
# Model Trained Using AutoTrain

- Problem type: Multi-class Classification

## Validation Metrics

|                       | precision | recall | f1-score | support |
|-----------------------|-----------|--------|----------|---------|
| World News            | 0.97      | 0.94   | 0.95     | 955     |
| Sports News           | 0.99      | 0.99   | 0.99     | 938     |
| Business News         | 0.93      | 0.89   | 0.91     | 991     |
| Science-Technology News | 0.88      | 0.95   | 0.91     | 916     |
| accuracy              |           |       | 0.94     | 3800    |
| macro avg             | 0.94      | 0.94   | 0.94     | 3800    |
| weighted avg          | 0.94      | 0.94   | 0.94     | 3800    |

{"language": ["en"], "tags": ["text-classification"], "widget": [{"text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\band of ultra-cynics, are seeing green again."}]}
transformers pytorch distilbert text-classification en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 

Model Trained Using AutoTrain
=============================


* Problem type: Multi-class Classification


Validation Metrics
------------------




TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
