---
base_model: dmis-lab/biobert-base-cased-v1.2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: devicebert-base-cased-v1.0
  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. -->

# devicebert-base-cased-v1.0

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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.6816
- Recall: 0.6691
- F1: 0.6753
- Accuracy: 0.8547

## 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: 1e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 101  | nan             | 0.5981    | 0.5740 | 0.5858 | 0.8131   |
| No log        | 2.0   | 202  | nan             | 0.6673    | 0.6197 | 0.6427 | 0.8424   |
| No log        | 3.0   | 303  | nan             | 0.6926    | 0.6673 | 0.6797 | 0.8498   |
| No log        | 4.0   | 404  | nan             | 0.686     | 0.6271 | 0.6552 | 0.8473   |
| 0.3891        | 5.0   | 505  | nan             | 0.6853    | 0.6490 | 0.6667 | 0.8539   |
| 0.3891        | 6.0   | 606  | nan             | 0.6857    | 0.7020 | 0.6938 | 0.8563   |
| 0.3891        | 7.0   | 707  | nan             | 0.6900    | 0.6673 | 0.6784 | 0.8580   |
| 0.3891        | 8.0   | 808  | nan             | 0.6795    | 0.6782 | 0.6789 | 0.8514   |
| 0.3891        | 9.0   | 909  | nan             | 0.6906    | 0.6691 | 0.6797 | 0.8571   |
| 0.1315        | 10.0  | 1010 | nan             | 0.6816    | 0.6691 | 0.6753 | 0.8547   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1