pytorch tensorboard bert token-classification dataset:bc2gm_corpus transformers generated_from_trainer model-index autotrain_compatible
---
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bioBERT-finrtuned-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: 0.7932528628907459
    - name: Recall
      type: recall
      value: 0.8373080692584123
    - name: F1
      type: f1
      value: 0.8146853146853147
    - name: Accuracy
      type: accuracy
      value: 0.9750375532003672
widget:
  - text: "JUP, AKT1, and AURKC are examples of genes"

---

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# bioBERT-finrtuned-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: 0.0887
- Precision: 0.7933
- Recall: 0.8373
- F1: 0.8147
- Accuracy: 0.9750

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0893        | 1.0   | 1563 | 0.0748          | 0.7447    | 0.8063 | 0.7743 | 0.9722   |
| 0.0507        | 2.0   | 3126 | 0.0773          | 0.7928    | 0.8275 | 0.8098 | 0.9739   |
| 0.0286        | 3.0   | 4689 | 0.0887          | 0.7933    | 0.8373 | 0.8147 | 0.9750   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
