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

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: 0.6965
- Precision: 0.6381
- Recall: 0.6865
- F1: 0.6614
- Accuracy: 0.8583

## 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  | 0.4123          | 0.6110    | 0.6694 | 0.6389 | 0.8542   |
| 0.4534        | 2.0   | 610  | 0.4023          | 0.6259    | 0.6848 | 0.6540 | 0.8586   |
| 0.4534        | 3.0   | 915  | 0.4384          | 0.6369    | 0.6991 | 0.6666 | 0.8615   |
| 0.2438        | 4.0   | 1220 | 0.4799          | 0.6445    | 0.6941 | 0.6684 | 0.8615   |
| 0.1551        | 5.0   | 1525 | 0.5190          | 0.6464    | 0.6908 | 0.6678 | 0.8628   |
| 0.1551        | 6.0   | 1830 | 0.5772          | 0.6454    | 0.6751 | 0.6599 | 0.8597   |
| 0.1044        | 7.0   | 2135 | 0.6141          | 0.6413    | 0.6881 | 0.6639 | 0.8586   |
| 0.1044        | 8.0   | 2440 | 0.6587          | 0.6353    | 0.6945 | 0.6636 | 0.8590   |
| 0.0755        | 9.0   | 2745 | 0.6856          | 0.6357    | 0.6905 | 0.6620 | 0.8580   |
| 0.0604        | 10.0  | 3050 | 0.6965          | 0.6381    | 0.6865 | 0.6614 | 0.8583   |


### Framework versions

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