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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ClinicalBERT_CRAFT_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: 0.1629
- Precision: 0.9605
- Recall: 0.9616
- F1: 0.9610
- Accuracy: 0.9602

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2622        | 1.0   | 695  | 0.1701          | 0.9555    | 0.9570 | 0.9563 | 0.9544   |
| 0.0947        | 2.0   | 1390 | 0.1616          | 0.9592    | 0.9606 | 0.9599 | 0.9589   |
| 0.0543        | 3.0   | 2085 | 0.1629          | 0.9605    | 0.9616 | 0.9610 | 0.9602   |


### Framework versions

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