transformers tensorboard safetensors distilbert token-classification generated_from_trainer base_model:medicalai/ClinicalBERT base_model:finetune:medicalai/ClinicalBERT autotrain_compatible endpoints_compatible region:us
---
base_model: medicalai/ClinicalBERT
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: ClinicalBERT_JNLPBA_NER_new
  results: []
---

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# ClinicalBERT_JNLPBA_NER_new

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: 0.1699
- Precision: 0.7855
- Recall: 0.8043
- F1: 0.7948
- Accuracy: 0.9439

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2204        | 1.0   | 1164 | 0.1821          | 0.7652    | 0.7719 | 0.7685 | 0.9380   |
| 0.1618        | 2.0   | 2328 | 0.1716          | 0.7884    | 0.7886 | 0.7885 | 0.9426   |
| 0.1338        | 3.0   | 3492 | 0.1699          | 0.7855    | 0.8043 | 0.7948 | 0.9439   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
