---
base_model: medicalai/ClinicalBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ClinicalBERT_JNLPBA_NER
  results: []
---

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# ClinicalBERT_JNLPBA_NER

This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1713
- Precision: 0.9452
- Recall: 0.9354
- F1: 0.9403
- Accuracy: 0.9427

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3475        | 1.0   | 582  | 0.1914          | 0.9330    | 0.9314 | 0.9322 | 0.9358   |
| 0.1835        | 2.0   | 1164 | 0.1746          | 0.9426    | 0.9332 | 0.9379 | 0.9408   |
| 0.158         | 3.0   | 1746 | 0.1713          | 0.9452    | 0.9354 | 0.9403 | 0.9427   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0