---
license: mit
tags:
- generated_from_trainer
datasets:
- bc4chemd_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: bc4chemd_ner
      type: bc4chemd_ner
      args: bc4chemd
    metrics:
    - name: Precision
      type: precision
      value: 0.8944236722550557
    - name: Recall
      type: recall
      value: 0.8777321865383098
    - name: F1
      type: f1
      value: 0.8859993229654115
    - name: Accuracy
      type: accuracy
      value: 0.9908228496683563
---

<!-- 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. -->

# bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner

This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc4chemd_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0641
- Precision: 0.8944
- Recall: 0.8777
- F1: 0.8860
- Accuracy: 0.9908

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.006         | 1.0   | 1918  | 0.0310          | 0.8697    | 0.8510 | 0.8602 | 0.9894   |
| 0.0097        | 2.0   | 3836  | 0.0345          | 0.8855    | 0.8637 | 0.8745 | 0.9898   |
| 0.0058        | 3.0   | 5754  | 0.0359          | 0.8733    | 0.8836 | 0.8784 | 0.9902   |
| 0.0014        | 4.0   | 7672  | 0.0440          | 0.8723    | 0.8842 | 0.8782 | 0.9903   |
| 0.0005        | 5.0   | 9590  | 0.0539          | 0.8862    | 0.8673 | 0.8766 | 0.9903   |
| 0.0001        | 6.0   | 11508 | 0.0558          | 0.8939    | 0.8628 | 0.8781 | 0.9904   |
| 0.0001        | 7.0   | 13426 | 0.0558          | 0.8846    | 0.8729 | 0.8787 | 0.9903   |
| 0.0012        | 8.0   | 15344 | 0.0635          | 0.8935    | 0.8696 | 0.8814 | 0.9905   |
| 0.0           | 9.0   | 17262 | 0.0624          | 0.8897    | 0.8831 | 0.8864 | 0.9908   |
| 0.0002        | 10.0  | 19180 | 0.0641          | 0.8944    | 0.8777 | 0.8860 | 0.9908   |


### Framework versions

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