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biobert-finetuned-ner

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results
Inference Endpoints
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biobert-finetuned-ner
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---
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: jnlpba
      type: jnlpba
      config: jnlpba
      split: train
      args: jnlpba
    metrics:
    - name: Precision
      type: precision
      value: 0.6550939663699308
    - name: Recall
      type: recall
      value: 0.7646040175479104
    - name: F1
      type: f1
      value: 0.7056253995312167
    - name: Accuracy
      type: accuracy
      value: 0.9107839603371846
---

<!-- 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-finetuned-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 jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5113
- Precision: 0.6551
- Recall: 0.7646
- F1: 0.7056
- Accuracy: 0.9108

## 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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1815        | 1.0   | 2319  | 0.2706          | 0.6538    | 0.7704 | 0.7073 | 0.9160   |
| 0.1226        | 2.0   | 4638  | 0.3230          | 0.6524    | 0.7675 | 0.7053 | 0.9118   |
| 0.0813        | 3.0   | 6957  | 0.3974          | 0.6483    | 0.7611 | 0.7002 | 0.9101   |
| 0.0521        | 4.0   | 9276  | 0.4529          | 0.6575    | 0.7652 | 0.7073 | 0.9121   |
| 0.0356        | 5.0   | 11595 | 0.5113          | 0.6551    | 0.7646 | 0.7056 | 0.9108   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1