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biobert-base-cased-v1.2-finetuned-ner

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biobert-base-cased-v1.2-finetuned-ner
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---
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.8396334478808706
    - name: Recall
      type: recall
      value: 0.8731387730792138
    - name: F1
      type: f1
      value: 0.856058394160584
    - name: Accuracy
      type: accuracy
      value: 0.9824805769647444
---

<!-- 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-base-cased-v1.2-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 ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0706
- Precision: 0.8396
- Recall: 0.8731
- F1: 0.8561
- Accuracy: 0.9825

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 340  | 0.0691          | 0.8190    | 0.7868 | 0.8026 | 0.9777   |
| 0.101         | 2.0   | 680  | 0.0700          | 0.8334    | 0.8553 | 0.8442 | 0.9807   |
| 0.0244        | 3.0   | 1020 | 0.0706          | 0.8396    | 0.8731 | 0.8561 | 0.9825   |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1