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mlma_nchan19_biogpt_gpt2

Token Classification
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gpt2
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mlma_nchan19_biogpt_gpt2
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---
license: mit
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mlma_nchan19_biogpt_gpt2
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ncbi_disease
      type: ncbi_disease
      config: ncbi_disease
      split: validation
      args: ncbi_disease
    metrics:
    - name: Precision
      type: precision
      value: 0.4473684210526316
    - name: Recall
      type: recall
      value: 0.5400254129606099
    - name: F1
      type: f1
      value: 0.48934945308002303
    - name: Accuracy
      type: accuracy
      value: 0.9576801898167957
---

<!-- 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. -->

# mlma_nchan19_biogpt_gpt2

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: 0.1551
- Precision: 0.4474
- Recall: 0.5400
- F1: 0.4893
- Accuracy: 0.9577

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2855        | 1.0   | 679  | 0.1675          | 0.3396    | 0.4130 | 0.3727 | 0.9456   |
| 0.1699        | 2.0   | 1358 | 0.1480          | 0.4084    | 0.5044 | 0.4514 | 0.9543   |
| 0.0965        | 3.0   | 2037 | 0.1551          | 0.4474    | 0.5400 | 0.4893 | 0.9577   |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3