## BERT-large finetuned on MNLI.

The [reference finetuned model](https://github.com/google-research/bert) has an accuracy of 86.05, we get 86.7:

```
{'eval_loss': 0.3984006643295288, 'eval_accuracy': 0.8667345899133979} 
```
{}
transformers pytorch jax bert text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 

## BERT-large finetuned on MNLI.

The reference finetuned model has an accuracy of 86.05, we get 86.7:


## BERT-large finetuned on MNLI.

The reference finetuned model has an accuracy of 86.05, we get 86.7:
TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 ## BERT-large finetuned on MNLI.

The reference finetuned model has an accuracy of 86.05, we get 86.7:
passage: TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us 
## BERT-large finetuned on MNLI.

The reference finetuned model has an accuracy of 86.05, we get 86.7: