Referensi:
wino_bias
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:wino_bias/wino_bias')
- Keterangan :
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).
- Lisensi : Lisensi MIT ( https://github.com/uclanlp/corefBias/blob/master/LICENSE )
- Versi : 4.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'train' | 150335 |
- Fitur :
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"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",
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}
tipe1_pro
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:wino_bias/type1_pro')
- Keterangan :
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).
- Lisensi : Lisensi MIT ( https://github.com/uclanlp/corefBias/blob/master/LICENSE )
- Versi : 1.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'test' | 396 |
'validation' | 396 |
- Fitur :
{
"document_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"part_number": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_number": {
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"ner_tags": {
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"B-PERSON",
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"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",
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"B-CARDINAL",
"I-CARDINAL",
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}
tipe1_anti
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:wino_bias/type1_anti')
- Keterangan :
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).
- Lisensi : Lisensi MIT ( https://github.com/uclanlp/corefBias/blob/master/LICENSE )
- Versi : 1.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'test' | 396 |
'validation' | 396 |
- Fitur :
{
"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": {
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"names": [
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"CD",
"DT",
"EX",
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"JJR",
"JJS",
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"NNPS",
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"NN|SYM",
"PDT",
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"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-",
"-"
],
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"parse_bit": {
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"predicate_lemma": {
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"B-GPE",
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"B-LOC",
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"B-LANGUAGE",
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"B-TIME",
"I-TIME",
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}
type2_pro
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:wino_bias/type2_pro')
- Keterangan :
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).
- Lisensi : Lisensi MIT ( https://github.com/uclanlp/corefBias/blob/master/LICENSE )
- Versi : 1.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'test' | 396 |
'validation' | 396 |
- Fitur :
{
"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"
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"pos_tags": {
"feature": {
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"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
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"PRP",
"PRP$",
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"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
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"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
"HYPH",
"XX",
"NFP",
"AFX",
"ADD",
"-LRB-",
"-RRB-",
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],
"names_file": null,
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"_type": "Sequence"
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"parse_bit": {
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"predicate_framenet_id": {
"feature": {
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"word_sense": {
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"ner_tags": {
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"B-FAC",
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"B-LOC",
"I-LOC",
"B-PRODUCT",
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"B-EVENT",
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"B-WORK_OF_ART",
"I-WORK_OF_ART",
"B-LAW",
"I-LAW",
"B-LANGUAGE",
"I-LANGUAGE",
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}
type2_anti
Gunakan perintah berikut untuk memuat kumpulan data ini di TFDS:
ds = tfds.load('huggingface:wino_bias/type2_anti')
- Keterangan :
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).
- Lisensi : Lisensi MIT ( https://github.com/uclanlp/corefBias/blob/master/LICENSE )
- Versi : 1.0.0
- Perpecahan :
| Membelah | Contoh |
|---|---|
'test' | 396 |
'validation' | 396 |
- Fitur :
{
"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": {
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"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
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"pos_tags": {
"feature": {
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"PDT",
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"PRP",
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"UH",
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"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
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"WP",
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"WRB",
"HYPH",
"XX",
"NFP",
"AFX",
"ADD",
"-LRB-",
"-RRB-",
"-"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"parse_bit": {
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"_type": "Value"
},
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"_type": "Sequence"
},
"predicate_lemma": {
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"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
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"predicate_framenet_id": {
"feature": {
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"ner_tags": {
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"B-GPE",
"I-GPE",
"B-LOC",
"I-LOC",
"B-PRODUCT",
"I-PRODUCT",
"B-EVENT",
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"B-WORK_OF_ART",
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"B-LAW",
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"B-TIME",
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],
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