Referencias:
palabras_de_genero
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/gendered_words')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'train' | 222 |
- Características :
{
"word_masculine": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_feminine": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
nombre_género
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/name_genders')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'yob1880' | 2000 |
'yob1881' | 1935 |
'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 |
'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 |
- Características :
{
"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"
}
}
nuevos_datos
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/new_data')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'train' | 2345 |
- Características :
{
"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"
}
}
funpedia
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/funpedia')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 2938 |
'train' | 23897 |
'validation' | 2984 |
- Características :
{
"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"
}
}
chat_imagen
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/image_chat')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 5000 |
'train' | 9997 |
'validation' | 338180 |
- Características :
{
"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"
}
}
mago
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/wizard')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 470 |
'train' | 10449 |
'validation' | 537 |
- Características :
{
"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_inferido
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/convai2_inferred')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 7801 |
'train' | 131438 |
'validation' | 7801 |
- Características :
{
"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"
}
}
luz_inferida
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/light_inferred')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 12765 |
'train' | 106122 |
'validation' | 6362 |
- Características :
{
"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_inferido
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/opensubtitles_inferred')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 49108 |
'train' | 351036 |
'validation' | 41957 |
- Características :
{
"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_inferido
Utilice el siguiente comando para cargar este conjunto de datos en TFDS:
ds = tfds.load('huggingface:md_gender_bias/yelp_inferred')
- Descripción :
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.
- Licencia : Licencia MIT
- Versión : 1.0.0
- Divisiones :
Dividir | Ejemplos |
---|---|
'test' | 534460 |
'train' | 2577862 |
'validation' | 4492 |
- Características :
{
"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"
}
}