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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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