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2023MLMA_LAB9_task2

Token Classification
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2023MLMA_LAB9_task2
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---
license: mit
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 2023MLMA_LAB9_task2
  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: 0.48903878583473864
    - name: Recall
      type: recall
      value: 0.5598455598455598
    - name: F1
      type: f1
      value: 0.522052205220522
    - name: Accuracy
      type: accuracy
      value: 0.9536349138434012
---

<!-- 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. -->

# 2023MLMA_LAB9_task2

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: 0.1611
- Precision: 0.4890
- Recall: 0.5598
- F1: 0.5221
- Accuracy: 0.9536

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3326        | 1.0   | 679  | 0.1749          | 0.4099    | 0.4546 | 0.4311 | 0.9449   |
| 0.175         | 2.0   | 1358 | 0.1616          | 0.4562    | 0.5125 | 0.4827 | 0.9511   |
| 0.1082        | 3.0   | 2037 | 0.1611          | 0.4890    | 0.5598 | 0.5221 | 0.9536   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3