transformers tensorboard safetensors bert token-classification generated_from_trainer dataset:ncbi_disease base_model:dmis-lab/biobert-v1.1 base_model:finetune:dmis-lab/biobert-v1.1 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: dmis-lab/biobert-v1.1
datasets:
- ncbi_disease
library_name: transformers
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: biobert-finetuned-ncbi
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - type: precision
      value: 0.837378640776699
      name: Precision
    - type: recall
      value: 0.8767471410419314
      name: Recall
    - type: f1
      value: 0.8566108007448789
      name: F1
    - type: accuracy
      value: 0.9854973235403959
      name: Accuracy
---

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# biobert-finetuned-ncbi

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1139
- Precision: 0.8374
- Recall: 0.8767
- F1: 0.8566
- Accuracy: 0.9855

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1049        | 1.0   | 680  | 0.0457          | 0.7929    | 0.8463 | 0.8187 | 0.9844   |
| 0.0332        | 2.0   | 1360 | 0.0498          | 0.7947    | 0.8806 | 0.8354 | 0.9852   |
| 0.0129        | 3.0   | 2040 | 0.0583          | 0.8196    | 0.8717 | 0.8448 | 0.9850   |
| 0.0058        | 4.0   | 2720 | 0.0817          | 0.8411    | 0.8679 | 0.8543 | 0.9850   |
| 0.0039        | 5.0   | 3400 | 0.0955          | 0.85      | 0.8640 | 0.8570 | 0.9852   |
| 0.0014        | 6.0   | 4080 | 0.1038          | 0.8222    | 0.8755 | 0.848  | 0.9850   |
| 0.0017        | 7.0   | 4760 | 0.1004          | 0.8427    | 0.8780 | 0.8600 | 0.9858   |
| 0.0007        | 8.0   | 5440 | 0.1115          | 0.8407    | 0.8717 | 0.8559 | 0.9855   |
| 0.0005        | 9.0   | 6120 | 0.1123          | 0.8348    | 0.8729 | 0.8534 | 0.9854   |
| 0.0002        | 10.0  | 6800 | 0.1139          | 0.8374    | 0.8767 | 0.8566 | 0.9855   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
