42
This model is a fine-tuned version of bert-large-uncased on the MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.8447
- Accuracy: 0.8634
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: not_parallel
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4274 | 1.0 | 12272 | 0.3892 | 0.8524 |
| 0.2844 | 2.0 | 24544 | 0.4079 | 0.8565 |
| 0.1589 | 3.0 | 36816 | 0.5033 | 0.8527 |
| 0.0877 | 4.0 | 49088 | 0.6624 | 0.8576 |
| 0.0426 | 5.0 | 61360 | 0.8447 | 0.8634 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.7.1
- Tokenizers 0.11.6
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