---
license: mit
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MLMA_lab9
  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: 0.12389380530973451
    - name: Recall
      type: recall
      value: 0.017789072426937738
    - name: F1
      type: f1
      value: 0.031111111111111107
    - name: Accuracy
      type: accuracy
      value: 0.9177455063979887
---

<!-- 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_lab9

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: 0.3328
- Precision: 0.1239
- Recall: 0.0178
- F1: 0.0311
- Accuracy: 0.9177

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2903        | 1.0   | 680  | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.2907        | 2.0   | 1360 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.2885        | 3.0   | 2040 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.2861        | 4.0   | 2720 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.2948        | 5.0   | 3400 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.2881        | 6.0   | 4080 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.292         | 7.0   | 4760 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.2882        | 8.0   | 5440 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.2905        | 9.0   | 6120 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |
| 0.2881        | 10.0  | 6800 | 0.3328          | 0.1239    | 0.0178 | 0.0311 | 0.9177   |


### Framework versions

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