pytorch tensorboard bert token-classification dataset:ncbi_disease transformers generated_from_trainer model-index autotrain_compatible
---
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bioBERT-NER-NCBI_disease
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.8136200716845878
    - name: Recall
      type: recall
      value: 0.8653113087674714
    - name: F1
      type: f1
      value: 0.8386699507389163
    - name: Accuracy
      type: accuracy
      value: 0.9850187265917603
      
widget:
  - text: "This model finds disease names such as Cholera, Cancer or COVID"
---


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# bioBERT-NER-NCBI_disease

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 ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0598
- Precision: 0.8136
- Recall: 0.8653
- F1: 0.8387
- Accuracy: 0.9850

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0972        | 1.0   | 680  | 0.0688          | 0.7435    | 0.7624 | 0.7528 | 0.9794   |
| 0.0397        | 2.0   | 1360 | 0.0508          | 0.7952    | 0.8780 | 0.8345 | 0.9840   |
| 0.0118        | 3.0   | 2040 | 0.0598          | 0.8136    | 0.8653 | 0.8387 | 0.9850   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
