Ссылки:
гендерные_слова
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/gendered_words')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 222 |
- Функции :
{
"word_masculine": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_feminine": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
name_genders
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/name_genders')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'yob1880' | 2000 г. |
'yob1881' | 1935 год |
'yob1882' | 2127 |
'yob1883' | 2084 |
'yob1884' | 2297 |
'yob1885' | 2294 |
'yob1886' | 2392 |
'yob1887' | 2373 |
'yob1888' | 2651 |
'yob1889' | 2590 |
'yob1890' | 2695 |
'yob1891' | 2660 |
'yob1892' | 2921 |
'yob1893' | 2831 |
'yob1894' | 2941 |
'yob1895' | 3049 |
'yob1896' | 3091 |
'yob1897' | 3028 |
'yob1898' | 3264 |
'yob1899' | 3042 |
'yob1900' | 3730 |
'yob1901' | 3153 |
'yob1902' | 3362 |
'yob1903' | 3389 |
'yob1904' | 3560 |
'yob1905' | 3655 |
'yob1906' | 3633 |
'yob1907' | 3948 |
'yob1908' | 4018 |
'yob1909' | 4227 |
'yob1910' | 4629 |
'yob1911' | 4867 |
'yob1912' | 6351 |
'yob1913' | 6968 |
'yob1914' | 7965 |
'yob1915' | 9357 |
'yob1916' | 9696 |
'yob1917' | 9913 |
'yob1918' | 10398 |
'yob1919' | 10369 |
'yob1920' | 10756 |
'yob1921' | 10857 |
'yob1922' | 10756 |
'yob1923' | 10643 |
'yob1924' | 10869 |
'yob1925' | 10638 |
'yob1926' | 10458 |
'yob1927' | 10406 |
'yob1928' | 10159 |
'yob1929' | 9820 |
'yob1930' | 9791 |
'yob1931' | 9298 |
'yob1932' | 9381 |
'yob1933' | 9013 |
'yob1934' | 9180 |
'yob1935' | 9037 |
'yob1936' | 8894 |
'yob1937' | 8946 |
'yob1938' | 9032 |
'yob1939' | 8918 |
'yob1940' | 8961 |
'yob1941' | 9085 |
'yob1942' | 9425 |
'yob1943' | 9408 |
'yob1944' | 9152 |
'yob1945' | 9025 |
'yob1946' | 9705 |
'yob1947' | 10371 |
'yob1948' | 10241 |
'yob1949' | 10269 |
'yob1950' | 10303 |
'yob1951' | 10462 |
'yob1952' | 10646 |
'yob1953' | 10837 |
'yob1954' | 10968 |
'yob1955' | 11115 |
'yob1956' | 11340 |
'yob1957' | 11564 |
'yob1958' | 11522 |
'yob1959' | 11767 |
'yob1960' | 11921 |
'yob1961' | 12182 |
'yob1962' | 12209 |
'yob1963' | 12282 |
'yob1964' | 12397 |
'yob1965' | 11952 |
'yob1966' | 12151 |
'yob1967' | 12397 |
'yob1968' | 12936 |
'yob1969' | 13749 |
'yob1970' | 14779 |
'yob1971' | 15295 |
'yob1972' | 15412 |
'yob1973' | 15682 |
'yob1974' | 16249 |
'yob1975' | 16944 |
'yob1976' | 17391 |
'yob1977' | 18175 |
'yob1978' | 18231 |
'yob1979' | 19039 |
'yob1980' | 19452 |
'yob1981' | 19475 |
'yob1982' | 19694 |
'yob1983' | 19407 |
'yob1984' | 19506 |
'yob1985' | 20085 г. |
'yob1986' | 20657 |
'yob1987' | 21406 |
'yob1988' | 22367 |
'yob1989' | 23775 |
'yob1990' | 24716 |
'yob1991' | 25109 |
'yob1992' | 25427 |
'yob1993' | 25966 |
'yob1994' | 25997 |
'yob1995' | 26080 |
'yob1996' | 26423 |
'yob1997' | 26970 |
'yob1998' | 27902 |
'yob1999' | 28552 |
'yob2000' | 29772 |
'yob2001' | 30274 |
'yob2002' | 30564 |
'yob2003' | 31185 |
'yob2004' | 32048 |
'yob2005' | 32549 |
'yob2006' | 34088 |
'yob2007' | 34961 |
'yob2008' | 35079 |
'yob2009' | 34709 |
'yob2010' | 34073 |
'yob2011' | 33908 |
'yob2012' | 33747 |
'yob2013' | 33282 |
'yob2014' | 33243 |
'yob2015' | 33121 |
'yob2016' | 33010 |
'yob2017' | 32590 |
'yob2018' | 32033 |
- Функции :
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"assigned_gender": {
"num_classes": 2,
"names": [
"M",
"F"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"count": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
}
новые_данные
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/new_data')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'train' | 2345 |
- Функции :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"labels": [
{
"num_classes": 6,
"names": [
"ABOUT:female",
"ABOUT:male",
"PARTNER:female",
"PARTNER:male",
"SELF:female",
"SELF:male"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
],
"class_type": {
"num_classes": 3,
"names": [
"about",
"partner",
"self"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"turker_gender": {
"num_classes": 5,
"names": [
"man",
"woman",
"nonbinary",
"prefer not to say",
"no answer"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"episode_done": {
"dtype": "bool_",
"id": null,
"_type": "Value"
},
"confidence": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
Фунпедия
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/funpedia')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'test' | 2938 |
'train' | 23897 |
'validation' | 2984 |
- Функции :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"persona": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gender": {
"num_classes": 3,
"names": [
"gender-neutral",
"female",
"male"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
image_chat
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/image_chat')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'test' | 5000 |
'train' | 9997 |
'validation' | 338180 |
- Функции :
{
"caption": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"male": {
"dtype": "bool_",
"id": null,
"_type": "Value"
},
"female": {
"dtype": "bool_",
"id": null,
"_type": "Value"
}
}
волшебник
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/wizard')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'test' | 470 |
'train' | 10449 |
'validation' | 537 |
- Функции :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"chosen_topic": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"gender": {
"num_classes": 3,
"names": [
"gender-neutral",
"female",
"male"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
}
}
convai2_inferred
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/convai2_inferred')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'test' | 7801 |
'train' | 131438 |
'validation' | 7801 |
- Функции :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"binary_label": {
"num_classes": 2,
"names": [
"ABOUT:female",
"ABOUT:male"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"binary_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"ternary_label": {
"num_classes": 3,
"names": [
"ABOUT:female",
"ABOUT:male",
"ABOUT:gender-neutral"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"ternary_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
}
}
Light_inferred
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/light_inferred')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'test' | 12765 |
'train' | 106122 |
'validation' | 6362 |
- Функции :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"binary_label": {
"num_classes": 2,
"names": [
"ABOUT:female",
"ABOUT:male"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"binary_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"ternary_label": {
"num_classes": 3,
"names": [
"ABOUT:female",
"ABOUT:male",
"ABOUT:gender-neutral"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"ternary_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
}
}
opensubtitles_inferred
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/opensubtitles_inferred')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'test' | 49108 |
'train' | 351036 |
'validation' | 41957 |
- Функции :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"binary_label": {
"num_classes": 2,
"names": [
"ABOUT:female",
"ABOUT:male"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"binary_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"ternary_label": {
"num_classes": 3,
"names": [
"ABOUT:female",
"ABOUT:male",
"ABOUT:gender-neutral"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"ternary_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
}
}
yelp_inferred
Используйте следующую команду, чтобы загрузить этот набор данных в TFDS:
ds = tfds.load('huggingface:md_gender_bias/yelp_inferred')
- Описание :
Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites.
Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers.
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models,
detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
- Лицензия : Лицензия MIT
- Версия : 1.0.0
- Расколы :
| Расколоть | Примеры |
|---|---|
'test' | 534460 |
'train' | 2577862 |
'validation' | 4492 |
- Функции :
{
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"binary_label": {
"num_classes": 2,
"names": [
"ABOUT:female",
"ABOUT:male"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"binary_score": {
"dtype": "float32",
"id": null,
"_type": "Value"
}
}