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bert-finetuned-MedicalChunk
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1762
- Precision: 0.2723
- Recall: 0.3065
- F1: 0.2884
- Accuracy: 0.9563
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: 8
- eval_batch_size: 8
- 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 | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 56 | 0.1631 | 0.0 | 0.0 | 0.0 | 0.9606 |
| No log | 2.0 | 112 | 0.1416 | 0.0638 | 0.0302 | 0.0410 | 0.9592 |
| No log | 3.0 | 168 | 0.1405 | 0.1982 | 0.2161 | 0.2067 | 0.9559 |
| No log | 4.0 | 224 | 0.1356 | 0.2771 | 0.2312 | 0.2521 | 0.9633 |
| No log | 5.0 | 280 | 0.1419 | 0.2928 | 0.2663 | 0.2789 | 0.9593 |
| No log | 6.0 | 336 | 0.1550 | 0.2732 | 0.2513 | 0.2618 | 0.9602 |
| No log | 7.0 | 392 | 0.1620 | 0.2732 | 0.2814 | 0.2772 | 0.9578 |
| No log | 8.0 | 448 | 0.1670 | 0.2585 | 0.3065 | 0.2805 | 0.9554 |
| 0.1137 | 9.0 | 504 | 0.1728 | 0.2553 | 0.3015 | 0.2765 | 0.9552 |
| 0.1137 | 10.0 | 560 | 0.1762 | 0.2723 | 0.3065 | 0.2884 | 0.9563 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
