BC5CDR_ClinicalBERT_NER
This model is a fine-tuned version of medicalai/ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:
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Loss: 0.1107
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Seqeval classification report: precision recall f1-score support
Chemical 0.71 0.73 0.72 10493 Disease 0.82 0.82 0.82 6944
micro avg 0.75 0.77 0.76 17437 macro avg 0.76 0.78 0.77 17437 weighted avg 0.75 0.77 0.76 17437
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report |
|---|---|---|---|---|
| No log | 1.0 | 143 | 0.1255 | precision recall f1-score support |
Chemical 0.67 0.68 0.68 10493
Disease 0.79 0.78 0.78 6944
micro avg 0.72 0.72 0.72 17437 macro avg 0.73 0.73 0.73 17437 weighted avg 0.72 0.72 0.72 17437 | | No log | 2.0 | 286 | 0.1160 | precision recall f1-score support
Chemical 0.69 0.71 0.70 10493
Disease 0.77 0.83 0.80 6944
micro avg 0.72 0.76 0.74 17437 macro avg 0.73 0.77 0.75 17437 weighted avg 0.72 0.76 0.74 17437 | | No log | 3.0 | 429 | 0.1107 | precision recall f1-score support
Chemical 0.71 0.73 0.72 10493
Disease 0.82 0.82 0.82 6944
micro avg 0.75 0.77 0.76 17437 macro avg 0.76 0.78 0.77 17437 weighted avg 0.75 0.77 0.76 17437 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0