References:
wino_bias
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:wino_bias/wino_bias')
- Description:
WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter).
- License: MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)
- Version: 4.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
150335 |
- Features:
{
"document_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"part_number": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_number": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"tokens": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"pos_tags": {
"feature": {
"num_classes": 54,
"names": [
"\"",
"''",
"#",
"$",
"(",
")",
",",
".",
":",
"``",
"CC",
"CD",
"DT",
"EX",
"FW",
"IN",
"JJ",
"JJR",
"JJS",
"LS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
"HYPH",
"XX",
"NFP",
"AFX",
"ADD",
"-LRB-",
"-RRB-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"parse_bit": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_lemma": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_framenet_id": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"word_sense": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"speaker": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"ner_tags": {
"feature": {
"num_classes": 38,
"names": [
"B-PERSON",
"I-PERSON",
"B-NORP",
"I-NORP",
"B-FAC",
"I-FAC",
"B-ORG",
"I-ORG",
"B-GPE",
"I-GPE",
"B-LOC",
"I-LOC",
"B-PRODUCT",
"I-PRODUCT",
"B-EVENT",
"I-EVENT",
"B-WORK_OF_ART",
"I-WORK_OF_ART",
"B-LAW",
"I-LAW",
"B-LANGUAGE",
"I-LANGUAGE",
"B-DATE",
"I-DATE",
"B-TIME",
"I-TIME",
"B-PERCENT",
"I-PERCENT",
"B-MONEY",
"I-MONEY",
"B-QUANTITY",
"I-QUANTITY",
"B-ORDINAL",
"I-ORDINAL",
"B-CARDINAL",
"I-CARDINAL",
"*",
"0"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"verbal_predicates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
type1_pro
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:wino_bias/type1_pro')
- Description:
WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter).
- License: MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'test' |
396 |
'validation' |
396 |
- Features:
{
"document_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"part_number": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_number": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"tokens": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"pos_tags": {
"feature": {
"num_classes": 55,
"names": [
"\"",
"''",
"#",
"$",
"(",
")",
",",
".",
":",
"``",
"CC",
"CD",
"DT",
"EX",
"FW",
"IN",
"JJ",
"JJR",
"JJS",
"LS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
"HYPH",
"XX",
"NFP",
"AFX",
"ADD",
"-LRB-",
"-RRB-",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"parse_bit": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_lemma": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_framenet_id": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"word_sense": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"speaker": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"ner_tags": {
"feature": {
"num_classes": 39,
"names": [
"B-PERSON",
"I-PERSON",
"B-NORP",
"I-NORP",
"B-FAC",
"I-FAC",
"B-ORG",
"I-ORG",
"B-GPE",
"I-GPE",
"B-LOC",
"I-LOC",
"B-PRODUCT",
"I-PRODUCT",
"B-EVENT",
"I-EVENT",
"B-WORK_OF_ART",
"I-WORK_OF_ART",
"B-LAW",
"I-LAW",
"B-LANGUAGE",
"I-LANGUAGE",
"B-DATE",
"I-DATE",
"B-TIME",
"I-TIME",
"B-PERCENT",
"I-PERCENT",
"B-MONEY",
"I-MONEY",
"B-QUANTITY",
"I-QUANTITY",
"B-ORDINAL",
"I-ORDINAL",
"B-CARDINAL",
"I-CARDINAL",
"*",
"0",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"verbal_predicates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"coreference_clusters": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
type1_anti
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:wino_bias/type1_anti')
- Description:
WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter).
- License: MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'test' |
396 |
'validation' |
396 |
- Features:
{
"document_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"part_number": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_number": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"tokens": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"pos_tags": {
"feature": {
"num_classes": 55,
"names": [
"\"",
"''",
"#",
"$",
"(",
")",
",",
".",
":",
"``",
"CC",
"CD",
"DT",
"EX",
"FW",
"IN",
"JJ",
"JJR",
"JJS",
"LS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
"HYPH",
"XX",
"NFP",
"AFX",
"ADD",
"-LRB-",
"-RRB-",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"parse_bit": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_lemma": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_framenet_id": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"word_sense": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"speaker": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"ner_tags": {
"feature": {
"num_classes": 39,
"names": [
"B-PERSON",
"I-PERSON",
"B-NORP",
"I-NORP",
"B-FAC",
"I-FAC",
"B-ORG",
"I-ORG",
"B-GPE",
"I-GPE",
"B-LOC",
"I-LOC",
"B-PRODUCT",
"I-PRODUCT",
"B-EVENT",
"I-EVENT",
"B-WORK_OF_ART",
"I-WORK_OF_ART",
"B-LAW",
"I-LAW",
"B-LANGUAGE",
"I-LANGUAGE",
"B-DATE",
"I-DATE",
"B-TIME",
"I-TIME",
"B-PERCENT",
"I-PERCENT",
"B-MONEY",
"I-MONEY",
"B-QUANTITY",
"I-QUANTITY",
"B-ORDINAL",
"I-ORDINAL",
"B-CARDINAL",
"I-CARDINAL",
"*",
"0",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"verbal_predicates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"coreference_clusters": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
type2_pro
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:wino_bias/type2_pro')
- Description:
WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter).
- License: MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'test' |
396 |
'validation' |
396 |
- Features:
{
"document_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"part_number": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_number": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"tokens": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"pos_tags": {
"feature": {
"num_classes": 55,
"names": [
"\"",
"''",
"#",
"$",
"(",
")",
",",
".",
":",
"``",
"CC",
"CD",
"DT",
"EX",
"FW",
"IN",
"JJ",
"JJR",
"JJS",
"LS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
"HYPH",
"XX",
"NFP",
"AFX",
"ADD",
"-LRB-",
"-RRB-",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"parse_bit": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_lemma": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_framenet_id": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"word_sense": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"speaker": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"ner_tags": {
"feature": {
"num_classes": 39,
"names": [
"B-PERSON",
"I-PERSON",
"B-NORP",
"I-NORP",
"B-FAC",
"I-FAC",
"B-ORG",
"I-ORG",
"B-GPE",
"I-GPE",
"B-LOC",
"I-LOC",
"B-PRODUCT",
"I-PRODUCT",
"B-EVENT",
"I-EVENT",
"B-WORK_OF_ART",
"I-WORK_OF_ART",
"B-LAW",
"I-LAW",
"B-LANGUAGE",
"I-LANGUAGE",
"B-DATE",
"I-DATE",
"B-TIME",
"I-TIME",
"B-PERCENT",
"I-PERCENT",
"B-MONEY",
"I-MONEY",
"B-QUANTITY",
"I-QUANTITY",
"B-ORDINAL",
"I-ORDINAL",
"B-CARDINAL",
"I-CARDINAL",
"*",
"0",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"verbal_predicates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"coreference_clusters": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
type2_anti
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:wino_bias/type2_anti')
- Description:
WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter).
- License: MIT License (https://github.com/uclanlp/corefBias/blob/master/LICENSE)
- Version: 1.0.0
- Splits:
| Split | Examples |
|---|---|
'test' |
396 |
'validation' |
396 |
- Features:
{
"document_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"part_number": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_number": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"tokens": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"pos_tags": {
"feature": {
"num_classes": 55,
"names": [
"\"",
"''",
"#",
"$",
"(",
")",
",",
".",
":",
"``",
"CC",
"CD",
"DT",
"EX",
"FW",
"IN",
"JJ",
"JJR",
"JJS",
"LS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
"HYPH",
"XX",
"NFP",
"AFX",
"ADD",
"-LRB-",
"-RRB-",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"parse_bit": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_lemma": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"predicate_framenet_id": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"word_sense": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"speaker": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"ner_tags": {
"feature": {
"num_classes": 39,
"names": [
"B-PERSON",
"I-PERSON",
"B-NORP",
"I-NORP",
"B-FAC",
"I-FAC",
"B-ORG",
"I-ORG",
"B-GPE",
"I-GPE",
"B-LOC",
"I-LOC",
"B-PRODUCT",
"I-PRODUCT",
"B-EVENT",
"I-EVENT",
"B-WORK_OF_ART",
"I-WORK_OF_ART",
"B-LAW",
"I-LAW",
"B-LANGUAGE",
"I-LANGUAGE",
"B-DATE",
"I-DATE",
"B-TIME",
"I-TIME",
"B-PERCENT",
"I-PERCENT",
"B-MONEY",
"I-MONEY",
"B-QUANTITY",
"I-QUANTITY",
"B-ORDINAL",
"I-ORDINAL",
"B-CARDINAL",
"I-CARDINAL",
"*",
"0",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"verbal_predicates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"coreference_clusters": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}