sakusakumura/dolly-14k-ines | [
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ja",
"license:cc-by-sa-3.0",
"region:us"
]
| 2023-11-03T12:44:16+00:00 | {"language": ["ja"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering", "summarization"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "index", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13572665, "num_examples": 14199}], "download_size": 7803782, "dataset_size": 13572665}} | 2023-11-04T02:58:13+00:00 | []
| [
"ja"
]
| TAGS
#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Japanese #license-cc-by-sa-3.0 #region-us
| # dolly-14k-ines
### Description
The dolly-14k-ines dataset is derived from the 'databricks-dolly-15k-ja', which is a machine-translated version of the 'databricks/dolly-15k'. The entries have been further converted to reflect the speech pattern of Ines Fujin, a character from "Umamusume Pretty Derby." The conversion process utilized a model specifically developed for this task, and entries that did not successfully emulate the character's textual speech style were omitted, resulting in a slightly smaller dataset. The foundational 'databricks-dolly-15k' dataset includes instruction-following records generated by Databricks employees and spans several behavioral categories, such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization, as defined in the InstructGPT paper.
This dataset is created and provided based on Umamusume Pretty Derby's derivative creation guidelines.
### License
This dataset is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License.
Additionally, to use this dataset, you must comply with the 'Umamusume Pretty Derby' derivative creation guidelines. The full text of the guidelines can be found at the link below.
Derivative creation guidelines for Umamusume Pretty Derby.
### Included Tasks
The tasks included in the dolly-14k-ines dataset mirror those from the original 'databricks-dolly-15k' dataset and encompass a variety of natural language processing scenarios:
- Creative Writing: Devise prompts that elicit a creative, open-ended written response, suitable for someone with general knowledge, and detailed enough to avoid the need for external research.
- Closed QA: Create complex questions that rely on factual accuracy, based on provided Wikipedia text excerpts, that involve human-level reasoning without necessitating specialized expertise.
- Open QA: Frame questions that can be answered with general world knowledge or minimal research, soliciting both factual and opinion-based responses without the need for reference material.
- Summarization: Condense information from a Wikipedia paragraph, ensuring the summarization task can be reasonably completed within a brief timeframe.
- Information Extraction: Extract specific information from a Wikipedia paragraph, where the text contains all the necessary details for formulating a response.
- Classification: Classify entities from given lists or categories, such as movie reviews or products, where the classification criteria are contained within the prompt itself.
- Brainstorming: Generate a diverse array of ideas in response to a given question, fostering creativity and a wide range of suggestions.
These tasks are originally defined in the 'databricks-dolly-15k' dataset and are retained in the dolly-14k-ines dataset to facilitate research and application in natural language processing, particularly for those interested in style-specific text adaptation or character-specific speech emulation.
### Updates
2023-11-04: Added description regarding license. When using this dataset, you must follow Umamusume Pretty Derby's derivative creation guidelines. | [
"# dolly-14k-ines",
"### Description\n\nThe dolly-14k-ines dataset is derived from the 'databricks-dolly-15k-ja', which is a machine-translated version of the 'databricks/dolly-15k'. The entries have been further converted to reflect the speech pattern of Ines Fujin, a character from \"Umamusume Pretty Derby.\" The conversion process utilized a model specifically developed for this task, and entries that did not successfully emulate the character's textual speech style were omitted, resulting in a slightly smaller dataset. The foundational 'databricks-dolly-15k' dataset includes instruction-following records generated by Databricks employees and spans several behavioral categories, such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization, as defined in the InstructGPT paper.\n\nThis dataset is created and provided based on Umamusume Pretty Derby's derivative creation guidelines.",
"### License\n\nThis dataset is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License.\n\nAdditionally, to use this dataset, you must comply with the 'Umamusume Pretty Derby' derivative creation guidelines. The full text of the guidelines can be found at the link below. \nDerivative creation guidelines for Umamusume Pretty Derby.",
"### Included Tasks\n\nThe tasks included in the dolly-14k-ines dataset mirror those from the original 'databricks-dolly-15k' dataset and encompass a variety of natural language processing scenarios:\n\n- Creative Writing: Devise prompts that elicit a creative, open-ended written response, suitable for someone with general knowledge, and detailed enough to avoid the need for external research.\n\n- Closed QA: Create complex questions that rely on factual accuracy, based on provided Wikipedia text excerpts, that involve human-level reasoning without necessitating specialized expertise.\n\n- Open QA: Frame questions that can be answered with general world knowledge or minimal research, soliciting both factual and opinion-based responses without the need for reference material.\n\n- Summarization: Condense information from a Wikipedia paragraph, ensuring the summarization task can be reasonably completed within a brief timeframe.\n\n- Information Extraction: Extract specific information from a Wikipedia paragraph, where the text contains all the necessary details for formulating a response.\n\n- Classification: Classify entities from given lists or categories, such as movie reviews or products, where the classification criteria are contained within the prompt itself.\n\n- Brainstorming: Generate a diverse array of ideas in response to a given question, fostering creativity and a wide range of suggestions.\n\nThese tasks are originally defined in the 'databricks-dolly-15k' dataset and are retained in the dolly-14k-ines dataset to facilitate research and application in natural language processing, particularly for those interested in style-specific text adaptation or character-specific speech emulation.",
"### Updates\n\n2023-11-04: Added description regarding license. When using this dataset, you must follow Umamusume Pretty Derby's derivative creation guidelines."
]
| [
"TAGS\n#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Japanese #license-cc-by-sa-3.0 #region-us \n",
"# dolly-14k-ines",
"### Description\n\nThe dolly-14k-ines dataset is derived from the 'databricks-dolly-15k-ja', which is a machine-translated version of the 'databricks/dolly-15k'. The entries have been further converted to reflect the speech pattern of Ines Fujin, a character from \"Umamusume Pretty Derby.\" The conversion process utilized a model specifically developed for this task, and entries that did not successfully emulate the character's textual speech style were omitted, resulting in a slightly smaller dataset. The foundational 'databricks-dolly-15k' dataset includes instruction-following records generated by Databricks employees and spans several behavioral categories, such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization, as defined in the InstructGPT paper.\n\nThis dataset is created and provided based on Umamusume Pretty Derby's derivative creation guidelines.",
"### License\n\nThis dataset is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License.\n\nAdditionally, to use this dataset, you must comply with the 'Umamusume Pretty Derby' derivative creation guidelines. The full text of the guidelines can be found at the link below. \nDerivative creation guidelines for Umamusume Pretty Derby.",
"### Included Tasks\n\nThe tasks included in the dolly-14k-ines dataset mirror those from the original 'databricks-dolly-15k' dataset and encompass a variety of natural language processing scenarios:\n\n- Creative Writing: Devise prompts that elicit a creative, open-ended written response, suitable for someone with general knowledge, and detailed enough to avoid the need for external research.\n\n- Closed QA: Create complex questions that rely on factual accuracy, based on provided Wikipedia text excerpts, that involve human-level reasoning without necessitating specialized expertise.\n\n- Open QA: Frame questions that can be answered with general world knowledge or minimal research, soliciting both factual and opinion-based responses without the need for reference material.\n\n- Summarization: Condense information from a Wikipedia paragraph, ensuring the summarization task can be reasonably completed within a brief timeframe.\n\n- Information Extraction: Extract specific information from a Wikipedia paragraph, where the text contains all the necessary details for formulating a response.\n\n- Classification: Classify entities from given lists or categories, such as movie reviews or products, where the classification criteria are contained within the prompt itself.\n\n- Brainstorming: Generate a diverse array of ideas in response to a given question, fostering creativity and a wide range of suggestions.\n\nThese tasks are originally defined in the 'databricks-dolly-15k' dataset and are retained in the dolly-14k-ines dataset to facilitate research and application in natural language processing, particularly for those interested in style-specific text adaptation or character-specific speech emulation.",
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]
| [
"passage: TAGS\n#task_categories-question-answering #task_categories-summarization #size_categories-10K<n<100K #language-Japanese #license-cc-by-sa-3.0 #region-us \n# dolly-14k-ines### Description\n\nThe dolly-14k-ines dataset is derived from the 'databricks-dolly-15k-ja', which is a machine-translated version of the 'databricks/dolly-15k'. The entries have been further converted to reflect the speech pattern of Ines Fujin, a character from \"Umamusume Pretty Derby.\" The conversion process utilized a model specifically developed for this task, and entries that did not successfully emulate the character's textual speech style were omitted, resulting in a slightly smaller dataset. The foundational 'databricks-dolly-15k' dataset includes instruction-following records generated by Databricks employees and spans several behavioral categories, such as brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization, as defined in the InstructGPT paper.\n\nThis dataset is created and provided based on Umamusume Pretty Derby's derivative creation guidelines.### License\n\nThis dataset is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License.\n\nAdditionally, to use this dataset, you must comply with the 'Umamusume Pretty Derby' derivative creation guidelines. The full text of the guidelines can be found at the link below. \nDerivative creation guidelines for Umamusume Pretty Derby."
]
|
0612325d98aa22d25a50b717d5a7c8d9d71c95c9 | # Dataset Card for "pill"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | brainer/pill | [
"region:us"
]
| 2023-11-03T13:57:09+00:00 | {"dataset_info": [{"config_name": "aihub/single/train", "features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 678413206.0, "num_examples": 500}], "download_size": 678444433, "dataset_size": 678413206.0}, {"config_name": "aihub/single/train/pill-part-0", "features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "K-027088", "1": "K-007843", "2": "K-031387", "3": "K-030085", "4": "K-023838", "5": "K-031391", "6": "K-046427", "7": "K-020401", "8": "K-029865", "9": "K-010450", "10": "K-006972", "11": "K-015279", "12": "K-033880", "13": "K-007157", "14": "K-031745", "15": "K-007388", "16": "K-019867", "17": "K-027179", "18": "K-029975", "19": "K-025691", "20": "K-032167", "21": "K-031472", "22": "K-020967", "23": "K-018119", "24": "K-029823", "25": "K-045925", "26": "K-016224", "27": "K-030820", "28": "K-034597", "29": "K-035206", "30": "K-042119", "31": "K-044834", "32": "K-027194", "33": "K-007243", "34": "K-027157", "35": "K-031214", "36": "K-018718", "37": "K-019904", "38": "K-033829", "39": "K-033024", "40": "K-010543", "41": "K-042785", "42": "K-012503", "43": "K-029768", "44": "K-037029", "45": "K-007059", "46": "K-006672", "47": "K-007402", "48": "K-023084", "49": "K-025275", "50": "K-017511", "51": "K-024143", "52": "K-029534", "53": "K-038152", "54": "K-038724", "55": "K-037578", "56": "K-042044", "57": "K-022713", "58": "K-034110", "59": "K-045403", "60": "K-022465", "61": "K-030518", "62": "K-034208", "63": "K-029159", "64": "K-041432", "65": "K-016912", "66": "K-006677", "67": "K-025495", "68": "K-019469", "69": "K-011566", "70": "K-012003", "71": "K-020769", "72": "K-025276", "73": "K-031178", "74": "K-032739", "75": "K-027185", "76": "K-014279", "77": "K-028864", "78": "K-018720", "79": "K-013105", "80": "K-029819", "81": "K-006664", "82": "K-027127", "83": "K-040336", "84": "K-016223", "85": "K-006284", "86": "K-025476", "87": "K-013592", "88": "K-007646", "89": "K-025336", "90": "K-027184", "91": "K-042041", "92": "K-006563", "93": "K-025143", "94": "K-017312", "95": "K-030690", "96": "K-040215", "97": "K-033198", "98": "K-006977", "99": "K-039815", "100": "K-027600", "101": "K-027777", "102": "K-006731", "103": "K-035649", "104": "K-011617", "105": "K-032990", "106": "K-007022", "107": "K-007055", "108": "K-025828", "109": "K-019843", "110": "K-025458", "111": "K-027228", "112": "K-036171", "113": "K-037566", "114": "K-013450", "115": "K-043670", "116": "K-040623", "117": "K-049428", "118": "K-007381", "119": "K-028828", "120": "K-025169", "121": "K-038622", "122": "K-045583", "123": "K-045862", "124": "K-025854", "125": "K-029451", "126": "K-033231", "127": "K-012758", "128": "K-026715", "129": "K-041917", "130": "K-016394", "131": "K-006883", "132": "K-033406", "133": "K-006762", "134": "K-027091", "135": "K-007317", "136": "K-043780", "137": "K-013828", "138": "K-021118", "139": "K-006984", "140": "K-018114", "141": "K-033146", "142": "K-007447", "143": "K-012420", "144": "K-024979", "145": "K-038212", "146": "K-044184", "147": "K-017258", "148": "K-024503", "149": "K-044458", "150": "K-029426", "151": "K-013536", "152": "K-024397", "153": "K-007223", "154": "K-006927", "155": "K-017714", "156": "K-025497", "157": "K-026093", "158": "K-027176", "159": "K-012501", "160": "K-030240", "161": "K-036353", "162": "K-027734", "163": "K-028468", "164": "K-040639", "165": "K-011686", "166": "K-010769", "167": "K-007144", "168": "K-021904", "169": "K-006421", "170": "K-044496", "171": "K-031917", "172": "K-046428", "173": "K-033069", "174": "K-007235", "175": "K-031702", "176": "K-031998", "177": "K-007821", "178": "K-031321", "179": "K-030307", "180": "K-012865", "181": "K-040530", "182": "K-041153", "183": "K-031223", "184": "K-019196", "185": "K-011695", "186": "K-033465", "187": "K-030308", "188": "K-023612", "189": "K-016804", "190": "K-031663", "191": "K-041667", "192": "K-029206", "193": "K-029972", "194": "K-031326", "195": "K-033326", "196": "K-032577", "197": "K-042269", "198": "K-030042", "199": "K-044529", "200": "K-011220", "201": "K-030044", "202": "K-031090", "203": "K-024057", "204": "K-024279", "205": "K-028074", "206": "K-028650", "207": "K-032936", "208": "K-042262", "209": "K-031492", "210": "K-025536", "211": "K-017506", "212": "K-027365", "213": "K-032657", "214": "K-043250", "215": "K-031282", "216": "K-014409", "217": "K-028081", "218": "K-035589", "219": "K-031934", "220": "K-037933", "221": "K-040317", "222": "K-027188", "223": "K-032735", "224": "K-036172", "225": "K-031467", "226": "K-035416", "227": "K-027771", "228": "K-007120", "229": "K-031050", "230": "K-011546", "231": "K-011780", "232": "K-033609", "233": "K-031625", "234": "K-032605", "235": "K-038183", "236": "K-039135", "237": "K-024854", "238": "K-011252", "239": "K-023310", "240": "K-027187", "241": "K-037446", "242": "K-037366", "243": "K-010614", "244": "K-025268", "245": "K-027688", "246": "K-018949", "247": "K-031466", "248": "K-013259", "249": "K-035328", "250": "K-020966", "251": "K-013020", "252": "K-013390", "253": "K-023599", "254": "K-027166", "255": "K-029864", "256": "K-033632", "257": "K-037501", "258": "K-023594", "259": "K-033185", "260": "K-038262", "261": "K-029909", "262": "K-030309", "263": "K-023720", "264": "K-013937", "265": "K-020582", "266": "K-017040", "267": "K-032906", "268": "K-033340", "269": "K-025545", "270": "K-038290", "271": "K-037284", "272": "K-020805", "273": "K-011544", "274": "K-024003", "275": "K-007328", "276": "K-006886", "277": "K-025227", "278": "K-025648", "279": "K-031376", "280": "K-006250", "281": "K-018969", "282": "K-035208", "283": "K-031142", "284": "K-038957", "285": "K-007325", "286": "K-014084", "287": "K-025532", "288": "K-006865", "289": "K-006562", "290": "K-039137", "291": "K-027840", "292": "K-035000", "293": "K-038972", "294": "K-010749", "295": "K-033059", "296": "K-022644", "297": "K-007337", "298": "K-030957", "299": "K-038460", "300": "K-041149", "301": "K-006681", "302": "K-034327", "303": "K-042616", "304": "K-007024", "305": "K-011833", "306": "K-018188", "307": "K-030091", "308": "K-007240", "309": "K-023248", "310": "K-042202", "311": "K-029916", "312": "K-034582", "313": "K-015537", "314": "K-025568", "315": "K-027525", "316": "K-024584", "317": "K-011274", "318": "K-016250", "319": "K-022377", "320": "K-033320", "321": "K-027436", "322": "K-034460", "323": "K-036954", "324": "K-041267", "325": "K-048028", "326": "K-007347", "327": "K-006435", "328": "K-011253", "329": "K-010931", "330": "K-020877", "331": "K-044852", "332": "K-032992", "333": "K-007131", "334": "K-025302", "335": "K-025304", "336": "K-029872", "337": "K-012502", "338": "K-036480", "339": "K-026545", "340": "K-025886", "341": "K-034056", "342": "K-035291", "343": "K-025316", "344": "K-025894", "345": "K-031624", "346": "K-006854", "347": "K-043251", "348": "K-044707", "349": "K-030063", "350": "K-007012", "351": "K-024995", "352": "K-025306", "353": "K-030232", "354": "K-047218", "355": "K-019161", "356": "K-030986", "357": "K-022095", "358": "K-032980", "359": "K-042014", "360": "K-027182", "361": "K-033046", "362": "K-025347", "363": "K-006842", "364": "K-028623", "365": "K-019860", "366": "K-007217", "367": "K-015530", "368": "K-029820", "369": "K-006432", "370": "K-011154", "371": "K-011845", "372": "K-030574", "373": "K-046737", "374": "K-036551", "375": "K-020006", "376": "K-046127", "377": "K-018409", "378": "K-032376", "379": "K-036784", "380": "K-038186", "381": "K-044818", "382": "K-025228", "383": "K-041666", "384": "K-025335", "385": "K-043689", "386": "K-012767", "387": "K-030062", "388": "K-016555", "389": "K-027567", "390": "K-013326", "391": "K-040531", "392": "K-038459", "393": "K-029774", "394": "K-029749", "395": "K-031695", "396": "K-021930", "397": "K-012081", "398": "K-013368", "399": "K-025500", "400": "K-027143", "401": "K-027181", "402": "K-027737", "403": "K-032243", "404": "K-033746", "405": "K-011648", "406": "K-012212", "407": "K-031974", "408": "K-027199", "409": "K-030148", "410": "K-012668", "411": "K-016193", "412": "K-027616", "413": "K-027093", "414": "K-027563", "415": "K-007143", "416": "K-029667", "417": "K-027129", "418": "K-006456", "419": "K-013263", "420": "K-016736", "421": "K-006686", "422": "K-022749", "423": "K-031319", "424": "K-014251", "425": "K-025470", "426": "K-012416", "427": "K-030096", "428": "K-033223", "429": "K-040339", "430": "K-022371", "431": "K-011942", "432": "K-033131", "433": "K-031705", "434": "K-031963", "435": "K-024350", "436": "K-019461", "437": "K-023779", "438": "K-041949", "439": "K-007268", "440": "K-020378", "441": "K-036307", "442": "K-040335", "443": "K-015230", "444": "K-024981", "445": "K-029066", "446": "K-012447", "447": "K-029919", "448": "K-032672", "449": "K-007820", "450": "K-013378", "451": "K-029818", "452": "K-031473", "453": "K-018203", "454": "K-040342", "455": "K-025519", "456": "K-043812", "457": "K-013543", "458": "K-031302", "459": "K-033171", "460": "K-011226", "461": "K-025211", "462": "K-046375", "463": "K-022704", "464": "K-028641", "465": "K-007014", "466": "K-032827", "467": "K-027398", "468": "K-031330", "469": "K-032809", "470": "K-031753", "471": "K-015236", "472": "K-023405", "473": "K-037500", "474": "K-042226", "475": "K-032808", "476": "K-029830", "477": "K-025531", "478": "K-043934", "479": "K-026281", "480": "K-028588", "481": "K-022705", "482": "K-040622", "483": "K-007073", "484": "K-044660", "485": "K-037086", "486": "K-019790", "487": "K-032662", "488": "K-025185", "489": "K-022176", "490": "K-031198", "491": "K-007061", "492": "K-023807", "493": "K-032900", "494": "K-018224", "495": "K-024631", "496": "K-016554", "497": "K-031211", "498": "K-029725", "499": "K-033830", "500": "K-038722", "501": "K-030008", "502": "K-027210", "503": "K-013684", "504": "K-013626", "505": "K-031545", "506": "K-014224", "507": "K-025000", "508": "K-013332", "509": "K-025184", "510": "K-027574", "511": "K-043946", "512": "K-025120", "513": "K-029910", "514": "K-044382", "515": "K-011846", "516": "K-026581", "517": "K-031701", "518": "K-020330", "519": "K-034827", "520": "K-011541", "521": "K-014102", "522": "K-033252", "523": "K-027615", "524": "K-023411", "525": "K-030037", "526": "K-025215", "527": "K-027602", "528": "K-032904", "529": "K-036625", "530": "K-007023", "531": "K-027693", "532": "K-019699", "533": "K-022963", "534": "K-043499", "535": "K-033000", "536": "K-016222", "537": "K-006697", "538": "K-016302", "539": "K-012266", "540": "K-025474", "541": "K-027126", "542": "K-028424", "543": "K-030033", "544": "K-033056", "545": "K-033204", "546": "K-041997", "547": "K-012769", "548": "K-031210", "549": "K-033134", "550": "K-007588", "551": "K-031329", "552": "K-006656", "553": "K-033091", "554": "K-033203", "555": "K-029533", "556": "K-023933", "557": "K-031918", "558": "K-044761", "559": "K-022362", "560": "K-025230", "561": "K-033876", "562": "K-029822", "563": "K-028423", "564": "K-010912", "565": "K-007822", "566": "K-007376", "567": "K-007145", "568": "K-031089", "569": "K-032832", "570": "K-034484", "571": "K-025319", "572": "K-025182", "573": "K-027653", "574": "K-017668", "575": "K-028073", "576": "K-023252", "577": "K-029998", "578": "K-021617", "579": "K-028901", "580": "K-014280", "581": "K-025033", "582": "K-013135", "583": "K-025149", "584": "K-014986", "585": "K-026894", "586": "K-035205", "587": "K-017479", "588": "K-020014", "589": "K-026694", "590": "K-017057", "591": "K-030239", "592": "K-026561", "593": "K-026993", "594": "K-025216", "595": "K-038185", "596": "K-025177", "597": "K-032104", "598": "K-006855", "599": "K-027776", "600": "K-011250", "601": "K-017482", "602": "K-007117", "603": "K-031328", "604": "K-006940", "605": "K-007316", "606": "K-032668", "607": "K-023092", "608": "K-034569", "609": "K-023729", "610": "K-049277", "611": "K-011166", "612": "K-033333", "613": "K-013256", "614": "K-048531", "615": "K-035879", "616": "K-019861", "617": "K-023740", "618": "K-025283", "619": "K-031194", "620": "K-025438", "621": "K-007060", "622": "K-030106", "623": "K-027399", "624": "K-027524", "625": "K-025437", "626": "K-007749", "627": "K-018305", "628": "K-032954", "629": "K-031605", "630": "K-029808", "631": "K-010528", "632": "K-011442", "633": "K-025262", "634": "K-022846", "635": "K-030043", "636": "K-037988", "637": "K-037043", "638": "K-033327", "639": "K-018254", "640": "K-022604", "641": "K-020834", "642": "K-027465", "643": "K-029760", "644": "K-011327", "645": "K-014729", "646": "K-039150", "647": "K-041691", "648": "K-024162", "649": "K-027735", "650": "K-025380", "651": "K-015759", "652": "K-032253", "653": "K-011481", "654": "K-044978", "655": "K-024765", "656": "K-031699", "657": "K-041947", "658": "K-007383", "659": "K-029251", "660": "K-006544", "661": "K-031130", "662": "K-025200", "663": 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"114": "K-013450", "115": "K-043670", "116": "K-040623", "117": "K-049428", "118": "K-007381", "119": "K-028828", "120": "K-025169", "121": "K-038622", "122": "K-045583", "123": "K-045862", "124": "K-025854", "125": "K-029451", "126": "K-033231", "127": "K-012758", "128": "K-026715", "129": "K-041917", "130": "K-016394", "131": "K-006883", "132": "K-033406", "133": "K-006762", "134": "K-027091", "135": "K-007317", "136": "K-043780", "137": "K-013828", "138": "K-021118", "139": "K-006984", "140": "K-018114", "141": "K-033146", "142": "K-007447", "143": "K-012420", "144": "K-024979", "145": "K-038212", "146": "K-044184", "147": "K-017258", "148": "K-024503", "149": "K-044458", "150": "K-029426", "151": "K-013536", "152": "K-024397", "153": "K-007223", "154": "K-006927", "155": "K-017714", "156": "K-025497", "157": "K-026093", "158": "K-027176", "159": "K-012501", "160": "K-030240", "161": "K-036353", "162": "K-027734", "163": "K-028468", "164": "K-040639", "165": "K-011686", "166": "K-010769", "167": "K-007144", "168": "K-021904", "169": "K-006421", "170": "K-044496", "171": "K-031917", "172": "K-046428", "173": "K-033069", "174": "K-007235", "175": "K-031702", "176": "K-031998", "177": "K-007821", "178": "K-031321", "179": "K-030307", "180": "K-012865", "181": "K-040530", "182": "K-041153", "183": "K-031223", "184": "K-019196", "185": "K-011695", "186": "K-033465", "187": "K-030308", "188": "K-023612", "189": "K-016804", "190": "K-031663", "191": "K-041667", "192": "K-029206", "193": "K-029972", "194": "K-031326", "195": "K-033326", "196": "K-032577", "197": "K-042269", "198": "K-030042", "199": "K-044529", "200": "K-011220", "201": "K-030044", "202": "K-031090", "203": "K-024057", "204": "K-024279", "205": "K-028074", "206": "K-028650", "207": "K-032936", "208": "K-042262", "209": "K-031492", "210": "K-025536", "211": "K-017506", "212": "K-027365", "213": "K-032657", "214": "K-043250", "215": "K-031282", "216": "K-014409", "217": "K-028081", "218": "K-035589", "219": "K-031934", "220": "K-037933", "221": "K-040317", "222": "K-027188", "223": "K-032735", "224": "K-036172", "225": "K-031467", "226": "K-035416", "227": "K-027771", "228": "K-007120", "229": "K-031050", "230": "K-011546", "231": "K-011780", "232": "K-033609", "233": "K-031625", "234": "K-032605", "235": "K-038183", "236": "K-039135", "237": "K-024854", "238": "K-011252", "239": "K-023310", "240": "K-027187", "241": "K-037446", "242": "K-037366", "243": "K-010614", "244": "K-025268", "245": "K-027688", "246": "K-018949", "247": "K-031466", "248": "K-013259", "249": "K-035328", "250": "K-020966", "251": "K-013020", "252": "K-013390", "253": "K-023599", "254": "K-027166", "255": "K-029864", "256": "K-033632", "257": "K-037501", "258": "K-023594", "259": "K-033185", "260": "K-038262", "261": "K-029909", "262": "K-030309", "263": "K-023720", "264": "K-013937", "265": "K-020582", "266": "K-017040", "267": "K-032906", "268": "K-033340", "269": "K-025545", "270": "K-038290", "271": "K-037284", "272": "K-020805", "273": "K-011544", "274": "K-024003", "275": "K-007328", "276": "K-006886", "277": "K-025227", "278": "K-025648", "279": "K-031376", "280": "K-006250", "281": "K-018969", "282": "K-035208", "283": "K-031142", "284": "K-038957", "285": "K-007325", "286": "K-014084", "287": "K-025532", "288": "K-006865", "289": "K-006562", "290": "K-039137", "291": "K-027840", "292": "K-035000", "293": "K-038972", "294": "K-010749", "295": "K-033059", "296": "K-022644", "297": "K-007337", "298": "K-030957", "299": "K-038460", "300": "K-041149", "301": "K-006681", "302": "K-034327", "303": "K-042616", "304": "K-007024", "305": "K-011833", "306": "K-018188", "307": "K-030091", "308": "K-007240", "309": "K-023248", "310": "K-042202", "311": "K-029916", "312": "K-034582", "313": "K-015537", "314": "K-025568", "315": "K-027525", "316": "K-024584", "317": "K-011274", "318": "K-016250", "319": "K-022377", "320": "K-033320", "321": "K-027436", "322": "K-034460", "323": "K-036954", "324": "K-041267", "325": "K-048028", "326": "K-007347", "327": "K-006435", "328": "K-011253", "329": "K-010931", "330": "K-020877", "331": "K-044852", "332": "K-032992", "333": "K-007131", "334": "K-025302", "335": "K-025304", "336": "K-029872", "337": "K-012502", "338": "K-036480", "339": "K-026545", "340": "K-025886", "341": "K-034056", "342": "K-035291", "343": "K-025316", "344": "K-025894", "345": "K-031624", "346": "K-006854", "347": "K-043251", "348": "K-044707", "349": "K-030063", "350": "K-007012", "351": "K-024995", "352": "K-025306", "353": "K-030232", "354": "K-047218", "355": "K-019161", "356": "K-030986", "357": "K-022095", "358": "K-032980", "359": "K-042014", "360": "K-027182", "361": "K-033046", "362": "K-025347", "363": "K-006842", "364": "K-028623", "365": "K-019860", "366": "K-007217", "367": "K-015530", "368": "K-029820", "369": "K-006432", "370": "K-011154", "371": "K-011845", "372": "K-030574", "373": "K-046737", "374": "K-036551", "375": "K-020006", "376": "K-046127", "377": "K-018409", "378": "K-032376", "379": "K-036784", "380": "K-038186", "381": "K-044818", "382": "K-025228", "383": "K-041666", "384": "K-025335", "385": "K-043689", "386": "K-012767", "387": "K-030062", "388": "K-016555", "389": "K-027567", "390": "K-013326", "391": "K-040531", "392": "K-038459", "393": "K-029774", "394": "K-029749", "395": "K-031695", "396": "K-021930", "397": "K-012081", "398": "K-013368", "399": "K-025500", "400": "K-027143", "401": "K-027181", "402": "K-027737", "403": "K-032243", "404": "K-033746", "405": "K-011648", "406": "K-012212", "407": "K-031974", "408": "K-027199", "409": "K-030148", "410": "K-012668", "411": "K-016193", "412": "K-027616", "413": "K-027093", "414": "K-027563", "415": "K-007143", "416": "K-029667", "417": "K-027129", "418": "K-006456", "419": "K-013263", "420": "K-016736", "421": "K-006686", "422": "K-022749", "423": "K-031319", "424": "K-014251", "425": "K-025470", "426": "K-012416", "427": "K-030096", "428": "K-033223", "429": "K-040339", "430": "K-022371", "431": "K-011942", "432": "K-033131", "433": "K-031705", "434": "K-031963", "435": "K-024350", "436": "K-019461", "437": "K-023779", "438": "K-041949", "439": "K-007268", "440": "K-020378", "441": "K-036307", "442": "K-040335", "443": "K-015230", "444": "K-024981", "445": "K-029066", "446": "K-012447", "447": "K-029919", "448": "K-032672", "449": "K-007820", "450": "K-013378", "451": "K-029818", "452": "K-031473", "453": "K-018203", "454": "K-040342", "455": "K-025519", "456": "K-043812", "457": "K-013543", "458": "K-031302", "459": "K-033171", "460": "K-011226", "461": "K-025211", "462": "K-046375", "463": "K-022704", "464": "K-028641", "465": "K-007014", "466": "K-032827", "467": "K-027398", "468": "K-031330", "469": "K-032809", "470": "K-031753", "471": "K-015236", "472": "K-023405", "473": "K-037500", "474": "K-042226", "475": "K-032808", "476": "K-029830", "477": "K-025531", "478": "K-043934", "479": "K-026281", "480": "K-028588", "481": "K-022705", "482": "K-040622", "483": "K-007073", "484": "K-044660", "485": "K-037086", "486": "K-019790", "487": "K-032662", "488": "K-025185", "489": "K-022176", "490": "K-031198", "491": "K-007061", "492": "K-023807", "493": "K-032900", "494": "K-018224", "495": "K-024631", "496": "K-016554", "497": "K-031211", "498": "K-029725", "499": "K-033830", "500": "K-038722", "501": "K-030008", "502": "K-027210", "503": "K-013684", "504": "K-013626", "505": "K-031545", "506": "K-014224", "507": "K-025000", "508": "K-013332", "509": "K-025184", "510": "K-027574", "511": "K-043946", "512": "K-025120", "513": "K-029910", "514": "K-044382", "515": "K-011846", "516": "K-026581", "517": "K-031701", "518": "K-020330", "519": "K-034827", "520": "K-011541", "521": "K-014102", "522": "K-033252", "523": "K-027615", "524": "K-023411", "525": "K-030037", "526": "K-025215", "527": "K-027602", "528": "K-032904", "529": "K-036625", "530": "K-007023", "531": "K-027693", "532": "K-019699", "533": "K-022963", "534": "K-043499", "535": "K-033000", "536": "K-016222", "537": "K-006697", "538": "K-016302", "539": "K-012266", "540": "K-025474", "541": "K-027126", "542": "K-028424", "543": "K-030033", "544": "K-033056", "545": "K-033204", "546": "K-041997", "547": "K-012769", "548": "K-031210", "549": "K-033134", "550": "K-007588", "551": "K-031329", "552": "K-006656", "553": "K-033091", "554": "K-033203", "555": "K-029533", "556": "K-023933", "557": "K-031918", "558": "K-044761", "559": "K-022362", "560": "K-025230", "561": "K-033876", "562": "K-029822", "563": "K-028423", "564": "K-010912", "565": "K-007822", "566": "K-007376", "567": "K-007145", "568": "K-031089", "569": "K-032832", "570": "K-034484", "571": "K-025319", "572": "K-025182", "573": "K-027653", "574": "K-017668", "575": "K-028073", "576": "K-023252", "577": "K-029998", "578": "K-021617", "579": "K-028901", "580": "K-014280", "581": "K-025033", "582": "K-013135", "583": "K-025149", "584": "K-014986", "585": "K-026894", "586": "K-035205", "587": "K-017479", "588": "K-020014", "589": "K-026694", "590": "K-017057", "591": "K-030239", "592": "K-026561", "593": "K-026993", "594": "K-025216", "595": "K-038185", "596": "K-025177", "597": "K-032104", "598": "K-006855", "599": "K-027776", "600": "K-011250", "601": "K-017482", "602": "K-007117", "603": "K-031328", "604": "K-006940", "605": "K-007316", "606": "K-032668", "607": "K-023092", "608": "K-034569", "609": "K-023729", "610": "K-049277", "611": "K-011166", "612": "K-033333", "613": "K-013256", "614": "K-048531", "615": "K-035879", "616": "K-019861", "617": "K-023740", "618": "K-025283", "619": "K-031194", "620": "K-025438", "621": "K-007060", "622": "K-030106", "623": "K-027399", "624": "K-027524", "625": "K-025437", "626": "K-007749", "627": "K-018305", "628": "K-032954", "629": "K-031605", "630": "K-029808", "631": "K-010528", "632": "K-011442", "633": "K-025262", "634": "K-022846", "635": "K-030043", "636": "K-037988", "637": "K-037043", "638": "K-033327", "639": "K-018254", "640": "K-022604", "641": "K-020834", "642": "K-027465", "643": "K-029760", "644": "K-011327", "645": "K-014729", "646": "K-039150", "647": "K-041691", "648": "K-024162", "649": "K-027735", "650": "K-025380", "651": "K-015759", "652": "K-032253", "653": "K-011481", "654": "K-044978", "655": "K-024765", "656": "K-031699", "657": "K-041947", "658": "K-007383", "659": "K-029251", "660": "K-006544", "661": "K-031130", "662": "K-025200", "663": "K-037298", "664": "K-007136", "665": "K-027926", "666": "K-031876", "667": "K-033042", "668": "K-019963", "669": "K-031481", "670": "K-036798", "671": "K-006287", "672": "K-012246", "673": "K-032951", "674": "K-031327", "675": "K-031494", "676": "K-021426", "677": "K-040244", "678": "K-012249", "679": "K-026818", "680": "K-030045", "681": "K-020665", "682": "K-032190", "683": "K-038182", "684": "K-012564", "685": "K-025367", "686": "K-015270", "687": "K-025481", "688": "K-040265", "689": "K-032997", "690": "K-011613", "691": 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"194": "K-031326", "195": "K-033326", "196": "K-032577", "197": "K-042269", "198": "K-030042", "199": "K-044529", "200": "K-011220", "201": "K-030044", "202": "K-031090", "203": "K-024057", "204": "K-024279", "205": "K-028074", "206": "K-028650", "207": "K-032936", "208": "K-042262", "209": "K-031492", "210": "K-025536", "211": "K-017506", "212": "K-027365", "213": "K-032657", "214": "K-043250", "215": "K-031282", "216": "K-014409", "217": "K-028081", "218": "K-035589", "219": "K-031934", "220": "K-037933", "221": "K-040317", "222": "K-027188", "223": "K-032735", "224": "K-036172", "225": "K-031467", "226": "K-035416", "227": "K-027771", "228": "K-007120", "229": "K-031050", "230": "K-011546", "231": "K-011780", "232": "K-033609", "233": "K-031625", "234": "K-032605", "235": "K-038183", "236": "K-039135", "237": "K-024854", "238": "K-011252", "239": "K-023310", "240": "K-027187", "241": "K-037446", "242": "K-037366", "243": "K-010614", "244": "K-025268", "245": "K-027688", "246": "K-018949", "247": "K-031466", "248": "K-013259", "249": "K-035328", "250": "K-020966", "251": "K-013020", "252": "K-013390", "253": "K-023599", "254": "K-027166", "255": "K-029864", "256": "K-033632", "257": "K-037501", "258": "K-023594", "259": "K-033185", "260": "K-038262", "261": "K-029909", "262": "K-030309", "263": "K-023720", "264": "K-013937", "265": "K-020582", "266": "K-017040", "267": "K-032906", "268": "K-033340", "269": "K-025545", "270": "K-038290", "271": "K-037284", "272": "K-020805", "273": "K-011544", "274": "K-024003", "275": "K-007328", "276": "K-006886", "277": "K-025227", "278": "K-025648", "279": "K-031376", "280": "K-006250", "281": "K-018969", "282": "K-035208", "283": "K-031142", "284": "K-038957", "285": "K-007325", "286": "K-014084", "287": "K-025532", "288": "K-006865", "289": "K-006562", "290": "K-039137", "291": "K-027840", "292": "K-035000", "293": "K-038972", "294": "K-010749", "295": "K-033059", "296": "K-022644", "297": "K-007337", "298": "K-030957", "299": "K-038460", "300": "K-041149", "301": "K-006681", "302": "K-034327", "303": "K-042616", "304": "K-007024", "305": "K-011833", "306": "K-018188", "307": "K-030091", "308": "K-007240", "309": "K-023248", "310": "K-042202", "311": "K-029916", "312": "K-034582", "313": "K-015537", "314": "K-025568", "315": "K-027525", "316": "K-024584", "317": "K-011274", "318": "K-016250", "319": "K-022377", "320": "K-033320", "321": "K-027436", "322": "K-034460", "323": "K-036954", "324": "K-041267", "325": "K-048028", "326": "K-007347", "327": "K-006435", "328": "K-011253", "329": "K-010931", "330": "K-020877", "331": "K-044852", "332": "K-032992", "333": "K-007131", "334": "K-025302", "335": "K-025304", "336": "K-029872", "337": "K-012502", "338": "K-036480", "339": "K-026545", "340": "K-025886", "341": "K-034056", "342": "K-035291", "343": "K-025316", "344": "K-025894", "345": "K-031624", "346": "K-006854", "347": "K-043251", "348": "K-044707", "349": "K-030063", "350": "K-007012", "351": "K-024995", "352": "K-025306", "353": "K-030232", "354": "K-047218", "355": "K-019161", "356": "K-030986", "357": "K-022095", "358": "K-032980", "359": "K-042014", "360": "K-027182", "361": "K-033046", "362": "K-025347", "363": "K-006842", "364": "K-028623", "365": "K-019860", "366": "K-007217", "367": "K-015530", "368": "K-029820", "369": "K-006432", "370": "K-011154", "371": "K-011845", "372": "K-030574", "373": "K-046737", "374": "K-036551", "375": "K-020006", "376": "K-046127", "377": "K-018409", "378": "K-032376", "379": "K-036784", "380": "K-038186", "381": "K-044818", "382": "K-025228", "383": "K-041666", "384": "K-025335", "385": "K-043689", "386": "K-012767", "387": "K-030062", "388": "K-016555", "389": "K-027567", "390": "K-013326", "391": "K-040531", "392": "K-038459", "393": "K-029774", "394": "K-029749", "395": "K-031695", "396": "K-021930", "397": "K-012081", "398": "K-013368", "399": "K-025500", "400": "K-027143", "401": "K-027181", "402": "K-027737", "403": "K-032243", "404": "K-033746", "405": "K-011648", "406": "K-012212", "407": "K-031974", "408": "K-027199", "409": "K-030148", "410": "K-012668", "411": "K-016193", "412": "K-027616", "413": "K-027093", "414": "K-027563", "415": "K-007143", "416": "K-029667", "417": "K-027129", "418": "K-006456", "419": "K-013263", "420": "K-016736", "421": "K-006686", "422": "K-022749", "423": "K-031319", "424": "K-014251", "425": "K-025470", "426": "K-012416", "427": "K-030096", "428": "K-033223", "429": "K-040339", "430": "K-022371", "431": "K-011942", "432": "K-033131", "433": "K-031705", "434": "K-031963", "435": "K-024350", "436": "K-019461", "437": "K-023779", "438": "K-041949", "439": "K-007268", "440": "K-020378", "441": "K-036307", "442": "K-040335", "443": "K-015230", "444": "K-024981", "445": "K-029066", "446": "K-012447", "447": "K-029919", "448": "K-032672", "449": "K-007820", "450": "K-013378", "451": "K-029818", "452": "K-031473", "453": "K-018203", "454": "K-040342", "455": "K-025519", "456": "K-043812", "457": "K-013543", "458": "K-031302", "459": "K-033171", "460": "K-011226", "461": "K-025211", "462": "K-046375", "463": "K-022704", "464": "K-028641", "465": "K-007014", "466": "K-032827", "467": "K-027398", "468": "K-031330", "469": "K-032809", "470": "K-031753", "471": "K-015236", "472": "K-023405", "473": "K-037500", "474": "K-042226", "475": "K-032808", "476": "K-029830", "477": "K-025531", "478": "K-043934", "479": "K-026281", "480": "K-028588", "481": "K-022705", "482": "K-040622", "483": "K-007073", "484": "K-044660", "485": "K-037086", "486": "K-019790", "487": "K-032662", "488": "K-025185", "489": "K-022176", "490": "K-031198", "491": "K-007061", "492": "K-023807", "493": "K-032900", "494": "K-018224", "495": "K-024631", "496": "K-016554", "497": "K-031211", "498": "K-029725", "499": "K-033830", "500": "K-038722", "501": "K-030008", "502": "K-027210", "503": "K-013684", "504": "K-013626", "505": "K-031545", "506": "K-014224", "507": "K-025000", "508": "K-013332", 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"K-033876", "562": "K-029822", "563": "K-028423", "564": "K-010912", "565": "K-007822", "566": "K-007376", "567": "K-007145", "568": "K-031089", "569": "K-032832", "570": "K-034484", "571": "K-025319", "572": "K-025182", "573": "K-027653", "574": "K-017668", "575": "K-028073", "576": "K-023252", "577": "K-029998", "578": "K-021617", "579": "K-028901", "580": "K-014280", "581": "K-025033", "582": "K-013135", "583": "K-025149", "584": "K-014986", "585": "K-026894", "586": "K-035205", "587": "K-017479", "588": "K-020014", "589": "K-026694", "590": "K-017057", "591": "K-030239", "592": "K-026561", "593": "K-026993", "594": "K-025216", "595": "K-038185", "596": "K-025177", "597": "K-032104", "598": "K-006855", "599": "K-027776", "600": "K-011250", "601": "K-017482", "602": "K-007117", "603": "K-031328", "604": "K-006940", "605": "K-007316", "606": "K-032668", "607": "K-023092", "608": "K-034569", "609": "K-023729", "610": "K-049277", "611": "K-011166", "612": "K-033333", "613": "K-013256", 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"274": "K-024003", "275": "K-007328", "276": "K-006886", "277": "K-025227", "278": "K-025648", "279": "K-031376", "280": "K-006250", "281": "K-018969", "282": "K-035208", "283": "K-031142", "284": "K-038957", "285": "K-007325", "286": "K-014084", "287": "K-025532", "288": "K-006865", "289": "K-006562", "290": "K-039137", "291": "K-027840", "292": "K-035000", "293": "K-038972", "294": "K-010749", "295": "K-033059", "296": "K-022644", "297": "K-007337", "298": "K-030957", "299": "K-038460", "300": "K-041149", "301": "K-006681", "302": "K-034327", "303": "K-042616", "304": "K-007024", "305": "K-011833", "306": "K-018188", "307": "K-030091", "308": "K-007240", "309": "K-023248", "310": "K-042202", "311": "K-029916", "312": "K-034582", "313": "K-015537", "314": "K-025568", "315": "K-027525", "316": "K-024584", "317": "K-011274", "318": "K-016250", "319": "K-022377", "320": "K-033320", "321": "K-027436", "322": "K-034460", "323": "K-036954", "324": "K-041267", "325": "K-048028", "326": "K-007347", "327": "K-006435", "328": "K-011253", "329": "K-010931", "330": "K-020877", "331": "K-044852", "332": "K-032992", "333": "K-007131", "334": "K-025302", "335": "K-025304", "336": "K-029872", "337": "K-012502", "338": "K-036480", "339": "K-026545", "340": "K-025886", "341": "K-034056", "342": "K-035291", "343": "K-025316", "344": "K-025894", "345": "K-031624", "346": "K-006854", "347": "K-043251", "348": "K-044707", "349": "K-030063", "350": "K-007012", "351": "K-024995", "352": "K-025306", "353": "K-030232", "354": "K-047218", "355": "K-019161", "356": "K-030986", "357": "K-022095", "358": "K-032980", "359": "K-042014", "360": "K-027182", "361": "K-033046", "362": "K-025347", "363": "K-006842", "364": "K-028623", "365": "K-019860", "366": "K-007217", "367": "K-015530", "368": "K-029820", "369": "K-006432", "370": "K-011154", "371": "K-011845", "372": "K-030574", "373": "K-046737", "374": "K-036551", "375": "K-020006", "376": "K-046127", "377": "K-018409", "378": "K-032376", "379": "K-036784", "380": "K-038186", "381": "K-044818", "382": "K-025228", "383": "K-041666", "384": "K-025335", "385": "K-043689", "386": "K-012767", "387": "K-030062", "388": "K-016555", "389": "K-027567", "390": "K-013326", "391": "K-040531", "392": "K-038459", "393": "K-029774", "394": "K-029749", "395": "K-031695", "396": "K-021930", "397": "K-012081", "398": "K-013368", "399": "K-025500", "400": "K-027143", "401": "K-027181", "402": "K-027737", "403": "K-032243", "404": "K-033746", "405": "K-011648", "406": "K-012212", "407": "K-031974", "408": "K-027199", "409": "K-030148", "410": "K-012668", "411": "K-016193", "412": "K-027616", "413": "K-027093", "414": "K-027563", "415": "K-007143", "416": "K-029667", "417": "K-027129", "418": "K-006456", "419": "K-013263", "420": "K-016736", "421": "K-006686", "422": "K-022749", "423": "K-031319", "424": "K-014251", "425": "K-025470", "426": "K-012416", "427": "K-030096", "428": "K-033223", "429": "K-040339", "430": "K-022371", "431": "K-011942", "432": "K-033131", "433": "K-031705", "434": "K-031963", "435": "K-024350", "436": "K-019461", "437": "K-023779", "438": "K-041949", "439": "K-007268", "440": "K-020378", "441": "K-036307", "442": "K-040335", "443": "K-015230", "444": "K-024981", "445": "K-029066", "446": "K-012447", "447": "K-029919", "448": "K-032672", "449": "K-007820", "450": "K-013378", "451": "K-029818", "452": "K-031473", "453": "K-018203", "454": "K-040342", "455": "K-025519", "456": "K-043812", "457": "K-013543", "458": "K-031302", "459": "K-033171", "460": "K-011226", "461": "K-025211", "462": "K-046375", "463": "K-022704", "464": "K-028641", "465": "K-007014", "466": "K-032827", "467": "K-027398", "468": "K-031330", "469": "K-032809", "470": "K-031753", "471": "K-015236", "472": "K-023405", "473": "K-037500", "474": "K-042226", "475": "K-032808", "476": "K-029830", "477": "K-025531", "478": "K-043934", "479": "K-026281", "480": "K-028588", "481": "K-022705", "482": "K-040622", "483": "K-007073", "484": "K-044660", "485": "K-037086", "486": "K-019790", "487": "K-032662", "488": "K-025185", "489": "K-022176", "490": "K-031198", "491": "K-007061", "492": "K-023807", "493": "K-032900", "494": "K-018224", "495": "K-024631", "496": "K-016554", "497": "K-031211", "498": "K-029725", "499": "K-033830", "500": "K-038722", "501": "K-030008", "502": "K-027210", "503": "K-013684", "504": "K-013626", "505": "K-031545", "506": "K-014224", "507": "K-025000", "508": "K-013332", "509": "K-025184", "510": "K-027574", "511": "K-043946", "512": "K-025120", "513": "K-029910", "514": "K-044382", "515": "K-011846", "516": "K-026581", "517": "K-031701", "518": "K-020330", "519": "K-034827", "520": "K-011541", "521": "K-014102", "522": "K-033252", "523": "K-027615", "524": "K-023411", "525": "K-030037", "526": "K-025215", "527": "K-027602", "528": "K-032904", "529": "K-036625", "530": "K-007023", "531": "K-027693", "532": "K-019699", "533": "K-022963", "534": "K-043499", "535": "K-033000", "536": "K-016222", "537": "K-006697", "538": "K-016302", "539": "K-012266", "540": "K-025474", "541": "K-027126", "542": "K-028424", "543": "K-030033", "544": "K-033056", "545": "K-033204", "546": "K-041997", "547": "K-012769", "548": "K-031210", "549": "K-033134", "550": "K-007588", "551": "K-031329", "552": "K-006656", "553": "K-033091", "554": "K-033203", "555": "K-029533", "556": "K-023933", "557": "K-031918", "558": "K-044761", "559": "K-022362", "560": "K-025230", "561": "K-033876", "562": "K-029822", "563": "K-028423", "564": "K-010912", "565": "K-007822", "566": "K-007376", "567": "K-007145", "568": "K-031089", "569": "K-032832", "570": "K-034484", "571": "K-025319", "572": "K-025182", "573": "K-027653", "574": "K-017668", "575": "K-028073", "576": "K-023252", "577": "K-029998", "578": "K-021617", "579": "K-028901", "580": "K-014280", "581": "K-025033", "582": "K-013135", "583": "K-025149", "584": "K-014986", "585": "K-026894", "586": "K-035205", "587": "K-017479", "588": "K-020014", "589": "K-026694", "590": "K-017057", "591": "K-030239", "592": "K-026561", "593": "K-026993", "594": "K-025216", "595": "K-038185", "596": "K-025177", "597": "K-032104", "598": "K-006855", "599": "K-027776", "600": "K-011250", "601": "K-017482", "602": "K-007117", "603": "K-031328", "604": "K-006940", "605": "K-007316", "606": "K-032668", "607": "K-023092", "608": "K-034569", "609": "K-023729", "610": "K-049277", "611": "K-011166", "612": "K-033333", "613": "K-013256", "614": "K-048531", "615": "K-035879", "616": "K-019861", "617": "K-023740", "618": "K-025283", "619": "K-031194", "620": "K-025438", "621": "K-007060", "622": "K-030106", "623": "K-027399", "624": "K-027524", "625": "K-025437", "626": "K-007749", "627": "K-018305", "628": "K-032954", "629": "K-031605", "630": "K-029808", "631": "K-010528", "632": "K-011442", "633": "K-025262", "634": "K-022846", "635": "K-030043", "636": "K-037988", "637": "K-037043", "638": "K-033327", "639": "K-018254", "640": "K-022604", "641": "K-020834", "642": "K-027465", "643": "K-029760", "644": "K-011327", "645": "K-014729", "646": "K-039150", "647": "K-041691", "648": "K-024162", "649": "K-027735", "650": "K-025380", "651": "K-015759", "652": "K-032253", "653": "K-011481", "654": "K-044978", "655": "K-024765", "656": "K-031699", "657": "K-041947", "658": "K-007383", "659": "K-029251", "660": "K-006544", "661": "K-031130", "662": "K-025200", "663": "K-037298", "664": "K-007136", "665": "K-027926", "666": "K-031876", "667": "K-033042", "668": "K-019963", "669": "K-031481", "670": "K-036798", "671": "K-006287", "672": "K-012246", "673": "K-032951", "674": "K-031327", "675": "K-031494", "676": "K-021426", "677": "K-040244", "678": "K-012249", "679": "K-026818", "680": "K-030045", "681": "K-020665", "682": "K-032190", "683": "K-038182", "684": "K-012564", "685": "K-025367", "686": "K-015270", "687": "K-025481", "688": "K-040265", "689": "K-032997", "690": "K-011613", "691": "K-031766", "692": "K-045265", "693": "K-025194", "694": "K-038461", "695": "K-044395", "696": "K-023208", "697": "K-041948", "698": "K-019700", "699": "K-012207", "700": "K-016550", "701": "K-022706", "702": "K-045102", "703": "K-026636", "704": "K-033009", "705": "K-044257", "706": "K-034680", "707": "K-006980", "708": "K-027092", "709": "K-029786", "710": "K-029905", "711": "K-019553", "712": "K-046429", "713": "K-027235", "714": "K-014733", "715": "K-010554", "716": "K-032138", "717": "K-040709", "718": "K-032083", "719": "K-025285", "720": "K-048127", "721": "K-027652", "722": "K-031680", "723": "K-017574", "724": "K-023804", "725": "K-025728", "726": "K-040996", "727": "K-006861", "728": "K-014587", "729": "K-047626", "730": "K-030143", "731": "K-030233", "732": "K-030242", "733": "K-029892", "734": "K-027606", "735": "K-030005", "736": "K-031168", "737": "K-030840", "738": "K-025218", "739": "K-030145", "740": "K-017525", "741": "K-023829", "742": "K-007103", "743": "K-015499", "744": "K-023336", "745": "K-029929", "746": 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"799": "K-010545", "800": "K-006564", "801": "K-028122", "802": "K-016645", "803": "K-016551", "804": "K-030599", "805": "K-025162", "806": "K-038105", "807": "K-040099", "808": "K-015257", "809": "K-029962", "810": "K-007348", "811": "K-023337", "812": "K-016722", "813": "K-045402", "814": "K-018757", "815": "K-033332", "816": "K-031365", "817": "K-006273", "818": "K-027080", "819": "K-036163", "820": "K-012137", "821": "K-011524", "822": "K-025485", "823": "K-025571", "824": "K-029851", "825": "K-029946", "826": "K-007146", "827": "K-029991", "828": "K-032852", "829": "K-030053", "830": "K-017522", "831": "K-012775", "832": "K-042835", "833": "K-011647", "834": "K-025238", "835": "K-025175", "836": "K-027626", "837": "K-031490", "838": "K-019299", "839": "K-046430", "840": "K-015258", "841": "K-013474", "842": "K-029980", "843": "K-038184", "844": "K-012244", "845": "K-027391", "846": "K-026741", "847": "K-023718", "848": "K-045103", "849": "K-031132", "850": "K-024468", "851": "K-033275", "852": "K-016233", "853": "K-033284", "854": "K-006960", "855": "K-014180", "856": "K-032607", "857": "K-023424", "858": "K-016230", "859": "K-031544", "860": "K-033060", "861": "K-013900", "862": "K-027587", "863": "K-036170", "864": "K-016799", "865": "K-029345", "866": "K-017008", "867": "K-033588", "868": "K-014317", "869": "K-007567", "870": "K-030001", "871": "K-041657", "872": "K-011015", "873": "K-018141", "874": "K-011029", "875": "K-007645", "876": "K-040671", "877": "K-044706", "878": "K-030177", "879": "K-012187", "880": "K-030064", "881": "K-018110", "882": "K-013829", "883": "K-022978", "884": "K-029873", "885": "K-007397", "886": "K-010995", "887": "K-013905", "888": "K-032738", "889": "K-014567", "890": "K-020379", "891": "K-038712", "892": "K-011543", "893": "K-017108", "894": "K-017587", "895": "K-026059", "896": "K-038245", "897": "K-027128", "898": "K-020251", "899": "K-033003", "900": "K-038855", "901": "K-022183", "902": "K-042120", "903": "K-030936", 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"K-033465", "187": "K-030308", "188": "K-023612", "189": "K-016804", "190": "K-031663", "191": "K-041667", "192": "K-029206", "193": "K-029972", "194": "K-031326", "195": "K-033326", "196": "K-032577", "197": "K-042269", "198": "K-030042", "199": "K-044529", "200": "K-011220", "201": "K-030044", "202": "K-031090", "203": "K-024057", "204": "K-024279", "205": "K-028074", "206": "K-028650", "207": "K-032936", "208": "K-042262", "209": "K-031492", "210": "K-025536", "211": "K-017506", "212": "K-027365", "213": "K-032657", "214": "K-043250", "215": "K-031282", "216": "K-014409", "217": "K-028081", "218": "K-035589", "219": "K-031934", "220": "K-037933", "221": "K-040317", "222": "K-027188", "223": "K-032735", "224": "K-036172", "225": "K-031467", "226": "K-035416", "227": "K-027771", "228": "K-007120", "229": "K-031050", "230": "K-011546", "231": "K-011780", "232": "K-033609", "233": "K-031625", "234": "K-032605", "235": "K-038183", "236": "K-039135", "237": "K-024854", "238": "K-011252", "239": "K-023310", "240": "K-027187", "241": "K-037446", "242": "K-037366", "243": "K-010614", "244": "K-025268", "245": "K-027688", "246": "K-018949", "247": "K-031466", "248": "K-013259", "249": "K-035328", "250": "K-020966", "251": "K-013020", "252": "K-013390", "253": "K-023599", "254": "K-027166", "255": "K-029864", "256": "K-033632", "257": "K-037501", "258": "K-023594", "259": "K-033185", "260": "K-038262", "261": "K-029909", "262": "K-030309", "263": "K-023720", "264": "K-013937", "265": "K-020582", "266": "K-017040", "267": "K-032906", "268": "K-033340", "269": "K-025545", "270": "K-038290", "271": "K-037284", "272": "K-020805", "273": "K-011544", "274": "K-024003", "275": "K-007328", "276": "K-006886", "277": "K-025227", "278": "K-025648", "279": "K-031376", "280": "K-006250", "281": "K-018969", "282": "K-035208", "283": "K-031142", "284": "K-038957", "285": "K-007325", "286": "K-014084", "287": "K-025532", "288": "K-006865", "289": "K-006562", "290": "K-039137", "291": "K-027840", "292": "K-035000", "293": "K-038972", "294": "K-010749", "295": "K-033059", "296": "K-022644", "297": "K-007337", "298": "K-030957", "299": "K-038460", "300": "K-041149", "301": "K-006681", "302": "K-034327", "303": "K-042616", "304": "K-007024", "305": "K-011833", "306": "K-018188", "307": "K-030091", "308": "K-007240", "309": "K-023248", "310": "K-042202", "311": "K-029916", "312": "K-034582", "313": "K-015537", "314": "K-025568", "315": "K-027525", "316": "K-024584", "317": "K-011274", "318": "K-016250", "319": "K-022377", "320": "K-033320", "321": "K-027436", "322": "K-034460", "323": "K-036954", "324": "K-041267", "325": "K-048028", "326": "K-007347", "327": "K-006435", "328": "K-011253", "329": "K-010931", "330": "K-020877", "331": "K-044852", "332": "K-032992", "333": "K-007131", "334": "K-025302", "335": "K-025304", "336": "K-029872", "337": "K-012502", "338": "K-036480", "339": "K-026545", "340": "K-025886", "341": "K-034056", "342": "K-035291", "343": "K-025316", "344": "K-025894", "345": "K-031624", "346": "K-006854", "347": "K-043251", "348": "K-044707", "349": "K-030063", "350": "K-007012", "351": "K-024995", "352": "K-025306", "353": "K-030232", "354": "K-047218", "355": "K-019161", "356": "K-030986", "357": "K-022095", "358": "K-032980", "359": "K-042014", "360": "K-027182", "361": "K-033046", "362": "K-025347", "363": "K-006842", "364": "K-028623", "365": "K-019860", "366": "K-007217", "367": "K-015530", "368": "K-029820", "369": "K-006432", "370": "K-011154", "371": "K-011845", "372": "K-030574", "373": "K-046737", "374": "K-036551", "375": "K-020006", "376": "K-046127", "377": "K-018409", "378": "K-032376", "379": "K-036784", "380": "K-038186", "381": "K-044818", "382": "K-025228", "383": "K-041666", "384": "K-025335", "385": "K-043689", "386": "K-012767", "387": "K-030062", "388": "K-016555", "389": "K-027567", "390": "K-013326", "391": "K-040531", "392": "K-038459", "393": "K-029774", "394": "K-029749", "395": "K-031695", "396": "K-021930", "397": "K-012081", "398": "K-013368", "399": "K-025500", "400": "K-027143", "401": "K-027181", "402": "K-027737", "403": "K-032243", "404": "K-033746", "405": "K-011648", "406": "K-012212", "407": "K-031974", "408": "K-027199", "409": "K-030148", "410": "K-012668", "411": "K-016193", "412": "K-027616", "413": "K-027093", "414": "K-027563", "415": "K-007143", "416": "K-029667", "417": "K-027129", "418": "K-006456", "419": "K-013263", "420": "K-016736", "421": "K-006686", "422": "K-022749", "423": "K-031319", "424": "K-014251", "425": "K-025470", "426": "K-012416", "427": "K-030096", "428": "K-033223", "429": "K-040339", "430": "K-022371", "431": "K-011942", "432": "K-033131", "433": "K-031705", "434": "K-031963", "435": "K-024350", "436": "K-019461", "437": "K-023779", "438": "K-041949", "439": "K-007268", "440": "K-020378", "441": "K-036307", "442": "K-040335", "443": "K-015230", "444": "K-024981", "445": "K-029066", "446": "K-012447", "447": "K-029919", "448": "K-032672", "449": "K-007820", "450": "K-013378", "451": "K-029818", "452": "K-031473", "453": "K-018203", "454": "K-040342", "455": "K-025519", "456": "K-043812", "457": "K-013543", "458": "K-031302", "459": "K-033171", "460": "K-011226", "461": "K-025211", "462": "K-046375", "463": "K-022704", "464": "K-028641", "465": "K-007014", "466": "K-032827", "467": "K-027398", "468": "K-031330", "469": "K-032809", "470": "K-031753", "471": "K-015236", "472": "K-023405", "473": "K-037500", "474": "K-042226", "475": "K-032808", "476": "K-029830", "477": "K-025531", "478": "K-043934", "479": "K-026281", "480": "K-028588", "481": "K-022705", "482": "K-040622", "483": "K-007073", "484": "K-044660", "485": "K-037086", "486": "K-019790", "487": "K-032662", "488": "K-025185", "489": "K-022176", "490": "K-031198", "491": "K-007061", "492": "K-023807", "493": "K-032900", "494": "K-018224", "495": "K-024631", "496": "K-016554", "497": "K-031211", "498": "K-029725", "499": "K-033830", "500": "K-038722", "501": "K-030008", "502": "K-027210", "503": "K-013684", "504": "K-013626", "505": "K-031545", "506": "K-014224", "507": "K-025000", "508": "K-013332", "509": "K-025184", "510": "K-027574", "511": "K-043946", "512": "K-025120", "513": "K-029910", "514": "K-044382", "515": "K-011846", "516": "K-026581", "517": "K-031701", "518": "K-020330", "519": "K-034827", "520": "K-011541", "521": "K-014102", "522": "K-033252", "523": "K-027615", "524": "K-023411", "525": "K-030037", "526": "K-025215", "527": "K-027602", "528": "K-032904", "529": "K-036625", "530": "K-007023", "531": "K-027693", "532": "K-019699", "533": "K-022963", "534": "K-043499", "535": "K-033000", "536": "K-016222", "537": "K-006697", "538": "K-016302", "539": "K-012266", "540": "K-025474", "541": "K-027126", "542": "K-028424", "543": "K-030033", "544": "K-033056", "545": "K-033204", "546": "K-041997", "547": "K-012769", "548": "K-031210", "549": "K-033134", "550": "K-007588", "551": "K-031329", "552": "K-006656", "553": "K-033091", "554": "K-033203", "555": "K-029533", "556": "K-023933", "557": "K-031918", "558": "K-044761", "559": "K-022362", "560": "K-025230", "561": "K-033876", "562": "K-029822", "563": "K-028423", "564": "K-010912", "565": "K-007822", "566": "K-007376", "567": "K-007145", "568": "K-031089", "569": "K-032832", "570": "K-034484", "571": "K-025319", "572": "K-025182", "573": "K-027653", "574": "K-017668", "575": "K-028073", "576": "K-023252", "577": "K-029998", "578": "K-021617", "579": "K-028901", "580": "K-014280", "581": "K-025033", "582": "K-013135", "583": "K-025149", "584": "K-014986", "585": "K-026894", "586": "K-035205", "587": "K-017479", "588": "K-020014", "589": "K-026694", "590": "K-017057", "591": "K-030239", "592": "K-026561", "593": "K-026993", "594": "K-025216", "595": "K-038185", "596": "K-025177", "597": "K-032104", "598": "K-006855", "599": "K-027776", "600": "K-011250", "601": "K-017482", "602": "K-007117", "603": "K-031328", "604": "K-006940", "605": "K-007316", "606": "K-032668", "607": "K-023092", "608": "K-034569", "609": "K-023729", "610": "K-049277", "611": "K-011166", "612": "K-033333", "613": "K-013256", "614": "K-048531", "615": "K-035879", "616": "K-019861", "617": "K-023740", "618": "K-025283", "619": "K-031194", "620": "K-025438", "621": "K-007060", "622": "K-030106", "623": "K-027399", "624": "K-027524", "625": "K-025437", "626": "K-007749", "627": "K-018305", "628": "K-032954", "629": "K-031605", "630": "K-029808", "631": "K-010528", "632": "K-011442", "633": "K-025262", "634": "K-022846", "635": "K-030043", "636": "K-037988", "637": "K-037043", "638": "K-033327", "639": "K-018254", "640": "K-022604", "641": "K-020834", "642": "K-027465", "643": "K-029760", "644": "K-011327", "645": "K-014729", "646": "K-039150", "647": "K-041691", "648": "K-024162", "649": "K-027735", "650": "K-025380", "651": "K-015759", "652": "K-032253", "653": "K-011481", "654": "K-044978", "655": "K-024765", "656": "K-031699", "657": "K-041947", "658": "K-007383", "659": "K-029251", "660": "K-006544", "661": "K-031130", "662": "K-025200", "663": "K-037298", "664": "K-007136", "665": "K-027926", "666": "K-031876", "667": "K-033042", "668": "K-019963", "669": "K-031481", "670": "K-036798", "671": "K-006287", "672": "K-012246", "673": "K-032951", "674": "K-031327", "675": "K-031494", "676": "K-021426", "677": "K-040244", "678": "K-012249", "679": "K-026818", "680": "K-030045", "681": "K-020665", "682": "K-032190", "683": "K-038182", "684": "K-012564", "685": "K-025367", "686": "K-015270", "687": "K-025481", "688": "K-040265", "689": "K-032997", "690": "K-011613", "691": "K-031766", "692": "K-045265", "693": "K-025194", "694": "K-038461", "695": "K-044395", "696": "K-023208", "697": "K-041948", "698": "K-019700", "699": "K-012207", "700": "K-016550", "701": "K-022706", "702": "K-045102", "703": "K-026636", "704": "K-033009", "705": "K-044257", "706": "K-034680", "707": "K-006980", "708": "K-027092", "709": "K-029786", "710": "K-029905", "711": "K-019553", "712": "K-046429", "713": "K-027235", "714": "K-014733", "715": "K-010554", "716": "K-032138", "717": "K-040709", "718": "K-032083", "719": "K-025285", "720": "K-048127", "721": "K-027652", "722": "K-031680", "723": "K-017574", "724": "K-023804", "725": "K-025728", "726": "K-040996", "727": "K-006861", "728": "K-014587", "729": "K-047626", "730": "K-030143", "731": "K-030233", "732": "K-030242", "733": "K-029892", "734": "K-027606", "735": "K-030005", "736": "K-031168", "737": "K-030840", "738": "K-025218", "739": "K-030145", "740": "K-017525", "741": "K-023829", "742": "K-007103", "743": "K-015499", "744": "K-023336", "745": "K-029929", "746": "K-031258", "747": "K-040998", "748": "K-041293", "749": "K-023719", "750": "K-031563", "751": "K-016251", "752": "K-027733", "753": "K-031999", "754": "K-034681", "755": "K-007303", "756": "K-028813", "757": "K-035659", "758": "K-035209", "759": "K-035207", "760": "K-035837", "761": "K-018641", "762": "K-029886", "763": "K-024752", "764": "K-029200", "765": "K-033287", "766": "K-036982", "767": "K-011783", "768": "K-038582", "769": "K-043935", "770": "K-006323", "771": "K-041998", "772": "K-027722", "773": "K-032012", "774": "K-013007", "775": "K-012256", "776": "K-023357", "777": "K-028773", "778": "K-031195", "779": "K-042118", "780": "K-037589", "781": "K-007339", "782": "K-044921", "783": "K-032883", "784": "K-043542", "785": "K-006941", "786": "K-039810", "787": "K-027087", "788": "K-027741", "789": "K-027607", "790": "K-025214", "791": "K-033208", "792": "K-022712", "793": "K-027440", "794": "K-028360", "795": "K-041692", "796": "K-011705", "797": "K-041290", "798": "K-006835", "799": "K-010545", "800": "K-006564", "801": "K-028122", "802": "K-016645", "803": "K-016551", "804": "K-030599", "805": "K-025162", "806": "K-038105", "807": "K-040099", "808": "K-015257", "809": "K-029962", "810": "K-007348", "811": "K-023337", "812": "K-016722", "813": "K-045402", "814": "K-018757", "815": "K-033332", "816": 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"K-017040", "267": "K-032906", "268": "K-033340", "269": "K-025545", "270": "K-038290", "271": "K-037284", "272": "K-020805", "273": "K-011544", "274": "K-024003", "275": "K-007328", "276": "K-006886", "277": "K-025227", "278": "K-025648", "279": "K-031376", "280": "K-006250", "281": "K-018969", "282": "K-035208", "283": "K-031142", "284": "K-038957", "285": "K-007325", "286": "K-014084", "287": "K-025532", "288": "K-006865", "289": "K-006562", "290": "K-039137", "291": "K-027840", "292": "K-035000", "293": "K-038972", "294": "K-010749", "295": "K-033059", "296": "K-022644", "297": "K-007337", "298": "K-030957", "299": "K-038460", "300": "K-041149", "301": "K-006681", "302": "K-034327", "303": "K-042616", "304": "K-007024", "305": "K-011833", "306": "K-018188", "307": "K-030091", "308": "K-007240", "309": "K-023248", "310": "K-042202", "311": "K-029916", "312": "K-034582", "313": "K-015537", "314": "K-025568", "315": "K-027525", "316": "K-024584", "317": "K-011274", "318": "K-016250", "319": "K-022377", "320": "K-033320", "321": "K-027436", "322": "K-034460", "323": "K-036954", "324": "K-041267", "325": "K-048028", "326": "K-007347", "327": "K-006435", "328": "K-011253", "329": "K-010931", "330": "K-020877", "331": "K-044852", "332": "K-032992", "333": "K-007131", "334": "K-025302", "335": "K-025304", "336": "K-029872", "337": "K-012502", "338": "K-036480", "339": "K-026545", "340": "K-025886", "341": "K-034056", "342": "K-035291", "343": "K-025316", "344": "K-025894", "345": "K-031624", "346": "K-006854", "347": "K-043251", "348": "K-044707", "349": "K-030063", "350": "K-007012", "351": "K-024995", "352": "K-025306", "353": "K-030232", "354": "K-047218", "355": "K-019161", "356": "K-030986", "357": "K-022095", "358": "K-032980", "359": "K-042014", "360": "K-027182", "361": "K-033046", "362": "K-025347", "363": "K-006842", "364": "K-028623", "365": "K-019860", "366": "K-007217", "367": "K-015530", "368": "K-029820", "369": "K-006432", "370": "K-011154", "371": "K-011845", "372": "K-030574", "373": "K-046737", "374": "K-036551", "375": "K-020006", "376": "K-046127", "377": "K-018409", "378": "K-032376", "379": "K-036784", "380": "K-038186", "381": "K-044818", "382": "K-025228", "383": "K-041666", "384": "K-025335", "385": "K-043689", "386": "K-012767", "387": "K-030062", "388": "K-016555", "389": "K-027567", "390": "K-013326", "391": "K-040531", "392": "K-038459", "393": "K-029774", "394": "K-029749", "395": "K-031695", "396": "K-021930", "397": "K-012081", "398": "K-013368", "399": "K-025500", "400": "K-027143", "401": "K-027181", "402": "K-027737", "403": "K-032243", "404": "K-033746", "405": "K-011648", "406": "K-012212", "407": "K-031974", "408": "K-027199", "409": "K-030148", "410": "K-012668", "411": "K-016193", "412": "K-027616", "413": "K-027093", "414": "K-027563", "415": "K-007143", "416": "K-029667", "417": "K-027129", "418": "K-006456", "419": "K-013263", "420": "K-016736", "421": "K-006686", "422": "K-022749", "423": "K-031319", "424": "K-014251", "425": "K-025470", "426": "K-012416", "427": "K-030096", "428": "K-033223", "429": "K-040339", "430": "K-022371", "431": "K-011942", "432": "K-033131", "433": "K-031705", "434": "K-031963", "435": "K-024350", "436": "K-019461", "437": "K-023779", "438": "K-041949", "439": "K-007268", "440": "K-020378", "441": "K-036307", "442": "K-040335", "443": "K-015230", "444": "K-024981", "445": "K-029066", "446": "K-012447", "447": "K-029919", "448": "K-032672", "449": "K-007820", "450": "K-013378", "451": "K-029818", "452": "K-031473", "453": "K-018203", "454": "K-040342", "455": "K-025519", "456": "K-043812", "457": "K-013543", "458": "K-031302", "459": "K-033171", "460": "K-011226", "461": "K-025211", "462": "K-046375", "463": "K-022704", "464": "K-028641", "465": "K-007014", "466": "K-032827", "467": "K-027398", "468": "K-031330", "469": "K-032809", "470": "K-031753", "471": "K-015236", "472": "K-023405", "473": "K-037500", "474": "K-042226", "475": "K-032808", "476": "K-029830", "477": "K-025531", "478": "K-043934", "479": "K-026281", "480": "K-028588", "481": "K-022705", "482": "K-040622", "483": "K-007073", "484": "K-044660", "485": "K-037086", "486": "K-019790", "487": "K-032662", "488": "K-025185", "489": "K-022176", "490": "K-031198", "491": "K-007061", "492": "K-023807", "493": "K-032900", "494": "K-018224", "495": "K-024631", "496": "K-016554", "497": "K-031211", "498": "K-029725", "499": "K-033830", "500": "K-038722", "501": "K-030008", "502": "K-027210", "503": "K-013684", "504": "K-013626", "505": "K-031545", "506": "K-014224", "507": "K-025000", "508": "K-013332", "509": "K-025184", "510": "K-027574", "511": "K-043946", "512": "K-025120", "513": "K-029910", "514": "K-044382", "515": "K-011846", "516": "K-026581", "517": "K-031701", "518": "K-020330", "519": "K-034827", "520": "K-011541", "521": "K-014102", "522": "K-033252", "523": "K-027615", "524": "K-023411", "525": "K-030037", "526": "K-025215", "527": "K-027602", "528": "K-032904", "529": "K-036625", "530": "K-007023", "531": "K-027693", "532": "K-019699", "533": "K-022963", "534": "K-043499", "535": "K-033000", "536": "K-016222", "537": "K-006697", "538": "K-016302", "539": "K-012266", "540": "K-025474", "541": "K-027126", "542": "K-028424", "543": "K-030033", "544": "K-033056", "545": "K-033204", "546": "K-041997", "547": "K-012769", "548": "K-031210", "549": "K-033134", "550": "K-007588", "551": "K-031329", "552": "K-006656", "553": "K-033091", "554": "K-033203", "555": "K-029533", "556": "K-023933", "557": "K-031918", "558": "K-044761", "559": "K-022362", "560": "K-025230", "561": "K-033876", "562": "K-029822", "563": "K-028423", "564": "K-010912", "565": "K-007822", "566": "K-007376", "567": "K-007145", "568": "K-031089", "569": "K-032832", "570": "K-034484", "571": "K-025319", "572": "K-025182", "573": "K-027653", "574": "K-017668", "575": "K-028073", "576": "K-023252", "577": "K-029998", "578": "K-021617", "579": "K-028901", "580": "K-014280", "581": "K-025033", "582": "K-013135", "583": "K-025149", "584": "K-014986", "585": "K-026894", "586": "K-035205", "587": "K-017479", "588": "K-020014", "589": "K-026694", "590": "K-017057", "591": "K-030239", "592": "K-026561", "593": "K-026993", "594": "K-025216", "595": "K-038185", "596": "K-025177", "597": "K-032104", "598": "K-006855", "599": "K-027776", "600": "K-011250", "601": "K-017482", "602": "K-007117", "603": "K-031328", "604": "K-006940", "605": "K-007316", "606": "K-032668", "607": "K-023092", "608": "K-034569", "609": "K-023729", "610": "K-049277", "611": "K-011166", "612": "K-033333", "613": "K-013256", "614": "K-048531", "615": "K-035879", "616": "K-019861", "617": "K-023740", "618": "K-025283", "619": "K-031194", "620": "K-025438", "621": "K-007060", "622": "K-030106", "623": "K-027399", "624": "K-027524", "625": "K-025437", "626": "K-007749", "627": "K-018305", "628": "K-032954", "629": "K-031605", "630": "K-029808", "631": "K-010528", "632": "K-011442", "633": "K-025262", "634": "K-022846", "635": "K-030043", "636": "K-037988", "637": "K-037043", "638": "K-033327", "639": "K-018254", "640": "K-022604", "641": "K-020834", "642": "K-027465", "643": "K-029760", "644": "K-011327", "645": "K-014729", "646": "K-039150", "647": "K-041691", "648": "K-024162", "649": "K-027735", "650": "K-025380", "651": "K-015759", "652": "K-032253", "653": "K-011481", "654": "K-044978", "655": "K-024765", "656": "K-031699", "657": "K-041947", "658": "K-007383", "659": "K-029251", "660": "K-006544", "661": "K-031130", "662": "K-025200", "663": "K-037298", "664": "K-007136", "665": "K-027926", "666": "K-031876", "667": "K-033042", "668": "K-019963", "669": "K-031481", "670": "K-036798", "671": "K-006287", "672": "K-012246", "673": "K-032951", "674": "K-031327", "675": "K-031494", "676": "K-021426", "677": "K-040244", "678": "K-012249", "679": "K-026818", "680": "K-030045", "681": "K-020665", "682": "K-032190", "683": "K-038182", "684": "K-012564", "685": "K-025367", "686": "K-015270", "687": "K-025481", "688": "K-040265", "689": "K-032997", "690": "K-011613", "691": "K-031766", "692": "K-045265", "693": "K-025194", "694": "K-038461", "695": "K-044395", "696": "K-023208", "697": "K-041948", "698": "K-019700", "699": "K-012207", "700": "K-016550", "701": "K-022706", "702": "K-045102", "703": "K-026636", "704": "K-033009", "705": "K-044257", "706": "K-034680", "707": "K-006980", "708": "K-027092", "709": "K-029786", "710": "K-029905", "711": "K-019553", "712": "K-046429", "713": "K-027235", "714": "K-014733", "715": "K-010554", "716": "K-032138", "717": "K-040709", "718": "K-032083", "719": "K-025285", "720": "K-048127", "721": "K-027652", "722": "K-031680", "723": "K-017574", "724": "K-023804", "725": "K-025728", "726": "K-040996", "727": "K-006861", "728": "K-014587", "729": "K-047626", "730": "K-030143", "731": "K-030233", "732": "K-030242", "733": "K-029892", "734": "K-027606", "735": "K-030005", "736": "K-031168", "737": "K-030840", "738": "K-025218", 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"K-033208", "792": "K-022712", "793": "K-027440", "794": "K-028360", "795": "K-041692", "796": "K-011705", "797": "K-041290", "798": "K-006835", "799": "K-010545", "800": "K-006564", "801": "K-028122", "802": "K-016645", "803": "K-016551", "804": "K-030599", "805": "K-025162", "806": "K-038105", "807": "K-040099", "808": "K-015257", "809": "K-029962", "810": "K-007348", "811": "K-023337", "812": "K-016722", "813": "K-045402", "814": "K-018757", "815": "K-033332", "816": "K-031365", "817": "K-006273", "818": "K-027080", "819": "K-036163", "820": "K-012137", "821": "K-011524", "822": "K-025485", "823": "K-025571", "824": "K-029851", "825": "K-029946", "826": "K-007146", "827": "K-029991", "828": "K-032852", "829": "K-030053", "830": "K-017522", "831": "K-012775", "832": "K-042835", "833": "K-011647", "834": "K-025238", "835": "K-025175", "836": "K-027626", "837": "K-031490", "838": "K-019299", "839": "K-046430", "840": "K-015258", "841": "K-013474", "842": "K-029980", "843": "K-038184", 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"179": "K-030307", "180": "K-012865", "181": "K-040530", "182": "K-041153", "183": "K-031223", "184": "K-019196", "185": "K-011695", "186": "K-033465", "187": "K-030308", "188": "K-023612", "189": "K-016804", "190": "K-031663", "191": "K-041667", "192": "K-029206", "193": "K-029972", "194": "K-031326", "195": "K-033326", "196": "K-032577", "197": "K-042269", "198": "K-030042", "199": "K-044529", "200": "K-011220", "201": "K-030044", "202": "K-031090", "203": "K-024057", "204": "K-024279", "205": "K-028074", "206": "K-028650", "207": "K-032936", "208": "K-042262", "209": "K-031492", "210": "K-025536", "211": "K-017506", "212": "K-027365", "213": "K-032657", "214": "K-043250", "215": "K-031282", "216": "K-014409", "217": "K-028081", "218": "K-035589", "219": "K-031934", "220": "K-037933", "221": "K-040317", "222": "K-027188", "223": "K-032735", "224": "K-036172", "225": "K-031467", "226": "K-035416", "227": "K-027771", "228": "K-007120", "229": "K-031050", "230": "K-011546", "231": "K-011780", "232": "K-033609", "233": "K-031625", "234": "K-032605", "235": "K-038183", "236": "K-039135", "237": "K-024854", "238": "K-011252", "239": "K-023310", "240": "K-027187", "241": "K-037446", "242": "K-037366", "243": "K-010614", "244": "K-025268", "245": "K-027688", "246": "K-018949", "247": "K-031466", "248": "K-013259", "249": "K-035328", "250": "K-020966", "251": "K-013020", "252": "K-013390", "253": "K-023599", "254": "K-027166", "255": "K-029864", "256": "K-033632", "257": "K-037501", "258": "K-023594", "259": "K-033185", "260": "K-038262", "261": "K-029909", "262": "K-030309", "263": "K-023720", "264": "K-013937", "265": "K-020582", "266": "K-017040", "267": "K-032906", "268": "K-033340", "269": "K-025545", "270": "K-038290", "271": "K-037284", "272": "K-020805", "273": "K-011544", "274": "K-024003", "275": "K-007328", "276": "K-006886", "277": "K-025227", "278": "K-025648", "279": "K-031376", "280": "K-006250", "281": "K-018969", "282": "K-035208", "283": "K-031142", "284": "K-038957", "285": "K-007325", "286": "K-014084", "287": "K-025532", "288": "K-006865", "289": "K-006562", "290": "K-039137", "291": "K-027840", "292": "K-035000", "293": "K-038972", "294": "K-010749", "295": "K-033059", "296": "K-022644", "297": "K-007337", "298": "K-030957", "299": "K-038460", "300": "K-041149", "301": "K-006681", "302": "K-034327", "303": "K-042616", "304": "K-007024", "305": "K-011833", "306": "K-018188", "307": "K-030091", "308": "K-007240", "309": "K-023248", "310": "K-042202", "311": "K-029916", "312": "K-034582", "313": "K-015537", "314": "K-025568", "315": "K-027525", "316": "K-024584", "317": "K-011274", "318": "K-016250", "319": "K-022377", "320": "K-033320", "321": "K-027436", "322": "K-034460", "323": "K-036954", "324": "K-041267", "325": "K-048028", "326": "K-007347", "327": "K-006435", "328": "K-011253", "329": "K-010931", "330": "K-020877", "331": "K-044852", "332": "K-032992", "333": "K-007131", "334": "K-025302", "335": "K-025304", "336": "K-029872", "337": "K-012502", "338": "K-036480", "339": "K-026545", "340": "K-025886", "341": "K-034056", "342": "K-035291", "343": "K-025316", "344": "K-025894", "345": "K-031624", "346": "K-006854", "347": "K-043251", "348": "K-044707", "349": "K-030063", "350": "K-007012", "351": "K-024995", "352": "K-025306", "353": "K-030232", "354": "K-047218", "355": "K-019161", "356": "K-030986", "357": "K-022095", "358": "K-032980", "359": "K-042014", "360": "K-027182", "361": "K-033046", "362": "K-025347", "363": "K-006842", "364": "K-028623", "365": "K-019860", "366": "K-007217", "367": "K-015530", "368": "K-029820", "369": "K-006432", "370": "K-011154", "371": "K-011845", "372": "K-030574", "373": "K-046737", "374": "K-036551", "375": "K-020006", "376": "K-046127", "377": "K-018409", "378": "K-032376", "379": "K-036784", "380": "K-038186", "381": "K-044818", "382": "K-025228", "383": "K-041666", "384": "K-025335", "385": "K-043689", "386": "K-012767", "387": "K-030062", "388": "K-016555", "389": "K-027567", "390": "K-013326", "391": "K-040531", "392": "K-038459", "393": "K-029774", "394": "K-029749", "395": "K-031695", "396": "K-021930", "397": "K-012081", "398": "K-013368", "399": "K-025500", "400": "K-027143", "401": "K-027181", "402": "K-027737", "403": "K-032243", "404": "K-033746", "405": "K-011648", "406": "K-012212", "407": "K-031974", "408": "K-027199", "409": "K-030148", "410": "K-012668", "411": "K-016193", "412": "K-027616", "413": "K-027093", "414": "K-027563", "415": "K-007143", "416": "K-029667", "417": "K-027129", "418": "K-006456", "419": "K-013263", "420": "K-016736", "421": "K-006686", "422": "K-022749", "423": "K-031319", "424": "K-014251", "425": "K-025470", "426": "K-012416", "427": "K-030096", "428": "K-033223", "429": "K-040339", "430": "K-022371", "431": "K-011942", "432": "K-033131", "433": "K-031705", "434": "K-031963", "435": "K-024350", "436": "K-019461", "437": "K-023779", "438": "K-041949", "439": "K-007268", "440": "K-020378", "441": "K-036307", "442": "K-040335", "443": "K-015230", "444": "K-024981", "445": "K-029066", "446": "K-012447", "447": "K-029919", "448": "K-032672", "449": "K-007820", "450": "K-013378", "451": "K-029818", "452": "K-031473", "453": "K-018203", "454": "K-040342", "455": "K-025519", "456": "K-043812", "457": "K-013543", "458": "K-031302", "459": "K-033171", "460": "K-011226", "461": "K-025211", "462": "K-046375", "463": "K-022704", "464": "K-028641", "465": "K-007014", "466": "K-032827", "467": "K-027398", "468": "K-031330", "469": "K-032809", "470": "K-031753", "471": "K-015236", "472": "K-023405", "473": "K-037500", "474": "K-042226", "475": "K-032808", "476": "K-029830", "477": "K-025531", "478": "K-043934", "479": "K-026281", "480": "K-028588", "481": "K-022705", "482": "K-040622", "483": "K-007073", "484": "K-044660", "485": "K-037086", "486": "K-019790", "487": "K-032662", "488": "K-025185", "489": "K-022176", "490": "K-031198", "491": "K-007061", "492": "K-023807", "493": "K-032900", "494": "K-018224", "495": "K-024631", "496": "K-016554", "497": "K-031211", "498": "K-029725", "499": "K-033830", "500": "K-038722", "501": "K-030008", "502": "K-027210", "503": "K-013684", "504": "K-013626", "505": "K-031545", "506": "K-014224", "507": "K-025000", "508": "K-013332", "509": "K-025184", "510": "K-027574", "511": "K-043946", "512": "K-025120", "513": "K-029910", "514": "K-044382", "515": "K-011846", "516": "K-026581", "517": "K-031701", "518": "K-020330", "519": "K-034827", "520": "K-011541", "521": "K-014102", "522": "K-033252", "523": "K-027615", "524": "K-023411", "525": "K-030037", "526": "K-025215", "527": "K-027602", "528": "K-032904", "529": "K-036625", "530": "K-007023", "531": "K-027693", "532": "K-019699", "533": "K-022963", "534": "K-043499", "535": "K-033000", "536": "K-016222", "537": "K-006697", "538": "K-016302", "539": "K-012266", "540": "K-025474", "541": "K-027126", "542": "K-028424", "543": "K-030033", "544": "K-033056", "545": "K-033204", "546": "K-041997", "547": "K-012769", "548": "K-031210", "549": "K-033134", "550": "K-007588", "551": "K-031329", "552": "K-006656", "553": "K-033091", "554": "K-033203", "555": "K-029533", "556": "K-023933", "557": "K-031918", "558": "K-044761", "559": "K-022362", "560": "K-025230", "561": "K-033876", "562": "K-029822", "563": "K-028423", "564": "K-010912", "565": "K-007822", "566": "K-007376", "567": "K-007145", "568": "K-031089", "569": "K-032832", "570": "K-034484", "571": "K-025319", "572": "K-025182", "573": "K-027653", "574": "K-017668", "575": "K-028073", "576": "K-023252", "577": "K-029998", "578": "K-021617", "579": "K-028901", "580": "K-014280", "581": "K-025033", "582": "K-013135", "583": "K-025149", "584": "K-014986", "585": "K-026894", "586": "K-035205", "587": "K-017479", "588": "K-020014", "589": "K-026694", "590": "K-017057", "591": "K-030239", "592": "K-026561", "593": "K-026993", "594": "K-025216", "595": "K-038185", "596": "K-025177", "597": "K-032104", "598": "K-006855", "599": "K-027776", "600": "K-011250", "601": "K-017482", "602": "K-007117", "603": "K-031328", "604": "K-006940", "605": "K-007316", "606": "K-032668", "607": "K-023092", "608": "K-034569", "609": "K-023729", "610": "K-049277", "611": "K-011166", "612": "K-033333", "613": "K-013256", "614": "K-048531", "615": "K-035879", "616": "K-019861", "617": "K-023740", "618": "K-025283", "619": "K-031194", "620": "K-025438", "621": "K-007060", "622": "K-030106", "623": "K-027399", "624": "K-027524", "625": "K-025437", "626": "K-007749", "627": "K-018305", "628": "K-032954", "629": "K-031605", "630": "K-029808", "631": "K-010528", "632": "K-011442", "633": "K-025262", "634": "K-022846", "635": "K-030043", "636": "K-037988", "637": "K-037043", "638": "K-033327", "639": "K-018254", "640": "K-022604", "641": "K-020834", "642": "K-027465", "643": "K-029760", "644": "K-011327", "645": "K-014729", "646": "K-039150", "647": "K-041691", "648": "K-024162", "649": "K-027735", "650": "K-025380", "651": "K-015759", "652": "K-032253", "653": "K-011481", "654": "K-044978", "655": "K-024765", "656": "K-031699", "657": "K-041947", "658": "K-007383", "659": "K-029251", "660": "K-006544", "661": "K-031130", "662": "K-025200", "663": "K-037298", "664": "K-007136", "665": "K-027926", "666": "K-031876", "667": "K-033042", "668": "K-019963", "669": "K-031481", "670": "K-036798", "671": "K-006287", "672": "K-012246", "673": "K-032951", "674": "K-031327", "675": "K-031494", "676": "K-021426", "677": "K-040244", "678": "K-012249", "679": "K-026818", "680": "K-030045", "681": "K-020665", "682": "K-032190", "683": "K-038182", "684": "K-012564", "685": "K-025367", "686": "K-015270", "687": "K-025481", "688": "K-040265", "689": "K-032997", "690": "K-011613", "691": "K-031766", "692": "K-045265", "693": "K-025194", "694": "K-038461", "695": "K-044395", "696": "K-023208", "697": "K-041948", "698": "K-019700", "699": "K-012207", "700": "K-016550", "701": "K-022706", "702": "K-045102", "703": "K-026636", "704": "K-033009", "705": "K-044257", "706": "K-034680", "707": "K-006980", "708": "K-027092", "709": "K-029786", "710": "K-029905", "711": "K-019553", "712": "K-046429", "713": "K-027235", "714": "K-014733", "715": "K-010554", "716": "K-032138", "717": "K-040709", "718": "K-032083", "719": "K-025285", "720": "K-048127", "721": "K-027652", "722": "K-031680", "723": "K-017574", "724": "K-023804", "725": "K-025728", "726": "K-040996", "727": "K-006861", "728": "K-014587", "729": "K-047626", "730": "K-030143", "731": "K-030233", "732": "K-030242", "733": "K-029892", "734": "K-027606", "735": "K-030005", "736": "K-031168", "737": "K-030840", "738": "K-025218", "739": "K-030145", "740": "K-017525", "741": "K-023829", "742": "K-007103", "743": "K-015499", "744": "K-023336", "745": "K-029929", "746": "K-031258", "747": "K-040998", "748": "K-041293", "749": "K-023719", "750": "K-031563", "751": "K-016251", "752": "K-027733", "753": "K-031999", "754": "K-034681", "755": "K-007303", "756": 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"469": "K-032809", "470": "K-031753", "471": "K-015236", "472": "K-023405", "473": "K-037500", "474": "K-042226", "475": "K-032808", "476": "K-029830", "477": "K-025531", "478": "K-043934", "479": "K-026281", "480": "K-028588", "481": "K-022705", "482": "K-040622", "483": "K-007073", "484": "K-044660", "485": "K-037086", "486": "K-019790", "487": "K-032662", "488": "K-025185", "489": "K-022176", "490": "K-031198", "491": "K-007061", "492": "K-023807", "493": "K-032900", "494": "K-018224", "495": "K-024631", "496": "K-016554", "497": "K-031211", "498": "K-029725", "499": "K-033830", "500": "K-038722", "501": "K-030008", "502": "K-027210", "503": "K-013684", "504": "K-013626", "505": "K-031545", "506": "K-014224", "507": "K-025000", "508": "K-013332", "509": "K-025184", "510": "K-027574", "511": "K-043946", "512": "K-025120", "513": "K-029910", "514": "K-044382", "515": "K-011846", "516": "K-026581", "517": "K-031701", "518": "K-020330", "519": "K-034827", "520": "K-011541", "521": "K-014102", "522": "K-033252", "523": "K-027615", "524": "K-023411", "525": "K-030037", "526": "K-025215", "527": "K-027602", "528": "K-032904", "529": "K-036625", "530": "K-007023", "531": "K-027693", "532": "K-019699", "533": "K-022963", "534": "K-043499", "535": "K-033000", "536": "K-016222", "537": "K-006697", "538": "K-016302", "539": "K-012266", "540": "K-025474", "541": "K-027126", "542": "K-028424", "543": "K-030033", "544": "K-033056", "545": "K-033204", "546": "K-041997", "547": "K-012769", "548": "K-031210", "549": "K-033134", "550": "K-007588", "551": "K-031329", "552": "K-006656", "553": "K-033091", "554": "K-033203", "555": "K-029533", "556": "K-023933", "557": "K-031918", "558": "K-044761", "559": "K-022362", "560": "K-025230", "561": "K-033876", "562": "K-029822", "563": "K-028423", "564": "K-010912", "565": "K-007822", "566": "K-007376", "567": "K-007145", "568": "K-031089", "569": "K-032832", "570": "K-034484", "571": "K-025319", "572": "K-025182", "573": "K-027653", 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[{"split": "train", "path": "aihub/single/train/pill-part-4000/train-*"}]}, {"config_name": "aihub/single/train/pill-part-4500", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-4500/train-*"}]}, {"config_name": "aihub/single/train/pill-part-500", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-500/train-*"}]}, {"config_name": "aihub/single/train/pill-part-5000", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-5000/train-*"}]}, {"config_name": "aihub/single/train/pill-part-5500", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-5500/train-*"}]}, {"config_name": "aihub/single/train/pill-part-6000", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-6000/train-*"}]}, {"config_name": "aihub/single/train/pill-part-6500", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-6500/train-*"}]}, {"config_name": "aihub/single/train/pill-part-7000", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-7000/train-*"}]}, {"config_name": "aihub/single/train/pill-part-7500", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-7500/train-*"}]}, {"config_name": "aihub/single/train/pill-part-8000", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-8000/train-*"}]}, {"config_name": "aihub/single/train/pill-part-8500", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-8500/train-*"}]}, {"config_name": "aihub/single/train/pill-part-9000", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-9000/train-*"}]}, {"config_name": "aihub/single/train/pill-part-9500", "data_files": [{"split": "train", "path": "aihub/single/train/pill-part-9500/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "server-image", "data_files": [{"split": "train", "path": "server-image/train-*"}]}]} | 2024-01-10T14:09:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pill"
More Information needed | [
"# Dataset Card for \"pill\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pill\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pill\"\n\nMore Information needed"
]
|
814f31ee481acdcf9322b176428281d1064ed699 | # Dataset Card for Maize and Arabidopsis gene expression
Plant Gene expression data used for benchmarking sequence to gene expression prediction ML models.
### Dataset Description
Species included are Maize and Arabidopsis thaliana. Dataset includes gene expression values for leaf and root tissues.
Within the `tasks` folder, datasets are broken down by `species-task-tissue`. Genomes in the `genomes` folders include the annotation and the GFF files associated with that specific genome.
All tasks are split by 80% train, 10% validation, and 10% test.
## Dataset Structure
```
dataset
genomes/
Arabidopsis_thaliana/
annotation.fa
ath.gff
Zea_mays/
annotation.fa
ath.gff
tasks/
species-task-tissue/
train.tsv
validate.tsv
test.tsv
```
- **Curated by:** Taylor Ferebee, Travis Wrightsman, Jingjing Zhai, Aaron Gokaslan, Volodymyr Kuleshov, Edward S. Buckler
- **Repository:** [https://github.com/maize-genetics/expression-survey]
- **Paper:** PLExBench: A benchmarking suite for predicting gene expression in plants
- **License:** MIT
### Dataset Sources
| | | | | | | | | | | | |
|-|-|-|-|-|-|-|-|-|-|-|-|
|sample_name|species|genotype|library_layout|library_selection|reads_location|organ|age|condition|replicate|batch|reference|
|SRR505743|Arabidopsis_thaliana|Col-0|single-read|random|sra|root|seedling|controlled|1|1|SRP013631|
|SRR505744|Arabidopsis_thaliana|Col-0|single-read|random|sra|leaf|seedling|controlled|1|1|SRP013631|
|SRR953400|Arabidopsis_thaliana|Col-0|single-read|random|sra|leaf|seeding|controlled|1|1|PRJNA215448|
|SRR1005386|Arabidopsis_thaliana|Col-0|single-read|random|sra|leaf|seedling|controlled|1|1|PRJNA222364|
|SRR578947|Arabidopsis_thaliana|Col-0|single-read|random|sra|root|seedling|controlled|1|1|SRP013631|
|SRR578948|Arabidopsis_thaliana|Col-0|single-read|random|sra|root|seedling|controlled|1|1|SRP013631|
|ERR2096663|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166|
|ERR2096664|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166|
|ERR2096665|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166|
|ERR2096666|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166|
|ERR2096667|Zea_mays|B73|paired-end|polyA|sra|leaf|seedling|controlled|1|1|PRJEB22166|
|ERR3773807|Zea_mays|B73|paired-end|polyA|sra|root|seedling|controlled|1|1|PRJEB35943|
|ERR3773808|Zea_mays|B73|paired-end|polyA|sra|root|seedling|controlled|1|1|PRJEB35943|
|ERR986091|Zea_mays|B73|paired-end|random|sra|root|seedling|controlled|1|1|PRJEB10406|
### Curation Rationale
To choose experiments for leaf and root tissues, we focused on datasets that have been used in a recent study and can be found in
multiple databases.
#### Data Collection and Processing
In the max gene expression datasets, for each gene, we take the maximum transcript per million TPM value over experiments. Similarly, for the
absolute expression datasets, we take the mean TPM value over experiments. Finally, for the on-off ex-
pression, we assign 1 to a gene if it has a TPM value in one of the tissues. To create train-test-validation
splits, we use orthogroup guided splitting as introduced by Washburn et al. 2019. Then, we split the training test sets so
that we train on 80% of the orthogroups and test on 10%. Note that for each of the task-based datasets, we
keep the same train-test-validate split.
**BibTeX:**
## Dataset Card Authors
Taylor Ferebee ([email protected])
## Dataset Card Contact
Taylor Ferebee ([email protected]), Cinta Romay, Edward S. Buckler | maize-genetics/plexbench-base | [
"license:mit",
"biology",
"plants",
"gene expression",
"region:us"
]
| 2023-11-03T13:57:58+00:00 | {"license": "mit", "pretty_name": "Maize and Arabidopsis gene expression", "tags": ["biology", "plants", "gene expression"]} | 2023-11-13T01:01:28+00:00 | []
| []
| TAGS
#license-mit #biology #plants #gene expression #region-us
| Dataset Card for Maize and Arabidopsis gene expression
======================================================
Plant Gene expression data used for benchmarking sequence to gene expression prediction ML models.
### Dataset Description
Species included are Maize and Arabidopsis thaliana. Dataset includes gene expression values for leaf and root tissues.
Within the 'tasks' folder, datasets are broken down by 'species-task-tissue'. Genomes in the 'genomes' folders include the annotation and the GFF files associated with that specific genome.
All tasks are split by 80% train, 10% validation, and 10% test.
Dataset Structure
-----------------
* Curated by: Taylor Ferebee, Travis Wrightsman, Jingjing Zhai, Aaron Gokaslan, Volodymyr Kuleshov, Edward S. Buckler
* Repository: [URL
* Paper: PLExBench: A benchmarking suite for predicting gene expression in plants
* License: MIT
### Dataset Sources
### Curation Rationale
To choose experiments for leaf and root tissues, we focused on datasets that have been used in a recent study and can be found in
multiple databases.
#### Data Collection and Processing
In the max gene expression datasets, for each gene, we take the maximum transcript per million TPM value over experiments. Similarly, for the
absolute expression datasets, we take the mean TPM value over experiments. Finally, for the on-off ex-
pression, we assign 1 to a gene if it has a TPM value in one of the tissues. To create train-test-validation
splits, we use orthogroup guided splitting as introduced by Washburn et al. 2019. Then, we split the training test sets so
that we train on 80% of the orthogroups and test on 10%. Note that for each of the task-based datasets, we
keep the same train-test-validate split.
BibTeX:
Dataset Card Authors
--------------------
Taylor Ferebee (tf259@URL)
Dataset Card Contact
--------------------
Taylor Ferebee (tf259@URL), Cinta Romay, Edward S. Buckler
| [
"### Dataset Description\n\n\nSpecies included are Maize and Arabidopsis thaliana. Dataset includes gene expression values for leaf and root tissues.\nWithin the 'tasks' folder, datasets are broken down by 'species-task-tissue'. Genomes in the 'genomes' folders include the annotation and the GFF files associated with that specific genome.\nAll tasks are split by 80% train, 10% validation, and 10% test.\n\n\nDataset Structure\n-----------------\n\n\n* Curated by: Taylor Ferebee, Travis Wrightsman, Jingjing Zhai, Aaron Gokaslan, Volodymyr Kuleshov, Edward S. Buckler\n* Repository: [URL\n* Paper: PLExBench: A benchmarking suite for predicting gene expression in plants\n* License: MIT",
"### Dataset Sources",
"### Curation Rationale\n\n\nTo choose experiments for leaf and root tissues, we focused on datasets that have been used in a recent study and can be found in\nmultiple databases.",
"#### Data Collection and Processing\n\n\nIn the max gene expression datasets, for each gene, we take the maximum transcript per million TPM value over experiments. Similarly, for the\nabsolute expression datasets, we take the mean TPM value over experiments. Finally, for the on-off ex-\npression, we assign 1 to a gene if it has a TPM value in one of the tissues. To create train-test-validation\nsplits, we use orthogroup guided splitting as introduced by Washburn et al. 2019. Then, we split the training test sets so\nthat we train on 80% of the orthogroups and test on 10%. Note that for each of the task-based datasets, we\nkeep the same train-test-validate split.\n\n\nBibTeX:\n\n\nDataset Card Authors\n--------------------\n\n\nTaylor Ferebee (tf259@URL)\n\n\nDataset Card Contact\n--------------------\n\n\nTaylor Ferebee (tf259@URL), Cinta Romay, Edward S. Buckler"
]
| [
"TAGS\n#license-mit #biology #plants #gene expression #region-us \n",
"### Dataset Description\n\n\nSpecies included are Maize and Arabidopsis thaliana. Dataset includes gene expression values for leaf and root tissues.\nWithin the 'tasks' folder, datasets are broken down by 'species-task-tissue'. Genomes in the 'genomes' folders include the annotation and the GFF files associated with that specific genome.\nAll tasks are split by 80% train, 10% validation, and 10% test.\n\n\nDataset Structure\n-----------------\n\n\n* Curated by: Taylor Ferebee, Travis Wrightsman, Jingjing Zhai, Aaron Gokaslan, Volodymyr Kuleshov, Edward S. Buckler\n* Repository: [URL\n* Paper: PLExBench: A benchmarking suite for predicting gene expression in plants\n* License: MIT",
"### Dataset Sources",
"### Curation Rationale\n\n\nTo choose experiments for leaf and root tissues, we focused on datasets that have been used in a recent study and can be found in\nmultiple databases.",
"#### Data Collection and Processing\n\n\nIn the max gene expression datasets, for each gene, we take the maximum transcript per million TPM value over experiments. Similarly, for the\nabsolute expression datasets, we take the mean TPM value over experiments. Finally, for the on-off ex-\npression, we assign 1 to a gene if it has a TPM value in one of the tissues. To create train-test-validation\nsplits, we use orthogroup guided splitting as introduced by Washburn et al. 2019. Then, we split the training test sets so\nthat we train on 80% of the orthogroups and test on 10%. Note that for each of the task-based datasets, we\nkeep the same train-test-validate split.\n\n\nBibTeX:\n\n\nDataset Card Authors\n--------------------\n\n\nTaylor Ferebee (tf259@URL)\n\n\nDataset Card Contact\n--------------------\n\n\nTaylor Ferebee (tf259@URL), Cinta Romay, Edward S. Buckler"
]
| [
20,
179,
6,
42,
222
]
| [
"passage: TAGS\n#license-mit #biology #plants #gene expression #region-us \n### Dataset Description\n\n\nSpecies included are Maize and Arabidopsis thaliana. Dataset includes gene expression values for leaf and root tissues.\nWithin the 'tasks' folder, datasets are broken down by 'species-task-tissue'. Genomes in the 'genomes' folders include the annotation and the GFF files associated with that specific genome.\nAll tasks are split by 80% train, 10% validation, and 10% test.\n\n\nDataset Structure\n-----------------\n\n\n* Curated by: Taylor Ferebee, Travis Wrightsman, Jingjing Zhai, Aaron Gokaslan, Volodymyr Kuleshov, Edward S. Buckler\n* Repository: [URL\n* Paper: PLExBench: A benchmarking suite for predicting gene expression in plants\n* License: MIT### Dataset Sources### Curation Rationale\n\n\nTo choose experiments for leaf and root tissues, we focused on datasets that have been used in a recent study and can be found in\nmultiple databases.#### Data Collection and Processing\n\n\nIn the max gene expression datasets, for each gene, we take the maximum transcript per million TPM value over experiments. Similarly, for the\nabsolute expression datasets, we take the mean TPM value over experiments. Finally, for the on-off ex-\npression, we assign 1 to a gene if it has a TPM value in one of the tissues. To create train-test-validation\nsplits, we use orthogroup guided splitting as introduced by Washburn et al. 2019. Then, we split the training test sets so\nthat we train on 80% of the orthogroups and test on 10%. Note that for each of the task-based datasets, we\nkeep the same train-test-validate split.\n\n\nBibTeX:\n\n\nDataset Card Authors\n--------------------\n\n\nTaylor Ferebee (tf259@URL)\n\n\nDataset Card Contact\n--------------------\n\n\nTaylor Ferebee (tf259@URL), Cinta Romay, Edward S. Buckler"
]
|
fda0c4d93f1b15f6360324b1964e480045134582 | # Dataset Card for "ArabicDarija-xP3x" part of "xP3x" by [Muennighoff](https://huggingface.co/Muennighoff)
## Find below part of the original dataset card
## Dataset Description
- **Repository:** https://github.com/bigscience-workshop/xmtf
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
> xP3x (Crosslingual Public Pool of Prompts eXtended) is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more! It is used for training future contenders of mT0 & BLOOMZ at project Aya @[C4AI](https://cohere.for.ai/) 🧡
>
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3) together with the file in this repository named `xp3x_create.py`. We provide this version to save processing time.
- **Languages:** 277
- **xP3 Dataset Family:**
<table>
<tr>
<th>Name</th>
<th>Explanation</th>
<th>Example models</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t>
<td>Mixture of 17 tasks in 277 languages with English prompts</td>
<td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
<td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
<td></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
</tr>
</table>
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | M-A-D/ArabicDarija-xP3x | [
"arxiv:2211.01786",
"region:us"
]
| 2023-11-03T14:25:43+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "template", "dtype": "string"}, {"name": "dataset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 308250831, "num_examples": 1223481}], "download_size": 129951272, "dataset_size": 308250831}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T14:29:36+00:00 | [
"2211.01786"
]
| []
| TAGS
#arxiv-2211.01786 #region-us
| Dataset Card for "ArabicDarija-xP3x" part of "xP3x" by Muennighoff
==================================================================
Find below part of the original dataset card
--------------------------------------------
Dataset Description
-------------------
* Repository: URL
* Paper: Crosslingual Generalization through Multitask Finetuning
* Point of Contact: Niklas Muennighoff
### Dataset Summary
>
> xP3x (Crosslingual Public Pool of Prompts eXtended) is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more! It is used for training future contenders of mT0 & BLOOMZ at project Aya @C4AI
>
>
>
* Creation: The dataset can be recreated using instructions available here together with the file in this repository named 'xp3x\_create.py'. We provide this version to save processing time.
* Languages: 277
* xP3 Dataset Family:
More Information needed
| [
"### Dataset Summary\n\n\n\n> \n> xP3x (Crosslingual Public Pool of Prompts eXtended) is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more! It is used for training future contenders of mT0 & BLOOMZ at project Aya @C4AI\n> \n> \n> \n\n\n* Creation: The dataset can be recreated using instructions available here together with the file in this repository named 'xp3x\\_create.py'. We provide this version to save processing time.\n* Languages: 277\n* xP3 Dataset Family:\n\n\n\nMore Information needed"
]
| [
"TAGS\n#arxiv-2211.01786 #region-us \n",
"### Dataset Summary\n\n\n\n> \n> xP3x (Crosslingual Public Pool of Prompts eXtended) is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more! It is used for training future contenders of mT0 & BLOOMZ at project Aya @C4AI\n> \n> \n> \n\n\n* Creation: The dataset can be recreated using instructions available here together with the file in this repository named 'xp3x\\_create.py'. We provide this version to save processing time.\n* Languages: 277\n* xP3 Dataset Family:\n\n\n\nMore Information needed"
]
| [
15,
156
]
| [
"passage: TAGS\n#arxiv-2211.01786 #region-us \n### Dataset Summary\n\n\n\n> \n> xP3x (Crosslingual Public Pool of Prompts eXtended) is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more! It is used for training future contenders of mT0 & BLOOMZ at project Aya @C4AI\n> \n> \n> \n\n\n* Creation: The dataset can be recreated using instructions available here together with the file in this repository named 'xp3x\\_create.py'. We provide this version to save processing time.\n* Languages: 277\n* xP3 Dataset Family:\n\n\n\nMore Information needed"
]
|
11d041d28763d86c828e34ff258d62421f89d586 | # Dataset Card for "anc_object_detect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ajanco/anc_object_detect | [
"region:us"
]
| 2023-11-03T14:41:43+00:00 | {"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "objects", "struct": [{"name": "area", "sequence": "float64"}, {"name": "bbox", "sequence": {"sequence": "float64"}}, {"name": "category", "sequence": "int64"}, {"name": "id", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 157851896.0, "num_examples": 132}], "download_size": 151292559, "dataset_size": 157851896.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T14:41:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "anc_object_detect"
More Information needed | [
"# Dataset Card for \"anc_object_detect\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"anc_object_detect\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"anc_object_detect\"\n\nMore Information needed"
]
|
5fdddf9c6f023258a1778a9b41d3c5a9b7e395a8 |
# **SEA Wikipedia Data Repository**
---
Welcome to SEA Wikipedia Data Repository. The datasets are extracted from [Wikipedia HF](https://huggingface.co/datasets/wikipedia) and processed using the scripts available in this repository for reproducibility purpose. Since Wikipedia iteslf has license [cc-by-sa 4.0](https://en.wikipedia.org/wiki/Wikipedia:Copyrights), we decided to follow this instead of Wikipedia HF data has of cc-by-sa 3.0 since it gives more rights to initial author/contributor.
# Getting Started #
### To read the datasets directly ###
Use one of the following code chunks to load it from HuggingFace Hub:
You can refer to the 2nd args of ```config name``` using the following script
```
dataset = load_dataset(
"sabilmakbar/sea_wiki",
"seawiki_dedup_all" # a config name, can be "seawiki_dedup_all" or "seawiki_with_countries_all", or "seawiki_with_countries_dedup_all" , defaults to "seawiki_dedup_all"
)
```
Or you can provide both ```lang``` and ```date_stamp``` (or just lang only by assuming the ```date_stamp``` will take the newest one)
```
dataset = load_dataset(
"sabilmakbar/sea_wiki",
lang = "id", # see README for complete lang choices
date_stamp="20230901"
)
```
Or you can provide a ```country``` params with similar fashion to ```lang``` args (providing both ```country``` and ```lang``` will prioritize the ```lang``` kwarg)
```
dataset = load_dataset(
"sabilmakbar/sea_wiki",
lang = "id", # see the splits for complete lang choices
date_stamp="20230901"
)
```
# **FAQS**
### What are the available languages provided in dataset and from which country?
You may check the following tables to understand the current coverage of this dataset (languages, countries, data size & volume). All tables are sorted by the leftmost column.
#### 1. Table of Countries and its Country Code
| Country Code | Country Name | Wiki Info |
| :---: | :---: | :---: |
| brn | Brunei | [Wiki Link](https://en.wikipedia.org/wiki/Brunei) |
| idn | Indonesia | [Wiki Link](https://en.wikipedia.org/wiki/Indonesia) |
| khm | Cambodia | [Wiki Link](https://en.wikipedia.org/wiki/Cambodia) |
| lao | Laos | [Wiki Link](https://en.wikipedia.org/wiki/Laos) |
| mmr | Myanmar | [Wiki Link](https://en.wikipedia.org/wiki/Myanmar) |
| mys | Malaysia | [Wiki Link](https://en.wikipedia.org/wiki/Malaysia) |
| phl | Philippines | [Wiki Link](https://en.wikipedia.org/wiki/Philippines) |
| sgp | Singapore | [Wiki Link](https://en.wikipedia.org/wiki/Singapore) |
| tha | Thailand | [Wiki Link](https://en.wikipedia.org/wiki/Thailand) |
| tls | East Timor | [Wiki Link](https://en.wikipedia.org/wiki/East_Timor) |
| vnm | Vietnam | [Wiki Link](https://en.wikipedia.org/wiki/Vietnam) |
#### 2. Table of Languages and Countries of its speakers
| ISO 639-3 Lang Code | Dataset Lang Code | Lang Name | Country Codes Spoken | Wiki Info | Total Data | Total Size (MiB rounded) |
| :---: | :---: | :---: | :---: | :--- | ---: | ---: |
| ace | ace | Acehnese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Acehnese_language) | 12979 | 4.72 |
| ban | ban | Balinese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Balinese_language) | 20611 | 17.19 |
| bcl | bcl | Central Bicolano | phl | [Wiki Link](https://en.wikipedia.org/wiki/Banjarese_language) | 14079 | 19.05 |
| bjn | bjn | Banjarese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Banjarese_language) | 10503 | 6.47 |
| bug | bug | Buginese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Buginese_language) | 9969 | 2.08 |
| bur | my | Burmese | mmr | [Wiki Link](https://en.wikipedia.org/wiki/Burmese_language) | 108819 | 298.49 |
| cbk | cbk_zam | Zamboanga Chavacano/Chavacano | phl | [Wiki Link](https://en.wikipedia.org/wiki/Chavacano) | 2242 | 1.51 |
| ceb | ceb | Cebuano | phl | [Wiki Link](https://en.wikipedia.org/wiki/Central_Bicolano_language) | 5815254 | 4,145.16 |
| gor | gor | Gorontalo | idn | [Wiki Link](https://en.wikipedia.org/wiki/Gorontalo_language) | 15290 | 5.93 |
| ilo | ilo | Ilokano | phl | [Wiki Link](https://en.wikipedia.org/wiki/Ilocano_language) | 15369 | 15.94 |
| ind | id | Indonesian | idn | [Wiki Link](https://en.wikipedia.org/wiki/Indonesian_language) | 662443 | 1,066.10 |
| jav | jv | Javanese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Javanese_language) | 73080 | 68.66 |
| khm | km | Khmer | khm | [Wiki Link](https://en.wikipedia.org/wiki/Khmer_language) | 11466 | 97.94 |
| lao | lo | Lao | lao | [Wiki Link](https://en.wikipedia.org/wiki/Lao_language) | 4897 | 14.22 |
| mad | mad | Madurese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Madurese_language) | 1192 | 1.54 |
| may | ms | Malay | mys, sgp, brn, idn | [Wiki Link](https://en.wikipedia.org/wiki/Malay_language) | 348045 | 395.57 |
| min | min | Minangkabau | idn | [Wiki Link](https://en.wikipedia.org/wiki/Minangkabau_language) | 225972 | 111.31 |
| mnw | mnw | Mon | mmr | [Wiki Link](https://en.wikipedia.org/wiki/Mon_language) | 3271 | 45.05 |
| nia | nia | Nias | idn | [Wiki Link](https://en.wikipedia.org/wiki/Nias_language) | 1714 | 2.05 |
| pag | pag | Pangasinan | phl | [Wiki Link](https://en.wikipedia.org/wiki/Pangasinan_language) | 1108 | 0.73 |
| pam | pam | Kapampangan | phl | [Wiki Link](https://en.wikipedia.org/wiki/Kapampangan_language) | 8932 | 7.83 |
| shn | shn | Shan | mmr | [Wiki Link](https://en.wikipedia.org/wiki/Shan_language) | 13662 | 32.06 |
| sun | su | Sundanese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Sundanese_language) | 61529 | 45.31 |
| tam | ta | Tamil | mys, sgp | [Wiki Link](https://en.wikipedia.org/wiki/Tamil_language) | 160580 | 771.58 |
| tgl | tl | Tagalog | phl | [Wiki Link](https://en.wikipedia.org/wiki/Tagalog_language) | 45121 | 81.34 |
| tha | th | Thai | tha | [Wiki Link](https://en.wikipedia.org/wiki/Thai_language) | 159666 | 965.95 |
| tet | tet | Tetum | tls, idn | [Wiki Link](https://en.wikipedia.org/wiki/Tetum_language) | 1464 | 1.38 |
| vie | vi | Vietnamese | vnm | [Wiki Link](https://en.wikipedia.org/wiki/Vietnamese_language) | 1287912 | 1,528.58 |
| war | war | Waray | phl | [Wiki Link](https://en.wikipedia.org/wiki/Waray_language) | 1266204 | 433.22 |
| (dialect) | map_bms | Banyumasan <br>(Dialect of Javanese) | idn | [Wiki Link](https://en.wikipedia.org/wiki/Banyumasan_dialect) | 11839 | 4.83 |
#### 3. Table of Token Statistics for Covered Languages
The token statistics is generated using ```tiktoken``` using encoder for GPT-4.
| Dataset Lang Code | Total Token | Avg Token per Article | Min Token | Max Token | Token Deciles List |
| :---: | ---: | ---: | ---: | ---: | :--- |
| ace | 1,370,829 | 105.61899992295247 | 3 | 9,659 | [38.0, 52.0, 54.0, 69.0, 76.0, 84.0, 90.0, 123.0, 126.0] |
| ban | 5,924,610 | 287.44893503469024 | 5 | 24,364 | [97.0, 144.0, 165.0, 187.0, 209.0, 245.0, 276.0, 315.0, 421.0] |
| bcl | 6,234,838 | 442.8466510405569 | 2 | 54,049 | [55.0, 95.0, 143.0, 179.0, 226.0, 304.0, 419.0, 587.0, 917.2] |
| bjn | 1,935,505 | 184.28115776444827 | 2 | 30,170 | [36.0, 38.0, 39.0, 40.0, 42.0, 51.0, 82.0, 151.0, 367.0] |
| bug | 553,693 | 55.54147858360919 | 1 | 13,951 | [31.0, 42.0, 43.0, 46.0, 48.0, 50.0, 52.0, 55.0, 57.0] |
| cbk_zam | 402,703 | 179.6177520071365 | 2 | 6,494 | [35.0, 41.2, 56.0, 69.0, 90.0, 120.0, 138.0, 155.0, 294.9] |
| ceb | 1,319,601,771 | 226.92074516435568 | 4 | 221,802 | [93.0, 108.0, 123.0, 136.0, 163.0, 207.0, 278.0, 377.0, 426.0] |
| gor | 1,575,766 | 103.05860039241334 | 2 | 5,525 | [55.0, 58.0, 60.0, 62.0, 64.0, 66.0, 69.0, 75.0, 96.0] |
| id | 325,411,713 | 491.22975561670967 | 1 | 198,597 | [54.0, 93.0, 123.0, 145.0, 180.0, 226.0, 332.0, 543.0, 1068.0] |
| ilo | 5,593,491 | 363.94632051532307 | 17 | 18,202 | [59.0, 80.0, 91.0, 111.0, 152.0, 213.0, 303.0, 461.0, 856.0] |
| jv | 23,528,314 | 321.95284619594963 | 2 | 342,156 | [48.0, 60.0, 75.0, 88.0, 117.0, 175.0, 270.0, 420.0, 772.0] |
| km | 54,559,721 | 4,758.391854177568 | 1 | 1,110,771 | [160.0, 293.0, 452.0, 693.0, 1032.0, 1609.0, 2644.0, 4745.0, 9607.0] |
| lo | 9,395,636 | 1,918.6514192362672 | 3 | 107,154 | [134.0, 184.2, 285.0, 494.0, 658.0, 894.6, 1258.0, 1971.2, 4153.8] |
| mad | 611,736 | 513.2013422818792 | 14 | 17,093 | [80.1, 110.2, 135.0, 161.0, 194.0, 242.0, 302.7, 531.4, 1167.1] |
| map_bms | 1,307,244 | 110.41844750401216 | 1 | 20,629 | [20.0, 21.0, 22.0, 24.0, 30.0, 35.0, 36.0, 38.0, 111.0] |
| min | 33,114,184 | 146.54109358681606 | 3 | 58,387 | [81.0, 91.0, 96.0, 108.0, 119.0, 135.0, 156.0, 168.0, 170.0] |
| mnw | 31,595,647 | 9,659.3234484867 | 6 | 1,450,765 | [425.0, 601.0, 629.0, 682.0, 763.0, 2103.0, 4255.0, 7724.0, 14517.0] |
| ms | 121,343,673 | 348.64363228892813 | 1 | 68,545 | [32.0, 40.0, 49.0, 63.0, 105.0, 138.0, 216.0, 362.0, 788.0] |
| my | 189,439,447 | 1,740.8673761015998 | 10 | 1,376,658 | [164.0, 269.0, 350.0, 508.0, 559.0, 578.0, 605.0, 892.4, 3369.0] |
| nia | 795,527 | 464.134772462077 | 8 | 18,650 | [59.0, 61.0, 63.0, 65.0, 67.0, 86.0, 239.1, 623.4, 1249.7] |
| pag | 222,366 | 200.6913357400722 | 5 | 10,143 | [31.0, 51.0, 73.0, 110.0, 118.0, 120.0, 127.0, 181.0, 355.8] |
| pam | 2,269,091 | 254.04064039408868 | 1 | 14,912 | [38.0, 56.0, 78.0, 108.0, 121.0, 150.0, 193.0, 289.0, 525.8] |
| shn | 23,125,637 | 1,692.6977748499487 | 2 | 204,094 | [460.0, 480.0, 585.0, 679.0, 715.0, 740.0, 756.0, 780.0, 1580.9] |
| su | 14,710,124 | 239.07627297697022 | 1 | 99,456 | [41.0, 43.0, 45.0, 49.0, 70.0, 146.0, 216.0, 219.0, 419.0] |
| ta | 376,043,508 | 2,341.782961763607 | 15 | 177,054 | [543.0, 700.0, 824.0, 1001.0, 1153.0, 1465.0, 1992.0, 2911.0, 4652.0] |
| tet | 487,016 | 332.6612021857924 | 4 | 24,287 | [30.3, 47.0, 66.9, 101.0, 164.0, 177.0, 187.0, 248.6, 604.4] |
| th | 330,964,733 | 2,072.8566695476807 | 1 | 289,150 | [231.0, 390.0, 546.0, 727.0, 969.0, 1276.0, 1741.0, 2533.0, 4361.0] |
| tl | 27,789,730 | 615.8934864032269 | 7 | 60,728 | [73.0, 116.0, 161.0, 214.0, 281.0, 360.0, 465.0, 666.0, 1136.0] |
| vi | 546,481,913 | 424.3161900813099 | 3 | 246,463 | [46.0, 64.0, 71.0, 80.0, 86.0, 92.0, 120.0, 240.0, 824.0] |
| war | 117,438,315 | 92.74833676090108 | 1 | 25,689 | [60.0, 77.0, 81.0, 84.0, 87.0, 90.0, 94.0, 99.0, 110.0] |
Some other languages in SEA that are already exists its Wiki Index at Wikimedia might be missing from this list. Any lang update PR is greatly appreciated!
### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?
The data available in here are processed with following flows:
1. Raw data is being deduplicated on ```title``` and ```text``` (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for unavailable informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.
2. Furthermore, the ```title``` and ```text``` data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars/UTF-8 chars validated).
The source code can be found on this Github Repo [SEA Wiki Github Source Code](https://github.com/sabilmakbar/sea_wiki)
### How do I extract new Wikipedia Dataset of SEA languages?
Please refer to the corresponding Github Repo for more detailed info [SEA Wiki Github Source Code](https://github.com/sabilmakbar/sea_wiki)
## Citation Info:
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"}
@ONLINE{wikipedia-hf,
title = "Huggingface Wikipedia Dataset",
url = "https://huggingface.co/datasets/wikipedia"}
```
| sabilmakbar/sea_wiki | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:Wikipedia",
"language:ace",
"language:ban",
"language:bcl",
"language:bjn",
"language:bug",
"language:cbk",
"language:ceb",
"language:gor",
"language:id",
"language:ilo",
"language:jv",
"language:km",
"language:lo",
"language:mad",
"language:min",
"language:mnw",
"language:ms",
"language:my",
"language:nia",
"language:pag",
"language:pam",
"language:shn",
"language:su",
"language:ta",
"language:th",
"language:tl",
"language:tet",
"language:vi",
"language:war",
"license:cc-by-sa-4.0",
"Wikipedia",
"Southeast Asia (SEA)",
"Dialect",
"Banyumasan Dialect of Javanese (Ngapak)",
"SEA-related Languages",
"SEA Local Languages",
"region:us"
]
| 2023-11-03T14:44:10+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["ace", "ban", "bcl", "bjn", "bug", "cbk", "ceb", "gor", "id", "ilo", "jv", "km", "lo", "mad", "min", "mnw", "ms", "my", "nia", "pag", "pam", "shn", "su", "ta", "th", "tl", "tet", "vi", "war"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "source_datasets": ["Wikipedia"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "Wikipedia Archive for SEA Languages", "tags": ["Wikipedia", "Southeast Asia (SEA)", "Dialect", "Banyumasan Dialect of Javanese (Ngapak)", "SEA-related Languages", "SEA Local Languages"], "dataset_info": [{"config_name": "seawiki_all", "features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "ace", "num_bytes": 4952102, "num_examples": 13003}, {"name": "ban", "num_bytes": 18198909, "num_examples": 20987}, {"name": "bcl", "num_bytes": 20258642, "num_examples": 15743}, {"name": "bjn", "num_bytes": 6792259, "num_examples": 10519}, {"name": "bug", "num_bytes": 3298561, "num_examples": 15880}, {"name": "cbk_zam", "num_bytes": 2033238, "num_examples": 3285}, {"name": "ceb", "num_bytes": 4572804910, "num_examples": 6302896}, {"name": "gor", "num_bytes": 6239133, "num_examples": 15359}, {"name": "id", "num_bytes": 1118834498, "num_examples": 665622}, {"name": "ilo", "num_bytes": 16719139, "num_examples": 15371}, {"name": "jv", "num_bytes": 72101470, "num_examples": 73380}, {"name": "km", "num_bytes": 103146669, "num_examples": 11994}, {"name": "lo", "num_bytes": 15240262, "num_examples": 5014}, {"name": "mad", "num_bytes": 1612542, "num_examples": 1192}, {"name": "map_bms", "num_bytes": 5221506, "num_examples": 13580}, {"name": "min", "num_bytes": 116824020, "num_examples": 227143}, {"name": "mnw", "num_bytes": 47321734, "num_examples": 3296}, {"name": "ms", "num_bytes": 419662356, "num_examples": 368628}, {"name": "my", "num_bytes": 313370839, "num_examples": 109310}, {"name": "nia", "num_bytes": 2153274, "num_examples": 1714}, {"name": "pag", "num_bytes": 1370162, "num_examples": 2665}, {"name": "pam", "num_bytes": 8218370, "num_examples": 9006}, {"name": "shn", "num_bytes": 33754296, "num_examples": 13945}, {"name": "su", "num_bytes": 47516268, "num_examples": 61555}, {"name": "ta", "num_bytes": 809156746, "num_examples": 160651}, {"name": "tet", "num_bytes": 1454499, "num_examples": 1468}, {"name": "th", "num_bytes": 1012930269, "num_examples": 159719}, {"name": "tl", "num_bytes": 85356818, "num_examples": 45341}, {"name": "vi", "num_bytes": 1603057633, "num_examples": 1288680}, {"name": "war", "num_bytes": 454304567, "num_examples": 1266394}], "download_size": 1829748651, "dataset_size": 10923905691}, {"config_name": "seawiki_dedup_all", "features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "ace", "num_bytes": 4944916, "num_examples": 12979}, {"name": "ban", "num_bytes": 18025267, "num_examples": 20611}, {"name": "bcl", "num_bytes": 19977232, "num_examples": 14079}, {"name": "bjn", "num_bytes": 6786207, "num_examples": 10503}, {"name": "bug", "num_bytes": 2182435, "num_examples": 9969}, {"name": "cbk_zam", "num_bytes": 1579651, "num_examples": 2242}, {"name": "ceb", "num_bytes": 4346511153, "num_examples": 5815254}, {"name": "gor", "num_bytes": 6217480, "num_examples": 15290}, {"name": "id", "num_bytes": 1117891512, "num_examples": 662443}, {"name": "ilo", "num_bytes": 16719001, "num_examples": 15369}, {"name": "jv", "num_bytes": 71997517, "num_examples": 73080}, {"name": "km", "num_bytes": 102698901, "num_examples": 11466}, {"name": "lo", "num_bytes": 14908444, "num_examples": 4897}, {"name": "mad", "num_bytes": 1612542, "num_examples": 1192}, {"name": "map_bms", "num_bytes": 5067489, "num_examples": 11839}, {"name": "min", "num_bytes": 116721269, "num_examples": 225972}, {"name": "mnw", "num_bytes": 47243333, "num_examples": 3271}, {"name": "ms", "num_bytes": 414783365, "num_examples": 348045}, {"name": "my", "num_bytes": 312990457, "num_examples": 108819}, {"name": "nia", "num_bytes": 2153274, "num_examples": 1714}, {"name": "pag", "num_bytes": 764869, "num_examples": 1108}, {"name": "pam", "num_bytes": 8205723, "num_examples": 8932}, {"name": "shn", "num_bytes": 33616591, "num_examples": 13662}, {"name": "su", "num_bytes": 47512744, "num_examples": 61529}, {"name": "ta", "num_bytes": 809061339, "num_examples": 160580}, {"name": "tet", "num_bytes": 1452151, "num_examples": 1464}, {"name": "th", "num_bytes": 1012868861, "num_examples": 159666}, {"name": "tl", "num_bytes": 85286023, "num_examples": 45121}, {"name": "vi", "num_bytes": 1602830022, "num_examples": 1287912}, {"name": "war", "num_bytes": 454266479, "num_examples": 1266204}], "download_size": 1811459996, "dataset_size": 10686876247}, {"config_name": "seawiki_with_countries_all", "features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "brn_ms", "num_bytes": 419662356, "num_examples": 368628}, {"name": "idn_ace", "num_bytes": 4952102, "num_examples": 13003}, {"name": "idn_ban", "num_bytes": 18198909, "num_examples": 20987}, {"name": "idn_bjn", "num_bytes": 6792259, "num_examples": 10519}, {"name": "idn_bug", "num_bytes": 3298561, "num_examples": 15880}, {"name": "idn_gor", "num_bytes": 6239133, "num_examples": 15359}, {"name": "idn_id", "num_bytes": 1118834498, "num_examples": 665622}, {"name": "idn_jv", "num_bytes": 72101470, "num_examples": 73380}, {"name": "idn_mad", "num_bytes": 1612542, "num_examples": 1192}, {"name": "idn_map_bms", "num_bytes": 5221506, "num_examples": 13580}, {"name": "idn_min", "num_bytes": 116824020, "num_examples": 227143}, {"name": "idn_ms", "num_bytes": 419662356, "num_examples": 368628}, {"name": "idn_nia", "num_bytes": 2153274, "num_examples": 1714}, {"name": "idn_su", "num_bytes": 47516268, "num_examples": 61555}, {"name": "idn_tet", "num_bytes": 1454499, "num_examples": 1468}, {"name": "khm_km", "num_bytes": 103146669, "num_examples": 11994}, {"name": "lao_lo", "num_bytes": 15240262, "num_examples": 5014}, {"name": "mmr_my", "num_bytes": 313370839, "num_examples": 109310}, {"name": "mmr_shn", "num_bytes": 33754296, "num_examples": 13945}, {"name": "mmr_mnw", "num_bytes": 47321734, "num_examples": 3296}, {"name": "mys_ms", "num_bytes": 419662356, "num_examples": 368628}, {"name": "mys_ta", "num_bytes": 809156746, "num_examples": 160651}, {"name": "phl_war", "num_bytes": 454304567, "num_examples": 1266394}, {"name": "phl_tl", "num_bytes": 85356818, "num_examples": 45341}, {"name": "phl_ilo", "num_bytes": 16719139, "num_examples": 15371}, {"name": "phl_bcl", "num_bytes": 20258642, "num_examples": 15743}, {"name": "phl_pam", "num_bytes": 8218370, "num_examples": 9006}, {"name": "phl_cbk_zam", "num_bytes": 2033238, "num_examples": 3285}, {"name": "phl_pag", "num_bytes": 1370162, "num_examples": 2665}, {"name": "phl_ceb", "num_bytes": 4572804910, "num_examples": 6302896}, {"name": "sgp_ms", "num_bytes": 419662356, "num_examples": 368628}, {"name": "sgp_ta", "num_bytes": 809156746, "num_examples": 160651}, {"name": "tha_th", "num_bytes": 1012930269, "num_examples": 159719}, {"name": "tha_mnw", "num_bytes": 47321734, "num_examples": 3296}, {"name": "tha_shn", "num_bytes": 33754296, "num_examples": 13945}, {"name": "tls_tet", "num_bytes": 1454499, "num_examples": 1468}, {"name": "vnm_vi", "num_bytes": 1603057633, "num_examples": 1288680}], "download_size": 1829748651, "dataset_size": 13074580034}, {"config_name": "seawiki_with_countries_dedup_all", "features": [{"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "brn_ms", "num_bytes": 414783365, "num_examples": 348045}, {"name": "idn_ace", "num_bytes": 4944916, "num_examples": 12979}, {"name": "idn_ban", "num_bytes": 18025267, "num_examples": 20611}, {"name": "idn_bjn", "num_bytes": 6786207, "num_examples": 10503}, {"name": "idn_bug", "num_bytes": 2182435, "num_examples": 9969}, {"name": "idn_gor", "num_bytes": 6217480, "num_examples": 15290}, {"name": "idn_id", "num_bytes": 1117891512, "num_examples": 662443}, {"name": "idn_jv", "num_bytes": 71997517, "num_examples": 73080}, {"name": "idn_mad", "num_bytes": 1612542, "num_examples": 1192}, {"name": "idn_map_bms", "num_bytes": 5067489, "num_examples": 11839}, {"name": "idn_min", "num_bytes": 116721269, "num_examples": 225972}, {"name": "idn_ms", "num_bytes": 414783365, "num_examples": 348045}, {"name": "idn_nia", "num_bytes": 2153274, "num_examples": 1714}, {"name": "idn_su", "num_bytes": 47512744, "num_examples": 61529}, {"name": "idn_tet", "num_bytes": 1452151, "num_examples": 1464}, {"name": "khm_km", "num_bytes": 102698901, "num_examples": 11466}, {"name": "lao_lo", "num_bytes": 14908444, "num_examples": 4897}, {"name": "mmr_my", "num_bytes": 312990457, "num_examples": 108819}, {"name": "mmr_shn", "num_bytes": 33616591, "num_examples": 13662}, {"name": "mmr_mnw", "num_bytes": 47243333, "num_examples": 3271}, {"name": "mys_ms", "num_bytes": 414783365, "num_examples": 348045}, {"name": "mys_ta", "num_bytes": 809061339, "num_examples": 160580}, {"name": "phl_war", "num_bytes": 454266479, "num_examples": 1266204}, {"name": "phl_tl", "num_bytes": 85286023, "num_examples": 45121}, {"name": "phl_ilo", "num_bytes": 16719001, "num_examples": 15369}, {"name": "phl_bcl", "num_bytes": 19977232, "num_examples": 14079}, {"name": "phl_pam", "num_bytes": 8205723, "num_examples": 8932}, {"name": "phl_cbk_zam", "num_bytes": 1579651, "num_examples": 2242}, {"name": "phl_pag", "num_bytes": 764869, "num_examples": 1108}, {"name": "phl_ceb", "num_bytes": 4346511153, "num_examples": 5815254}, {"name": "sgp_ms", "num_bytes": 414783365, "num_examples": 348045}, {"name": "sgp_ta", "num_bytes": 809061339, "num_examples": 160580}, {"name": "tha_th", "num_bytes": 1012868861, "num_examples": 159666}, {"name": "tha_mnw", "num_bytes": 47243333, "num_examples": 3271}, {"name": "tha_shn", "num_bytes": 33616591, "num_examples": 13662}, {"name": "tls_tet", "num_bytes": 1452151, "num_examples": 1464}, {"name": "vnm_vi", "num_bytes": 1602830022, "num_examples": 1287912}], "download_size": 1811459996, "dataset_size": 12822599756}]} | 2023-11-12T16:10:53+00:00 | []
| [
"ace",
"ban",
"bcl",
"bjn",
"bug",
"cbk",
"ceb",
"gor",
"id",
"ilo",
"jv",
"km",
"lo",
"mad",
"min",
"mnw",
"ms",
"my",
"nia",
"pag",
"pam",
"shn",
"su",
"ta",
"th",
"tl",
"tet",
"vi",
"war"
]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-Wikipedia #language-Achinese #language-Balinese #language-Central Bikol #language-Banjar #language-Buginese #language-Chavacano #language-Cebuano #language-Gorontalo #language-Indonesian #language-Iloko #language-Javanese #language-Khmer #language-Lao #language-Madurese #language-Minangkabau #language-Mon #language-Malay (macrolanguage) #language-Burmese #language-Nias #language-Pangasinan #language-Pampanga #language-Shan #language-Sundanese #language-Tamil #language-Thai #language-Tagalog #language-Tetum #language-Vietnamese #language-Waray (Philippines) #license-cc-by-sa-4.0 #Wikipedia #Southeast Asia (SEA) #Dialect #Banyumasan Dialect of Javanese (Ngapak) #SEA-related Languages #SEA Local Languages #region-us
| SEA Wikipedia Data Repository
=============================
---
Welcome to SEA Wikipedia Data Repository. The datasets are extracted from Wikipedia HF and processed using the scripts available in this repository for reproducibility purpose. Since Wikipedia iteslf has license cc-by-sa 4.0, we decided to follow this instead of Wikipedia HF data has of cc-by-sa 3.0 since it gives more rights to initial author/contributor.
Getting Started
===============
### To read the datasets directly
Use one of the following code chunks to load it from HuggingFace Hub:
You can refer to the 2nd args of using the following script
Or you can provide both and (or just lang only by assuming the will take the newest one)
Or you can provide a params with similar fashion to args (providing both and will prioritize the kwarg)
FAQS
====
### What are the available languages provided in dataset and from which country?
You may check the following tables to understand the current coverage of this dataset (languages, countries, data size & volume). All tables are sorted by the leftmost column.
#### 1. Table of Countries and its Country Code
#### 2. Table of Languages and Countries of its speakers
#### 3. Table of Token Statistics for Covered Languages
The token statistics is generated using using encoder for GPT-4.
Some other languages in SEA that are already exists its Wiki Index at Wikimedia might be missing from this list. Any lang update PR is greatly appreciated!
### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?
The data available in here are processed with following flows:
1. Raw data is being deduplicated on and (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for unavailable informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.
2. Furthermore, the and data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars/UTF-8 chars validated).
The source code can be found on this Github Repo SEA Wiki Github Source Code
### How do I extract new Wikipedia Dataset of SEA languages?
Please refer to the corresponding Github Repo for more detailed info SEA Wiki Github Source Code
Info:
| [
"### To read the datasets directly\n\n\nUse one of the following code chunks to load it from HuggingFace Hub:\nYou can refer to the 2nd args of using the following script\n\n\nOr you can provide both and (or just lang only by assuming the will take the newest one)\n\n\nOr you can provide a params with similar fashion to args (providing both and will prioritize the kwarg)\n\n\nFAQS\n====",
"### What are the available languages provided in dataset and from which country?\n\n\nYou may check the following tables to understand the current coverage of this dataset (languages, countries, data size & volume). All tables are sorted by the leftmost column.",
"#### 1. Table of Countries and its Country Code",
"#### 2. Table of Languages and Countries of its speakers",
"#### 3. Table of Token Statistics for Covered Languages\n\n\nThe token statistics is generated using using encoder for GPT-4.\n\n\n\nSome other languages in SEA that are already exists its Wiki Index at Wikimedia might be missing from this list. Any lang update PR is greatly appreciated!",
"### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?\n\n\nThe data available in here are processed with following flows:\n\n\n1. Raw data is being deduplicated on and (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for unavailable informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.\n2. Furthermore, the and data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars/UTF-8 chars validated).\nThe source code can be found on this Github Repo SEA Wiki Github Source Code",
"### How do I extract new Wikipedia Dataset of SEA languages?\n\n\nPlease refer to the corresponding Github Repo for more detailed info SEA Wiki Github Source Code\n\n\nInfo:"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-Wikipedia #language-Achinese #language-Balinese #language-Central Bikol #language-Banjar #language-Buginese #language-Chavacano #language-Cebuano #language-Gorontalo #language-Indonesian #language-Iloko #language-Javanese #language-Khmer #language-Lao #language-Madurese #language-Minangkabau #language-Mon #language-Malay (macrolanguage) #language-Burmese #language-Nias #language-Pangasinan #language-Pampanga #language-Shan #language-Sundanese #language-Tamil #language-Thai #language-Tagalog #language-Tetum #language-Vietnamese #language-Waray (Philippines) #license-cc-by-sa-4.0 #Wikipedia #Southeast Asia (SEA) #Dialect #Banyumasan Dialect of Javanese (Ngapak) #SEA-related Languages #SEA Local Languages #region-us \n",
"### To read the datasets directly\n\n\nUse one of the following code chunks to load it from HuggingFace Hub:\nYou can refer to the 2nd args of using the following script\n\n\nOr you can provide both and (or just lang only by assuming the will take the newest one)\n\n\nOr you can provide a params with similar fashion to args (providing both and will prioritize the kwarg)\n\n\nFAQS\n====",
"### What are the available languages provided in dataset and from which country?\n\n\nYou may check the following tables to understand the current coverage of this dataset (languages, countries, data size & volume). All tables are sorted by the leftmost column.",
"#### 1. Table of Countries and its Country Code",
"#### 2. Table of Languages and Countries of its speakers",
"#### 3. Table of Token Statistics for Covered Languages\n\n\nThe token statistics is generated using using encoder for GPT-4.\n\n\n\nSome other languages in SEA that are already exists its Wiki Index at Wikimedia might be missing from this list. Any lang update PR is greatly appreciated!",
"### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?\n\n\nThe data available in here are processed with following flows:\n\n\n1. Raw data is being deduplicated on and (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for unavailable informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.\n2. Furthermore, the and data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars/UTF-8 chars validated).\nThe source code can be found on this Github Repo SEA Wiki Github Source Code",
"### How do I extract new Wikipedia Dataset of SEA languages?\n\n\nPlease refer to the corresponding Github Repo for more detailed info SEA Wiki Github Source Code\n\n\nInfo:"
]
| [
312,
93,
59,
12,
15,
65,
183,
39
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-Wikipedia #language-Achinese #language-Balinese #language-Central Bikol #language-Banjar #language-Buginese #language-Chavacano #language-Cebuano #language-Gorontalo #language-Indonesian #language-Iloko #language-Javanese #language-Khmer #language-Lao #language-Madurese #language-Minangkabau #language-Mon #language-Malay (macrolanguage) #language-Burmese #language-Nias #language-Pangasinan #language-Pampanga #language-Shan #language-Sundanese #language-Tamil #language-Thai #language-Tagalog #language-Tetum #language-Vietnamese #language-Waray (Philippines) #license-cc-by-sa-4.0 #Wikipedia #Southeast Asia (SEA) #Dialect #Banyumasan Dialect of Javanese (Ngapak) #SEA-related Languages #SEA Local Languages #region-us \n### To read the datasets directly\n\n\nUse one of the following code chunks to load it from HuggingFace Hub:\nYou can refer to the 2nd args of using the following script\n\n\nOr you can provide both and (or just lang only by assuming the will take the newest one)\n\n\nOr you can provide a params with similar fashion to args (providing both and will prioritize the kwarg)\n\n\nFAQS\n====### What are the available languages provided in dataset and from which country?\n\n\nYou may check the following tables to understand the current coverage of this dataset (languages, countries, data size & volume). All tables are sorted by the leftmost column.#### 1. Table of Countries and its Country Code#### 2. Table of Languages and Countries of its speakers"
]
|
76497abbccc9cda37ffd7b25523e513fdde55a82 | # Dataset Card for "turkish_train_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Kamyar-zeinalipour/turkish_train_v1 | [
"region:us"
]
| 2023-11-03T15:03:51+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19942339, "num_examples": 54929}], "download_size": 4372293, "dataset_size": 19942339}} | 2023-11-03T15:03:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "turkish_train_v1"
More Information needed | [
"# Dataset Card for \"turkish_train_v1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"turkish_train_v1\"\n\nMore Information needed"
]
| [
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18
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"passage: TAGS\n#region-us \n# Dataset Card for \"turkish_train_v1\"\n\nMore Information needed"
]
|
c570ac47e9f71bfecc52bf277744136b09f61482 | # Dataset Card for "platform-urls-sample-roberta-tiny-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cc-platform-links/platform-urls-sample-roberta-tiny-filtered | [
"region:us"
]
| 2023-11-03T15:13:20+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "true_label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1738881, "num_examples": 20739}], "download_size": 756212, "dataset_size": 1738881}} | 2023-11-03T15:13:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "platform-urls-sample-roberta-tiny-filtered"
More Information needed | [
"# Dataset Card for \"platform-urls-sample-roberta-tiny-filtered\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"platform-urls-sample-roberta-tiny-filtered\"\n\nMore Information needed"
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25
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"passage: TAGS\n#region-us \n# Dataset Card for \"platform-urls-sample-roberta-tiny-filtered\"\n\nMore Information needed"
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|
a56a9166235ce5abdcea7f89c8c11ea29457d25e | # Dataset Card for "sw-consultancy-book"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ftang97/sw-consultancy-book | [
"region:us"
]
| 2023-11-03T15:23:17+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2592763, "num_examples": 1}], "download_size": 1529310, "dataset_size": 2592763}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T15:23:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sw-consultancy-book"
More Information needed | [
"# Dataset Card for \"sw-consultancy-book\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sw-consultancy-book\"\n\nMore Information needed"
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| [
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18
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"passage: TAGS\n#region-us \n# Dataset Card for \"sw-consultancy-book\"\n\nMore Information needed"
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|
7e0e5eeb5d38c64b1a08fb2016194cfb132ed533 | # Dataset Card for "sw-consultancy-agent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ftang97/sw-consultancy-agent | [
"region:us"
]
| 2023-11-03T15:23:23+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 2311272.0, "num_examples": 282}, {"name": "test", "num_bytes": 262272.0, "num_examples": 32}], "download_size": 1195336, "dataset_size": 2573544.0}} | 2023-11-03T15:23:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sw-consultancy-agent"
More Information needed | [
"# Dataset Card for \"sw-consultancy-agent\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sw-consultancy-agent\"\n\nMore Information needed"
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|
9b06622296fe472292cb9409f926706198507d62 | # Dataset Card for "reward_tuned_prompt_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | toilaluan/reward_tuned_prompt_v2 | [
"region:us"
]
| 2023-11-03T15:30:52+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "model_type", "dtype": "string"}, {"name": "request_id", "dtype": "int64"}, {"name": "topic", "dtype": "string"}, {"name": "reward", "dtype": "float64"}, {"name": "individual_rewards", "struct": [{"name": "clip_aesthetic_rewarder", "dtype": "float64"}, {"name": "pick_rewarder", "dtype": "float64"}, {"name": "image_rewarder", "dtype": "float64"}, {"name": "hps_v2_rewarder", "dtype": "float64"}]}], "splits": [{"name": "train", "num_bytes": 463200, "num_examples": 4500}], "download_size": 160515, "dataset_size": 463200}} | 2023-11-04T05:05:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "reward_tuned_prompt_v2"
More Information needed | [
"# Dataset Card for \"reward_tuned_prompt_v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"reward_tuned_prompt_v2\"\n\nMore Information needed"
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| [
6,
22
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|
278c802d72189d884140b49c9bc5fee6c16cb72d | # Dataset Card for "train_data_2a3_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | chreh/train_data_2a3_preprocessed | [
"region:us"
]
| 2023-11-03T15:32:53+00:00 | {} | 2023-11-03T16:20:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "train_data_2a3_preprocessed"
More Information needed | [
"# Dataset Card for \"train_data_2a3_preprocessed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"train_data_2a3_preprocessed\"\n\nMore Information needed"
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]
|
a1fc5d5fbe8fc39425927820124669c825ab8ad7 | # Dataset Card for "truthful_qa_pl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | szymonrucinski/truthful_qa_pl | [
"region:us"
]
| 2023-11-03T15:34:04+00:00 | {"configs": [{"config_name": "generation", "data_files": [{"split": "validation", "path": "generation/validation-*"}]}, {"config_name": "multiple_choice_1", "data_files": [{"split": "validation", "path": "multiple_choice_1/validation-*"}]}], "dataset_info": [{"config_name": "default", "features": [{"name": "index", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "best_answer", "dtype": "string"}, {"name": "correct_answers", "sequence": "string"}, {"name": "incorrect_answers", "sequence": "string"}, {"name": "type", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 529669, "num_examples": 817}], "download_size": 262081, "dataset_size": 529669}, {"config_name": "generation", "features": [{"name": "question", "dtype": "string"}, {"name": "best_answer", "dtype": "string"}, {"name": "correct_answers", "sequence": "string"}, {"name": "incorrect_answers", "sequence": "string"}, {"name": "type", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 523133, "num_examples": 817}], "download_size": 257197, "dataset_size": 523133}, {"config_name": "mc1", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "dtype": "string"}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 302759, "num_examples": 817}], "download_size": 145186, "dataset_size": 302759}, {"config_name": "multiple_choice_1", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "dtype": "string"}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 302759, "num_examples": 817}], "download_size": 145186, "dataset_size": 302759}]} | 2024-01-19T09:08:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "truthful_qa_pl"
More Information needed | [
"# Dataset Card for \"truthful_qa_pl\"\n\nMore Information needed"
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|
465f4e64e92e0b0afb6a60d039c71feb25f7c808 | # Dataset Card for "new_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Adminhuggingface/new_data | [
"region:us"
]
| 2023-11-03T15:34:38+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9234983.0, "num_examples": 56}], "download_size": 9232484, "dataset_size": 9234983.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T15:34:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "new_data"
More Information needed | [
"# Dataset Card for \"new_data\"\n\nMore Information needed"
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| [
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"# Dataset Card for \"new_data\"\n\nMore Information needed"
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|
e44fdb3fe13d85b932ae552f5bf154f40bfa4e2b | # Dataset Card for "sdu_es_train_topics_ud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tomashs/sdu_es_train_topics_ud | [
"region:us"
]
| 2023-11-03T15:43:37+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "short_form", "dtype": "string"}, {"name": "long_form", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "topic_vector", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 32142103, "num_examples": 22424}], "download_size": 7811041, "dataset_size": 32142103}} | 2023-11-03T15:43:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sdu_es_train_topics_ud"
More Information needed | [
"# Dataset Card for \"sdu_es_train_topics_ud\"\n\nMore Information needed"
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|
32dda9a7c9585dff95c7ebf72b82bd66d4166d16 | # Dataset Card for "wikilingua_data-wiki_cstnews_1024_results"
rouge= {'rouge1': 0.33338117664091194, 'rouge2': 0.12343918406188643, 'rougeL': 0.24055022727761752, 'rougeLsum': 0.24055022727761752}
bert= {'precision': 0.7445338318921713, 'recall': 0.7644032267581008, 'f1': 0.7533634330623368}
mover = 0.6152605452035328 | arthurmluz/wikilingua_data-wiki_cstnews_1024_results | [
"region:us"
]
| 2023-11-03T16:02:19+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 22895375, "num_examples": 8165}], "download_size": 13549331, "dataset_size": 22895375}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-13T19:37:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikilingua_data-wiki_cstnews_1024_results"
rouge= {'rouge1': 0.33338117664091194, 'rouge2': 0.12343918406188643, 'rougeL': 0.24055022727761752, 'rougeLsum': 0.24055022727761752}
bert= {'precision': 0.7445338318921713, 'recall': 0.7644032267581008, 'f1': 0.7533634330623368}
mover = 0.6152605452035328 | [
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]
|
48169b569c364ad5568cc126d7ae96bfc0667b2a | # AutoTrain Dataset for project: dvf
## Dataset Description
This dataset has been automatically processed by AutoTrain for project dvf.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_id_mutation": "2023-190",
"feat_date_mutation": "2023-01-05",
"feat_numero_disposition": 1,
"feat_nature_mutation": "Vente en l'\u00e9tat futur d'ach\u00e8vement",
"target": 195285.0,
"feat_adresse_numero": null,
"feat_adresse_suffixe": null,
"feat_adresse_nom_voie": "COLLONGES",
"feat_adresse_code_voie": "B044",
"feat_code_postal": 1550.0,
"feat_code_commune": 1109,
"feat_nom_commune": "Collonges",
"feat_code_departement": 1,
"feat_ancien_code_commune": null,
"feat_ancien_nom_commune": null,
"feat_id_parcelle": "011090000F0129",
"feat_ancien_id_parcelle": null,
"feat_numero_volume": null,
"feat_lot1_numero": 80.0,
"feat_lot1_surface_carrez": null,
"feat_lot2_numero": null,
"feat_lot2_surface_carrez": null,
"feat_lot3_numero": null,
"feat_lot3_surface_carrez": null,
"feat_lot4_numero": null,
"feat_lot4_surface_carrez": null,
"feat_lot5_numero": null,
"feat_lot5_surface_carrez": null,
"feat_nombre_lots": 1,
"feat_code_type_local": null,
"feat_type_local": null,
"feat_surface_reelle_bati": null,
"feat_nombre_pieces_principales": null,
"feat_code_nature_culture": null,
"feat_nature_culture": null,
"feat_code_nature_culture_speciale": null,
"feat_nature_culture_speciale": null,
"feat_surface_terrain": null,
"feat_longitude": 5.905016,
"feat_latitude": 46.13927
},
{
"feat_id_mutation": "2023-20",
"feat_date_mutation": "2023-01-05",
"feat_numero_disposition": 1,
"feat_nature_mutation": "Vente",
"target": 361800.0,
"feat_adresse_numero": null,
"feat_adresse_suffixe": null,
"feat_adresse_nom_voie": "PORT GALAND",
"feat_adresse_code_voie": "B082",
"feat_code_postal": 1800.0,
"feat_code_commune": 1378,
"feat_nom_commune": "Saint-Maurice-de-Gourdans",
"feat_code_departement": 1,
"feat_ancien_code_commune": null,
"feat_ancien_nom_commune": null,
"feat_id_parcelle": "013780000B1752",
"feat_ancien_id_parcelle": null,
"feat_numero_volume": null,
"feat_lot1_numero": null,
"feat_lot1_surface_carrez": null,
"feat_lot2_numero": null,
"feat_lot2_surface_carrez": null,
"feat_lot3_numero": null,
"feat_lot3_surface_carrez": null,
"feat_lot4_numero": null,
"feat_lot4_surface_carrez": null,
"feat_lot5_numero": null,
"feat_lot5_surface_carrez": null,
"feat_nombre_lots": 0,
"feat_code_type_local": null,
"feat_type_local": null,
"feat_surface_reelle_bati": null,
"feat_nombre_pieces_principales": null,
"feat_code_nature_culture": "S",
"feat_nature_culture": "sols",
"feat_code_nature_culture_speciale": null,
"feat_nature_culture_speciale": null,
"feat_surface_terrain": 204.0,
"feat_longitude": 5.207586,
"feat_latitude": 45.817613
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_id_mutation": "Value(dtype='string', id=None)",
"feat_date_mutation": "Value(dtype='string', id=None)",
"feat_numero_disposition": "Value(dtype='int64', id=None)",
"feat_nature_mutation": "Value(dtype='string', id=None)",
"target": "Value(dtype='float32', id=None)",
"feat_adresse_numero": "Value(dtype='float64', id=None)",
"feat_adresse_suffixe": "Value(dtype='string', id=None)",
"feat_adresse_nom_voie": "Value(dtype='string', id=None)",
"feat_adresse_code_voie": "Value(dtype='string', id=None)",
"feat_code_postal": "Value(dtype='float64', id=None)",
"feat_code_commune": "Value(dtype='int64', id=None)",
"feat_nom_commune": "Value(dtype='string', id=None)",
"feat_code_departement": "Value(dtype='int64', id=None)",
"feat_ancien_code_commune": "Value(dtype='float64', id=None)",
"feat_ancien_nom_commune": "Value(dtype='float64', id=None)",
"feat_id_parcelle": "Value(dtype='string', id=None)",
"feat_ancien_id_parcelle": "Value(dtype='float64', id=None)",
"feat_numero_volume": "Value(dtype='float64', id=None)",
"feat_lot1_numero": "Value(dtype='float64', id=None)",
"feat_lot1_surface_carrez": "Value(dtype='float64', id=None)",
"feat_lot2_numero": "Value(dtype='float64', id=None)",
"feat_lot2_surface_carrez": "Value(dtype='float64', id=None)",
"feat_lot3_numero": "Value(dtype='float64', id=None)",
"feat_lot3_surface_carrez": "Value(dtype='float64', id=None)",
"feat_lot4_numero": "Value(dtype='float64', id=None)",
"feat_lot4_surface_carrez": "Value(dtype='float64', id=None)",
"feat_lot5_numero": "Value(dtype='float64', id=None)",
"feat_lot5_surface_carrez": "Value(dtype='float64', id=None)",
"feat_nombre_lots": "Value(dtype='int64', id=None)",
"feat_code_type_local": "Value(dtype='float64', id=None)",
"feat_type_local": "Value(dtype='string', id=None)",
"feat_surface_reelle_bati": "Value(dtype='float64', id=None)",
"feat_nombre_pieces_principales": "Value(dtype='float64', id=None)",
"feat_code_nature_culture": "Value(dtype='string', id=None)",
"feat_nature_culture": "Value(dtype='string', id=None)",
"feat_code_nature_culture_speciale": "Value(dtype='string', id=None)",
"feat_nature_culture_speciale": "Value(dtype='string', id=None)",
"feat_surface_terrain": "Value(dtype='float64', id=None)",
"feat_longitude": "Value(dtype='float64', id=None)",
"feat_latitude": "Value(dtype='float64', 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 | 793 |
| valid | 204 |
| Antoine92Ptx/autotrain-data-dvf | [
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| 2023-11-03T16:08:31+00:00 | {} | 2023-11-03T16:21:08+00:00 | []
| []
| TAGS
#region-us
| AutoTrain Dataset for project: dvf
==================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project dvf.
### 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:
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|
bf4d0909c99c89e9921d12acffdb71fb80d89903 | # Dataset Card for "pictarine"
Dataset for training pictarineChatbot | Ceroxlol/pictarine | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"region:us"
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| 2023-11-03T16:53:38+00:00 | {"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "pictarineChatbot", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 110425, "num_examples": 520}], "download_size": 57760, "dataset_size": 110425}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T16:59:34+00:00 | []
| [
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| TAGS
#task_categories-question-answering #size_categories-n<1K #language-English #region-us
| # Dataset Card for "pictarine"
Dataset for training pictarineChatbot | [
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|
75ea8d2e54465d6104ddedeb22a283747fdd839b | # Dataset Card for "emrqaQA_medication_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hippocrates/emrqaQA_medication_train | [
"region:us"
]
| 2023-11-03T16:53:45+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24849580, "num_examples": 59928}, {"name": "valid", "num_bytes": 4286042, "num_examples": 10468}], "download_size": 0, "dataset_size": 29135622}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}]}]} | 2023-11-08T02:12:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "emrqaQA_medication_train"
More Information needed | [
"# Dataset Card for \"emrqaQA_medication_train\"\n\nMore Information needed"
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|
4d7dd4413c949054532faa54a6860bdc8c78d0e1 | # Dataset Card for "videos_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fmagot01/videos_0 | [
"region:us"
]
| 2023-11-03T17:17:58+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "video_data", "dtype": "binary"}, {"name": "duration_seconds", "dtype": "int64"}, {"name": "video_path", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5759485, "num_examples": 5}], "download_size": 5747829, "dataset_size": 5759485}} | 2023-11-03T17:17:59+00:00 | []
| []
| TAGS
#region-us
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More Information needed | [
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|
166131533fe5e1575196848cc42cd302db234f77 | # Dataset Card for "videos_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fmagot01/videos_1 | [
"region:us"
]
| 2023-11-03T17:18:20+00:00 | {"dataset_info": {"features": [{"name": "video_data", "dtype": "binary"}, {"name": "duration_seconds", "dtype": "int64"}, {"name": "video_path", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9417113, "num_examples": 10}], "download_size": 9403451, "dataset_size": 9417113}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T17:18:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "videos_1"
More Information needed | [
"# Dataset Card for \"videos_1\"\n\nMore Information needed"
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|
cfdcce8d9a7cbe1e642700b52e64792789e7abae | # Dataset Card for "ola_polyglot_3.8B_t2_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | eunbinni/ola_polyglot_3.8B_t2_data | [
"region:us"
]
| 2023-11-03T17:25:36+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 46498843, "num_examples": 139107}], "download_size": 28667291, "dataset_size": 46498843}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T17:25:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ola_polyglot_3.8B_t2_data"
More Information needed | [
"# Dataset Card for \"ola_polyglot_3.8B_t2_data\"\n\nMore Information needed"
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|
b7c9b2a9cc8a88daff4824fba34aa6ac035fba7f | # Dataset Card for "ola_polyglot_5.8B_t2_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | eunbinni/ola_polyglot_5.8B_t2_data | [
"region:us"
]
| 2023-11-03T17:28:47+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29998082, "num_examples": 107174}], "download_size": 18601058, "dataset_size": 29998082}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T17:28:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ola_polyglot_5.8B_t2_data"
More Information needed | [
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|
f913c6f8d4dae62fd365c498637e5283e6c488c8 | # Dataset Card for "ola_polyglot_12.8B_t2_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | eunbinni/ola_polyglot_12.8B_t2_data | [
"region:us"
]
| 2023-11-03T17:30:42+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13747294, "num_examples": 66716}], "download_size": 8606988, "dataset_size": 13747294}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T17:30:47+00:00 | []
| []
| TAGS
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| # Dataset Card for "ola_polyglot_12.8B_t2_data"
More Information needed | [
"# Dataset Card for \"ola_polyglot_12.8B_t2_data\"\n\nMore Information needed"
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|
343a6d7a0c46766f6c724f4e1b902a0f6a82f601 | # Dataset Card for "ola_llama2_7B_t3_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | eunbinni/ola_llama2_7B_t3_data | [
"region:us"
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| 2023-11-03T17:42:45+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10143063, "num_examples": 54803}], "download_size": 6348305, "dataset_size": 10143063}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T17:42:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ola_llama2_7B_t3_data"
More Information needed | [
"# Dataset Card for \"ola_llama2_7B_t3_data\"\n\nMore Information needed"
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|
6a69b9da4cc5734dfb42f568f7fa9ba6c86b0e95 | # Dataset Card for "ola_llama2_13B_t3_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | eunbinni/ola_llama2_13B_t3_data | [
"region:us"
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| 2023-11-03T17:44:03+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5297963, "num_examples": 34771}], "download_size": 3300876, "dataset_size": 5297963}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T17:44:07+00:00 | []
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| TAGS
#region-us
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|
feaf0cefb73c515a29e16afce70f6892f2a471ec | # Dataset Card for "ola_llama2_13B_t2_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | eunbinni/ola_llama2_13B_t2_data | [
"region:us"
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| 2023-11-03T17:46:47+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 259903337, "num_examples": 382990}], "download_size": 157712147, "dataset_size": 259903337}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T17:47:06+00:00 | []
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| TAGS
#region-us
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|
3cc8394a19b1e8bac5333722e02cbbfe87301132 | # Dataset Card for "OCR_redSeal_redo_02"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DataStudio/OCR_redSeal_redo_02 | [
"region:us"
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| 2023-11-03T18:07:24+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "Noise_level", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 962292803.875, "num_examples": 91977}], "download_size": 963706057, "dataset_size": 962292803.875}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T18:08:17+00:00 | []
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| TAGS
#region-us
| # Dataset Card for "OCR_redSeal_redo_02"
More Information needed | [
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93c456bedbb1fe1577de7553d6b033337628d65d | # Dataset Card for "OCR_redSeal_redo_01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DataStudio/OCR_redSeal_redo_01 | [
"region:us"
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| 2023-11-03T18:15:42+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "Noise_level", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1093092202.75, "num_examples": 115850}], "download_size": 1095497763, "dataset_size": 1093092202.75}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T19:05:47+00:00 | []
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| TAGS
#region-us
| # Dataset Card for "OCR_redSeal_redo_01"
More Information needed | [
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cc633501ab689432f70a96dec5fd82747019dbc4 | # Dataset Card for "OCR_redSeal_redo_03"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DataStudio/OCR_redSeal_redo_03 | [
"region:us"
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| 2023-11-03T18:20:09+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "Noise_level", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1017995085.875, "num_examples": 119737}], "download_size": 1020933188, "dataset_size": 1017995085.875}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T18:21:00+00:00 | []
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| TAGS
#region-us
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More Information needed | [
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5f9cc7a3a7bf09dd758fbebaa9b86f37aac9bab3 | # Dataset Card for "OCR_redSeal_redo_05"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DataStudio/OCR_redSeal_redo_05 | [
"region:us"
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| 2023-11-03T18:24:34+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "Noise_level", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 933784573.0, "num_examples": 97296}], "download_size": 935297916, "dataset_size": 933784573.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T19:06:39+00:00 | []
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39657b23f881699bb562bf9ba7f48d99b47cf226 | All instances from the `allegro/klej-cbd` (train, val, test) translated to English with Google Translate API.
Columns:
- `source` - text instance in Polish.
- `target` - text instance in English. | allegro/klej-cbd-en | [
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| 2023-11-03T18:30:15+00:00 | {"language": ["pl", "en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "pretty_name": "CBD dataset translated to English"} | 2023-11-05T15:40:39+00:00 | []
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Columns:
- 'source' - text instance in Polish.
- 'target' - text instance in English. | []
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|
38e1a9776c69feec1995a2c72ce3c4488e9a3128 | # Dataset Card for "wikilingua_data-wiki_temario_results"
rouge= {'rouge1': 0.32186631280345346, 'rouge2': 0.11643663933189041, 'rougeL': 0.22613896268852546, 'rougeLsum': 0.22613896268852546}
bert= {'precision': 0.7288013449272931, 'recall': 0.7668859778849245, 'f1': 0.7463569133178047}
mover = 0.6063795152381846 | arthurmluz/wikilingua_data-wiki_temario_results | [
"region:us"
]
| 2023-11-03T18:47:56+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "gen_summary", "dtype": "string"}, {"name": "rouge", "struct": [{"name": "rouge1", "dtype": "float64"}, {"name": "rouge2", "dtype": "float64"}, {"name": "rougeL", "dtype": "float64"}, {"name": "rougeLsum", "dtype": "float64"}]}, {"name": "bert", "struct": [{"name": "f1", "sequence": "float64"}, {"name": "hashcode", "dtype": "string"}, {"name": "precision", "sequence": "float64"}, {"name": "recall", "sequence": "float64"}]}, {"name": "moverScore", "dtype": "float64"}], "splits": [{"name": "validation", "num_bytes": 23695646, "num_examples": 8165}], "download_size": 14096829, "dataset_size": 23695646}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]} | 2023-11-13T19:38:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikilingua_data-wiki_temario_results"
rouge= {'rouge1': 0.32186631280345346, 'rouge2': 0.11643663933189041, 'rougeL': 0.22613896268852546, 'rougeLsum': 0.22613896268852546}
bert= {'precision': 0.7288013449272931, 'recall': 0.7668859778849245, 'f1': 0.7463569133178047}
mover = 0.6063795152381846 | [
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|
97b18200339d0e1a0a462d046ffdba3a710c2c55 | # Dataset Card for "OCR_redSeal_redo_04"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DataStudio/OCR_redSeal_redo_04 | [
"region:us"
]
| 2023-11-03T19:29:01+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "Noise_level", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 774249937.375, "num_examples": 72221}], "download_size": 774738042, "dataset_size": 774249937.375}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-04T08:25:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OCR_redSeal_redo_04"
More Information needed | [
"# Dataset Card for \"OCR_redSeal_redo_04\"\n\nMore Information needed"
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| [
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|
312013bda84a963fb34d494fc53c778a61265741 | # Dataset Card for "WiNER"
## WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition
## Sample
```json
{'id': '1',
'text': 'В договоре среди 5 старших князей упоминается Миндовг .',
'tokens': ['В',
'договоре',
'среди',
'5',
'старших',
'князей',
'упоминается',
'Миндовг',
'.'],
'pos_tags': ['PR', 'S', 'PR', 'NUM', 'A', 'S', 'V', 'S', 'SENT'],
'ner_tags': ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'I-PER', 'O']}
```
## Citation
[WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition](https://aclanthology.org/I17-1042/)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hfvladkon/WiNER | [
"region:us"
]
| 2023-11-03T21:31:33+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "pos_tags", "sequence": "string"}, {"name": "ner_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 133047685, "num_examples": 203286}], "download_size": 46621835, "dataset_size": 133047685}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T21:32:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "WiNER"
## WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition
## Sample
WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition
More Information needed | [
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]
|
f628f8eaa6c6e6394bf28975f4ac723c436cabd8 | # Dataset Card for "WizardLM_evol_instruct_V2_196k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vietgpt/WizardLM_evol_instruct_V2_196k | [
"region:us"
]
| 2023-11-03T21:34:26+00:00 | {"dataset_info": {"features": [{"name": "idx", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train_4096", "num_bytes": 336887141.02772725, "num_examples": 142745}, {"name": "train_2048", "num_bytes": 326970157.8297273, "num_examples": 138543}, {"name": "train_1024", "num_bytes": 256477446.94418183, "num_examples": 108674}], "download_size": 405629113, "dataset_size": 920334745.8016363}, "configs": [{"config_name": "default", "data_files": [{"split": "train_4096", "path": "data/train_4096-*"}, {"split": "train_2048", "path": "data/train_2048-*"}, {"split": "train_1024", "path": "data/train_1024-*"}]}]} | 2023-11-03T21:35:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "WizardLM_evol_instruct_V2_196k"
More Information needed | [
"# Dataset Card for \"WizardLM_evol_instruct_V2_196k\"\n\nMore Information needed"
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|
6d0f831ca22bf57918aba3980ae475c94d45b997 | This README file was generated on 03-11-2023 by David Bouget.
It was then updated on 07-11-2023 to add Hugging Face metadata by André Pedersen.
Last updated: 07-11-2023.
----------------------
ACCESSING THE DATASET
----------------------
To access the data, it is HIGHLY RECOMMENDED to use the Python API.
See this notebook on how to get started: https://colab.research.google.com/gist/andreped/6070d1d2914a9ce5847d4b3e687188b7/aeropath-load-dataset-example.ipynb
--------------------
GENERAL INFORMATION
--------------------
1. Title of Dataset: AeroPath
2. Publication and DOI: "AeroPath: An airway segmentation benchmark dataset with challenging pathology". https://arxiv.org/abs/2311.01138
3. Contact Information
Name: Erlend F. Hofstad
Institution: Medical Technology Department, SINTEF Digital, Trondheim
Email: [email protected]
Website: https://www.sintef.no/en/all-employees/employee/erlend.hofstad/
4. Contributors: Karen-Helene Støverud, Haakon Olav Leira, Erlend F. Hofstad, Andre Pedersen, David Bouget, and Thomas Langø.
5. Kind of data: computed tomography angiography (CTA) scans and binary annotation masks, all stored as NifTI files (*.nii.gz).
6. Date of data collection/generation: .
7. Geographic location: Trondheim, Norway.
8. Funding sources: the Ministry of Health and Care Services of Norway through the Norwegian National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy (MiDT) at St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway. The research leading to these results has in addition received funding from the Norwegian Financial Mechanism 2014-2021 under the project RO- NO2019-0138, 19/2020 “Improving Cancer Diagnostics in Flexible Endoscopy using Artificial Intelligence and Medical Robotics” IDEAR, Contract No. 19/2020.
9. Description of dataset:
General description and ethics approvals: The dataset contains 27 computed tomography angiography
(CTA) scans, acquired using the Thorax Lung protocol at St. Olavs hospital (Trondheim, Norway). The included patients (nine women), aged 52 to 84 (median 70), were all undergoing diagnostic tests for lung cancer and had a wide range of pathologies including malignant tumors, sarcoidosis, and emphysema.
---------------------------
SHARING/ACCESS INFORMATION
---------------------------
1. Licenses/Restrictions:CC-BY 4.0 (See license.md).
2. Recommended citation: See citation recommended at https://github.com/raidionics/AeroPath.
---------------------
DATA & FILE OVERVIEW
---------------------
```
1. File List:
README.md
license.md
└── 1/
└── 1_CT_HR.nii.gz
└── 1_CT_HR_label_airways.nii.gz
└── 1_CT_HR_label_lungs.nii.gz
.
.
.
└── 27/
└── 27_CT_HR.nii.gz
└── 27_CT_HR_label_airways.nii.gz
└── 27_CT_HR_label_lungs.nii.gz
```
---------------------------
METHODOLOGICAL INFORMATION
---------------------------
1. Description of sources and methods used for collection/generation of data:
Dataset statistics
Overall, the CT scan dimensions are covering [487 : 512] × [441 : 512] × [241 : 829] voxels, and the trans-axial voxel size ranges [0.68 : 0.76] × [0.68 : 0.75] mm2 with a reconstructed slice thickness of [0.5 : 1.25] mm.
Annotation procedures
The annotation process for generating the ground truth was performed in three steps. First, the largest components (i.e., trachea and the first branches) were extracted based on a region growing, or a grow-cut method. Due to leakage, the region growing method did not yield satisfactory results in all cases. Therefore, for certain cases, the grow-cut method in 3D Slicer was used instead. In the second step, BronchiNet was employed to segment the smaller peripheral airways. In the third and final step, the segmentations were refined manually. Bronchial fragments and missed segments were connected, before false positives and fragments that could not be connected based on visual inspection were removed. All manual corrections were performed using the default segment editor in 3D Slicer. The manual correction was performed by a trained engineer, supervised by a pulmonologist. Finally, all annotations were verified on a case-by-case basis by a pulmonologist. The final annotations from the AeroPath segmentation included on average 128 ± 56 branches per CT scan. | andreped/AeroPath | [
"task_categories:image-segmentation",
"size_categories:1B<n<10B",
"language:en",
"license:mit",
"medical",
"arxiv:2311.01138",
"region:us"
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| 2023-11-03T21:53:46+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["1B<n<10B"], "task_categories": ["image-segmentation"], "pretty_name": "AeroPath", "tags": ["medical"]} | 2023-11-13T22:36:21+00:00 | [
"2311.01138"
]
| [
"en"
]
| TAGS
#task_categories-image-segmentation #size_categories-1B<n<10B #language-English #license-mit #medical #arxiv-2311.01138 #region-us
| This README file was generated on 03-11-2023 by David Bouget.
It was then updated on 07-11-2023 to add Hugging Face metadata by André Pedersen.
Last updated: 07-11-2023.
----------------------
ACCESSING THE DATASET
----------------------
To access the data, it is HIGHLY RECOMMENDED to use the Python API.
See this notebook on how to get started: URL
--------------------
GENERAL INFORMATION
--------------------
1. Title of Dataset: AeroPath
2. Publication and DOI: "AeroPath: An airway segmentation benchmark dataset with challenging pathology". URL
3. Contact Information
Name: Erlend F. Hofstad
Institution: Medical Technology Department, SINTEF Digital, Trondheim
Email: erlend.hofstad@URL
Website: URL
4. Contributors: Karen-Helene Støverud, Haakon Olav Leira, Erlend F. Hofstad, Andre Pedersen, David Bouget, and Thomas Langø.
5. Kind of data: computed tomography angiography (CTA) scans and binary annotation masks, all stored as NifTI files (*.URL).
6. Date of data collection/generation: .
7. Geographic location: Trondheim, Norway.
8. Funding sources: the Ministry of Health and Care Services of Norway through the Norwegian National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy (MiDT) at St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway. The research leading to these results has in addition received funding from the Norwegian Financial Mechanism 2014-2021 under the project RO- NO2019-0138, 19/2020 “Improving Cancer Diagnostics in Flexible Endoscopy using Artificial Intelligence and Medical Robotics” IDEAR, Contract No. 19/2020.
9. Description of dataset:
General description and ethics approvals: The dataset contains 27 computed tomography angiography
(CTA) scans, acquired using the Thorax Lung protocol at St. Olavs hospital (Trondheim, Norway). The included patients (nine women), aged 52 to 84 (median 70), were all undergoing diagnostic tests for lung cancer and had a wide range of pathologies including malignant tumors, sarcoidosis, and emphysema.
---------------------------
SHARING/ACCESS INFORMATION
---------------------------
1. Licenses/Restrictions:CC-BY 4.0 (See URL).
2. Recommended citation: See citation recommended at URL
---------------------
DATA & FILE OVERVIEW
---------------------
---------------------------
METHODOLOGICAL INFORMATION
---------------------------
1. Description of sources and methods used for collection/generation of data:
Dataset statistics
Overall, the CT scan dimensions are covering [487 : 512] × [441 : 512] × [241 : 829] voxels, and the trans-axial voxel size ranges [0.68 : 0.76] × [0.68 : 0.75] mm2 with a reconstructed slice thickness of [0.5 : 1.25] mm.
Annotation procedures
The annotation process for generating the ground truth was performed in three steps. First, the largest components (i.e., trachea and the first branches) were extracted based on a region growing, or a grow-cut method. Due to leakage, the region growing method did not yield satisfactory results in all cases. Therefore, for certain cases, the grow-cut method in 3D Slicer was used instead. In the second step, BronchiNet was employed to segment the smaller peripheral airways. In the third and final step, the segmentations were refined manually. Bronchial fragments and missed segments were connected, before false positives and fragments that could not be connected based on visual inspection were removed. All manual corrections were performed using the default segment editor in 3D Slicer. The manual correction was performed by a trained engineer, supervised by a pulmonologist. Finally, all annotations were verified on a case-by-case basis by a pulmonologist. The final annotations from the AeroPath segmentation included on average 128 ± 56 branches per CT scan. | []
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c12ea1ff2a259c33fead76ed4b2a2917b3cb00c9 |
direct copy of [AgentInstruct](https://huggingface.co/datasets/THUDM/AgentInstruct) for use in Axolotl. | rchiang/mergedAgentInstruct | [
"license:apache-2.0",
"region:us"
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| 2023-11-03T21:53:51+00:00 | {"license": "apache-2.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "loss", "dtype": "bool"}, {"name": "value", "dtype": "string"}]}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8042511, "num_examples": 1866}], "download_size": 0, "dataset_size": 8042511}} | 2023-11-03T22:18:41+00:00 | []
| []
| TAGS
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|
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5769828831990edd34e3421521b97cc7532441d4 | # 4chan greentext, all synthesized from gpt3.5. can be used for whatever i dont care | Superintendent/4chan-greentext | [
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"license:mit",
"region:us"
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| 2023-11-03T22:14:20+00:00 | {"language": ["en"], "license": "mit"} | 2023-11-03T22:15:31+00:00 | []
| [
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| TAGS
#language-English #license-mit #region-us
| # 4chan greentext, all synthesized from gpt3.5. can be used for whatever i dont care | [
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295507f1186334a9d9fd173595bb655515060910 | # Dataset Card for "all_english_datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Intuit-GenSRF/all_english_datasets | [
"region:us"
]
| 2023-11-03T22:19:15+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}, {"name": "encoded_labels", "sequence": "int64"}, {"name": "lang", "dtype": "string"}, {"name": "has_toxic", "dtype": "int64"}, {"name": "has_profane", "dtype": "int64"}, {"name": "has_insult", "dtype": "int64"}, {"name": "has_hate", "dtype": "int64"}, {"name": "has_threat", "dtype": "int64"}, {"name": "has_sexual", "dtype": "int64"}, {"name": "has_offensive", "dtype": "int64"}, {"name": "has_selfharm", "dtype": "int64"}, {"name": "has_harassment", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1498751715, "num_examples": 2921884}], "download_size": 616223055, "dataset_size": 1498751715}} | 2023-11-03T22:19:49+00:00 | []
| []
| TAGS
#region-us
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More Information needed | [
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7481be33d9d2f0f10da6de63c7296c8289bbb636 | # Dataset Card for "all_spanish_datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Intuit-GenSRF/all_spanish_datasets | [
"region:us"
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| 2023-11-03T22:25:13+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}, {"name": "encoded_labels", "sequence": "int64"}, {"name": "lang", "dtype": "string"}, {"name": "has_toxic", "dtype": "int64"}, {"name": "has_profane", "dtype": "int64"}, {"name": "has_insult", "dtype": "int64"}, {"name": "has_hate", "dtype": "int64"}, {"name": "has_threat", "dtype": "int64"}, {"name": "has_sexual", "dtype": "int64"}, {"name": "has_offensive", "dtype": "int64"}, {"name": "has_selfharm", "dtype": "int64"}, {"name": "has_harassment", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1495796876, "num_examples": 2814389}], "download_size": 603996129, "dataset_size": 1495796876}} | 2023-11-03T22:25:47+00:00 | []
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More Information needed | [
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502a67fe387959aa1050e5ae57153d614cac42c5 | # Dataset Card for "all_french_datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Intuit-GenSRF/all_french_datasets | [
"region:us"
]
| 2023-11-03T22:31:04+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}, {"name": "encoded_labels", "sequence": "int64"}, {"name": "lang", "dtype": "string"}, {"name": "has_toxic", "dtype": "int64"}, {"name": "has_profane", "dtype": "int64"}, {"name": "has_insult", "dtype": "int64"}, {"name": "has_hate", "dtype": "int64"}, {"name": "has_threat", "dtype": "int64"}, {"name": "has_sexual", "dtype": "int64"}, {"name": "has_offensive", "dtype": "int64"}, {"name": "has_selfharm", "dtype": "int64"}, {"name": "has_harassment", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1135963065, "num_examples": 2124196}], "download_size": 522591191, "dataset_size": 1135963065}} | 2023-11-03T22:31:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "all_french_datasets"
More Information needed | [
"# Dataset Card for \"all_french_datasets\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"all_french_datasets\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"all_french_datasets\"\n\nMore Information needed"
]
|
91580c747ebe797029c3cf225bf0e0d40a853cac | # Dataset Card for "basic"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vietgpt/basic | [
"region:us"
]
| 2023-11-03T22:35:26+00:00 | {"dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 3024, "num_examples": 13}], "download_size": 3151, "dataset_size": 3024}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-03T22:35:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "basic"
More Information needed | [
"# Dataset Card for \"basic\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"basic\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"basic\"\n\nMore Information needed"
]
|
4e4cbfc54bdd12057eca414cdd2c10258cc3c69a | # Dataset Card for "grade_school_math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vietgpt/grade_school_math | [
"region:us"
]
| 2023-11-03T22:41:24+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4838661, "num_examples": 8792}], "download_size": 2398402, "dataset_size": 4838661}} | 2023-11-03T22:42:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "grade_school_math"
More Information needed | [
"# Dataset Card for \"grade_school_math\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"grade_school_math\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"grade_school_math\"\n\nMore Information needed"
]
|
b705fa3a8752f0e887746a2583e827ee01fb06d4 | # Dataset Card for "flan-10k-flat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sordonia/flan-10k-flat | [
"region:us"
]
| 2023-11-03T22:42:04+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "template_type", "dtype": "string"}, {"name": "template_idx", "dtype": "int64"}, {"name": "split", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16815984887, "num_examples": 10912677}], "download_size": 6978956537, "dataset_size": 16815984887}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T19:56:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "flan-10k-flat"
More Information needed | [
"# Dataset Card for \"flan-10k-flat\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"flan-10k-flat\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"flan-10k-flat\"\n\nMore Information needed"
]
|
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