---
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2-bc2gm-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: bc2gm_corpus
      type: bc2gm_corpus
      config: bc2gm_corpus
      split: train
      args: bc2gm_corpus
    metrics:
    - name: Precision
      type: precision
      value: 0.7988356059445381
    - name: Recall
      type: recall
      value: 0.8243478260869566
    - name: F1
      type: f1
      value: 0.8113912231559292
    - name: Accuracy
      type: accuracy
      value: 0.9772069842818806
---

<!-- 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-base-cased-v1.2-bc2gm-ner

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 the bc2gm_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1528
- Precision: 0.7988
- Recall: 0.8243
- F1: 0.8114
- Accuracy: 0.9772

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.057         | 1.0   | 782  | 0.0670          | 0.7446    | 0.8051 | 0.7736 | 0.9738   |
| 0.0586        | 2.0   | 1564 | 0.0689          | 0.7689    | 0.8106 | 0.7892 | 0.9755   |
| 0.0123        | 3.0   | 2346 | 0.0715          | 0.7846    | 0.8076 | 0.7959 | 0.9750   |
| 0.0002        | 4.0   | 3128 | 0.0896          | 0.7942    | 0.8199 | 0.8068 | 0.9767   |
| 0.0004        | 5.0   | 3910 | 0.1119          | 0.7971    | 0.8201 | 0.8084 | 0.9765   |
| 0.0004        | 6.0   | 4692 | 0.1192          | 0.7966    | 0.8337 | 0.8147 | 0.9768   |
| 0.013         | 7.0   | 5474 | 0.1274          | 0.7932    | 0.8266 | 0.8095 | 0.9773   |
| 0.0236        | 8.0   | 6256 | 0.1419          | 0.7976    | 0.8213 | 0.8093 | 0.9771   |
| 0.0004        | 9.0   | 7038 | 0.1519          | 0.8004    | 0.8261 | 0.8130 | 0.9772   |
| 0.0           | 10.0  | 7820 | 0.1528          | 0.7988    | 0.8243 | 0.8114 | 0.9772   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1