bert-finetuned-ner
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the ncbi_disease dataset. It achieves the following results on the evaluation set:
- Loss: 0.0623
- Precision: 0.7953
- Recall: 0.8590
- F1: 0.8259
- Accuracy: 0.9841
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.1204 | 1.0 | 680 | 0.0536 | 0.7417 | 0.8247 | 0.7810 | 0.9824 |
0.0386 | 2.0 | 1360 | 0.0542 | 0.7808 | 0.8463 | 0.8122 | 0.9831 |
0.0144 | 3.0 | 2040 | 0.0623 | 0.7953 | 0.8590 | 0.8259 | 0.9841 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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Base model
emilyalsentzer/Bio_ClinicalBERTDataset used to train adigo/bert-finetuned-ner
Evaluation results
- Precision on ncbi_diseasevalidation set self-reported0.795
- Recall on ncbi_diseasevalidation set self-reported0.859
- F1 on ncbi_diseasevalidation set self-reported0.826
- Accuracy on ncbi_diseasevalidation set self-reported0.984