metadata
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ClinicalBERT-full-finetuned-ner-pablo
results: []
ClinicalBERT-full-finetuned-ner-pablo
This model is a fine-tuned version of medicalai/ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1117
- Precision: 0.8051
- Recall: 0.7944
- F1: 0.7997
- Accuracy: 0.9702
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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.135 | 0.9998 | 2351 | 0.1292 | 0.7596 | 0.7329 | 0.7460 | 0.9649 |
| 0.0863 | 2.0 | 4703 | 0.1222 | 0.8064 | 0.7631 | 0.7841 | 0.9690 |
| 0.0554 | 2.9994 | 7053 | 0.1117 | 0.8051 | 0.7944 | 0.7997 | 0.9702 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1