ClinicalBERT-full-finetuned-ner-pablo
This model is a fine-tuned version of medicalai/ClinicalBERT on the n2c2 2018 dataset for the paper https://arxiv.org/abs/2409.19467. It achieves the following results on the evaluation set:
- Loss: 0.0810
- Precision: 0.7936
- Recall: 0.7896
- F1: 0.7916
- Accuracy: 0.9752
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: 5e-05
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 231 | 0.0939 | 0.7601 | 0.7551 | 0.7576 | 0.9723 |
No log | 2.0 | 462 | 0.0806 | 0.7821 | 0.7798 | 0.7810 | 0.9748 |
0.2422 | 3.0 | 693 | 0.0800 | 0.7928 | 0.7869 | 0.7899 | 0.9755 |
0.2422 | 4.0 | 924 | 0.0810 | 0.7936 | 0.7896 | 0.7916 | 0.9752 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Base model
medicalai/ClinicalBERT