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.
| Test Corpus | Accuracy |
|---|---|
| Matched | 0.8456 |
| Mismatched | 0.8484 |
- Downloads last month
- 3,436
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.