---
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_dir
  results: []
datasets:
- wisesight_sentiment
language:
- th
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wangchanberta-hyperopt-sentiment-01

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 Wisesight Sentiment dataset.
The model is optimized for binary sentiment classification tasks, targeting two labels: positive and negative.

It achieves the following results on the evaluation set:
- Loss: 0.3595
- Accuracy: 0.9103

## Model description

This model is intended for Thai language sentiment analysis, specifically designed to classify text as either positive or negative.

## Intended uses & limitations

- The model is only trained to recognize positive and negative sentiments and may not perform well on nuanced or multi-class sentiment tasks.
- The model is specialized for the Thai language and is not intended for multi-language or code-switching scenarios.

## Training and evaluation data

The model is trained on the Wisesight Sentiment dataset, which is a widely-used dataset for Thai NLP tasks.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2.5692051845867925e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 7
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.55  | 250  | 0.3128          | 0.8859   |
| 0.3913        | 1.09  | 500  | 0.2672          | 0.8942   |
| 0.3913        | 1.64  | 750  | 0.2860          | 0.9025   |
| 0.2172        | 2.19  | 1000 | 0.4044          | 0.9060   |
| 0.2172        | 2.74  | 1250 | 0.3738          | 0.9076   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3