---
base_model: dmis-lab/biobert-base-cased-v1.2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: NHS-dmis-binary
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# NHS-dmis-binary

This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4752
- Accuracy: 0.8158
- Precision: 0.8102
- Recall: 0.8064
- F1: 0.8081

## 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: 3e-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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0543        | 1.0   | 397  | 0.3985          | 0.8240   | 0.8232    | 0.8089 | 0.8141 |
| 0.1033        | 2.0   | 794  | 0.4902          | 0.7817   | 0.7913    | 0.7996 | 0.7811 |
| 2.162         | 3.0   | 1191 | 0.4752          | 0.8158   | 0.8102    | 0.8064 | 0.8081 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2