---
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fine-tune-wangchanberta-stock-thai
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# fine-tune-wangchanberta-stock-thai

This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0974
- Accuracy: 0.4010
- Precision: 0.3527
- Recall: 0.4010
- F1: 0.2439

## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0643        | 1.0   | 26   | 1.1045          | 0.3564   | 0.2361    | 0.3564 | 0.2750 |
| 1.08          | 2.0   | 52   | 1.0902          | 0.3960   | 0.1568    | 0.3960 | 0.2247 |
| 1.074         | 3.0   | 78   | 1.1016          | 0.3960   | 0.1568    | 0.3960 | 0.2247 |
| 1.0613        | 4.0   | 104  | 1.0929          | 0.3960   | 0.1568    | 0.3960 | 0.2247 |
| 1.0564        | 5.0   | 130  | 1.0990          | 0.3960   | 0.1568    | 0.3960 | 0.2247 |
| 1.053         | 6.0   | 156  | 1.1003          | 0.3960   | 0.1568    | 0.3960 | 0.2247 |
| 1.0498        | 7.0   | 182  | 1.0968          | 0.3911   | 0.1557    | 0.3911 | 0.2227 |
| 1.0444        | 8.0   | 208  | 1.0946          | 0.3911   | 0.1557    | 0.3911 | 0.2227 |
| 1.0418        | 9.0   | 234  | 1.0990          | 0.3960   | 0.1568    | 0.3960 | 0.2247 |
| 1.0385        | 10.0  | 260  | 1.0982          | 0.3960   | 0.3025    | 0.3960 | 0.2331 |
| 1.0352        | 11.0  | 286  | 1.0980          | 0.3911   | 0.1557    | 0.3911 | 0.2227 |
| 1.0401        | 12.0  | 312  | 1.1001          | 0.3911   | 0.1557    | 0.3911 | 0.2227 |
| 1.0395        | 13.0  | 338  | 1.0970          | 0.4010   | 0.3519    | 0.4010 | 0.2431 |
| 1.032         | 14.0  | 364  | 1.0971          | 0.4010   | 0.3519    | 0.4010 | 0.2431 |
| 1.0351        | 15.0  | 390  | 1.0977          | 0.4010   | 0.3527    | 0.4010 | 0.2439 |
| 1.0262        | 16.0  | 416  | 1.0970          | 0.4010   | 0.3527    | 0.4010 | 0.2439 |
| 1.0385        | 17.0  | 442  | 1.0970          | 0.4010   | 0.3527    | 0.4010 | 0.2439 |
| 1.031         | 18.0  | 468  | 1.0970          | 0.4010   | 0.3527    | 0.4010 | 0.2439 |
| 1.0313        | 19.0  | 494  | 1.0969          | 0.4010   | 0.3527    | 0.4010 | 0.2439 |
| 1.0429        | 20.0  | 520  | 1.0974          | 0.4010   | 0.3527    | 0.4010 | 0.2439 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1