---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-all
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# biobert-all

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7750
- Precision: 0.5990
- Recall: 0.6572
- F1: 0.6268
- Accuracy: 0.8385

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 363  | 0.4337          | 0.5819    | 0.6535 | 0.6156 | 0.8427   |
| 0.4325        | 2.0   | 726  | 0.4422          | 0.5912    | 0.6675 | 0.6270 | 0.8438   |
| 0.2832        | 3.0   | 1089 | 0.4720          | 0.6010    | 0.6422 | 0.6209 | 0.8443   |
| 0.2832        | 4.0   | 1452 | 0.5342          | 0.6076    | 0.6522 | 0.6291 | 0.8454   |
| 0.1948        | 5.0   | 1815 | 0.5969          | 0.6059    | 0.6594 | 0.6315 | 0.8415   |
| 0.1315        | 6.0   | 2178 | 0.6428          | 0.6051    | 0.6551 | 0.6291 | 0.8408   |
| 0.0987        | 7.0   | 2541 | 0.6933          | 0.5933    | 0.6649 | 0.6270 | 0.8384   |
| 0.0987        | 8.0   | 2904 | 0.7353          | 0.5949    | 0.6633 | 0.6273 | 0.8390   |
| 0.0762        | 9.0   | 3267 | 0.7640          | 0.5920    | 0.6623 | 0.6252 | 0.8389   |
| 0.0628        | 10.0  | 3630 | 0.7750          | 0.5990    | 0.6572 | 0.6268 | 0.8385   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1