LP-NLI Models
Collection
4 items • Updated
bert-mnli-classifier
This model is a fine-tuned version of bert-base-cased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.4604
- Accuracy: 0.8436
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4134 | 1.0 | 6136 | 0.4234 | 0.8384 |
| 0.39 | 2.0 | 12272 | 0.4150 | 0.8420 |
| 0.2299 | 3.0 | 18408 | 0.4604 | 0.8436 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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Dataset used to train gayanin/bert-mnli-classifier
Collection including gayanin/bert-mnli-classifier
Evaluation results
- Accuracy on glueself-reported0.844