|
--- |
|
language: en |
|
thumbnail: |
|
tags: |
|
- pytorch |
|
- text-classification |
|
datasets: |
|
- MNLI |
|
--- |
|
|
|
# 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 | |
|
|
|
|
