# **roberta-base model is fine tuned on news article abstracts and headlines.**

## Respective labels and thier categories 

- 0: 'ARTS & CULTURE',
- 1: 'BUSINESS & FINANCES',
- 2: 'COMEDY',
- 3: 'CRIME',
- 4: 'DIVORCE',
- 5: 'EDUCATION',
- 6: 'ENTERTAINMENT',
- 7: 'ENVIRONMENT',
- 8: 'FOOD & DRINK',
- 9: 'GROUPS VOICES',
- 10: 'HOME & LIVING',
- 11: 'IMPACT',
- 12: 'MEDIA',
- 13: 'MISCELLANEOUS',
- 14: 'PARENTING',
- 15: 'POLITICS',
- 16: 'RELIGION',
- 17: 'SCIENCE & TECH',
- 18: 'SPORTS',
- 19: 'STYLE & BEAUTY',
- 20: 'TRAVEL',
- 21: 'U.S. NEWS',
- 22: 'WEDDINGS',
- 23: 'WEIRD NEWS',
- 24: 'WELLNESS',
- 25: 'WOMEN',
- 26: 'WORLD NEWS'





{}
transformers pytorch roberta text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# roberta-base model is fine tuned on news article abstracts and headlines.

## Respective labels and thier categories 

- 0: 'ARTS & CULTURE',
- 1: 'BUSINESS & FINANCES',
- 2: 'COMEDY',
- 3: 'CRIME',
- 4: 'DIVORCE',
- 5: 'EDUCATION',
- 6: 'ENTERTAINMENT',
- 7: 'ENVIRONMENT',
- 8: 'FOOD & DRINK',
- 9: 'GROUPS VOICES',
- 10: 'HOME & LIVING',
- 11: 'IMPACT',
- 12: 'MEDIA',
- 13: 'MISCELLANEOUS',
- 14: 'PARENTING',
- 15: 'POLITICS',
- 16: 'RELIGION',
- 17: 'SCIENCE & TECH',
- 18: 'SPORTS',
- 19: 'STYLE & BEAUTY',
- 20: 'TRAVEL',
- 21: 'U.S. NEWS',
- 22: 'WEDDINGS',
- 23: 'WEIRD NEWS',
- 24: 'WELLNESS',
- 25: 'WOMEN',
- 26: 'WORLD NEWS'





# roberta-base model is fine tuned on news article abstracts and headlines. ## Respective labels and thier categories 

- 0: 'ARTS & CULTURE',
- 1: 'BUSINESS & FINANCES',
- 2: 'COMEDY',
- 3: 'CRIME',
- 4: 'DIVORCE',
- 5: 'EDUCATION',
- 6: 'ENTERTAINMENT',
- 7: 'ENVIRONMENT',
- 8: 'FOOD & DRINK',
- 9: 'GROUPS VOICES',
- 10: 'HOME & LIVING',
- 11: 'IMPACT',
- 12: 'MEDIA',
- 13: 'MISCELLANEOUS',
- 14: 'PARENTING',
- 15: 'POLITICS',
- 16: 'RELIGION',
- 17: 'SCIENCE & TECH',
- 18: 'SPORTS',
- 19: 'STYLE & BEAUTY',
- 20: 'TRAVEL',
- 21: 'U.S. NEWS',
- 22: 'WEDDINGS',
- 23: 'WEIRD NEWS',
- 24: 'WELLNESS',
- 25: 'WOMEN',
- 26: 'WORLD NEWS'
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # roberta-base model is fine tuned on news article abstracts and headlines. ## Respective labels and thier categories 

- 0: 'ARTS & CULTURE',
- 1: 'BUSINESS & FINANCES',
- 2: 'COMEDY',
- 3: 'CRIME',
- 4: 'DIVORCE',
- 5: 'EDUCATION',
- 6: 'ENTERTAINMENT',
- 7: 'ENVIRONMENT',
- 8: 'FOOD & DRINK',
- 9: 'GROUPS VOICES',
- 10: 'HOME & LIVING',
- 11: 'IMPACT',
- 12: 'MEDIA',
- 13: 'MISCELLANEOUS',
- 14: 'PARENTING',
- 15: 'POLITICS',
- 16: 'RELIGION',
- 17: 'SCIENCE & TECH',
- 18: 'SPORTS',
- 19: 'STYLE & BEAUTY',
- 20: 'TRAVEL',
- 21: 'U.S. NEWS',
- 22: 'WEDDINGS',
- 23: 'WEIRD NEWS',
- 24: 'WELLNESS',
- 25: 'WOMEN',
- 26: 'WORLD NEWS'
TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# roberta-base model is fine tuned on news article abstracts and headlines.## Respective labels and thier categories 

- 0: 'ARTS & CULTURE',
- 1: 'BUSINESS & FINANCES',
- 2: 'COMEDY',
- 3: 'CRIME',
- 4: 'DIVORCE',
- 5: 'EDUCATION',
- 6: 'ENTERTAINMENT',
- 7: 'ENVIRONMENT',
- 8: 'FOOD & DRINK',
- 9: 'GROUPS VOICES',
- 10: 'HOME & LIVING',
- 11: 'IMPACT',
- 12: 'MEDIA',
- 13: 'MISCELLANEOUS',
- 14: 'PARENTING',
- 15: 'POLITICS',
- 16: 'RELIGION',
- 17: 'SCIENCE & TECH',
- 18: 'SPORTS',
- 19: 'STYLE & BEAUTY',
- 20: 'TRAVEL',
- 21: 'U.S. NEWS',
- 22: 'WEDDINGS',
- 23: 'WEIRD NEWS',
- 24: 'WELLNESS',
- 25: 'WOMEN',
- 26: 'WORLD NEWS'