Referanslar:
cinsiyetli_kelimeler
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/gendered_words')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'train' | 222 |
- Özellikler :
{
"word_masculine": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"word_feminine": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
name_genders
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/name_genders')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'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 |
- Özellikler :
{
"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"
}
}
yeni_veriler
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/new_data')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'train' | 2345 |
- Özellikler :
{
"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"
}
}
eğlencepedisi
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/funpedia')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 2938 |
'train' | 23897 |
'validation' | 2984 |
- Özellikler :
{
"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"
}
}
resim_sohbet
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/image_chat')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 5000 |
'train' | 9997 |
'validation' | 338180 |
- Özellikler :
{
"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"
}
}
sihirbaz
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/wizard')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 470 |
'train' | 10449 |
'validation' | 537 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/convai2_inferred')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 7801 |
'train' | 131438 |
'validation' | 7801 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/light_inferred')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 12765 |
'train' | 106122 |
'validation' | 6362 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/opensubtitles_inferred')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 49108 |
'train' | 351036 |
'validation' | 41957 |
- Özellikler :
{
"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
Bu veri kümesini TFDS'ye yüklemek için aşağıdaki komutu kullanın:
ds = tfds.load('huggingface:md_gender_bias/yelp_inferred')
- Tanım :
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.
- Lisans : MIT Lisansı
- Sürüm : 1.0.0
- Bölünmeler :
| Bölmek | Örnekler |
|---|---|
'test' | 534460 |
'train' | 2577862 |
'validation' | 4492 |
- Özellikler :
{
"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"
}
}