PubMedBERT_BC5CDR_NER_new
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0805
- Precision: 0.9857
- Recall: 0.9821
- F1: 0.9839
- Accuracy: 0.9769
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 286 | 0.0837 | 0.9831 | 0.9782 | 0.9806 | 0.9725 |
| 0.126 | 2.0 | 572 | 0.0824 | 0.9847 | 0.9784 | 0.9815 | 0.9740 |
| 0.126 | 3.0 | 858 | 0.0805 | 0.9857 | 0.9821 | 0.9839 | 0.9769 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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