bert_large_subjqa_model
This model is a fine-tuned version of bert-large-uncased-whole-word-masking-finetuned-squad on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.5694
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 205 | 2.6886 |
| No log | 2.0 | 410 | 3.0812 |
| 1.3352 | 3.0 | 615 | 3.6959 |
| 1.3352 | 4.0 | 820 | 4.2810 |
| 0.5176 | 5.0 | 1025 | 4.5694 |
Framework versions
- Transformers 4.28.0
- Pytorch 1.13.0a0+d321be6
- Datasets 2.12.0
- Tokenizers 0.13.3