### BERT base model (uncased)
It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert)

### This model is trained on [News Category Dataset](https://www.kaggle.com/datasets/rmisra/news-category-dataset).

### Labels
Consists of 1-4 numbers which represents class of which 

    0  world
    1  sport
    2  business
    3  tech


{}
transformers pytorch bert text-classification arxiv:1810.04805 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 

### BERT base model (uncased)
It was introduced in this paper and first released in this repository

### This model is trained on News Category Dataset.

### Labels
Consists of 1-4 numbers which represents class of which 

    0  world
    1  sport
    2  business
    3  tech


### BERT base model (uncased)
It was introduced in this paper and first released in this repository ### This model is trained on News Category Dataset. ### Labels
Consists of 1-4 numbers which represents class of which 

    0  world
    1  sport
    2  business
    3  tech
TAGS
#transformers #pytorch #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 
 ### BERT base model (uncased)
It was introduced in this paper and first released in this repository ### This model is trained on News Category Dataset. ### Labels
Consists of 1-4 numbers which represents class of which 

    0  world
    1  sport
    2  business
    3  tech
TAGS
#transformers #pytorch #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 
### BERT base model (uncased)
It was introduced in this paper and first released in this repository### This model is trained on News Category Dataset.### Labels
Consists of 1-4 numbers which represents class of which 

    0  world
    1  sport
    2  business
    3  tech