md_gender_bias

Referências:

palavras_de_gênero

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/gendered_words')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'train' 222
  • Características :
{
    "word_masculine": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "word_feminine": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

nome_gêneros

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/name_genders')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'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"
    }
}

novos_dados

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/new_data')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'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"
    }
}

funpédia

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/funpedia')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'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"
    }
}

imagem_chat

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/image_chat')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'test' 5.000
'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

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/wizard')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'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

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/convai2_inferred')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'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

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/light_inferred')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'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_inferred

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/opensubtitles_inferred')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'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

Use o seguinte comando para carregar este conjunto de dados no TFDS:

ds = tfds.load('huggingface:md_gender_bias/yelp_inferred')
  • Descrição :
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.
  • Licença : Licença MIT
  • Versão : 1.0.0
  • Divisões :
Dividir Exemplos
'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"
    }
}