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

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: 0.8095
- Precision: 0.6591
- Recall: 0.7116
- F1: 0.6843
- Accuracy: 0.8236

## 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  | 0.5639          | 0.5973    | 0.6865 | 0.6388 | 0.8149   |
| 0.6983        | 2.0   | 726  | 0.5410          | 0.6263    | 0.7052 | 0.6634 | 0.8238   |
| 0.3859        | 3.0   | 1089 | 0.5557          | 0.6544    | 0.7011 | 0.6769 | 0.8245   |
| 0.3859        | 4.0   | 1452 | 0.5803          | 0.6579    | 0.7064 | 0.6813 | 0.8276   |
| 0.276         | 5.0   | 1815 | 0.6461          | 0.6598    | 0.7105 | 0.6842 | 0.8238   |
| 0.1944        | 6.0   | 2178 | 0.6995          | 0.6616    | 0.7120 | 0.6859 | 0.8237   |
| 0.1505        | 7.0   | 2541 | 0.7337          | 0.6563    | 0.7195 | 0.6865 | 0.8253   |
| 0.1505        | 8.0   | 2904 | 0.7710          | 0.6664    | 0.7120 | 0.6884 | 0.8255   |
| 0.1178        | 9.0   | 3267 | 0.8030          | 0.6541    | 0.7165 | 0.6838 | 0.8233   |
| 0.1006        | 10.0  | 3630 | 0.8095          | 0.6591    | 0.7116 | 0.6843 | 0.8236   |


### Framework versions

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