---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-finetuned-ner1
  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-finetuned-ner1

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: 0.6653
- Precision: 0.6417
- Recall: 0.6985
- F1: 0.6689
- Accuracy: 0.8611

## 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   | 305  | 0.4133          | 0.6172    | 0.6674 | 0.6413 | 0.8529   |
| 0.4433        | 2.0   | 610  | 0.4058          | 0.6121    | 0.6868 | 0.6473 | 0.8568   |
| 0.4433        | 3.0   | 915  | 0.4456          | 0.6323    | 0.7015 | 0.6651 | 0.8594   |
| 0.2431        | 4.0   | 1220 | 0.4708          | 0.6323    | 0.6925 | 0.6610 | 0.8612   |
| 0.1563        | 5.0   | 1525 | 0.5084          | 0.6434    | 0.6998 | 0.6704 | 0.8652   |
| 0.1563        | 6.0   | 1830 | 0.5655          | 0.6438    | 0.6801 | 0.6615 | 0.8607   |
| 0.1038        | 7.0   | 2135 | 0.6173          | 0.6385    | 0.6918 | 0.6641 | 0.8591   |
| 0.1038        | 8.0   | 2440 | 0.6352          | 0.6410    | 0.7011 | 0.6697 | 0.8608   |
| 0.0754        | 9.0   | 2745 | 0.6600          | 0.6406    | 0.6951 | 0.6668 | 0.8609   |
| 0.0599        | 10.0  | 3050 | 0.6653          | 0.6417    | 0.6985 | 0.6689 | 0.8611   |


### Framework versions

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