
# bert-base-uncased finetuned on MNLI

## Model Details and Training Data

We used the pretrained model from [bert-base-uncased](https://huggingface.co/bert-base-uncased) and finetuned it on [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) dataset. 

The training parameters were kept the same as [Devlin et al., 2019](https://arxiv.org/abs/1810.04805) (learning rate = 2e-5, training epochs = 3, max_sequence_len = 128 and batch_size = 32).

## Evaluation Results

The evaluation results are mentioned in the table below.

| Test Corpus | Accuracy |
|:---:|:---------:|
| Matched | 0.8456 |
| Mismatched | 0.8484 |


{"language": "en", "tags": ["pytorch", "text-classification"], "datasets": ["MNLI"]}
transformers pytorch jax bert text-classification en dataset:MNLI arxiv:1810.04805 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #bert #text-classification #en #dataset-MNLI #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased finetuned on MNLI
===================================


Model Details and Training Data
-------------------------------


We used the pretrained model from bert-base-uncased and finetuned it on MultiNLI dataset.


The training parameters were kept the same as Devlin et al., 2019 (learning rate = 2e-5, training epochs = 3, max\_sequence\_len = 128 and batch\_size = 32).


Evaluation Results
------------------


The evaluation results are mentioned in the table below.




TAGS
#transformers #pytorch #jax #bert #text-classification #en #dataset-MNLI #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #jax #bert #text-classification #en #dataset-MNLI #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us 
