model
This model is a fine-tuned version of Tsubasaz/clinical-pubmed-bert-base-512 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3511
- Precision: 0.6103
- Recall: 0.5640
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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall |
|---|---|---|---|---|---|
| No log | 1.0 | 128 | 0.4393 | 0.0 | 0.0 |
| No log | 2.0 | 256 | 0.3958 | 0.5714 | 0.1706 |
| No log | 3.0 | 384 | 0.3785 | 0.5690 | 0.3128 |
| 0.4046 | 4.0 | 512 | 0.3676 | 0.5789 | 0.5213 |
| 0.4046 | 5.0 | 640 | 0.3606 | 0.6532 | 0.3839 |
| 0.4046 | 6.0 | 768 | 0.3597 | 0.6549 | 0.4408 |
| 0.4046 | 7.0 | 896 | 0.3584 | 0.6376 | 0.4502 |
| 0.3046 | 8.0 | 1024 | 0.3518 | 0.6310 | 0.5024 |
| 0.3046 | 9.0 | 1152 | 0.3511 | 0.6133 | 0.5261 |
| 0.3046 | 10.0 | 1280 | 0.3511 | 0.6103 | 0.5640 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0