---
license: mit
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: bc2gm_corpus
      type: bc2gm_corpus
      args: bc2gm_corpus
    metrics:
    - name: Precision
      type: precision
      value: 0.7853881278538812
    - name: Recall
      type: recall
      value: 0.8158102766798419
    - name: F1
      type: f1
      value: 0.8003101977510663
    - name: Accuracy
      type: accuracy
      value: 0.9758965601366187
---

<!-- 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. -->

# bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc2gm_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1505
- Precision: 0.7854
- Recall: 0.8158
- F1: 0.8003
- Accuracy: 0.9759

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0981        | 1.0   | 782  | 0.0712          | 0.7228    | 0.7948 | 0.7571 | 0.9724   |
| 0.0509        | 2.0   | 1564 | 0.0687          | 0.7472    | 0.8199 | 0.7818 | 0.9746   |
| 0.0121        | 3.0   | 2346 | 0.0740          | 0.7725    | 0.8011 | 0.7866 | 0.9747   |
| 0.0001        | 4.0   | 3128 | 0.1009          | 0.7618    | 0.8251 | 0.7922 | 0.9741   |
| 0.0042        | 5.0   | 3910 | 0.1106          | 0.7757    | 0.8185 | 0.7965 | 0.9754   |
| 0.0015        | 6.0   | 4692 | 0.1182          | 0.7812    | 0.8111 | 0.7958 | 0.9758   |
| 0.0001        | 7.0   | 5474 | 0.1283          | 0.7693    | 0.8275 | 0.7973 | 0.9753   |
| 0.0072        | 8.0   | 6256 | 0.1376          | 0.7863    | 0.8158 | 0.8008 | 0.9762   |
| 0.0045        | 9.0   | 7038 | 0.1468          | 0.7856    | 0.8180 | 0.8015 | 0.9761   |
| 0.0           | 10.0  | 7820 | 0.1505          | 0.7854    | 0.8158 | 0.8003 | 0.9759   |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1