metadata
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ClinicalBERT_BioNLP13CG_NER
results: []
ClinicalBERT_BioNLP13CG_NER
This model is a fine-tuned version of medicalai/ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3426
- Precision: 0.7090
- Recall: 0.6958
- F1: 0.7023
- Accuracy: 0.9104
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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 0.99 | 95 | 0.4756 | 0.6077 | 0.5579 | 0.5817 | 0.8777 |
| No log | 2.0 | 191 | 0.3626 | 0.6999 | 0.6889 | 0.6944 | 0.9068 |
| No log | 2.98 | 285 | 0.3426 | 0.7090 | 0.6958 | 0.7023 | 0.9104 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0