transformers pytorch tensorboard gpt2 token-classification generated_from_trainer dataset:ncbi_disease license:mit model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- ncbi_disease
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: biogpt-finetuned-ner
  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: 0.4699792960662526
      name: Precision
    - type: recall
      value: 0.576874205844981
      name: Recall
    - type: f1
      value: 0.517969195664575
      name: F1
    - type: accuracy
      value: 0.9581469524291104
      name: Accuracy
---

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# biogpt-finetuned-ner

This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1728
- Precision: 0.4700
- Recall: 0.5769
- F1: 0.5180
- Accuracy: 0.9581

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1029        | 1.0   | 679  | 0.1493          | 0.4228    | 0.5845 | 0.4907 | 0.9554   |
| 0.076         | 2.0   | 1358 | 0.1756          | 0.4138    | 0.5705 | 0.4797 | 0.9533   |
| 0.0516        | 3.0   | 2037 | 0.1728          | 0.4700    | 0.5769 | 0.5180 | 0.9581   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
