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66d8ae067c3116a92362ab17ca34b0e59623c94d
|
# Dataset Card for "MULTI_VALUE_qqp_who_which"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_who_which
|
[
"region:us"
] |
2023-04-04T00:01:32+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 123327, "num_examples": 597}, {"name": "test", "num_bytes": 1360682, "num_examples": 6621}, {"name": "train", "num_bytes": 1252712, "num_examples": 5952}], "download_size": 1652306, "dataset_size": 2736721}}
|
2023-04-04T00:01:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_who_which"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_who_which\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_who_which\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_who_which\"\n\nMore Information needed"
] |
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f3dbbc893c7e6a63da77813dc509ed6d0e351d75
|
# Dataset Card for "MULTI_VALUE_qqp_give_passive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_give_passive
|
[
"region:us"
] |
2023-04-04T00:01:35+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 50869, "num_examples": 265}, {"name": "test", "num_bytes": 494406, "num_examples": 2672}, {"name": "train", "num_bytes": 480669, "num_examples": 2514}], "download_size": 603887, "dataset_size": 1025944}}
|
2023-04-04T00:01:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_give_passive"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_give_passive\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_give_passive\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_give_passive\"\n\nMore Information needed"
] |
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b2472b23171b2ea57fc2ded2a60336aaff1e5799
|
# Dataset Card for "MULTI_VALUE_qqp_double_past"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_double_past
|
[
"region:us"
] |
2023-04-04T00:01:49+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 261054, "num_examples": 1490}, {"name": "test", "num_bytes": 2434647, "num_examples": 13904}, {"name": "train", "num_bytes": 2438286, "num_examples": 13894}], "download_size": 3216145, "dataset_size": 5133987}}
|
2023-04-04T00:01:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_double_past"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_double_past\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_double_past\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_double_past\"\n\nMore Information needed"
] |
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98ccdc49064b775433fecde9fc0d46a12f019cda
|
# Dataset Card for "MULTI_VALUE_qqp_invariant_tag_amnt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_invariant_tag_amnt
|
[
"region:us"
] |
2023-04-04T00:01:58+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 215577, "num_examples": 1209}, {"name": "test", "num_bytes": 2515389, "num_examples": 13862}, {"name": "train", "num_bytes": 1995047, "num_examples": 11077}], "download_size": 2842573, "dataset_size": 4726013}}
|
2023-04-04T00:02:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_invariant_tag_amnt"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_invariant_tag_amnt\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_invariant_tag_amnt\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_invariant_tag_amnt\"\n\nMore Information needed"
] |
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] |
b6c8b1d8b350a84eb788326bf189efde4219a4a7
|
# Dataset Card for "MULTI_VALUE_qqp_their_them"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_their_them
|
[
"region:us"
] |
2023-04-04T00:02:01+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 173047, "num_examples": 889}, {"name": "test", "num_bytes": 1848693, "num_examples": 9528}, {"name": "train", "num_bytes": 1672751, "num_examples": 8499}], "download_size": 2172071, "dataset_size": 3694491}}
|
2023-04-04T00:02:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_their_them"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_their_them\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_their_them\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_their_them\"\n\nMore Information needed"
] |
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1026912677a9df2bc544d86dcfd77e5dea14f260
|
# Dataset Card for "MULTI_VALUE_qqp_generalized_third_person_s"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_generalized_third_person_s
|
[
"region:us"
] |
2023-04-04T00:02:03+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 2528804, "num_examples": 15387}, {"name": "test", "num_bytes": 24868182, "num_examples": 150753}, {"name": "train", "num_bytes": 22908072, "num_examples": 139262}], "download_size": 31084547, "dataset_size": 50305058}}
|
2023-04-04T00:02:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_generalized_third_person_s"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_generalized_third_person_s\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_generalized_third_person_s\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_generalized_third_person_s\"\n\nMore Information needed"
] |
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8406c63b494ee113b99c176a4a2f6662d3ea3c10
|
# Dataset Card for "MULTI_VALUE_qqp_me_coordinate_subjects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_me_coordinate_subjects
|
[
"region:us"
] |
2023-04-04T00:02:04+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 4841, "num_examples": 19}, {"name": "test", "num_bytes": 50642, "num_examples": 197}, {"name": "train", "num_bytes": 37708, "num_examples": 145}], "download_size": 65241, "dataset_size": 93191}}
|
2023-04-04T00:02:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_me_coordinate_subjects"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_me_coordinate_subjects\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_me_coordinate_subjects\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_me_coordinate_subjects\"\n\nMore Information needed"
] |
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884e5bdd305b9d4dbeccc75ec9d9341ee7c406e1
|
# Dataset Card for "MULTI_VALUE_qqp_completive_finish"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_completive_finish
|
[
"region:us"
] |
2023-04-04T00:02:05+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 285757, "num_examples": 1494}, {"name": "test", "num_bytes": 2705534, "num_examples": 13910}, {"name": "train", "num_bytes": 2562760, "num_examples": 13233}], "download_size": 3297573, "dataset_size": 5554051}}
|
2023-04-04T00:02:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_completive_finish"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_completive_finish\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_completive_finish\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_completive_finish\"\n\nMore Information needed"
] |
[
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] |
ac5bdba9729bb4377538a571486124d19bcf90c3
|
# Dataset Card for "MULTI_VALUE_qqp_my_i"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_my_i
|
[
"region:us"
] |
2023-04-04T00:02:21+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 793695, "num_examples": 4654}, {"name": "test", "num_bytes": 8457698, "num_examples": 48427}, {"name": "train", "num_bytes": 7362359, "num_examples": 42844}], "download_size": 10033208, "dataset_size": 16613752}}
|
2023-04-04T00:02:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_my_i"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_my_i\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_my_i\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_my_i\"\n\nMore Information needed"
] |
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3db7b7fa793c08848e7f2d946e47d7386fd8157e
|
# Dataset Card for "MULTI_VALUE_qqp_remove_det_indefinite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_remove_det_indefinite
|
[
"region:us"
] |
2023-04-04T00:02:27+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1799625, "num_examples": 10884}, {"name": "test", "num_bytes": 18844785, "num_examples": 113284}, {"name": "train", "num_bytes": 16217313, "num_examples": 97728}], "download_size": 23182430, "dataset_size": 36861723}}
|
2023-04-04T00:02:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_remove_det_indefinite"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_remove_det_indefinite\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_remove_det_indefinite\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_remove_det_indefinite\"\n\nMore Information needed"
] |
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a81b6d66d60a2c7ede13208a0c6b307c8854cf29
|
# Dataset Card for "MULTI_VALUE_qqp_aint_before_main"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_aint_before_main
|
[
"region:us"
] |
2023-04-04T00:02:33+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 331722, "num_examples": 1686}, {"name": "test", "num_bytes": 3212678, "num_examples": 16334}, {"name": "train", "num_bytes": 2992938, "num_examples": 14994}], "download_size": 3988727, "dataset_size": 6537338}}
|
2023-04-04T00:02:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_aint_before_main"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_aint_before_main\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_aint_before_main\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_aint_before_main\"\n\nMore Information needed"
] |
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] |
6c1da8ea96d6b23883ef3b0012a98d0b66346c0f
|
# Dataset Card for "MULTI_VALUE_qqp_got_gotten"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_got_gotten
|
[
"region:us"
] |
2023-04-04T00:02:33+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 5293, "num_examples": 23}, {"name": "test", "num_bytes": 99842, "num_examples": 460}, {"name": "train", "num_bytes": 65617, "num_examples": 295}], "download_size": 104322, "dataset_size": 170752}}
|
2023-04-04T00:02:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_got_gotten"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_got_gotten\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_got_gotten\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_got_gotten\"\n\nMore Information needed"
] |
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] |
7807b7c750398211825051bf17450fefde912f73
|
# Dataset Card for "MULTI_VALUE_qqp_who_at"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_who_at
|
[
"region:us"
] |
2023-04-04T00:02:49+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 121227, "num_examples": 597}, {"name": "test", "num_bytes": 1338416, "num_examples": 6621}, {"name": "train", "num_bytes": 1231970, "num_examples": 5952}], "download_size": 1647252, "dataset_size": 2691613}}
|
2023-04-04T00:02:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_who_at"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_who_at\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_who_at\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_who_at\"\n\nMore Information needed"
] |
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0feca99a49fb342ecb8ebea0c494827f182c6dd4
|
# Dataset Card for "MULTI_VALUE_qqp_synthetic_superlative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_synthetic_superlative
|
[
"region:us"
] |
2023-04-04T00:03:08+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 107779, "num_examples": 683}, {"name": "test", "num_bytes": 1174008, "num_examples": 7649}, {"name": "train", "num_bytes": 969120, "num_examples": 6298}], "download_size": 1169822, "dataset_size": 2250907}}
|
2023-04-04T00:03:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_synthetic_superlative"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_synthetic_superlative\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_synthetic_superlative\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_synthetic_superlative\"\n\nMore Information needed"
] |
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875e63ea0e0789cb5b531f56365a2a6807dc9fd6
|
# Dataset Card for "MULTI_VALUE_qqp_existential_got"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_existential_got
|
[
"region:us"
] |
2023-04-04T00:03:24+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 324059, "num_examples": 1859}, {"name": "test", "num_bytes": 3200542, "num_examples": 18620}, {"name": "train", "num_bytes": 2815329, "num_examples": 16171}], "download_size": 3967475, "dataset_size": 6339930}}
|
2023-04-04T00:03:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_existential_got"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_existential_got\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_existential_got\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_existential_got\"\n\nMore Information needed"
] |
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7dfd3508e3e8b8f78a011af33ec3368322ee910d
|
# Dataset Card for "MULTI_VALUE_qqp_irrealis_be_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_irrealis_be_done
|
[
"region:us"
] |
2023-04-04T00:03:26+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 569355, "num_examples": 2766}, {"name": "test", "num_bytes": 5610655, "num_examples": 27990}, {"name": "train", "num_bytes": 5166930, "num_examples": 25186}], "download_size": 6979796, "dataset_size": 11346940}}
|
2023-04-04T00:03:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_irrealis_be_done"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_irrealis_be_done\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_irrealis_be_done\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_irrealis_be_done\"\n\nMore Information needed"
] |
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e2d340dd8447916c419ce86626529432d1f41646
|
# Dataset Card for "MULTI_VALUE_qqp_relativizer_where"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_relativizer_where
|
[
"region:us"
] |
2023-04-04T00:03:28+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 363726, "num_examples": 1820}, {"name": "test", "num_bytes": 3454497, "num_examples": 17679}, {"name": "train", "num_bytes": 3186941, "num_examples": 15990}], "download_size": 4159938, "dataset_size": 7005164}}
|
2023-04-04T00:03:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_relativizer_where"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_relativizer_where\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_relativizer_where\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_relativizer_where\"\n\nMore Information needed"
] |
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1c095c64348b2845b4428ed6cd3f5455b39c6d37
|
# Dataset Card for "MULTI_VALUE_qqp_drop_copula_be_locative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_drop_copula_be_locative
|
[
"region:us"
] |
2023-04-04T00:03:59+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 230999, "num_examples": 1408}, {"name": "test", "num_bytes": 2447761, "num_examples": 15005}, {"name": "train", "num_bytes": 2019627, "num_examples": 12431}], "download_size": 2903431, "dataset_size": 4698387}}
|
2023-04-04T00:04:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_drop_copula_be_locative"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_drop_copula_be_locative\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_drop_copula_be_locative\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_drop_copula_be_locative\"\n\nMore Information needed"
] |
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e46542816d5de57f66dab2aa71f08790588f6ef2
|
# Dataset Card for "MULTI_VALUE_qqp_she_inanimate_objects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_she_inanimate_objects
|
[
"region:us"
] |
2023-04-04T00:04:03+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 304875, "num_examples": 1513}, {"name": "test", "num_bytes": 3031389, "num_examples": 14873}, {"name": "train", "num_bytes": 2810110, "num_examples": 13532}], "download_size": 3860078, "dataset_size": 6146374}}
|
2023-04-04T00:04:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_she_inanimate_objects"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_she_inanimate_objects\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_she_inanimate_objects\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_she_inanimate_objects\"\n\nMore Information needed"
] |
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] |
3573711431896492c311332786c09c548d8afa7f
|
# Dataset Card for "MULTI_VALUE_qqp_quotative_like"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_quotative_like
|
[
"region:us"
] |
2023-04-04T00:04:21+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 3908, "num_examples": 15}, {"name": "test", "num_bytes": 44512, "num_examples": 190}, {"name": "train", "num_bytes": 35296, "num_examples": 146}], "download_size": 59657, "dataset_size": 83716}}
|
2023-04-04T00:04:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_quotative_like"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_quotative_like\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_quotative_like\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_quotative_like\"\n\nMore Information needed"
] |
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9716abc95ac9bae9bcc0ab1ef56185731c27861a
|
# Dataset Card for "MULTI_VALUE_qqp_myself_coordinate_subjects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_myself_coordinate_subjects
|
[
"region:us"
] |
2023-04-04T00:04:25+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 4921, "num_examples": 19}, {"name": "test", "num_bytes": 51462, "num_examples": 197}, {"name": "train", "num_bytes": 38308, "num_examples": 145}], "download_size": 65501, "dataset_size": 94691}}
|
2023-04-04T00:04:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_myself_coordinate_subjects"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_myself_coordinate_subjects\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_myself_coordinate_subjects\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_myself_coordinate_subjects\"\n\nMore Information needed"
] |
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f64bdf4cc2e13bd4314a9b3a3f869c38d0d05ad3
|
# Dataset Card for "MULTI_VALUE_qqp_for_complementizer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_for_complementizer
|
[
"region:us"
] |
2023-04-04T00:04:34+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1347745, "num_examples": 7337}, {"name": "test", "num_bytes": 13279052, "num_examples": 72304}, {"name": "train", "num_bytes": 11913209, "num_examples": 64615}], "download_size": 16593315, "dataset_size": 26540006}}
|
2023-04-04T00:04:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_for_complementizer"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_for_complementizer\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_for_complementizer\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_for_complementizer\"\n\nMore Information needed"
] |
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262e5001de6b7ca0cafd2b1bdce68dbfded84c65
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# Dataset Card for "MULTI_VALUE_qqp_drop_copula_be_AP"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_drop_copula_be_AP
|
[
"region:us"
] |
2023-04-04T00:04:39+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 574829, "num_examples": 3667}, {"name": "test", "num_bytes": 6086644, "num_examples": 38655}, {"name": "train", "num_bytes": 5201008, "num_examples": 32930}], "download_size": 7404705, "dataset_size": 11862481}}
|
2023-04-04T00:04:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_drop_copula_be_AP"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_drop_copula_be_AP\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_drop_copula_be_AP\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_drop_copula_be_AP\"\n\nMore Information needed"
] |
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] |
926bbfe376e8dc3d6824080eb93f78fa101e0620
|
# Dataset Card for "MULTI_VALUE_qqp_fronting_pobj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_fronting_pobj
|
[
"region:us"
] |
2023-04-04T00:04:41+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 4180098, "num_examples": 26730}, {"name": "test", "num_bytes": 41875431, "num_examples": 266523}, {"name": "train", "num_bytes": 37497905, "num_examples": 239726}], "download_size": 51514741, "dataset_size": 83553434}}
|
2023-04-04T00:04:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_fronting_pobj"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_fronting_pobj\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_fronting_pobj\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_fronting_pobj\"\n\nMore Information needed"
] |
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aa1dbaf4078a6aa40d45a455c500ae8f8955fa82
|
# Dataset Card for "MULTI_VALUE_qqp_possessives_belong"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_possessives_belong
|
[
"region:us"
] |
2023-04-04T00:04:54+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1810077, "num_examples": 9983}, {"name": "test", "num_bytes": 18044755, "num_examples": 98443}, {"name": "train", "num_bytes": 16625757, "num_examples": 91164}], "download_size": 21769659, "dataset_size": 36480589}}
|
2023-04-04T00:05:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_possessives_belong"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_possessives_belong\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_possessives_belong\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_possessives_belong\"\n\nMore Information needed"
] |
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a031337ec75f2a6fabe7e6748e82b613c2c75cd2
|
# Dataset Card for "MULTI_VALUE_qqp_comparative_as_to"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_comparative_as_to
|
[
"region:us"
] |
2023-04-04T00:04:55+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 41598, "num_examples": 238}, {"name": "test", "num_bytes": 441033, "num_examples": 2501}, {"name": "train", "num_bytes": 372913, "num_examples": 2084}], "download_size": 520599, "dataset_size": 855544}}
|
2023-04-04T00:05:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_comparative_as_to"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_comparative_as_to\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_comparative_as_to\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_comparative_as_to\"\n\nMore Information needed"
] |
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05c4e306cbc222662cd0b18caaf39682be1476f4
|
# Dataset Card for "MULTI_VALUE_qqp_benefactive_dative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_benefactive_dative
|
[
"region:us"
] |
2023-04-04T00:04:59+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 563127, "num_examples": 3350}, {"name": "test", "num_bytes": 6098962, "num_examples": 35548}, {"name": "train", "num_bytes": 5134332, "num_examples": 30404}], "download_size": 7027073, "dataset_size": 11796421}}
|
2023-04-04T00:05:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_benefactive_dative"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_benefactive_dative\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_benefactive_dative\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_benefactive_dative\"\n\nMore Information needed"
] |
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] |
5d8fc149cbe1b4a716948aad01862ef29fcd9e17
|
# Dataset Card for "MULTI_VALUE_qqp_double_determiners"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_double_determiners
|
[
"region:us"
] |
2023-04-04T00:04:59+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 860, "num_examples": 4}, {"name": "test", "num_bytes": 10802, "num_examples": 50}, {"name": "train", "num_bytes": 5810, "num_examples": 27}], "download_size": 19442, "dataset_size": 17472}}
|
2023-04-04T00:05:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_double_determiners"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_double_determiners\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_double_determiners\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_double_determiners\"\n\nMore Information needed"
] |
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94538a80508242cbbb3e6ae67bd484d0bcebadbd
|
# Dataset Card for "MULTI_VALUE_qqp_nasal_possessive_pron"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_nasal_possessive_pron
|
[
"region:us"
] |
2023-04-04T00:05:36+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1490411, "num_examples": 8656}, {"name": "test", "num_bytes": 14928230, "num_examples": 85065}, {"name": "train", "num_bytes": 13708192, "num_examples": 79093}], "download_size": 18451759, "dataset_size": 30126833}}
|
2023-04-04T00:05:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_nasal_possessive_pron"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_nasal_possessive_pron\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_nasal_possessive_pron\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_nasal_possessive_pron\"\n\nMore Information needed"
] |
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06b2b549e8f882bf815ba081b3fd31488d2bc9d1
|
# Dataset Card for "MULTI_VALUE_qqp_nomo_existential"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_nomo_existential
|
[
"region:us"
] |
2023-04-04T00:05:40+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 243, "num_examples": 1}, {"name": "test", "num_bytes": 10894, "num_examples": 47}, {"name": "train", "num_bytes": 8850, "num_examples": 41}], "download_size": 22998, "dataset_size": 19987}}
|
2023-04-04T00:05:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_nomo_existential"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_nomo_existential\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_nomo_existential\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_nomo_existential\"\n\nMore Information needed"
] |
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] |
c8153971e655c7bf5868c9bd8f399c1d079dba54
|
# Dataset Card for "MULTI_VALUE_qqp_who_as"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_who_as
|
[
"region:us"
] |
2023-04-04T00:05:41+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 121227, "num_examples": 597}, {"name": "test", "num_bytes": 1338416, "num_examples": 6621}, {"name": "train", "num_bytes": 1231970, "num_examples": 5952}], "download_size": 1647775, "dataset_size": 2691613}}
|
2023-04-04T00:05:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_who_as"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_who_as\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_who_as\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_who_as\"\n\nMore Information needed"
] |
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d867b02fcee2efe824e65628a07a18938dbb31f6
|
# Dataset Card for "MULTI_VALUE_qqp_reduced_relative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_reduced_relative
|
[
"region:us"
] |
2023-04-04T00:05:47+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 233739, "num_examples": 1186}, {"name": "test", "num_bytes": 2220506, "num_examples": 11410}, {"name": "train", "num_bytes": 2149800, "num_examples": 10834}], "download_size": 2872278, "dataset_size": 4604045}}
|
2023-04-04T00:05:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_reduced_relative"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_reduced_relative\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_reduced_relative\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_reduced_relative\"\n\nMore Information needed"
] |
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cd3dc19db3058b84e5fc08b01408e57377789731
|
# Dataset Card for "MULTI_VALUE_qqp_regularized_reflexives_object_pronouns"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_regularized_reflexives_object_pronouns
|
[
"region:us"
] |
2023-04-04T00:05:52+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 53130, "num_examples": 321}, {"name": "test", "num_bytes": 546897, "num_examples": 3329}, {"name": "train", "num_bytes": 484763, "num_examples": 2952}], "download_size": 563266, "dataset_size": 1084790}}
|
2023-04-04T00:05:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_regularized_reflexives_object_pronouns"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_regularized_reflexives_object_pronouns\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_regularized_reflexives_object_pronouns\"\n\nMore Information needed"
] |
[
6,
32
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_regularized_reflexives_object_pronouns\"\n\nMore Information needed"
] |
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] |
7baaa735e5bc96dc44ce7ee07289794ceec41682
|
# Dataset Card for "MULTI_VALUE_qqp_bare_past_tense"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_bare_past_tense
|
[
"region:us"
] |
2023-04-04T00:05:55+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 533663, "num_examples": 2736}, {"name": "test", "num_bytes": 5377066, "num_examples": 27550}, {"name": "train", "num_bytes": 4888061, "num_examples": 24820}], "download_size": 6896663, "dataset_size": 10798790}}
|
2023-04-04T00:06:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_bare_past_tense"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_bare_past_tense\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_bare_past_tense\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_bare_past_tense\"\n\nMore Information needed"
] |
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e61bee3a4f27004b4a327a74759409fafdb12b03
|
# Dataset Card for "MULTI_VALUE_qqp_to_infinitive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_to_infinitive
|
[
"region:us"
] |
2023-04-04T00:06:09+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 99296, "num_examples": 532}, {"name": "test", "num_bytes": 955905, "num_examples": 5172}, {"name": "train", "num_bytes": 908661, "num_examples": 4788}], "download_size": 1182186, "dataset_size": 1963862}}
|
2023-04-04T00:06:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_to_infinitive"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_to_infinitive\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_to_infinitive\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_to_infinitive\"\n\nMore Information needed"
] |
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bf4e97dc3abc0d361fea083233041fd59c90d1e6
|
# Dataset Card for "MULTI_VALUE_qqp_no_gender_distinction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_no_gender_distinction
|
[
"region:us"
] |
2023-04-04T00:06:19+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 278265, "num_examples": 1329}, {"name": "test", "num_bytes": 2932247, "num_examples": 14086}, {"name": "train", "num_bytes": 2642642, "num_examples": 12471}], "download_size": 3666148, "dataset_size": 5853154}}
|
2023-04-04T00:06:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_no_gender_distinction"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_no_gender_distinction\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_no_gender_distinction\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_no_gender_distinction\"\n\nMore Information needed"
] |
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] |
9effd9422a30ee5a23dabc2a0c836d6c84fdc4fb
|
# Dataset Card for "MULTI_VALUE_qqp_finna_future"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_finna_future
|
[
"region:us"
] |
2023-04-04T00:06:22+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 198403, "num_examples": 1028}, {"name": "test", "num_bytes": 1773000, "num_examples": 9289}, {"name": "train", "num_bytes": 1769246, "num_examples": 9240}], "download_size": 2180207, "dataset_size": 3740649}}
|
2023-04-04T00:06:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_finna_future"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_finna_future\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_finna_future\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_finna_future\"\n\nMore Information needed"
] |
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2cb6dd4fb75610a0d7082287e5d4e21c964ffe62
|
# Dataset Card for "MULTI_VALUE_qqp_mass_noun_plurals"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_mass_noun_plurals
|
[
"region:us"
] |
2023-04-04T00:06:31+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 3027659, "num_examples": 17941}, {"name": "test", "num_bytes": 30098899, "num_examples": 179332}, {"name": "train", "num_bytes": 27349324, "num_examples": 162207}], "download_size": 37381541, "dataset_size": 60475882}}
|
2023-04-04T00:06:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_mass_noun_plurals"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_mass_noun_plurals\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_mass_noun_plurals\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_mass_noun_plurals\"\n\nMore Information needed"
] |
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cba57b4fe4c341b03cc5c746e6aff5c5e2f1361d
|
# Dataset Card for "MULTI_VALUE_qqp_do_tense_marker"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_do_tense_marker
|
[
"region:us"
] |
2023-04-04T00:06:34+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 3830332, "num_examples": 23704}, {"name": "test", "num_bytes": 38772904, "num_examples": 237824}, {"name": "train", "num_bytes": 34535330, "num_examples": 213623}], "download_size": 46051920, "dataset_size": 77138566}}
|
2023-04-04T00:06:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_do_tense_marker"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_do_tense_marker\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_do_tense_marker\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_do_tense_marker\"\n\nMore Information needed"
] |
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] |
ff1efe711911334b0d1930b163f8994582b026bc
|
# Dataset Card for "MULTI_VALUE_qqp_degree_adj_for_adv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_degree_adj_for_adv
|
[
"region:us"
] |
2023-04-04T00:06:53+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 31558, "num_examples": 188}, {"name": "test", "num_bytes": 336732, "num_examples": 1958}, {"name": "train", "num_bytes": 294601, "num_examples": 1673}], "download_size": 389113, "dataset_size": 662891}}
|
2023-04-04T00:06:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_degree_adj_for_adv"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_degree_adj_for_adv\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_degree_adj_for_adv\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_degree_adj_for_adv\"\n\nMore Information needed"
] |
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] |
90a7a588173fb57730a144cfb080fb731a7c1cdf
|
# Dataset Card for "MULTI_VALUE_qqp_progressives"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_progressives
|
[
"region:us"
] |
2023-04-04T00:06:54+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 3004290, "num_examples": 17783}, {"name": "test", "num_bytes": 29307911, "num_examples": 172968}, {"name": "train", "num_bytes": 26904243, "num_examples": 158928}], "download_size": 36682981, "dataset_size": 59216444}}
|
2023-04-04T00:07:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_progressives"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_progressives\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_progressives\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_progressives\"\n\nMore Information needed"
] |
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6de0433cf5b6b875cfffbb5a861fcad21d760ed5
|
# Dataset Card for "MULTI_VALUE_qqp_not_preverbal_negator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_not_preverbal_negator
|
[
"region:us"
] |
2023-04-04T00:06:58+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 255092, "num_examples": 1281}, {"name": "test", "num_bytes": 2419258, "num_examples": 12249}, {"name": "train", "num_bytes": 2272011, "num_examples": 11176}], "download_size": 3042453, "dataset_size": 4946361}}
|
2023-04-04T00:07:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_not_preverbal_negator"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_not_preverbal_negator\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_not_preverbal_negator\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_not_preverbal_negator\"\n\nMore Information needed"
] |
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] |
89657236533540adee79a41a668db6166f6853cc
|
# Dataset Card for "MULTI_VALUE_qqp_transitive_suffix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_transitive_suffix
|
[
"region:us"
] |
2023-04-04T00:07:02+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 2842170, "num_examples": 16844}, {"name": "test", "num_bytes": 28411698, "num_examples": 167470}, {"name": "train", "num_bytes": 25420238, "num_examples": 150324}], "download_size": 35393943, "dataset_size": 56674106}}
|
2023-04-04T00:07:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_transitive_suffix"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_transitive_suffix\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_transitive_suffix\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_transitive_suffix\"\n\nMore Information needed"
] |
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ec127697b6698704e02084cd16cf9c5f36ab133f
|
# Dataset Card for "MULTI_VALUE_qqp_present_perfect_ever"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_present_perfect_ever
|
[
"region:us"
] |
2023-04-04T00:07:05+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 282286, "num_examples": 1494}, {"name": "test", "num_bytes": 2674842, "num_examples": 13910}, {"name": "train", "num_bytes": 2532159, "num_examples": 13233}], "download_size": 3302161, "dataset_size": 5489287}}
|
2023-04-04T00:07:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_present_perfect_ever"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_present_perfect_ever\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_present_perfect_ever\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_present_perfect_ever\"\n\nMore Information needed"
] |
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] |
e7a1e32822fb4484b7937eee8e632511fabb0f70
|
# Dataset Card for "MULTI_VALUE_qqp_non_coordinated_obj_subj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_non_coordinated_obj_subj
|
[
"region:us"
] |
2023-04-04T00:07:21+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 626180, "num_examples": 3289}, {"name": "test", "num_bytes": 6106978, "num_examples": 31990}, {"name": "train", "num_bytes": 5800083, "num_examples": 30191}], "download_size": 7870233, "dataset_size": 12533241}}
|
2023-04-04T00:07:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_non_coordinated_obj_subj"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_non_coordinated_obj_subj\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_non_coordinated_obj_subj\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_non_coordinated_obj_subj\"\n\nMore Information needed"
] |
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f080921e332fec3f81c2b96767c3f3d49566f8de
|
# Dataset Card for "MULTI_VALUE_qqp_uninflect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_uninflect
|
[
"region:us"
] |
2023-04-04T00:07:22+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1296805, "num_examples": 7673}, {"name": "test", "num_bytes": 12786457, "num_examples": 75749}, {"name": "train", "num_bytes": 11784591, "num_examples": 69250}], "download_size": 16436312, "dataset_size": 25867853}}
|
2023-04-04T00:07:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_uninflect"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_uninflect\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_uninflect\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_uninflect\"\n\nMore Information needed"
] |
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a6a5331f317cf783ff6fbb7e93a25d8a3c1418d7
|
# Dataset Card for "MULTI_VALUE_qqp_serial_verb_give"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_serial_verb_give
|
[
"region:us"
] |
2023-04-04T00:07:33+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 12208, "num_examples": 65}, {"name": "test", "num_bytes": 120104, "num_examples": 656}, {"name": "train", "num_bytes": 99639, "num_examples": 538}], "download_size": 136302, "dataset_size": 231951}}
|
2023-04-04T00:07:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_serial_verb_give"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_serial_verb_give\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_serial_verb_give\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_serial_verb_give\"\n\nMore Information needed"
] |
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efa2967017f7779fb615584857a67df21f57cdc5
|
# Dataset Card for "MULTI_VALUE_qqp_plural_to_singular_human"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_plural_to_singular_human
|
[
"region:us"
] |
2023-04-04T00:07:51+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1168101, "num_examples": 6683}, {"name": "test", "num_bytes": 11495581, "num_examples": 66497}, {"name": "train", "num_bytes": 10432978, "num_examples": 59450}], "download_size": 14217567, "dataset_size": 23096660}}
|
2023-04-04T00:07:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_plural_to_singular_human"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_plural_to_singular_human\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_plural_to_singular_human\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_plural_to_singular_human\"\n\nMore Information needed"
] |
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b49bb5c6ab99f880bdf2cacac48a4571c1c2c52a
|
# Dataset Card for "MULTI_VALUE_qqp_acomp_focusing_like"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_acomp_focusing_like
|
[
"region:us"
] |
2023-04-04T00:07:52+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1396999, "num_examples": 8022}, {"name": "test", "num_bytes": 14576606, "num_examples": 83799}, {"name": "train", "num_bytes": 12672982, "num_examples": 72367}], "download_size": 17791138, "dataset_size": 28646587}}
|
2023-04-04T00:07:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_acomp_focusing_like"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_acomp_focusing_like\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_acomp_focusing_like\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_acomp_focusing_like\"\n\nMore Information needed"
] |
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ba36d905b62c21379c0a58a716604b5e56d03f43
|
# Dataset Card for "MULTI_VALUE_qqp_negative_inversion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_negative_inversion
|
[
"region:us"
] |
2023-04-04T00:07:52+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 5678, "num_examples": 25}, {"name": "test", "num_bytes": 98378, "num_examples": 428}, {"name": "train", "num_bytes": 77374, "num_examples": 330}], "download_size": 107133, "dataset_size": 181430}}
|
2023-04-04T00:07:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_negative_inversion"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_negative_inversion\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_negative_inversion\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_negative_inversion\"\n\nMore Information needed"
] |
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] |
d1b319c32768c4caab4882ade97ba6523d21f038
|
# Dataset Card for "MULTI_VALUE_qqp_for_to_pupose"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_for_to_pupose
|
[
"region:us"
] |
2023-04-04T00:08:09+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 554737, "num_examples": 3474}, {"name": "test", "num_bytes": 5563205, "num_examples": 34426}, {"name": "train", "num_bytes": 5069715, "num_examples": 31944}], "download_size": 6023731, "dataset_size": 11187657}}
|
2023-04-04T00:08:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_for_to_pupose"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_for_to_pupose\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_for_to_pupose\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_for_to_pupose\"\n\nMore Information needed"
] |
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28cea51927e9119990c389c922e08f7067b3c231
|
# Dataset Card for "MULTI_VALUE_qqp_fixin_future"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_fixin_future
|
[
"region:us"
] |
2023-04-04T00:08:09+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 205059, "num_examples": 1028}, {"name": "test", "num_bytes": 1831091, "num_examples": 9289}, {"name": "train", "num_bytes": 1828129, "num_examples": 9240}], "download_size": 2196943, "dataset_size": 3864279}}
|
2023-04-04T00:08:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_fixin_future"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_fixin_future\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_fixin_future\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_fixin_future\"\n\nMore Information needed"
] |
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8bed650085a3ea881c9eed5a221224e0154329fc
|
# Dataset Card for "MULTI_VALUE_qqp_double_comparative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_double_comparative
|
[
"region:us"
] |
2023-04-04T00:08:18+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 219481, "num_examples": 1220}, {"name": "test", "num_bytes": 2499840, "num_examples": 13800}, {"name": "train", "num_bytes": 2006391, "num_examples": 11047}], "download_size": 2866930, "dataset_size": 4725712}}
|
2023-04-04T00:08:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_double_comparative"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_double_comparative\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_double_comparative\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_double_comparative\"\n\nMore Information needed"
] |
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15153e5c7e118e24c00b96e067be2443eb0b6d3f
|
# Dataset Card for "MULTI_VALUE_qqp_definite_abstract"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_definite_abstract
|
[
"region:us"
] |
2023-04-04T00:08:30+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 840463, "num_examples": 4737}, {"name": "test", "num_bytes": 7921652, "num_examples": 44767}, {"name": "train", "num_bytes": 7560356, "num_examples": 42436}], "download_size": 9919049, "dataset_size": 16322471}}
|
2023-04-04T00:08:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_definite_abstract"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_definite_abstract\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_definite_abstract\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_definite_abstract\"\n\nMore Information needed"
] |
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] |
7593fed80e00878bc1463096149ab0512b5b114c
|
# Dataset Card for "MULTI_VALUE_qqp_say_complementizer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_say_complementizer
|
[
"region:us"
] |
2023-04-04T00:08:34+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 131067, "num_examples": 614}, {"name": "test", "num_bytes": 1311590, "num_examples": 6202}, {"name": "train", "num_bytes": 1212547, "num_examples": 5505}], "download_size": 1612005, "dataset_size": 2655204}}
|
2023-04-04T00:08:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_say_complementizer"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_say_complementizer\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_say_complementizer\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_say_complementizer\"\n\nMore Information needed"
] |
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] |
927cf85927eeafd0acb0a07fbbfc614a5e59f4c9
|
# Dataset Card for "MULTI_VALUE_qqp_drop_aux_be_progressive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_drop_aux_be_progressive
|
[
"region:us"
] |
2023-04-04T00:08:36+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 270824, "num_examples": 1553}, {"name": "test", "num_bytes": 2461834, "num_examples": 14195}, {"name": "train", "num_bytes": 2398053, "num_examples": 13719}], "download_size": 3124623, "dataset_size": 5130711}}
|
2023-04-04T00:08:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_drop_aux_be_progressive"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_drop_aux_be_progressive\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_drop_aux_be_progressive\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_drop_aux_be_progressive\"\n\nMore Information needed"
] |
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b51d18ada196d749e9a69f2e32afe3f12ccd47f1
|
# Dataset Card for "MULTI_VALUE_qqp_drop_aux_wh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_drop_aux_wh
|
[
"region:us"
] |
2023-04-04T00:08:59+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 4830871, "num_examples": 33522}, {"name": "test", "num_bytes": 47375999, "num_examples": 325344}, {"name": "train", "num_bytes": 43406035, "num_examples": 301000}], "download_size": 58949718, "dataset_size": 95612905}}
|
2023-04-04T00:09:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_drop_aux_wh"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_drop_aux_wh\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_drop_aux_wh\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_drop_aux_wh\"\n\nMore Information needed"
] |
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d6068c45622273a329363ad018348a9e627afba3
|
# Dataset Card for "MULTI_VALUE_qqp_reduplicate_interrogative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_reduplicate_interrogative
|
[
"region:us"
] |
2023-04-04T00:09:11+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 3315252, "num_examples": 20784}, {"name": "test", "num_bytes": 32557362, "num_examples": 202214}, {"name": "train", "num_bytes": 29744573, "num_examples": 186546}], "download_size": 37443550, "dataset_size": 65617187}}
|
2023-04-04T00:09:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_reduplicate_interrogative"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_reduplicate_interrogative\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_reduplicate_interrogative\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_reduplicate_interrogative\"\n\nMore Information needed"
] |
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c2dfad0d91ac1b26fcbc195b6df1f1a279ccf700
|
# Dataset Card for "MULTI_VALUE_qqp_relativizer_doubling"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_relativizer_doubling
|
[
"region:us"
] |
2023-04-04T00:09:43+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 496425, "num_examples": 2407}, {"name": "test", "num_bytes": 4904516, "num_examples": 24140}, {"name": "train", "num_bytes": 4515480, "num_examples": 21763}], "download_size": 6010527, "dataset_size": 9916421}}
|
2023-04-04T00:09:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_relativizer_doubling"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_relativizer_doubling\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_relativizer_doubling\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_relativizer_doubling\"\n\nMore Information needed"
] |
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f981747e7a9fa04f27d723525970d29f7ed135ac
|
# Dataset Card for "MULTI_VALUE_qqp_possessives_for_post"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_possessives_for_post
|
[
"region:us"
] |
2023-04-04T00:10:54+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1825480, "num_examples": 9983}, {"name": "test", "num_bytes": 18192433, "num_examples": 98443}, {"name": "train", "num_bytes": 16766881, "num_examples": 91164}], "download_size": 22183214, "dataset_size": 36784794}}
|
2023-04-04T00:10:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_possessives_for_post"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_possessives_for_post\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_possessives_for_post\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_possessives_for_post\"\n\nMore Information needed"
] |
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ab59802ef4878655cba4d6584e1a60b0b99087b8
|
# Dataset Card for "MULTI_VALUE_qqp_completive_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_completive_done
|
[
"region:us"
] |
2023-04-04T00:10:56+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 312738, "num_examples": 1592}, {"name": "test", "num_bytes": 2962055, "num_examples": 14898}, {"name": "train", "num_bytes": 2800205, "num_examples": 14143}], "download_size": 3751305, "dataset_size": 6074998}}
|
2023-04-04T00:11:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_completive_done"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_completive_done\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_completive_done\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_completive_done\"\n\nMore Information needed"
] |
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c426114bcc8a2288602299ba6c0181986b05ac68
|
# Dataset Card for "MULTI_VALUE_qqp_who_what"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_who_what
|
[
"region:us"
] |
2023-04-04T00:11:50+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 476008, "num_examples": 2385}, {"name": "test", "num_bytes": 4702599, "num_examples": 23944}, {"name": "train", "num_bytes": 4329233, "num_examples": 21586}], "download_size": 5782997, "dataset_size": 9507840}}
|
2023-04-04T00:11:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_who_what"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_who_what\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_who_what\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_who_what\"\n\nMore Information needed"
] |
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] |
6d16db591ee7b55159d97b7d4bba805d9be2c66c
|
# 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)
|
lordgrim18/indian_food_images
|
[
"region:us"
] |
2023-04-04T00:23:29+00:00
|
{"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": 1448837662.6960664, "num_examples": 5642}, {"name": "test", "num_bytes": 164371269.52193332, "num_examples": 627}], "download_size": 1599385105, "dataset_size": 1613208932.2179997}}
|
2023-04-04T00:25:03+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"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"indian_food_images\"\n\nMore Information needed"
] |
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8f729ba625012bd320b31eeffea34b9194608ef2
|
# Dataset Card for "MULTI_VALUE_qqp_it_is_non_referential"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_it_is_non_referential
|
[
"region:us"
] |
2023-04-04T01:17:50+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 358821, "num_examples": 2127}, {"name": "test", "num_bytes": 3738950, "num_examples": 21997}, {"name": "train", "num_bytes": 3186907, "num_examples": 18760}], "download_size": 4535420, "dataset_size": 7284678}}
|
2023-04-04T01:17:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_it_is_non_referential"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_it_is_non_referential\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_it_is_non_referential\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_it_is_non_referential\"\n\nMore Information needed"
] |
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396e410bdd45d93d775782b3530663cdf8c3d53d
|
# Dataset Card for "MULTI_VALUE_qqp_no_preverbal_negator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_no_preverbal_negator
|
[
"region:us"
] |
2023-04-04T01:19:49+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 253517, "num_examples": 1281}, {"name": "test", "num_bytes": 2404581, "num_examples": 12249}, {"name": "train", "num_bytes": 2258255, "num_examples": 11176}], "download_size": 3046484, "dataset_size": 4916353}}
|
2023-04-04T01:19:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_no_preverbal_negator"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_no_preverbal_negator\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_no_preverbal_negator\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_no_preverbal_negator\"\n\nMore Information needed"
] |
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9c1992d530996f416543c9d1445091d0aa9118e7
|
# Dataset Card for "MULTI_VALUE_qqp_remove_det_definite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_remove_det_definite
|
[
"region:us"
] |
2023-04-04T01:20:02+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 2275368, "num_examples": 13608}, {"name": "test", "num_bytes": 22477928, "num_examples": 136071}, {"name": "train", "num_bytes": 20410027, "num_examples": 122030}], "download_size": 28607208, "dataset_size": 45163323}}
|
2023-04-04T01:20:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_remove_det_definite"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_remove_det_definite\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_remove_det_definite\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_remove_det_definite\"\n\nMore Information needed"
] |
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] |
59d28e1b1fd76a049e935995710fe2cef209c547
|
# Dataset Card for "MULTI_VALUE_qqp_perfect_slam"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_perfect_slam
|
[
"region:us"
] |
2023-04-04T01:20:13+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 233668, "num_examples": 1217}, {"name": "test", "num_bytes": 2194042, "num_examples": 11275}, {"name": "train", "num_bytes": 2076075, "num_examples": 10705}], "download_size": 2693590, "dataset_size": 4503785}}
|
2023-04-04T01:20:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_perfect_slam"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_perfect_slam\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_perfect_slam\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_perfect_slam\"\n\nMore Information needed"
] |
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620e7cd62bada0a63bb994924f1269d9f69b67cc
|
# Dataset Card for "MULTI_VALUE_qqp_indef_one"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_indef_one
|
[
"region:us"
] |
2023-04-04T01:20:18+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 2270650, "num_examples": 13219}, {"name": "test", "num_bytes": 23668466, "num_examples": 137338}, {"name": "train", "num_bytes": 20579019, "num_examples": 119383}], "download_size": 28922383, "dataset_size": 46518135}}
|
2023-04-04T01:20:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_indef_one"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_indef_one\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_indef_one\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_indef_one\"\n\nMore Information needed"
] |
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] |
1ece0557690846800dc13eb5f40eafbb1fdc0405
|
# Dataset Card for "MULTI_VALUE_qqp_past_been"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_past_been
|
[
"region:us"
] |
2023-04-04T01:21:30+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 336352, "num_examples": 1746}, {"name": "test", "num_bytes": 3279250, "num_examples": 17227}, {"name": "train", "num_bytes": 3033939, "num_examples": 15738}], "download_size": 4093680, "dataset_size": 6649541}}
|
2023-04-04T01:21:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_past_been"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_past_been\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_past_been\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_past_been\"\n\nMore Information needed"
] |
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] |
8c8a17b9da698688487028ef2ea9fbe42d26dada
|
# Dataset Card for "MULTI_VALUE_qqp_come_future"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_come_future
|
[
"region:us"
] |
2023-04-04T01:21:33+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 206279, "num_examples": 1028}, {"name": "test", "num_bytes": 1841644, "num_examples": 9289}, {"name": "train", "num_bytes": 1838904, "num_examples": 9240}], "download_size": 2199195, "dataset_size": 3886827}}
|
2023-04-04T01:21:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_come_future"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_come_future\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_come_future\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_come_future\"\n\nMore Information needed"
] |
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0057223ece619935cef6339e9203d97ae33f093a
|
# Dataset Card for "MULTI_VALUE_qqp_existential_you_have"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_existential_you_have
|
[
"region:us"
] |
2023-04-04T01:23:02+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 334749, "num_examples": 1855}, {"name": "test", "num_bytes": 3278678, "num_examples": 18476}, {"name": "train", "num_bytes": 2894888, "num_examples": 16080}], "download_size": 3970712, "dataset_size": 6508315}}
|
2023-04-04T01:23:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_existential_you_have"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_existential_you_have\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_existential_you_have\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_existential_you_have\"\n\nMore Information needed"
] |
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e6161edaf886a0ce54a81f3fd48cfd5e8fbbefa5
|
# Dataset Card for "MULTI_VALUE_qqp_plural_preposed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_plural_preposed
|
[
"region:us"
] |
2023-04-04T01:23:23+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 1637630, "num_examples": 9333}, {"name": "test", "num_bytes": 16390540, "num_examples": 93867}, {"name": "train", "num_bytes": 14677543, "num_examples": 83260}], "download_size": 20059460, "dataset_size": 32705713}}
|
2023-04-04T01:23:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_plural_preposed"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_plural_preposed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_plural_preposed\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_plural_preposed\"\n\nMore Information needed"
] |
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003943c3149e334cb2d272aaf2a938cd00a7f965
|
# Dataset Card for "MULTI_VALUE_qqp_definite_for_indefinite_articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liuyanchen1015/MULTI_VALUE_qqp_definite_for_indefinite_articles
|
[
"region:us"
] |
2023-04-04T01:23:29+00:00
|
{"dataset_info": {"features": [{"name": "question1", "dtype": "string"}, {"name": "question2", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "value_score", "dtype": "int64"}], "splits": [{"name": "dev", "num_bytes": 2270650, "num_examples": 13219}, {"name": "test", "num_bytes": 23668466, "num_examples": 137338}, {"name": "train", "num_bytes": 20579019, "num_examples": 119383}], "download_size": 28797821, "dataset_size": 46518135}}
|
2023-04-04T01:23:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MULTI_VALUE_qqp_definite_for_indefinite_articles"
More Information needed
|
[
"# Dataset Card for \"MULTI_VALUE_qqp_definite_for_indefinite_articles\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MULTI_VALUE_qqp_definite_for_indefinite_articles\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"MULTI_VALUE_qqp_definite_for_indefinite_articles\"\n\nMore Information needed"
] |
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e80222b626096097b987d4f843900c95694395c5
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_1880
|
[
"region:us"
] |
2023-04-04T01:48:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 610291, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_dtd_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 603919, "num_examples": 1880}, {"name": "fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices", "num_bytes": 773380, "num_examples": 1880}], "download_size": 452561, "dataset_size": 1987590}}
|
2023-05-22T17:15:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_1880"
More Information needed
|
[
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_1880\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_1880\"\n\nMore Information needed"
] |
[
6,
36
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_A_ns_1880\"\n\nMore Information needed"
] |
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c7d6d4c4e6fd0bdd6b371c55df37cc042dc6ea36
|
# Dataset Card for "faust_code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dhuck/faust_code
|
[
"region:us"
] |
2023-04-04T02:21:34+00:00
|
{"dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "repository", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "download_url", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28372010, "num_examples": 4222}], "download_size": 10561733, "dataset_size": 28372010}}
|
2023-04-10T17:17:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "faust_code"
More Information needed
|
[
"# Dataset Card for \"faust_code\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"faust_code\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"faust_code\"\n\nMore Information needed"
] |
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8c46dcee1feb63580f86adc0d54e30576dd2cee5
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_6149"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_6149
|
[
"region:us"
] |
2023-04-04T02:55:43+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 2426155, "num_examples": 6149}], "download_size": 252424, "dataset_size": 2426155}}
|
2023-04-04T02:55:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_6149"
More Information needed
|
[
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_6149\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_6149\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_6149\"\n\nMore Information needed"
] |
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] |
061e67157105b0c47e914866528cd1527ff56135
|
# Dataset Card for "logo-captioning-BLIP-BrandInfo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Babypotatotang/logo-captioning-BLIP-BrandInfo
|
[
"region:us"
] |
2023-04-04T03:21:24+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 166722232.001, "num_examples": 12911}, {"name": "test", "num_bytes": 41832785.436, "num_examples": 3228}], "download_size": 209310011, "dataset_size": 208555017.43699998}}
|
2023-04-04T03:48:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "logo-captioning-BLIP-BrandInfo"
More Information needed
|
[
"# Dataset Card for \"logo-captioning-BLIP-BrandInfo\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"logo-captioning-BLIP-BrandInfo\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"logo-captioning-BLIP-BrandInfo\"\n\nMore Information needed"
] |
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] |
1edff17cb03b1978b8b361d0ed65fa3aa10cabc1
|
# Dataset Card for C4-K8-1M
|
crumb/C4-K8-1M
|
[
"region:us"
] |
2023-04-04T03:29:20+00:00
|
{}
|
2023-04-05T13:08:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for C4-K8-1M
|
[
"# Dataset Card for C4-K8-1M"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for C4-K8-1M"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for C4-K8-1M"
] |
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57960a6d26072e85c068b47d192258a97d1e3412
|
# Dataset Card for "logo-captioning-BLIP-BrandInfoWBP"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Babypotatotang/logo-captioning-BLIP-BrandInfoWBP
|
[
"region:us"
] |
2023-04-04T04:03:29+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 321581037.08, "num_examples": 24080}, {"name": "test", "num_bytes": 82453208.54, "num_examples": 6021}], "download_size": 265975818, "dataset_size": 404034245.62}}
|
2023-04-04T05:23:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "logo-captioning-BLIP-BrandInfoWBP"
More Information needed
|
[
"# Dataset Card for \"logo-captioning-BLIP-BrandInfoWBP\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"logo-captioning-BLIP-BrandInfoWBP\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"logo-captioning-BLIP-BrandInfoWBP\"\n\nMore Information needed"
] |
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a7906d16e8d8a7dbb7e64acdca674c4711fe6ae6
|
# Clean Intel Scene Classification Dataset
This is a cleaned version of the [Intel Scene Classification Dataset](https://www.kaggle.com/datasets/puneet6060/intel-image-classification). The cleaned version removes broken files, duplicates and outliers.
## Disclaimer
You are bound to the usage license of the original dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license. We provide no warranty or guarantee of accuracy or completeness.
|
dnth/clean-intel-scene-classification
|
[
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-04-04T04:31:29+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"]}
|
2023-04-04T07:38:53+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us
|
# Clean Intel Scene Classification Dataset
This is a cleaned version of the Intel Scene Classification Dataset. The cleaned version removes broken files, duplicates and outliers.
## Disclaimer
You are bound to the usage license of the original dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license. We provide no warranty or guarantee of accuracy or completeness.
|
[
"# Clean Intel Scene Classification Dataset\n\nThis is a cleaned version of the Intel Scene Classification Dataset. The cleaned version removes broken files, duplicates and outliers.",
"## Disclaimer\nYou are bound to the usage license of the original dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license. We provide no warranty or guarantee of accuracy or completeness."
] |
[
"TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n",
"# Clean Intel Scene Classification Dataset\n\nThis is a cleaned version of the Intel Scene Classification Dataset. The cleaned version removes broken files, duplicates and outliers.",
"## Disclaimer\nYou are bound to the usage license of the original dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license. We provide no warranty or guarantee of accuracy or completeness."
] |
[
41,
39,
54
] |
[
"passage: TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n# Clean Intel Scene Classification Dataset\n\nThis is a cleaned version of the Intel Scene Classification Dataset. The cleaned version removes broken files, duplicates and outliers.## Disclaimer\nYou are bound to the usage license of the original dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license. We provide no warranty or guarantee of accuracy or completeness."
] |
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] |
c424633b80984fa0e838f0ee5be01dda13e34b19
|
# Dataset Card for "english_quotes_copy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ybelkada/english_quotes_copy
|
[
"region:us"
] |
2023-04-04T05:13:24+00:00
|
{"dataset_info": {"features": [{"name": "quote", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 598359, "num_examples": 2508}], "download_size": 349107, "dataset_size": 598359}}
|
2023-04-04T05:13:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "english_quotes_copy"
More Information needed
|
[
"# Dataset Card for \"english_quotes_copy\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"english_quotes_copy\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"english_quotes_copy\"\n\nMore Information needed"
] |
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26264dde89b4e1a031e813a9a62caa0bf81efb5b
|
# Dataset Card for "common_voice_mr_11_0_copy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ybelkada/common_voice_mr_11_0_copy
|
[
"region:us"
] |
2023-04-04T05:14:54+00:00
|
{"dataset_info": {"features": [{"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "int64"}, {"name": "down_votes", "dtype": "int64"}, {"name": "age", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "segment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 81761699.0, "num_examples": 2245}, {"name": "validation", "num_bytes": 65082681.0, "num_examples": 1682}, {"name": "test", "num_bytes": 69247449.0, "num_examples": 1816}, {"name": "other", "num_bytes": 109682091.0, "num_examples": 2819}, {"name": "invalidated", "num_bytes": 90463060.0, "num_examples": 2237}], "download_size": 407562763, "dataset_size": 416236980.0}}
|
2023-04-04T05:15:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "common_voice_mr_11_0_copy"
More Information needed
|
[
"# Dataset Card for \"common_voice_mr_11_0_copy\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"common_voice_mr_11_0_copy\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"common_voice_mr_11_0_copy\"\n\nMore Information needed"
] |
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2460081807eaa47ffc279d42f609bbaa1f5c3123
|
# Dataset Card for "ta_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
livinNector/ta_ner
|
[
"region:us"
] |
2023-04-04T06:12:46+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PER", "2": "I-PER", "3": "B-ORG", "4": "I-ORG", "5": "B-LOC", "6": "I-LOC"}}}}], "splits": [{"name": "train", "num_bytes": 191152277.0, "num_examples": 518348}, {"name": "validation", "num_bytes": 1637859.0, "num_examples": 5381}, {"name": "test", "num_bytes": 905672.0, "num_examples": 3369}], "download_size": 50425637, "dataset_size": 193695808.0}}
|
2023-04-25T02:59:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ta_ner"
More Information needed
|
[
"# Dataset Card for \"ta_ner\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ta_ner\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ta_ner\"\n\nMore Information needed"
] |
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] |
2cbab7689628afcef8f57d3e87481f5d001f3c9e
|
# T<sup>2</sup>Ranking
## Introduction
T<sup>2</sup>Ranking is a large-scale Chinese benchmark for passage ranking. The details about T<sup>2</sup>Ranking are elaborated in [this paper](https://arxiv.org/abs/2304.03679#).
Passage ranking are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). The goal of passage ranking is to compile a search result list ordered in terms of relevance to the query from a large passage collection. Typically, Passage ranking involves two stages: passage retrieval and passage re-ranking.
To support the passage ranking research, various benchmark datasets are constructed. However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues.
To address this problem, we introduce T<sup>2</sup>Ranking, a large-scale Chinese benchmark for passage ranking. T<sup>2</sup>Ranking comprises more than 300K queries and over 2M unique passages from real- world search engines. Specifically, we sample question-based search queries from user logs of the Sogou search engine, a popular search system in China. For each query, we extract the content of corresponding documents from different search engines. After model-based passage segmentation and clustering-based passage de-duplication, a large-scale passage corpus is obtained. For a given query and its corresponding passages, we hire expert annotators to provide 4-level relevance judgments of each query-passage pair.
<div align=center><img width="600" height="200" src="https://github.com/THUIR/T2Ranking/blob/main/pic/stat.png?raw=true"/></div>
<div align=center>Table 1: The data statistics of datasets commonly used in passage ranking. FR(SR): First (Second)- stage of passage ranking, i.e., passage Retrieval (Re-ranking).</div>
Compared with existing datasets, T<sup>2</sup>Ranking dataset has the following characteristics and advantages:
* The proposed dataset focus on the Chinese search scenario, and has advantages in data scale compared with existing Chinese passage ranking datasets, which can better support the design of deep learning algorithms
* The proposed dataset has a large number of fine-grained relevance annotations, which is helpful for mining fine-grained relationship between queries and passages and constructing more accurate ranking algorithms.
* By retrieving passage results from multiple commercial search engines and providing complete annotation, we ease the false negative problem to some extent, which is beneficial to providing more accurate evaluation.
* We design multiple strategies to ensure the high quality of our dataset, such as using a passage segment model and a passage clustering model to enhance the semantic integrity and diversity of passages and employing active learning for annotation method to improve the efficiency and quality of data annotation.
## Data Download
The whole dataset is placed in [huggingface](https://huggingface.co/datasets/THUIR/T2Ranking), and the data formats are presented in the following table.
<div class="center">
| Description| Filename|Num Records|Format|
|-------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|----------:|-----------------------------------:|
| Collection | collection.tsv | 2,303,643 | tsv: pid, passage |
| Queries Train | queries.train.tsv | 258,042 | tsv: qid, query |
| Queries Dev | queries.dev.tsv | 24,832 | tsv: qid, query |
| Queries Test | queries.test.tsv | 24,832 | tsv: qid, query |
| Qrels Train for re-ranking | qrels.train.tsv | 1,613,421 | TREC qrels format |
| Qrels Dev for re-ranking | qrels.dev.tsv | 400,536 | TREC qrels format |
| Qrels Retrieval Train | qrels.retrieval.train.tsv | 744,663 | tsv: qid, pid |
| Qrels Retrieval Dev | qrels.retrieval.dev.tsv | 118,933 | tsv: qid, pid |
| BM25 Negatives | train.bm25.tsv | 200,359,731 | tsv: qid, pid, index |
| Hard Negatives | train.mined.tsv | 200,376,001 | tsv: qid, pid, index, score |
</div>
You can download the dataset by running the following command:
```bash
git lfs install
git clone https://huggingface.co/datasets/THUIR/T2Ranking
```
After downloading, you can find the following files in the folder:
```
├── data
│ ├── collection.tsv
│ ├── qrels.dev.tsv
│ ├── qrels.retrieval.dev.tsv
│ ├── qrels.retrieval.train.tsv
│ ├── qrels.train.tsv
│ ├── queries.dev.tsv
│ ├── queries.test.tsv
│ ├── queries.train.tsv
│ ├── train.bm25.tsv
│ └── train.mined.tsv
├── script
│ ├── train_cross_encoder.sh
│ └── train_dual_encoder.sh
└── src
├── convert2trec.py
├── dataset_factory.py
├── modeling.py
├── msmarco_eval.py
├── train_cross_encoder.py
├── train_dual_encoder.py
└── utils.py
```
## Training and Evaluation
The dual-encoder can be trained by running the following command:
```bash
sh script/train_dual_encoder.sh
```
After training the model, you can evaluate the model by running the following command:
```bash
python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.top1000.step20
```
The cross-encoder can be trained by running the following command:
```bash
sh script/train_cross_encoder.sh
```
After training the model, you can evaluate the model by running the following command:
```bash
python src/convert2trec.py output/res.step-20 && python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.step-20.trec && path_to/trec_eval -m ndcg_cut.5 data/qrels.dev.tsv res.step-20.trec
```
BM25 on DEV set
```bash
#####################
MRR @10: 0.35894801237316354
QueriesRanked: 24831
recall@1: 0.05098711868967141
recall@1000: 0.7464097131133757
recall@50: 0.4942572226146033
#####################
```
DPR w/o hard negatives on DEV set
```bash
#####################
MRR @10: 0.4856112079562753
QueriesRanked: 24831
recall@1: 0.07367235058688999
recall@1000: 0.9082753169878586
recall@50: 0.7099350889583964
#####################
```
DPR w/ hard negatives on DEV set
```bash
#####################
MRR @10: 0.5166915171959451
QueriesRanked: 24831
recall@1: 0.08047455688965123
recall@1000: 0.9135220125786163
recall@50: 0.7327044025157232
#####################
```
BM25 retrieved+CE reranked on DEV set
```bash
#####################
MRR @10: 0.5188107959009376
QueriesRanked: 24831
recall@1: 0.08545219116806242
recall@1000: 0.7464097131133757
recall@50: 0.595298153566744
#####################
ndcg_cut_20 all 0.4405
ndcg_cut_100 all 0.4705
#####################
```
DPR retrieved+CE reranked on DEV set
```bash
#####################
MRR @10: 0.5508822816845231
QueriesRanked: 24831
recall@1: 0.08903406988867588
recall@1000: 0.9135220125786163
recall@50: 0.7393720781623112
#####################
ndcg_cut_20 all 0.5131
ndcg_cut_100 all 0.5564
#####################
```
## License
The dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
## Citation
If you use this dataset in your research, please cite our paper:
```
@misc{xie2023t2ranking,
title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking},
author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},
year={2023},
eprint={2304.03679},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
|
THUIR/T2Ranking
|
[
"task_categories:text-retrieval",
"size_categories:1M<n<10M",
"language:zh",
"license:apache-2.0",
"arxiv:2304.03679",
"region:us"
] |
2023-04-04T06:15:08+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-retrieval"]}
|
2023-07-03T07:46:32+00:00
|
[
"2304.03679"
] |
[
"zh"
] |
TAGS
#task_categories-text-retrieval #size_categories-1M<n<10M #language-Chinese #license-apache-2.0 #arxiv-2304.03679 #region-us
|
T2Ranking
=========
Introduction
------------
T2Ranking is a large-scale Chinese benchmark for passage ranking. The details about T2Ranking are elaborated in this paper.
Passage ranking are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). The goal of passage ranking is to compile a search result list ordered in terms of relevance to the query from a large passage collection. Typically, Passage ranking involves two stages: passage retrieval and passage re-ranking.
To support the passage ranking research, various benchmark datasets are constructed. However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues.
To address this problem, we introduce T2Ranking, a large-scale Chinese benchmark for passage ranking. T2Ranking comprises more than 300K queries and over 2M unique passages from real- world search engines. Specifically, we sample question-based search queries from user logs of the Sogou search engine, a popular search system in China. For each query, we extract the content of corresponding documents from different search engines. After model-based passage segmentation and clustering-based passage de-duplication, a large-scale passage corpus is obtained. For a given query and its corresponding passages, we hire expert annotators to provide 4-level relevance judgments of each query-passage pair.
: First (Second)- stage of passage ranking, i.e., passage Retrieval (Re-ranking).</div>
<p>Compared with existing datasets, T<sup>2</sup>Ranking dataset has the following characteristics and advantages:</p>
<ul>
<li>The proposed dataset focus on the Chinese search scenario, and has advantages in data scale compared with existing Chinese passage ranking datasets, which can better support the design of deep learning algorithms</li>
<li>The proposed dataset has a large number of fine-grained relevance annotations, which is helpful for mining fine-grained relationship between queries and passages and constructing more accurate ranking algorithms.</li>
<li>By retrieving passage results from multiple commercial search engines and providing complete annotation, we ease the false negative problem to some extent, which is beneficial to providing more accurate evaluation.</li>
<li>We design multiple strategies to ensure the high quality of our dataset, such as using a passage segment model and a passage clustering model to enhance the semantic integrity and diversity of passages and employing active learning for annotation method to improve the efficiency and quality of data annotation.</li>
</ul>
<h2>Data Download</h2>
<p>The whole dataset is placed in huggingface, and the data formats are presented in the following table.</p>
<div class=)
You can download the dataset by running the following command:
After downloading, you can find the following files in the folder:
Training and Evaluation
-----------------------
The dual-encoder can be trained by running the following command:
After training the model, you can evaluate the model by running the following command:
The cross-encoder can be trained by running the following command:
After training the model, you can evaluate the model by running the following command:
BM25 on DEV set
DPR w/o hard negatives on DEV set
DPR w/ hard negatives on DEV set
BM25 retrieved+CE reranked on DEV set
DPR retrieved+CE reranked on DEV set
License
-------
The dataset is licensed under the Apache License 2.0.
If you use this dataset in your research, please cite our paper:
|
[] |
[
"TAGS\n#task_categories-text-retrieval #size_categories-1M<n<10M #language-Chinese #license-apache-2.0 #arxiv-2304.03679 #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#task_categories-text-retrieval #size_categories-1M<n<10M #language-Chinese #license-apache-2.0 #arxiv-2304.03679 #region-us \n"
] |
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eb784358c5486bd09896509aa261b449d60432d0
|
# Dataset Card for Dataset Arabic-Tweets
## Dataset Description
- **Homepage:** https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus
- **Paper:** https://ieeexplore.ieee.org/document/10022652
### Dataset Summary
This dataset has been collected from twitter which is more than 41 GB of clean data of Arabic Tweets with nearly 4-billion Arabic words (12-million unique Arabic words).
### Languages
Arabic
### Source Data
Twitter
### Example on data loading using streaming:
```py
from datasets import load_dataset
dataset = load_dataset("pain/Arabic-Tweets",split='train', streaming=True)
print(next(iter(dataset)))
```
### Example on data loading without streaming "It will be downloaded locally":
```py
from datasets import load_dataset
dataset = load_dataset("pain/Arabic-Tweets",split='train')
print(dataset["train"][0])
```
#### Initial Data Collection and Normalization
The collected data comprises 100 GB of Twitter raw data. Only tweets with Arabic characters were crawled. It was observed that the new data contained a large number of Persian tweets as well as many Arabic words with repeated characters. Because of this and in order to improve the data efficiency the raw data was processed as follows: hashtags, mentions, and links were removed; tweets that contain Persian characters, 3 consecutive characters, or a singlecharacter word were dropped out; normalization of Arabic letters was considered.
This has resulted in more than 41 GB of clean data with nearly 4-billion Arabic words (12-million unique Arabic words).
## Considerations for Using the Data
- This data has been collected to create a language model. The tweets published without checking the tweets data. Therefore, we are not responsible for any tweets content at all.
### Licensing Information
[Creative Commons Attribution](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@INPROCEEDINGS{10022652,
author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
title={MASC: Massive Arabic Speech Corpus},
year={2023},
volume={},
number={},
pages={1006-1013},
doi={10.1109/SLT54892.2023.10022652}}
```
|
pain/Arabic-Tweets
|
[
"language:ar",
"license:cc-by-4.0",
"region:us"
] |
2023-04-04T06:56:20+00:00
|
{"language": ["ar"], "license": "cc-by-4.0"}
|
2023-04-08T09:02:07+00:00
|
[] |
[
"ar"
] |
TAGS
#language-Arabic #license-cc-by-4.0 #region-us
|
# Dataset Card for Dataset Arabic-Tweets
## Dataset Description
- Homepage: URL
- Paper: URL
### Dataset Summary
This dataset has been collected from twitter which is more than 41 GB of clean data of Arabic Tweets with nearly 4-billion Arabic words (12-million unique Arabic words).
### Languages
Arabic
### Source Data
Twitter
### Example on data loading using streaming:
### Example on data loading without streaming "It will be downloaded locally":
#### Initial Data Collection and Normalization
The collected data comprises 100 GB of Twitter raw data. Only tweets with Arabic characters were crawled. It was observed that the new data contained a large number of Persian tweets as well as many Arabic words with repeated characters. Because of this and in order to improve the data efficiency the raw data was processed as follows: hashtags, mentions, and links were removed; tweets that contain Persian characters, 3 consecutive characters, or a singlecharacter word were dropped out; normalization of Arabic letters was considered.
This has resulted in more than 41 GB of clean data with nearly 4-billion Arabic words (12-million unique Arabic words).
## Considerations for Using the Data
- This data has been collected to create a language model. The tweets published without checking the tweets data. Therefore, we are not responsible for any tweets content at all.
### Licensing Information
Creative Commons Attribution
|
[
"# Dataset Card for Dataset Arabic-Tweets",
"## Dataset Description\n\n- Homepage: URL\n- Paper: URL",
"### Dataset Summary\n\nThis dataset has been collected from twitter which is more than 41 GB of clean data of Arabic Tweets with nearly 4-billion Arabic words (12-million unique Arabic words).",
"### Languages\n\nArabic",
"### Source Data\n\nTwitter",
"### Example on data loading using streaming:",
"### Example on data loading without streaming \"It will be downloaded locally\":",
"#### Initial Data Collection and Normalization\n\nThe collected data comprises 100 GB of Twitter raw data. Only tweets with Arabic characters were crawled. It was observed that the new data contained a large number of Persian tweets as well as many Arabic words with repeated characters. Because of this and in order to improve the data efficiency the raw data was processed as follows: hashtags, mentions, and links were removed; tweets that contain Persian characters, 3 consecutive characters, or a singlecharacter word were dropped out; normalization of Arabic letters was considered. \n\nThis has resulted in more than 41 GB of clean data with nearly 4-billion Arabic words (12-million unique Arabic words).",
"## Considerations for Using the Data\n\n- This data has been collected to create a language model. The tweets published without checking the tweets data. Therefore, we are not responsible for any tweets content at all.",
"### Licensing Information\n\nCreative Commons Attribution"
] |
[
"TAGS\n#language-Arabic #license-cc-by-4.0 #region-us \n",
"# Dataset Card for Dataset Arabic-Tweets",
"## Dataset Description\n\n- Homepage: URL\n- Paper: URL",
"### Dataset Summary\n\nThis dataset has been collected from twitter which is more than 41 GB of clean data of Arabic Tweets with nearly 4-billion Arabic words (12-million unique Arabic words).",
"### Languages\n\nArabic",
"### Source Data\n\nTwitter",
"### Example on data loading using streaming:",
"### Example on data loading without streaming \"It will be downloaded locally\":",
"#### Initial Data Collection and Normalization\n\nThe collected data comprises 100 GB of Twitter raw data. Only tweets with Arabic characters were crawled. It was observed that the new data contained a large number of Persian tweets as well as many Arabic words with repeated characters. Because of this and in order to improve the data efficiency the raw data was processed as follows: hashtags, mentions, and links were removed; tweets that contain Persian characters, 3 consecutive characters, or a singlecharacter word were dropped out; normalization of Arabic letters was considered. \n\nThis has resulted in more than 41 GB of clean data with nearly 4-billion Arabic words (12-million unique Arabic words).",
"## Considerations for Using the Data\n\n- This data has been collected to create a language model. The tweets published without checking the tweets data. Therefore, we are not responsible for any tweets content at all.",
"### Licensing Information\n\nCreative Commons Attribution"
] |
[
20,
12,
12,
43,
5,
5,
11,
20,
154,
46,
9
] |
[
"passage: TAGS\n#language-Arabic #license-cc-by-4.0 #region-us \n# Dataset Card for Dataset Arabic-Tweets## Dataset Description\n\n- Homepage: URL\n- Paper: URL### Dataset Summary\n\nThis dataset has been collected from twitter which is more than 41 GB of clean data of Arabic Tweets with nearly 4-billion Arabic words (12-million unique Arabic words).### Languages\n\nArabic### Source Data\n\nTwitter### Example on data loading using streaming:### Example on data loading without streaming \"It will be downloaded locally\":#### Initial Data Collection and Normalization\n\nThe collected data comprises 100 GB of Twitter raw data. Only tweets with Arabic characters were crawled. It was observed that the new data contained a large number of Persian tweets as well as many Arabic words with repeated characters. Because of this and in order to improve the data efficiency the raw data was processed as follows: hashtags, mentions, and links were removed; tweets that contain Persian characters, 3 consecutive characters, or a singlecharacter word were dropped out; normalization of Arabic letters was considered. \n\nThis has resulted in more than 41 GB of clean data with nearly 4-billion Arabic words (12-million unique Arabic words).## Considerations for Using the Data\n\n- This data has been collected to create a language model. The tweets published without checking the tweets data. Therefore, we are not responsible for any tweets content at all.### Licensing Information\n\nCreative Commons Attribution"
] |
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] |
4eaf3232994679665bfb9295469d76b14fd47596
|
# Dataset Card for "hotpot_FiD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/hotpot_FiD
|
[
"region:us"
] |
2023-04-04T07:11:00+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "supporting_facts", "sequence": [{"name": "title", "dtype": "string"}, {"name": "sent_id", "dtype": "int32"}]}, {"name": "context", "sequence": [{"name": "title", "dtype": "string"}, {"name": "sentences", "sequence": "string"}]}, {"name": "para_list", "sequence": "string"}, {"name": "output", "dtype": "string"}, {"name": "gold_para", "sequence": "int64"}, {"name": "act_idxs", "sequence": "int64"}, {"name": "input10", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1749489303, "num_examples": 90447}, {"name": "validation", "num_bytes": 143793645, "num_examples": 7405}], "download_size": 1048534101, "dataset_size": 1893282948}}
|
2023-04-04T07:12:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hotpot_FiD"
More Information needed
|
[
"# Dataset Card for \"hotpot_FiD\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hotpot_FiD\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"hotpot_FiD\"\n\nMore Information needed"
] |
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-0.09958707541227341
] |
2c42a20dc8716aff5473c8507d6d78d9d2e6d553
|
# Dataset Card for "tib_slides_missing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gigant/tib_slides_missing
|
[
"region:us"
] |
2023-04-04T07:38:28+00:00
|
{"dataset_info": {"features": [{"name": "slide", "dtype": "image"}, {"name": "frames", "sequence": "int32"}, {"name": "timestamp", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 9488974488.254, "num_examples": 31567}], "download_size": 7810059369, "dataset_size": 9488974488.254}}
|
2023-04-04T19:14:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tib_slides_missing"
More Information needed
|
[
"# Dataset Card for \"tib_slides_missing\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tib_slides_missing\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tib_slides_missing\"\n\nMore Information needed"
] |
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] |
9519d95f23dc49212ec8e956ffc978589e7824bf
|
## Prepraration
```
pip3 install -r requirements.txt
```
## Data Cleaning
1. merge two raw json files and json beautify the merged file
```
python merge.py sharegpt_90k_raw_dataset/sg_90k_part1.json sharegpt_90k_raw_dataset/sg_90k_part2.json sharegpt_20230401_html_unformatted.json
python pretty_json.py --in sharegpt_20230401_html_unformatted.json --out sharegpt_20230401_html.json
```
2. (Optional) Verify the json file
```
if jq empty sharegpt_20230401_html.json 2>/dev/null; then
echo "JSON is valid"
else
echo "JSON is invalid"
fi
jq length sharegpt_90k_raw_dataset/sg_90k_part1.json
jq length sharegpt_90k_raw_dataset/sg_90k_part2.json
jq length sharegpt_20230401_html.json
```
3. clean data - remove html tags etc
```
python3 clean_sharegpt.py --in sharegpt_20230401_html.json --out sharegpt_20230401_clean.json
....
100%|███████████████████████████████████████████████████████████████████| 90665/90665 [06:32<00:00, 230.98it/s]
total: 90665, skip: 13745, new: 76920
```
4. Filter dataset by language
```
python3 optional_clean.py --in sharegpt_20230401_clean.json --out sharegpt_20230401_clean_lang_zh.json --lang zh
....
return 6240 out of 76920, start dump ...
python3 optional_clean.py --in sharegpt_20230401_clean.json --out sharegpt_20230401_clean_lang_en.json --lang en
...
return 55413 out of 76920, start dump ...
```
> Note: the code itself doesn't support languange list, I didn't change the code for adpation. You can change the code to support more languages. Instead, I just filter two languages I need and merge the `sharegpt_20230401_clean_lang_zh.json` and `sharegpt_20230401_clean_lang_en.json` into `sharegpt_20230401_clean_lang.json`.
5. Split the long conversation
```
python3 split_long_conversation.py --in sharegpt_20230401_clean_lang.json --out sharegpt_20230401_clean_lang_split.json --model-name /home/ubuntu/llama-13b-hf/
...
total: 61653, new: 126032
```
Ok, now we have the cleaned dataset `sharegpt_20230401_clean_lang_split.json` which should be used for finetuning.
|
philschmid/sharegpt-raw
|
[
"license:other",
"region:us"
] |
2023-04-04T07:52:59+00:00
|
{"license": "other", "duplicated_from": "jeffwan/sharegpt_vicuna"}
|
2023-04-04T07:52:59+00:00
|
[] |
[] |
TAGS
#license-other #region-us
|
## Prepraration
## Data Cleaning
1. merge two raw json files and json beautify the merged file
2. (Optional) Verify the json file
3. clean data - remove html tags etc
4. Filter dataset by language
> Note: the code itself doesn't support languange list, I didn't change the code for adpation. You can change the code to support more languages. Instead, I just filter two languages I need and merge the 'sharegpt_20230401_clean_lang_zh.json' and 'sharegpt_20230401_clean_lang_en.json' into 'sharegpt_20230401_clean_lang.json'.
5. Split the long conversation
Ok, now we have the cleaned dataset 'sharegpt_20230401_clean_lang_split.json' which should be used for finetuning.
|
[
"## Prepraration",
"## Data Cleaning\n\n1. merge two raw json files and json beautify the merged file\n\n\n\n2. (Optional) Verify the json file\n\n\n\n3. clean data - remove html tags etc\n\n\n\n4. Filter dataset by language\n\n\n\n> Note: the code itself doesn't support languange list, I didn't change the code for adpation. You can change the code to support more languages. Instead, I just filter two languages I need and merge the 'sharegpt_20230401_clean_lang_zh.json' and 'sharegpt_20230401_clean_lang_en.json' into 'sharegpt_20230401_clean_lang.json'. \n\n\n5. Split the long conversation\n\n\n\nOk, now we have the cleaned dataset 'sharegpt_20230401_clean_lang_split.json' which should be used for finetuning."
] |
[
"TAGS\n#license-other #region-us \n",
"## Prepraration",
"## Data Cleaning\n\n1. merge two raw json files and json beautify the merged file\n\n\n\n2. (Optional) Verify the json file\n\n\n\n3. clean data - remove html tags etc\n\n\n\n4. Filter dataset by language\n\n\n\n> Note: the code itself doesn't support languange list, I didn't change the code for adpation. You can change the code to support more languages. Instead, I just filter two languages I need and merge the 'sharegpt_20230401_clean_lang_zh.json' and 'sharegpt_20230401_clean_lang_en.json' into 'sharegpt_20230401_clean_lang.json'. \n\n\n5. Split the long conversation\n\n\n\nOk, now we have the cleaned dataset 'sharegpt_20230401_clean_lang_split.json' which should be used for finetuning."
] |
[
11,
4,
200
] |
[
"passage: TAGS\n#license-other #region-us \n## Prepraration## Data Cleaning\n\n1. merge two raw json files and json beautify the merged file\n\n\n\n2. (Optional) Verify the json file\n\n\n\n3. clean data - remove html tags etc\n\n\n\n4. Filter dataset by language\n\n\n\n> Note: the code itself doesn't support languange list, I didn't change the code for adpation. You can change the code to support more languages. Instead, I just filter two languages I need and merge the 'sharegpt_20230401_clean_lang_zh.json' and 'sharegpt_20230401_clean_lang_en.json' into 'sharegpt_20230401_clean_lang.json'. \n\n\n5. Split the long conversation\n\n\n\nOk, now we have the cleaned dataset 'sharegpt_20230401_clean_lang_split.json' which should be used for finetuning."
] |
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49953230f8c219677f97de6bb773410baecae1e9
|
# Dataset Card for "gen.3.facades"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lansinuote/gen.3.facades
|
[
"region:us"
] |
2023-04-04T08:00:15+00:00
|
{"dataset_info": {"features": [{"name": "a", "dtype": "image"}, {"name": "b", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 79532776.0, "num_examples": 606}], "download_size": 79244859, "dataset_size": 79532776.0}}
|
2023-04-04T08:02:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gen.3.facades"
More Information needed
|
[
"# Dataset Card for \"gen.3.facades\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gen.3.facades\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gen.3.facades\"\n\nMore Information needed"
] |
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] |
cc82c191ba06bef54b715d3cf1500f0ca8e46f4e
|
# Dataset Card for "lane_master"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Efferbach/lane_master
|
[
"region:us"
] |
2023-04-04T08:57:32+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 89415329.0, "num_examples": 129}], "download_size": 89404625, "dataset_size": 89415329.0}}
|
2023-04-08T07:39:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "lane_master"
More Information needed
|
[
"# Dataset Card for \"lane_master\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"lane_master\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"lane_master\"\n\nMore Information needed"
] |
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7c6ea441fa7a319a3bd909b230ad6f6aeb17f8a4
|
# Dataset Card for Letras Carnaval Cádiz
<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz/blob/main/README_es.md">Español</a>
<p>
</h4>
## Dataset Description
- **Homepage:** https://letrascarnavalcadiz.com
- **Repository:** https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz
- **Point of Contact:** [email protected]
### Changelog
|Release|Description|
|-|-|
|v1.0| Initial release of the dataset. Included more than 1K lyrics. It is necessary to verify the accuracy of the data, especially the subset midaccurate. |
### Dataset Summary
This dataset is a comprehensive collection of lyrics from the Carnaval de Cádiz, a significant cultural heritage of the city of Cádiz, Spain. Despite its cultural importance, there has been a lack of a structured database for these lyrics, hindering research and public access to this cultural heritage. This dataset aims to address this gap.
The dataset was created by the Cádiz AI Learning Community, a branch of the non-profit association Spain AI, and was developed by Iván Romero Reyna and Jesús Federico Franco Medinilla, students of the Specialization Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the 2022-2023 academic year. The project is supervised by Jesús Carlos Avecilla de la Herrán, a computational linguist.
Collaboration is encouraged, with individuals able to verify the different records of the dataset at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com), ensuring the transcription of the lyrics and all data are correct. New lyrics can also be added to the dataset. Corrections and additions are not immediately reflected in the dataset but are updated periodically.
For more information or to report a problem, you can write to [[email protected]](mailto:[email protected]).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in Spanish, reflecting the language of the Carnaval de Cádiz.
## Dataset Structure
### Data Instances
A typical instance in the dataset is formatted in JSON and contains the following fields:
```json
{
"id": "9de8647521b728c45ff45c1c11208708d055397fd7781b31cf91b473dff224d5",
"authors": ["Juan Carlos Aragón Becerra"],
"song_type": 2,
"year": "2018",
"group": "Los Mafiosos",
"group_type": 2,
"lyrics": [
"Mujer va llegando el momento",
"de ser la que lleve la rienda",
"el camino ha sido largo y polvoriento",
"pero ya no habrá varón que te detenga",
"gritad larga vida a la reina",
"que va a comenzar tu gobierno",
"ojalá no heredes nada",
"de aquel macho que te odiaba",
"porque en el fondo sabía",
"que ya tú te le acercabas",
"y el contigo no podía",
"ten en cuenta cuando hagas justicia",
"de volver a nivelar la balanza",
"y aguantar aunque tragando saliva",
"el deseo de venganza",
"de ser oh humano fatal",
"de ser o que puedo entender",
"tan solo con una mirada",
"la llaga que baña tu alma y tu piel",
"que te sirva la experiencia",
"del macho de la manada",
"la fuerza no vale nada",
"si no es con la inteligencia",
"y ojalá que tu conciencia",
"a mí me brinde la suerte",
"de nunca volver a verte",
"con los pies en una iglesia",
"que ella fue quien escribió",
"que ella fue quien escribió",
"la historia contra vosotras",
"y encima se la cobró",
"y encima se la cobró",
"con mil millones de devotas",
"ojalá que tu corona y tu bandera",
"abran paso a una vida nueva",
"como un mundo en primavera",
"ojalá que a ti no te envenene el poder",
"y que no dejes nunca de ser la mujer",
"que siempre fue nuestra gran compañera"
]
}
```
The `id` field uniquely identifies each instance in the dataset, providing a way to reference specific entries. The `authors`, `song_type`, `year`, `group`, and `group_type` fields provide context for the lyrics, while the `lyrics` field itself contains the actual text of the song. The relationships between these fields are implicit in the structure of the dataset, with each instance representing a single song from the Carnaval de Cádiz.
### Data Fields
`id`
Unique identifier for each song in the dataset. A SHA-256 hash calculated from the first four verses of the lyrics and the group name, with all spaces removed and converted to lowercase (string).
`authors`
List of authors who have written the song (string array).
`song_type`
The type of song (1: presentación, 2: pasodoble/tango, 3: cuplé, 4: estribillo, 5: popurrí, 6: cuarteta).
`year`
Year the song was written or performed (string).
`group`
Name of the group that performed the song (string).
`group_type`
The type of the group (1: coro, 2: comparsa, 3: chirigota, 4: cuarteto).
`lyrics`
The lyrics of the song, represented as an array of verses (string array).
### Data Splits
This dataset does not have traditional training, validation, and test splits. Instead, it is divided into two subsets: "accurate" and "midaccurate".
The "accurate" subset contains 958 instances. All fields of first 957 instances in this subset have been obtained through web scraping and have undergone at least one human review for accuracy. The rest have been added by users at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com).
The "midaccurate" subset contains 226 instances. The 'group' and 'lyrics' fields in this subset were collected through web scraping, but the remaining fields were filled in by querying language models connected to the Internet. Therefore, the data in these fields may not be accurate.
| Subset | Instances |
|-------------|----------:|
| Accurate | 958 |
| Midaccurate | 226 |
Please note that the division into subsets is based on the method and reliability of data collection, rather than a random or stratified split typically used in machine learning tasks. Users of the dataset should consider this when deciding how to use the data.
## Dataset Creation
### Curation Rationale
The dataset was created to address a significant need in the cultural heritage of the city of Cádiz, Spain. The Carnaval de Cádiz is a major cultural event, yet there was no structured database of its lyrics that could be consulted for research or public access. This lack of a structured database hindered the exploration and appreciation of this cultural heritage. The dataset was curated to respond to this need.
### Source Data
#### Initial Data Collection and Normalization
The initial collection of lyrics was carried out through automatic scraping of various websites and multimedia content on the Internet. To maximize the number of records with minimal effort, all collection is being done using different Artificial Intelligence models.
#### Who are the source language producers?
The source language producers of the dataset are the authors and performers of the songs from the Carnaval de Cádiz. These include a wide range of individuals and groups who have participated in the Carnaval over the years. The dataset does not include self-reported demographic or identity information for these individuals or groups.
The data in the dataset was collected from two websites: https://www.alsondelcarnaval.es and http://letrasdesdeelparaiso.blogspot.com. The first 957 instances of "accurate" subset of the dataset was collected from the former, while the "midaccurate" subset was collected from the latter. The data was extracted through automatic web scraping, and in the case of the "midaccurate" subset, some fields were filled in by querying language models connected to the Internet.
The rest of "accurate" subset have been added by users at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com).
### Personal and Sensitive Information
The only sensitive information in the dataset is the names and surnames of the authors of the lyrics.
## Considerations for Using the Data
### Social Impact of Dataset
The use of this dataset has significant social impact.
Firstly, this dataset can positively contribute to the understanding and preservation of Cadiz's culture and traditions, as the Carnaval de Cádiz is an integral part of the city's cultural identity. By providing an accessible and easily searchable resource for carnival song lyrics, this dataset can assist cultural researchers, linguists, and the general public in better understanding and appreciating the rich tradition of the Carnaval de Cádiz.
Additionally, this dataset can be utilized to enhance natural language processing (NLP) technologies in Spanish, a language that can sometimes be underrepresented in NLP research. By providing a high-quality, culture-specific Spanish text corpus, this dataset can aid in improving the accuracy and cultural relevance of Spanish NLP models.
However, there are also risks associated with the use of this dataset. For instance, if used to train text generation models, these models could generate content that reinforces cultural stereotypes or perpetuates existing biases. Moreover, the automatic interpretation of carnival song lyrics can be challenging due to cultural and linguistic subtleties, and errors in this interpretation could lead to misunderstandings or misrepresentations of Cadiz's culture.
Finally, although this dataset does not contain a low-resource or underrepresented language, it does focus on a specific cultural tradition from a specific region of Spain. Therefore, its use can impact the Cadiz community by helping to preserve and disseminate its unique culture and traditions.
### Discussion of Biases
The dataset is subject to several biases due to the nature of the data collection and the historical context of the Cadiz Carnival.
Firstly, there is a temporal bias in the dataset. More recent lyrics are overrepresented compared to older ones, as there is more information available on the internet about modern groups. This may lead to a skewed understanding of the evolution of the Carnival's themes over time.
Secondly, the dataset exhibits a popularity bias. Lyrics from more popular groups are overrepresented because individuals have chosen to write about them more frequently. This could potentially limit the diversity of styles and themes represented in the dataset.
Thirdly, there is a competition bias. Lyrics from groups that advanced further in the competition stages are overrepresented, resulting in more available lyrics from these groups. This might lead to an overemphasis on the styles and themes that tend to be more successful in the competition.
Lastly, the dataset reflects a gender bias. Given that there have historically been more male authors than female authors in the Cadiz Carnival, the majority of the dataset consists of lyrics written by men. This could potentially limit the representation of diverse perspectives and themes in the lyrics.
To mitigate these biases, we actively encourage the participation of the community. By verifying the different records of the dataset, reviewing the transcription of the lyrics and all the data for accuracy, and adding new lyrics, we hope to broaden the diversity and representation.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- Iván Romero Reyna. Student of the Specialisation Course in Artificial Intelligence and Big Data at [IES Rafael Alberti](https://iesrafaelalberti.es) during the academic year 2022-2023.
- Jesús Federico Franco Medinilla. Student of the Specialisation Course in Artificial Intelligence and Big Data at [IES Rafael Alberti](https://iesrafaelalberti.es) during the academic year 2022-2023.
- Jesús Carlos Avecilla de la Herrán. Promoter in [Cádiz AI](https://www.spain-ai.com).
### Licensing Information
[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0)
### Citation Information
```
@misc{letrascarnavalcadiz2023,
author = {Romero Reyna, Iván and Franco Medinilla, Jesús Federico and Avecilla de la Herrán, Jesús Carlos},
title = {letras-carnaval-cadiz},
year = {2023},
url = {https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz}
}
```
### Contributions
Thanks to [@ivanro](https://huggingface.co/ivanro), [@jframed281](https://huggingface.co/jframed281) for adding this dataset.
Thanks to all the reviewers and contributors at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com).
|
IES-Rafael-Alberti/letras-carnaval-cadiz
|
[
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"lyrics",
"carnival",
"cadiz",
"region:us"
] |
2023-04-04T09:34:51+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated"], "language": ["es"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": [], "task_ids": [], "pretty_name": "letrascarnavalcadiz", "tags": ["lyrics", "carnival", "cadiz"]}
|
2024-01-15T19:33:43+00:00
|
[] |
[
"es"
] |
TAGS
#annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-cc-by-sa-4.0 #lyrics #carnival #cadiz #region-us
|
Dataset Card for Letras Carnaval Cádiz
======================================
!logo
####
**English** |
<a href="URL>Español
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Point of Contact: contacto@URL
### Changelog
### Dataset Summary
This dataset is a comprehensive collection of lyrics from the Carnaval de Cádiz, a significant cultural heritage of the city of Cádiz, Spain. Despite its cultural importance, there has been a lack of a structured database for these lyrics, hindering research and public access to this cultural heritage. This dataset aims to address this gap.
The dataset was created by the Cádiz AI Learning Community, a branch of the non-profit association Spain AI, and was developed by Iván Romero Reyna and Jesús Federico Franco Medinilla, students of the Specialization Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the 2022-2023 academic year. The project is supervised by Jesús Carlos Avecilla de la Herrán, a computational linguist.
Collaboration is encouraged, with individuals able to verify the different records of the dataset at URL, ensuring the transcription of the lyrics and all data are correct. New lyrics can also be added to the dataset. Corrections and additions are not immediately reflected in the dataset but are updated periodically.
For more information or to report a problem, you can write to contacto@URL.
### Supported Tasks and Leaderboards
### Languages
The dataset is in Spanish, reflecting the language of the Carnaval de Cádiz.
Dataset Structure
-----------------
### Data Instances
A typical instance in the dataset is formatted in JSON and contains the following fields:
The 'id' field uniquely identifies each instance in the dataset, providing a way to reference specific entries. The 'authors', 'song\_type', 'year', 'group', and 'group\_type' fields provide context for the lyrics, while the 'lyrics' field itself contains the actual text of the song. The relationships between these fields are implicit in the structure of the dataset, with each instance representing a single song from the Carnaval de Cádiz.
### Data Fields
'id'
Unique identifier for each song in the dataset. A SHA-256 hash calculated from the first four verses of the lyrics and the group name, with all spaces removed and converted to lowercase (string).
'authors'
List of authors who have written the song (string array).
'song\_type'
The type of song (1: presentación, 2: pasodoble/tango, 3: cuplé, 4: estribillo, 5: popurrí, 6: cuarteta).
'year'
Year the song was written or performed (string).
'group'
Name of the group that performed the song (string).
'group\_type'
The type of the group (1: coro, 2: comparsa, 3: chirigota, 4: cuarteto).
'lyrics'
The lyrics of the song, represented as an array of verses (string array).
### Data Splits
This dataset does not have traditional training, validation, and test splits. Instead, it is divided into two subsets: "accurate" and "midaccurate".
The "accurate" subset contains 958 instances. All fields of first 957 instances in this subset have been obtained through web scraping and have undergone at least one human review for accuracy. The rest have been added by users at URL.
The "midaccurate" subset contains 226 instances. The 'group' and 'lyrics' fields in this subset were collected through web scraping, but the remaining fields were filled in by querying language models connected to the Internet. Therefore, the data in these fields may not be accurate.
Please note that the division into subsets is based on the method and reliability of data collection, rather than a random or stratified split typically used in machine learning tasks. Users of the dataset should consider this when deciding how to use the data.
Dataset Creation
----------------
### Curation Rationale
The dataset was created to address a significant need in the cultural heritage of the city of Cádiz, Spain. The Carnaval de Cádiz is a major cultural event, yet there was no structured database of its lyrics that could be consulted for research or public access. This lack of a structured database hindered the exploration and appreciation of this cultural heritage. The dataset was curated to respond to this need.
### Source Data
#### Initial Data Collection and Normalization
The initial collection of lyrics was carried out through automatic scraping of various websites and multimedia content on the Internet. To maximize the number of records with minimal effort, all collection is being done using different Artificial Intelligence models.
#### Who are the source language producers?
The source language producers of the dataset are the authors and performers of the songs from the Carnaval de Cádiz. These include a wide range of individuals and groups who have participated in the Carnaval over the years. The dataset does not include self-reported demographic or identity information for these individuals or groups.
The data in the dataset was collected from two websites: URL and URL. The first 957 instances of "accurate" subset of the dataset was collected from the former, while the "midaccurate" subset was collected from the latter. The data was extracted through automatic web scraping, and in the case of the "midaccurate" subset, some fields were filled in by querying language models connected to the Internet.
The rest of "accurate" subset have been added by users at URL.
### Personal and Sensitive Information
The only sensitive information in the dataset is the names and surnames of the authors of the lyrics.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
The use of this dataset has significant social impact.
Firstly, this dataset can positively contribute to the understanding and preservation of Cadiz's culture and traditions, as the Carnaval de Cádiz is an integral part of the city's cultural identity. By providing an accessible and easily searchable resource for carnival song lyrics, this dataset can assist cultural researchers, linguists, and the general public in better understanding and appreciating the rich tradition of the Carnaval de Cádiz.
Additionally, this dataset can be utilized to enhance natural language processing (NLP) technologies in Spanish, a language that can sometimes be underrepresented in NLP research. By providing a high-quality, culture-specific Spanish text corpus, this dataset can aid in improving the accuracy and cultural relevance of Spanish NLP models.
However, there are also risks associated with the use of this dataset. For instance, if used to train text generation models, these models could generate content that reinforces cultural stereotypes or perpetuates existing biases. Moreover, the automatic interpretation of carnival song lyrics can be challenging due to cultural and linguistic subtleties, and errors in this interpretation could lead to misunderstandings or misrepresentations of Cadiz's culture.
Finally, although this dataset does not contain a low-resource or underrepresented language, it does focus on a specific cultural tradition from a specific region of Spain. Therefore, its use can impact the Cadiz community by helping to preserve and disseminate its unique culture and traditions.
### Discussion of Biases
The dataset is subject to several biases due to the nature of the data collection and the historical context of the Cadiz Carnival.
Firstly, there is a temporal bias in the dataset. More recent lyrics are overrepresented compared to older ones, as there is more information available on the internet about modern groups. This may lead to a skewed understanding of the evolution of the Carnival's themes over time.
Secondly, the dataset exhibits a popularity bias. Lyrics from more popular groups are overrepresented because individuals have chosen to write about them more frequently. This could potentially limit the diversity of styles and themes represented in the dataset.
Thirdly, there is a competition bias. Lyrics from groups that advanced further in the competition stages are overrepresented, resulting in more available lyrics from these groups. This might lead to an overemphasis on the styles and themes that tend to be more successful in the competition.
Lastly, the dataset reflects a gender bias. Given that there have historically been more male authors than female authors in the Cadiz Carnival, the majority of the dataset consists of lyrics written by men. This could potentially limit the representation of diverse perspectives and themes in the lyrics.
To mitigate these biases, we actively encourage the participation of the community. By verifying the different records of the dataset, reviewing the transcription of the lyrics and all the data for accuracy, and adding new lyrics, we hope to broaden the diversity and representation.
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
* Iván Romero Reyna. Student of the Specialisation Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the academic year 2022-2023.
* Jesús Federico Franco Medinilla. Student of the Specialisation Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the academic year 2022-2023.
* Jesús Carlos Avecilla de la Herrán. Promoter in Cádiz AI.
### Licensing Information
CC BY-SA 4.0
### Contributions
Thanks to @ivanro, @jframed281 for adding this dataset.
Thanks to all the reviewers and contributors at URL.
|
[
"#### \n\n**English** |\n <a href=\"URL>Español\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Point of Contact: contacto@URL",
"### Changelog",
"### Dataset Summary\n\n\nThis dataset is a comprehensive collection of lyrics from the Carnaval de Cádiz, a significant cultural heritage of the city of Cádiz, Spain. Despite its cultural importance, there has been a lack of a structured database for these lyrics, hindering research and public access to this cultural heritage. This dataset aims to address this gap.\n\n\nThe dataset was created by the Cádiz AI Learning Community, a branch of the non-profit association Spain AI, and was developed by Iván Romero Reyna and Jesús Federico Franco Medinilla, students of the Specialization Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the 2022-2023 academic year. The project is supervised by Jesús Carlos Avecilla de la Herrán, a computational linguist.\n\n\nCollaboration is encouraged, with individuals able to verify the different records of the dataset at URL, ensuring the transcription of the lyrics and all data are correct. New lyrics can also be added to the dataset. Corrections and additions are not immediately reflected in the dataset but are updated periodically.\n\n\nFor more information or to report a problem, you can write to contacto@URL.",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThe dataset is in Spanish, reflecting the language of the Carnaval de Cádiz.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA typical instance in the dataset is formatted in JSON and contains the following fields:\n\n\nThe 'id' field uniquely identifies each instance in the dataset, providing a way to reference specific entries. The 'authors', 'song\\_type', 'year', 'group', and 'group\\_type' fields provide context for the lyrics, while the 'lyrics' field itself contains the actual text of the song. The relationships between these fields are implicit in the structure of the dataset, with each instance representing a single song from the Carnaval de Cádiz.",
"### Data Fields\n\n\n'id'\n\n\nUnique identifier for each song in the dataset. A SHA-256 hash calculated from the first four verses of the lyrics and the group name, with all spaces removed and converted to lowercase (string).\n\n\n'authors'\n\n\nList of authors who have written the song (string array).\n\n\n'song\\_type'\n\n\nThe type of song (1: presentación, 2: pasodoble/tango, 3: cuplé, 4: estribillo, 5: popurrí, 6: cuarteta).\n\n\n'year'\n\n\nYear the song was written or performed (string).\n\n\n'group'\n\n\nName of the group that performed the song (string).\n\n\n'group\\_type'\n\n\nThe type of the group (1: coro, 2: comparsa, 3: chirigota, 4: cuarteto).\n\n\n'lyrics'\n\n\nThe lyrics of the song, represented as an array of verses (string array).",
"### Data Splits\n\n\nThis dataset does not have traditional training, validation, and test splits. Instead, it is divided into two subsets: \"accurate\" and \"midaccurate\".\n\n\nThe \"accurate\" subset contains 958 instances. All fields of first 957 instances in this subset have been obtained through web scraping and have undergone at least one human review for accuracy. The rest have been added by users at URL.\n\n\nThe \"midaccurate\" subset contains 226 instances. The 'group' and 'lyrics' fields in this subset were collected through web scraping, but the remaining fields were filled in by querying language models connected to the Internet. Therefore, the data in these fields may not be accurate.\n\n\n\nPlease note that the division into subsets is based on the method and reliability of data collection, rather than a random or stratified split typically used in machine learning tasks. Users of the dataset should consider this when deciding how to use the data.\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was created to address a significant need in the cultural heritage of the city of Cádiz, Spain. The Carnaval de Cádiz is a major cultural event, yet there was no structured database of its lyrics that could be consulted for research or public access. This lack of a structured database hindered the exploration and appreciation of this cultural heritage. The dataset was curated to respond to this need.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe initial collection of lyrics was carried out through automatic scraping of various websites and multimedia content on the Internet. To maximize the number of records with minimal effort, all collection is being done using different Artificial Intelligence models.",
"#### Who are the source language producers?\n\n\nThe source language producers of the dataset are the authors and performers of the songs from the Carnaval de Cádiz. These include a wide range of individuals and groups who have participated in the Carnaval over the years. The dataset does not include self-reported demographic or identity information for these individuals or groups.\n\n\nThe data in the dataset was collected from two websites: URL and URL. The first 957 instances of \"accurate\" subset of the dataset was collected from the former, while the \"midaccurate\" subset was collected from the latter. The data was extracted through automatic web scraping, and in the case of the \"midaccurate\" subset, some fields were filled in by querying language models connected to the Internet.\n\n\nThe rest of \"accurate\" subset have been added by users at URL.",
"### Personal and Sensitive Information\n\n\nThe only sensitive information in the dataset is the names and surnames of the authors of the lyrics.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe use of this dataset has significant social impact.\n\n\nFirstly, this dataset can positively contribute to the understanding and preservation of Cadiz's culture and traditions, as the Carnaval de Cádiz is an integral part of the city's cultural identity. By providing an accessible and easily searchable resource for carnival song lyrics, this dataset can assist cultural researchers, linguists, and the general public in better understanding and appreciating the rich tradition of the Carnaval de Cádiz.\n\n\nAdditionally, this dataset can be utilized to enhance natural language processing (NLP) technologies in Spanish, a language that can sometimes be underrepresented in NLP research. By providing a high-quality, culture-specific Spanish text corpus, this dataset can aid in improving the accuracy and cultural relevance of Spanish NLP models.\n\n\nHowever, there are also risks associated with the use of this dataset. For instance, if used to train text generation models, these models could generate content that reinforces cultural stereotypes or perpetuates existing biases. Moreover, the automatic interpretation of carnival song lyrics can be challenging due to cultural and linguistic subtleties, and errors in this interpretation could lead to misunderstandings or misrepresentations of Cadiz's culture.\n\n\nFinally, although this dataset does not contain a low-resource or underrepresented language, it does focus on a specific cultural tradition from a specific region of Spain. Therefore, its use can impact the Cadiz community by helping to preserve and disseminate its unique culture and traditions.",
"### Discussion of Biases\n\n\nThe dataset is subject to several biases due to the nature of the data collection and the historical context of the Cadiz Carnival.\n\n\nFirstly, there is a temporal bias in the dataset. More recent lyrics are overrepresented compared to older ones, as there is more information available on the internet about modern groups. This may lead to a skewed understanding of the evolution of the Carnival's themes over time.\n\n\nSecondly, the dataset exhibits a popularity bias. Lyrics from more popular groups are overrepresented because individuals have chosen to write about them more frequently. This could potentially limit the diversity of styles and themes represented in the dataset.\n\n\nThirdly, there is a competition bias. Lyrics from groups that advanced further in the competition stages are overrepresented, resulting in more available lyrics from these groups. This might lead to an overemphasis on the styles and themes that tend to be more successful in the competition.\n\n\nLastly, the dataset reflects a gender bias. Given that there have historically been more male authors than female authors in the Cadiz Carnival, the majority of the dataset consists of lyrics written by men. This could potentially limit the representation of diverse perspectives and themes in the lyrics.\n\n\nTo mitigate these biases, we actively encourage the participation of the community. By verifying the different records of the dataset, reviewing the transcription of the lyrics and all the data for accuracy, and adding new lyrics, we hope to broaden the diversity and representation.",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Iván Romero Reyna. Student of the Specialisation Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the academic year 2022-2023.\n* Jesús Federico Franco Medinilla. Student of the Specialisation Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the academic year 2022-2023.\n* Jesús Carlos Avecilla de la Herrán. Promoter in Cádiz AI.",
"### Licensing Information\n\n\nCC BY-SA 4.0",
"### Contributions\n\n\nThanks to @ivanro, @jframed281 for adding this dataset.\n\n\nThanks to all the reviewers and contributors at URL."
] |
[
"TAGS\n#annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-cc-by-sa-4.0 #lyrics #carnival #cadiz #region-us \n",
"#### \n\n**English** |\n <a href=\"URL>Español\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Point of Contact: contacto@URL",
"### Changelog",
"### Dataset Summary\n\n\nThis dataset is a comprehensive collection of lyrics from the Carnaval de Cádiz, a significant cultural heritage of the city of Cádiz, Spain. Despite its cultural importance, there has been a lack of a structured database for these lyrics, hindering research and public access to this cultural heritage. This dataset aims to address this gap.\n\n\nThe dataset was created by the Cádiz AI Learning Community, a branch of the non-profit association Spain AI, and was developed by Iván Romero Reyna and Jesús Federico Franco Medinilla, students of the Specialization Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the 2022-2023 academic year. The project is supervised by Jesús Carlos Avecilla de la Herrán, a computational linguist.\n\n\nCollaboration is encouraged, with individuals able to verify the different records of the dataset at URL, ensuring the transcription of the lyrics and all data are correct. New lyrics can also be added to the dataset. Corrections and additions are not immediately reflected in the dataset but are updated periodically.\n\n\nFor more information or to report a problem, you can write to contacto@URL.",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nThe dataset is in Spanish, reflecting the language of the Carnaval de Cádiz.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA typical instance in the dataset is formatted in JSON and contains the following fields:\n\n\nThe 'id' field uniquely identifies each instance in the dataset, providing a way to reference specific entries. The 'authors', 'song\\_type', 'year', 'group', and 'group\\_type' fields provide context for the lyrics, while the 'lyrics' field itself contains the actual text of the song. The relationships between these fields are implicit in the structure of the dataset, with each instance representing a single song from the Carnaval de Cádiz.",
"### Data Fields\n\n\n'id'\n\n\nUnique identifier for each song in the dataset. A SHA-256 hash calculated from the first four verses of the lyrics and the group name, with all spaces removed and converted to lowercase (string).\n\n\n'authors'\n\n\nList of authors who have written the song (string array).\n\n\n'song\\_type'\n\n\nThe type of song (1: presentación, 2: pasodoble/tango, 3: cuplé, 4: estribillo, 5: popurrí, 6: cuarteta).\n\n\n'year'\n\n\nYear the song was written or performed (string).\n\n\n'group'\n\n\nName of the group that performed the song (string).\n\n\n'group\\_type'\n\n\nThe type of the group (1: coro, 2: comparsa, 3: chirigota, 4: cuarteto).\n\n\n'lyrics'\n\n\nThe lyrics of the song, represented as an array of verses (string array).",
"### Data Splits\n\n\nThis dataset does not have traditional training, validation, and test splits. Instead, it is divided into two subsets: \"accurate\" and \"midaccurate\".\n\n\nThe \"accurate\" subset contains 958 instances. All fields of first 957 instances in this subset have been obtained through web scraping and have undergone at least one human review for accuracy. The rest have been added by users at URL.\n\n\nThe \"midaccurate\" subset contains 226 instances. The 'group' and 'lyrics' fields in this subset were collected through web scraping, but the remaining fields were filled in by querying language models connected to the Internet. Therefore, the data in these fields may not be accurate.\n\n\n\nPlease note that the division into subsets is based on the method and reliability of data collection, rather than a random or stratified split typically used in machine learning tasks. Users of the dataset should consider this when deciding how to use the data.\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was created to address a significant need in the cultural heritage of the city of Cádiz, Spain. The Carnaval de Cádiz is a major cultural event, yet there was no structured database of its lyrics that could be consulted for research or public access. This lack of a structured database hindered the exploration and appreciation of this cultural heritage. The dataset was curated to respond to this need.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe initial collection of lyrics was carried out through automatic scraping of various websites and multimedia content on the Internet. To maximize the number of records with minimal effort, all collection is being done using different Artificial Intelligence models.",
"#### Who are the source language producers?\n\n\nThe source language producers of the dataset are the authors and performers of the songs from the Carnaval de Cádiz. These include a wide range of individuals and groups who have participated in the Carnaval over the years. The dataset does not include self-reported demographic or identity information for these individuals or groups.\n\n\nThe data in the dataset was collected from two websites: URL and URL. The first 957 instances of \"accurate\" subset of the dataset was collected from the former, while the \"midaccurate\" subset was collected from the latter. The data was extracted through automatic web scraping, and in the case of the \"midaccurate\" subset, some fields were filled in by querying language models connected to the Internet.\n\n\nThe rest of \"accurate\" subset have been added by users at URL.",
"### Personal and Sensitive Information\n\n\nThe only sensitive information in the dataset is the names and surnames of the authors of the lyrics.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nThe use of this dataset has significant social impact.\n\n\nFirstly, this dataset can positively contribute to the understanding and preservation of Cadiz's culture and traditions, as the Carnaval de Cádiz is an integral part of the city's cultural identity. By providing an accessible and easily searchable resource for carnival song lyrics, this dataset can assist cultural researchers, linguists, and the general public in better understanding and appreciating the rich tradition of the Carnaval de Cádiz.\n\n\nAdditionally, this dataset can be utilized to enhance natural language processing (NLP) technologies in Spanish, a language that can sometimes be underrepresented in NLP research. By providing a high-quality, culture-specific Spanish text corpus, this dataset can aid in improving the accuracy and cultural relevance of Spanish NLP models.\n\n\nHowever, there are also risks associated with the use of this dataset. For instance, if used to train text generation models, these models could generate content that reinforces cultural stereotypes or perpetuates existing biases. Moreover, the automatic interpretation of carnival song lyrics can be challenging due to cultural and linguistic subtleties, and errors in this interpretation could lead to misunderstandings or misrepresentations of Cadiz's culture.\n\n\nFinally, although this dataset does not contain a low-resource or underrepresented language, it does focus on a specific cultural tradition from a specific region of Spain. Therefore, its use can impact the Cadiz community by helping to preserve and disseminate its unique culture and traditions.",
"### Discussion of Biases\n\n\nThe dataset is subject to several biases due to the nature of the data collection and the historical context of the Cadiz Carnival.\n\n\nFirstly, there is a temporal bias in the dataset. More recent lyrics are overrepresented compared to older ones, as there is more information available on the internet about modern groups. This may lead to a skewed understanding of the evolution of the Carnival's themes over time.\n\n\nSecondly, the dataset exhibits a popularity bias. Lyrics from more popular groups are overrepresented because individuals have chosen to write about them more frequently. This could potentially limit the diversity of styles and themes represented in the dataset.\n\n\nThirdly, there is a competition bias. Lyrics from groups that advanced further in the competition stages are overrepresented, resulting in more available lyrics from these groups. This might lead to an overemphasis on the styles and themes that tend to be more successful in the competition.\n\n\nLastly, the dataset reflects a gender bias. Given that there have historically been more male authors than female authors in the Cadiz Carnival, the majority of the dataset consists of lyrics written by men. This could potentially limit the representation of diverse perspectives and themes in the lyrics.\n\n\nTo mitigate these biases, we actively encourage the participation of the community. By verifying the different records of the dataset, reviewing the transcription of the lyrics and all the data for accuracy, and adding new lyrics, we hope to broaden the diversity and representation.",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Iván Romero Reyna. Student of the Specialisation Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the academic year 2022-2023.\n* Jesús Federico Franco Medinilla. Student of the Specialisation Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the academic year 2022-2023.\n* Jesús Carlos Avecilla de la Herrán. Promoter in Cádiz AI.",
"### Licensing Information\n\n\nCC BY-SA 4.0",
"### Contributions\n\n\nThanks to @ivanro, @jframed281 for adding this dataset.\n\n\nThanks to all the reviewers and contributors at URL."
] |
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240,
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[
"passage: TAGS\n#annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-cc-by-sa-4.0 #lyrics #carnival #cadiz #region-us \n#### \n\n**English** |\n <a href=\"URL>Español\n\n\nDataset Description\n-------------------\n\n\n* Homepage: URL\n* Repository: URL\n* Point of Contact: contacto@URL### Changelog### Dataset Summary\n\n\nThis dataset is a comprehensive collection of lyrics from the Carnaval de Cádiz, a significant cultural heritage of the city of Cádiz, Spain. Despite its cultural importance, there has been a lack of a structured database for these lyrics, hindering research and public access to this cultural heritage. This dataset aims to address this gap.\n\n\nThe dataset was created by the Cádiz AI Learning Community, a branch of the non-profit association Spain AI, and was developed by Iván Romero Reyna and Jesús Federico Franco Medinilla, students of the Specialization Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the 2022-2023 academic year. The project is supervised by Jesús Carlos Avecilla de la Herrán, a computational linguist.\n\n\nCollaboration is encouraged, with individuals able to verify the different records of the dataset at URL, ensuring the transcription of the lyrics and all data are correct. New lyrics can also be added to the dataset. Corrections and additions are not immediately reflected in the dataset but are updated periodically.\n\n\nFor more information or to report a problem, you can write to contacto@URL.### Supported Tasks and Leaderboards### Languages\n\n\nThe dataset is in Spanish, reflecting the language of the Carnaval de Cádiz.\n\n\nDataset Structure\n-----------------",
"passage: ### Data Instances\n\n\nA typical instance in the dataset is formatted in JSON and contains the following fields:\n\n\nThe 'id' field uniquely identifies each instance in the dataset, providing a way to reference specific entries. The 'authors', 'song\\_type', 'year', 'group', and 'group\\_type' fields provide context for the lyrics, while the 'lyrics' field itself contains the actual text of the song. The relationships between these fields are implicit in the structure of the dataset, with each instance representing a single song from the Carnaval de Cádiz.### Data Fields\n\n\n'id'\n\n\nUnique identifier for each song in the dataset. A SHA-256 hash calculated from the first four verses of the lyrics and the group name, with all spaces removed and converted to lowercase (string).\n\n\n'authors'\n\n\nList of authors who have written the song (string array).\n\n\n'song\\_type'\n\n\nThe type of song (1: presentación, 2: pasodoble/tango, 3: cuplé, 4: estribillo, 5: popurrí, 6: cuarteta).\n\n\n'year'\n\n\nYear the song was written or performed (string).\n\n\n'group'\n\n\nName of the group that performed the song (string).\n\n\n'group\\_type'\n\n\nThe type of the group (1: coro, 2: comparsa, 3: chirigota, 4: cuarteto).\n\n\n'lyrics'\n\n\nThe lyrics of the song, represented as an array of verses (string array).### Data Splits\n\n\nThis dataset does not have traditional training, validation, and test splits. Instead, it is divided into two subsets: \"accurate\" and \"midaccurate\".\n\n\nThe \"accurate\" subset contains 958 instances. All fields of first 957 instances in this subset have been obtained through web scraping and have undergone at least one human review for accuracy. The rest have been added by users at URL.\n\n\nThe \"midaccurate\" subset contains 226 instances. The 'group' and 'lyrics' fields in this subset were collected through web scraping, but the remaining fields were filled in by querying language models connected to the Internet. Therefore, the data in these fields may not be accurate.\n\n\n\nPlease note that the division into subsets is based on the method and reliability of data collection, rather than a random or stratified split typically used in machine learning tasks. Users of the dataset should consider this when deciding how to use the data.\n\n\nDataset Creation\n----------------",
"passage: ### Curation Rationale\n\n\nThe dataset was created to address a significant need in the cultural heritage of the city of Cádiz, Spain. The Carnaval de Cádiz is a major cultural event, yet there was no structured database of its lyrics that could be consulted for research or public access. This lack of a structured database hindered the exploration and appreciation of this cultural heritage. The dataset was curated to respond to this need.### Source Data#### Initial Data Collection and Normalization\n\n\nThe initial collection of lyrics was carried out through automatic scraping of various websites and multimedia content on the Internet. To maximize the number of records with minimal effort, all collection is being done using different Artificial Intelligence models.#### Who are the source language producers?\n\n\nThe source language producers of the dataset are the authors and performers of the songs from the Carnaval de Cádiz. These include a wide range of individuals and groups who have participated in the Carnaval over the years. The dataset does not include self-reported demographic or identity information for these individuals or groups.\n\n\nThe data in the dataset was collected from two websites: URL and URL. The first 957 instances of \"accurate\" subset of the dataset was collected from the former, while the \"midaccurate\" subset was collected from the latter. The data was extracted through automatic web scraping, and in the case of the \"midaccurate\" subset, some fields were filled in by querying language models connected to the Internet.\n\n\nThe rest of \"accurate\" subset have been added by users at URL.### Personal and Sensitive Information\n\n\nThe only sensitive information in the dataset is the names and surnames of the authors of the lyrics.\n\n\nConsiderations for Using the Data\n---------------------------------"
] |
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] |
53fa949d97339bb8ad82b6d46ce004512b6266f7
|
# Dataset Card for RISCBAC
RISCBAC was created using [RISC](https://github.com/GRAAL-Research/risc), an open-source Python package data generator. RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English.
It contains 10,000 English and French insurance contracts generated using the same seed. Thus, contracts share the same deterministic synthetic data (RISCBAC can be used as an aligned dataset). RISC can be used to generate more data for RISCBAC.
# Data Instances
## Default (`'fr'`)
The default data instance is the French version of the dataset. The dataset is comprised of 10,000 synthetic automobile insurance contracts.
## Other Option
The other data instance option is `"en"`. The dataset is comprised of 10,000 synthetic automobile insurance contracts.
# Citation Information
```
@misc{beaucheminrisc,
title={{RISC: Generating Realistic Synthetic Bilingual Insurance
Contract}},
author={David Beauchemin and Richard Khoury},
year={2023},
eprint={2304.04212},
archivePrefix={arXiv}
}
```
|
davebulaval/RISCBAC
|
[
"task_categories:summarization",
"task_categories:question-answering",
"task_categories:translation",
"multilinguality:monolingual",
"multilinguality:aligned",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:fr",
"license:cc-by-4.0",
"unsupervised",
"arxiv:2304.04212",
"doi:10.57967/hf/0501",
"region:us"
] |
2023-04-04T09:48:51+00:00
|
{"language": ["en", "fr"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual", "aligned"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization", "question-answering", "translation"], "pretty_name": "Realistic Bilingual Synthetic Automobile Insurance Contract", "tags": ["unsupervised"], "dataset_info": {"download_size": 376971, "dataset_size": 611048}, "viewer": true}
|
2023-08-10T21:04:01+00:00
|
[
"2304.04212"
] |
[
"en",
"fr"
] |
TAGS
#task_categories-summarization #task_categories-question-answering #task_categories-translation #multilinguality-monolingual #multilinguality-aligned #size_categories-10K<n<100K #source_datasets-original #language-English #language-French #license-cc-by-4.0 #unsupervised #arxiv-2304.04212 #doi-10.57967/hf/0501 #region-us
|
# Dataset Card for RISCBAC
RISCBAC was created using RISC, an open-source Python package data generator. RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English.
It contains 10,000 English and French insurance contracts generated using the same seed. Thus, contracts share the same deterministic synthetic data (RISCBAC can be used as an aligned dataset). RISC can be used to generate more data for RISCBAC.
# Data Instances
## Default (''fr'')
The default data instance is the French version of the dataset. The dataset is comprised of 10,000 synthetic automobile insurance contracts.
## Other Option
The other data instance option is '"en"'. The dataset is comprised of 10,000 synthetic automobile insurance contracts.
|
[
"# Dataset Card for RISCBAC\n\nRISCBAC was created using RISC, an open-source Python package data generator. RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English.\n\nIt contains 10,000 English and French insurance contracts generated using the same seed. Thus, contracts share the same deterministic synthetic data (RISCBAC can be used as an aligned dataset). RISC can be used to generate more data for RISCBAC.",
"# Data Instances",
"## Default (''fr'')\nThe default data instance is the French version of the dataset. The dataset is comprised of 10,000 synthetic automobile insurance contracts.",
"## Other Option\nThe other data instance option is '\"en\"'. The dataset is comprised of 10,000 synthetic automobile insurance contracts."
] |
[
"TAGS\n#task_categories-summarization #task_categories-question-answering #task_categories-translation #multilinguality-monolingual #multilinguality-aligned #size_categories-10K<n<100K #source_datasets-original #language-English #language-French #license-cc-by-4.0 #unsupervised #arxiv-2304.04212 #doi-10.57967/hf/0501 #region-us \n",
"# Dataset Card for RISCBAC\n\nRISCBAC was created using RISC, an open-source Python package data generator. RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English.\n\nIt contains 10,000 English and French insurance contracts generated using the same seed. Thus, contracts share the same deterministic synthetic data (RISCBAC can be used as an aligned dataset). RISC can be used to generate more data for RISCBAC.",
"# Data Instances",
"## Default (''fr'')\nThe default data instance is the French version of the dataset. The dataset is comprised of 10,000 synthetic automobile insurance contracts.",
"## Other Option\nThe other data instance option is '\"en\"'. The dataset is comprised of 10,000 synthetic automobile insurance contracts."
] |
[
117,
115,
5,
38,
31
] |
[
"passage: TAGS\n#task_categories-summarization #task_categories-question-answering #task_categories-translation #multilinguality-monolingual #multilinguality-aligned #size_categories-10K<n<100K #source_datasets-original #language-English #language-French #license-cc-by-4.0 #unsupervised #arxiv-2304.04212 #doi-10.57967/hf/0501 #region-us \n# Dataset Card for RISCBAC\n\nRISCBAC was created using RISC, an open-source Python package data generator. RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English.\n\nIt contains 10,000 English and French insurance contracts generated using the same seed. Thus, contracts share the same deterministic synthetic data (RISCBAC can be used as an aligned dataset). RISC can be used to generate more data for RISCBAC.# Data Instances## Default (''fr'')\nThe default data instance is the French version of the dataset. The dataset is comprised of 10,000 synthetic automobile insurance contracts.## Other Option\nThe other data instance option is '\"en\"'. The dataset is comprised of 10,000 synthetic automobile insurance contracts."
] |
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] |
563ecc7f6ceb6d9a93e543b6466398edb1339ba0
|
# Dataset Card for "DiagTrast"
## Table of Content
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Team members](#team-members)
## Dataset Description
### Dataset Summary
For the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the "Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)". The mental disorders included are:
- Narcissistic personality disorder.
- Histrionic personality disorder.
- Borderline personality disorder.
- Antisocial personality disorder.
- Schizotypal personality disorder.
### Supported Tasks and Leaderboards
- text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.
### Languages
This dataset of statements is in Spanish only.
## Dataset Structure
### Data Instances
A typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).
The following is a JSON-formatted example of a typical case in this dataset:
```
{
'Sintoma': "Su comportamiento es a menudo extraño y excéntrico, como llevar ropa que no coincide o actuar de una manera inapropiada en situaciones sociales.",
'Padecimiento': "Trastornos de la personalidad esquizotípica",
'Padecimiento_cat': 2,
'Sintoma_limpia ': "comportamiento menudo extraño excentrico llevar ropa coincide actuar manera inapropiada situaciones sociales"
}
```
### Data Fields
- `Sintoma`: a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.
- `Padecimiento`: a string that indicates the disorder according to DSM-5.
- `Padecimiento_cat`: an integer representing the `Padecimiento` field, this field can be used as a label in a text-classification model.
- `Sintoma_Limpia`: a string, this field is the clean text of the `Sintoma` field. For the text-classification task, is advisable to use this field instead of the "Padecimiento" field to reduce the noise that punctuation marks, articles and connectors generate in the models.
### Data Splits
The data were not split into training and test subsets, instead having a single set with the following distribution:
| Disorder | Records |
| - | - |
| Narcissistic personality disorder| 250 |
| Histrionic personality disorder | 250 |
| Borderline personality disorder | 358 |
| Antisocial personality disorder | 250 |
| Schizotypal personality disorder | 225 |
## Dataset Creation
### Curation Rationale
It was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.
### Source Data
Data was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.
### Annotations
#### Annotation process
The generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder.
### Discussion of Biases
It should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality.
### Other Known Limitations
We have only generated data for 5 of the disorders described in the DSM-5.
## Team members
- [Alberto Martín Garrido](https://huggingface.co/Stremie)
- [Edgar Mencia](https://huggingface.co/edmenciab)
- [Miguel Ángel Solís Orozco](https://huggingface.co/homosapienssapiens)
- [Jose Carlos Vílchez Villegas](https://huggingface.co/JCarlos)
|
hackathon-somos-nlp-2023/DiagTrast
|
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:es",
"license:mit",
"mental",
"medical",
"disorder",
"region:us"
] |
2023-04-04T09:53:27+00:00
|
{"language": ["es"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "DiagTrast", "dataset_info": {"features": [{"name": "Sintoma", "dtype": "string"}, {"name": "Padecimiento", "dtype": "string"}, {"name": "Padecimiento_cat", "dtype": "int64"}, {"name": "Sintoma_limpia", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 524464, "num_examples": 1333}], "download_size": 232511, "dataset_size": 524464}, "tags": ["mental", "medical", "disorder"]}
|
2023-04-09T21:38:37+00:00
|
[] |
[
"es"
] |
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #license-mit #mental #medical #disorder #region-us
|
Dataset Card for "DiagTrast"
============================
Table of Content
----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
* Considerations for Using the Data
+ Social Impact of Dataset
+ Discussion of Biases
+ Other Known Limitations
* Team members
Dataset Description
-------------------
### Dataset Summary
For the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the "Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)". The mental disorders included are:
* Narcissistic personality disorder.
* Histrionic personality disorder.
* Borderline personality disorder.
* Antisocial personality disorder.
* Schizotypal personality disorder.
### Supported Tasks and Leaderboards
* text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.
### Languages
This dataset of statements is in Spanish only.
Dataset Structure
-----------------
### Data Instances
A typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).
The following is a JSON-formatted example of a typical case in this dataset:
### Data Fields
* 'Sintoma': a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.
* 'Padecimiento': a string that indicates the disorder according to DSM-5.
* 'Padecimiento\_cat': an integer representing the 'Padecimiento' field, this field can be used as a label in a text-classification model.
* 'Sintoma\_Limpia': a string, this field is the clean text of the 'Sintoma' field. For the text-classification task, is advisable to use this field instead of the "Padecimiento" field to reduce the noise that punctuation marks, articles and connectors generate in the models.
### Data Splits
The data were not split into training and test subsets, instead having a single set with the following distribution:
Dataset Creation
----------------
### Curation Rationale
It was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.
### Source Data
Data was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.
### Annotations
#### Annotation process
The generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
We hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder.
### Discussion of Biases
It should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality.
### Other Known Limitations
We have only generated data for 5 of the disorders described in the DSM-5.
Team members
------------
* Alberto Martín Garrido
* Edgar Mencia
* Miguel Ángel Solís Orozco
* Jose Carlos Vílchez Villegas
|
[
"### Dataset Summary\n\n\nFor the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the \"Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)\". The mental disorders included are:\n\n\n* Narcissistic personality disorder.\n* Histrionic personality disorder.\n* Borderline personality disorder.\n* Antisocial personality disorder.\n* Schizotypal personality disorder.",
"### Supported Tasks and Leaderboards\n\n\n* text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.",
"### Languages\n\n\nThis dataset of statements is in Spanish only.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).\n\n\nThe following is a JSON-formatted example of a typical case in this dataset:",
"### Data Fields\n\n\n* 'Sintoma': a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.\n* 'Padecimiento': a string that indicates the disorder according to DSM-5.\n* 'Padecimiento\\_cat': an integer representing the 'Padecimiento' field, this field can be used as a label in a text-classification model.\n* 'Sintoma\\_Limpia': a string, this field is the clean text of the 'Sintoma' field. For the text-classification task, is advisable to use this field instead of the \"Padecimiento\" field to reduce the noise that punctuation marks, articles and connectors generate in the models.",
"### Data Splits\n\n\nThe data were not split into training and test subsets, instead having a single set with the following distribution:\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nIt was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.",
"### Source Data\n\n\nData was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.",
"### Annotations",
"#### Annotation process\n\n\nThe generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nWe hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder.",
"### Discussion of Biases\n\n\nIt should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality.",
"### Other Known Limitations\n\n\nWe have only generated data for 5 of the disorders described in the DSM-5.\n\n\nTeam members\n------------\n\n\n* Alberto Martín Garrido\n* Edgar Mencia\n* Miguel Ángel Solís Orozco\n* Jose Carlos Vílchez Villegas"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #license-mit #mental #medical #disorder #region-us \n",
"### Dataset Summary\n\n\nFor the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the \"Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)\". The mental disorders included are:\n\n\n* Narcissistic personality disorder.\n* Histrionic personality disorder.\n* Borderline personality disorder.\n* Antisocial personality disorder.\n* Schizotypal personality disorder.",
"### Supported Tasks and Leaderboards\n\n\n* text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.",
"### Languages\n\n\nThis dataset of statements is in Spanish only.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).\n\n\nThe following is a JSON-formatted example of a typical case in this dataset:",
"### Data Fields\n\n\n* 'Sintoma': a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.\n* 'Padecimiento': a string that indicates the disorder according to DSM-5.\n* 'Padecimiento\\_cat': an integer representing the 'Padecimiento' field, this field can be used as a label in a text-classification model.\n* 'Sintoma\\_Limpia': a string, this field is the clean text of the 'Sintoma' field. For the text-classification task, is advisable to use this field instead of the \"Padecimiento\" field to reduce the noise that punctuation marks, articles and connectors generate in the models.",
"### Data Splits\n\n\nThe data were not split into training and test subsets, instead having a single set with the following distribution:\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nIt was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.",
"### Source Data\n\n\nData was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.",
"### Annotations",
"#### Annotation process\n\n\nThe generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nWe hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder.",
"### Discussion of Biases\n\n\nIt should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality.",
"### Other Known Limitations\n\n\nWe have only generated data for 5 of the disorders described in the DSM-5.\n\n\nTeam members\n------------\n\n\n* Alberto Martín Garrido\n* Edgar Mencia\n* Miguel Ángel Solís Orozco\n* Jose Carlos Vílchez Villegas"
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[
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #license-mit #mental #medical #disorder #region-us \n### Dataset Summary\n\n\nFor the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the \"Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)\". The mental disorders included are:\n\n\n* Narcissistic personality disorder.\n* Histrionic personality disorder.\n* Borderline personality disorder.\n* Antisocial personality disorder.\n* Schizotypal personality disorder.### Supported Tasks and Leaderboards\n\n\n* text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.### Languages\n\n\nThis dataset of statements is in Spanish only.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).\n\n\nThe following is a JSON-formatted example of a typical case in this dataset:",
"passage: ### Data Fields\n\n\n* 'Sintoma': a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.\n* 'Padecimiento': a string that indicates the disorder according to DSM-5.\n* 'Padecimiento\\_cat': an integer representing the 'Padecimiento' field, this field can be used as a label in a text-classification model.\n* 'Sintoma\\_Limpia': a string, this field is the clean text of the 'Sintoma' field. For the text-classification task, is advisable to use this field instead of the \"Padecimiento\" field to reduce the noise that punctuation marks, articles and connectors generate in the models.### Data Splits\n\n\nThe data were not split into training and test subsets, instead having a single set with the following distribution:\n\n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nIt was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.### Source Data\n\n\nData was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.### Annotations#### Annotation process\n\n\nThe generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nWe hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder."
] |
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] |
0f26fdab2f6f556744d68bdc6b2b218ac9ec7007
|
# Dataset Card for "sitcoms_captioned-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/sitcoms_captioned-augmented
|
[
"region:us"
] |
2023-04-04T11:19:51+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3079485599.75, "num_examples": 11490}], "download_size": 3078348034, "dataset_size": 3079485599.75}}
|
2023-04-04T11:21:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sitcoms_captioned-augmented"
More Information needed
|
[
"# Dataset Card for \"sitcoms_captioned-augmented\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sitcoms_captioned-augmented\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sitcoms_captioned-augmented\"\n\nMore Information needed"
] |
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fcdaaefdd4b8d49ec623da9abd62feb7bfec0f10
|
# Dataset Card for "portuguese-mapa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arubenruben/portuguese-mapa
|
[
"region:us"
] |
2023-04-04T11:19:55+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-PESSOA", "2": "I-PESSOA", "3": "B-ORGANIZACAO", "4": "I-ORGANIZACAO", "5": "B-LOCAL", "6": "I-LOCAL", "7": "B-TEMPO", "8": "I-TEMPO", "9": "B-VALOR", "10": "I-VALOR"}}}}], "splits": [{"name": "train", "num_bytes": 970478, "num_examples": 1086}, {"name": "validation", "num_bytes": 119282, "num_examples": 105}, {"name": "test", "num_bytes": 335581, "num_examples": 390}], "download_size": 218401, "dataset_size": 1425341}}
|
2023-05-01T17:03:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "portuguese-mapa"
More Information needed
|
[
"# Dataset Card for \"portuguese-mapa\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"portuguese-mapa\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"portuguese-mapa\"\n\nMore Information needed"
] |
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] |
073bb12639aa140045d92d0928b71860c54704a4
|
# Dataset Card for "dataset-tokenized-mdeberta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sleoruiz/dataset-tokenized-mdeberta
|
[
"region:us"
] |
2023-04-04T12:04:25+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2852368832, "num_examples": 406552}, {"name": "test", "num_bytes": 713856952, "num_examples": 101747}], "download_size": 319209887, "dataset_size": 3566225784}}
|
2023-04-04T12:05:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dataset-tokenized-mdeberta"
More Information needed
|
[
"# Dataset Card for \"dataset-tokenized-mdeberta\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dataset-tokenized-mdeberta\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dataset-tokenized-mdeberta\"\n\nMore Information needed"
] |
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] |
abb3705e6c68c767854f35a1d9fd90d84f088393
|
# smiles-transformers dataset
TODO: Add references to the datasets we curated
## dataset features
- name: text
- Molecule SMILES : string
- name: formula
- Molecular formula : string
- name: NumHDonors
- Number of hidrogen bond donors : int
- name: NumHAcceptors
- Number of hidrogen bond acceptors : int
- name: MolLogP
- Wildman-Crippen LogP : float
- name: NumHeteroatoms
- Number of hetero atoms: int
- name: RingCount
- Number of rings : int
- name: NumRotatableBonds
- Number of rotable bonds : int
- name: NumAromaticBonds
- Number of aromatic bonds : int
- name: NumAcidGroups
- Number of acid groups : int
- name: NumBasicGroups
- Number of basic groups : int
- name: Apol
## citation information
|
maykcaldas/smiles-transformers
|
[
"size_categories:100M<n<1B",
"language:en",
"license:mit",
"region:us"
] |
2023-04-04T12:10:48+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["100M<n<1B"], "pretty_name": "smiles-transformer-dataset", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "formula", "dtype": "string"}, {"name": "NumHDonors", "dtype": "int64"}, {"name": "NumHAcceptors", "dtype": "int64"}, {"name": "MolLogP", "dtype": "float64"}, {"name": "NumHeteroatoms", "dtype": "int64"}, {"name": "RingCount", "dtype": "int64"}, {"name": "NumRotatableBonds", "dtype": "int64"}, {"name": "NumAromaticBonds", "dtype": "int64"}, {"name": "NumAcidGroups", "dtype": "int64"}, {"name": "NumBasicGroups", "dtype": "int64"}, {"name": "Apol", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 136431671689, "num_examples": 908086717}, {"name": "test", "num_bytes": 7437928022, "num_examples": 50487919}, {"name": "validation", "num_bytes": 7621324737, "num_examples": 50605067}], "download_size": 34998665406, "dataset_size": 151490924448}}
|
2023-04-04T21:02:47+00:00
|
[] |
[
"en"
] |
TAGS
#size_categories-100M<n<1B #language-English #license-mit #region-us
|
# smiles-transformers dataset
TODO: Add references to the datasets we curated
## dataset features
- name: text
- Molecule SMILES : string
- name: formula
- Molecular formula : string
- name: NumHDonors
- Number of hidrogen bond donors : int
- name: NumHAcceptors
- Number of hidrogen bond acceptors : int
- name: MolLogP
- Wildman-Crippen LogP : float
- name: NumHeteroatoms
- Number of hetero atoms: int
- name: RingCount
- Number of rings : int
- name: NumRotatableBonds
- Number of rotable bonds : int
- name: NumAromaticBonds
- Number of aromatic bonds : int
- name: NumAcidGroups
- Number of acid groups : int
- name: NumBasicGroups
- Number of basic groups : int
- name: Apol
## citation information
|
[
"# smiles-transformers dataset\n\n TODO: Add references to the datasets we curated",
"## dataset features\n\n - name: text\n - Molecule SMILES : string\n - name: formula\n - Molecular formula : string\n - name: NumHDonors\n - Number of hidrogen bond donors : int\n - name: NumHAcceptors\n - Number of hidrogen bond acceptors : int\n - name: MolLogP\n - Wildman-Crippen LogP : float\n - name: NumHeteroatoms\n - Number of hetero atoms: int\n - name: RingCount\n - Number of rings : int\n - name: NumRotatableBonds\n - Number of rotable bonds : int\n - name: NumAromaticBonds\n - Number of aromatic bonds : int\n - name: NumAcidGroups\n - Number of acid groups : int\n - name: NumBasicGroups\n - Number of basic groups : int\n - name: Apol",
"## citation information"
] |
[
"TAGS\n#size_categories-100M<n<1B #language-English #license-mit #region-us \n",
"# smiles-transformers dataset\n\n TODO: Add references to the datasets we curated",
"## dataset features\n\n - name: text\n - Molecule SMILES : string\n - name: formula\n - Molecular formula : string\n - name: NumHDonors\n - Number of hidrogen bond donors : int\n - name: NumHAcceptors\n - Number of hidrogen bond acceptors : int\n - name: MolLogP\n - Wildman-Crippen LogP : float\n - name: NumHeteroatoms\n - Number of hetero atoms: int\n - name: RingCount\n - Number of rings : int\n - name: NumRotatableBonds\n - Number of rotable bonds : int\n - name: NumAromaticBonds\n - Number of aromatic bonds : int\n - name: NumAcidGroups\n - Number of acid groups : int\n - name: NumBasicGroups\n - Number of basic groups : int\n - name: Apol",
"## citation information"
] |
[
27,
22,
185,
4
] |
[
"passage: TAGS\n#size_categories-100M<n<1B #language-English #license-mit #region-us \n# smiles-transformers dataset\n\n TODO: Add references to the datasets we curated## dataset features\n\n - name: text\n - Molecule SMILES : string\n - name: formula\n - Molecular formula : string\n - name: NumHDonors\n - Number of hidrogen bond donors : int\n - name: NumHAcceptors\n - Number of hidrogen bond acceptors : int\n - name: MolLogP\n - Wildman-Crippen LogP : float\n - name: NumHeteroatoms\n - Number of hetero atoms: int\n - name: RingCount\n - Number of rings : int\n - name: NumRotatableBonds\n - Number of rotable bonds : int\n - name: NumAromaticBonds\n - Number of aromatic bonds : int\n - name: NumAcidGroups\n - Number of acid groups : int\n - name: NumBasicGroups\n - Number of basic groups : int\n - name: Apol## citation information"
] |
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987111c7c262a3c1255f5da4ecc545782280e794
|
# Dataset Card for "pushshift-reddit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fddemarco/pushshift-reddit-comments
|
[
"region:us"
] |
2023-04-04T12:22:08+00:00
|
{"dataset_info": {"features": [{"name": "author", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "controversiality", "dtype": "int64"}, {"name": "created_utc", "dtype": "int64"}, {"name": "link_id", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "subreddit", "dtype": "string"}, {"name": "subreddit_id", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1920019700, "num_examples": 15034827}], "download_size": 1920019700, "dataset_size": 1920019700}}
|
2023-05-14T16:19:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pushshift-reddit"
More Information needed
|
[
"# Dataset Card for \"pushshift-reddit\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pushshift-reddit\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pushshift-reddit\"\n\nMore Information needed"
] |
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e0c92d3b5c6176463cb8db86b1767f3af0c2b18f
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_A_T_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_A_T_ns_100
|
[
"region:us"
] |
2023-04-04T12:55:10+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 58390, "num_examples": 100}], "download_size": 14948, "dataset_size": 58390}}
|
2023-04-04T13:26:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_A_T_ns_100"
More Information needed
|
[
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_T_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_T_ns_100\"\n\nMore Information needed"
] |
[
6,
36
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_T_ns_100\"\n\nMore Information needed"
] |
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84cc57a7c0c8963d7293edaecbd4ff8638083a1d
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_100
|
[
"region:us"
] |
2023-04-04T12:56:56+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 43386, "num_examples": 100}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 43408, "num_examples": 100}], "download_size": 20841, "dataset_size": 86794}}
|
2023-04-05T02:31:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_100"
More Information needed
|
[
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_100\"\n\nMore Information needed"
] |
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