---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 2023MLMA_LAB9_task5
  results: []
---

<!-- 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_task5

This model is a fine-tuned version of [yujie07/2023MLMA_LAB9_task2](https://huggingface.co/yujie07/2023MLMA_LAB9_task2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1449
- Precision: 0.5578
- Recall: 0.5875
- F1: 0.5723
- Accuracy: 0.9525

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2325        | 1.0   | 591  | 0.1697          | 0.5117    | 0.4192 | 0.4608 | 0.9418   |
| 0.151         | 2.0   | 1182 | 0.1448          | 0.5302    | 0.5765 | 0.5524 | 0.9503   |
| 0.1139        | 3.0   | 1773 | 0.1449          | 0.5578    | 0.5875 | 0.5723 | 0.9525   |


### Framework versions

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