training_bert_model
This model is a fine-tuned version of distilbert-base-uncased on the fact verification dataset. It achieves the following results on the evaluation set:
- Loss: 1.0866
- Accuracy: 0.4318
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 11 | 1.1001 | 0.3182 |
| No log | 2.0 | 22 | 1.0924 | 0.3864 |
| No log | 3.0 | 33 | 1.0881 | 0.4091 |
| No log | 4.0 | 44 | 1.0866 | 0.4318 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.11.0