# Text classifier using DistilBERT to determine Partisanship
## This is one of many single-class partisanship models

label_0 refers to "left" while label_1 refers to "other".  

This model was trained on 40,000 articles. 

### Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens will result in inaccurate results.  
{}
transformers pytorch tf distilbert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# Text classifier using DistilBERT to determine Partisanship
## This is one of many single-class partisanship models

label_0 refers to "left" while label_1 refers to "other".  

This model was trained on 40,000 articles. 

### Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens will result in inaccurate results.  
# Text classifier using DistilBERT to determine Partisanship ## This is one of many single-class partisanship models

label_0 refers to "left" while label_1 refers to "other".  

This model was trained on 40,000 articles. ### Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens will result in inaccurate results.
TAGS
#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # Text classifier using DistilBERT to determine Partisanship ## This is one of many single-class partisanship models

label_0 refers to "left" while label_1 refers to "other".  

This model was trained on 40,000 articles. ### Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens will result in inaccurate results.
passage: TAGS
#transformers #pytorch #tf #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# Text classifier using DistilBERT to determine Partisanship## This is one of many single-class partisanship models

label_0 refers to "left" while label_1 refers to "other".  

This model was trained on 40,000 articles.### Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens will result in inaccurate results.