---
base_model: poom-sci/WangchanBERTa-finetuned-sentiment
datasets:
- pythainlp/wisesight_sentiment
language:
- th
library_name: transformers
license: apache-2.0
pipeline_tag: text-classification
tags:
- generated_from_trainer
model-index:
- name: sentiment-thai-text-model
  results: []
---

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# sentiment-thai-text-model

This model is a fine-tuned version of [poom-sci/WangchanBERTa-finetuned-sentiment](https://huggingface.co/poom-sci/WangchanBERTa-finetuned-sentiment) on an pythainlp/wisesight_sentiment.

## Model description

This model is a fine-tuned version of poom-sci/WangchanBERTa-finetuned-sentiment, specifically tailored for sentiment analysis on Thai-language texts. The fine-tuning was performed to improve performance on a custom Thai dataset for sentiment classification. The model is based on WangchanBERTa, a powerful transformer-based language model developed for Thai by the National Electronics and Computer Technology Center (NECTEC) in Thailand.

## Intended uses & limitations

This model is designed to perform sentiment analysis, categorizing input text into three classes: positive, neutral, and negative. It can be used in a variety of natural language processing (NLP) applications such as:

Social media sentiment analysis
Product or service reviews sentiment classification
Customer feedback processing

Limitations:
Language: The model is specialized for Thai text and may not perform well with other languages.
Generalization: The model's performance depends on the quality and diversity of the dataset used for fine-tuning. It may not generalize well to domains that differ significantly from the training data.
Ambiguity: Handling of highly ambiguous or sarcastic sentences may still be challenging.

## Training and evaluation data

The model was fine-tuned on a sentiment classification dataset composed of Thai-language text. The dataset includes sentences and texts from multiple domains, such as social media, product reviews, and general user feedback, labeled into three categories:

Positive: Indicates that the text expresses positive sentiment.
Neutral: Indicates that the text is neutral or objective in sentiment.
Negative: Indicates that the text expresses negative sentiment.
More details on the dataset used can be provided upon request.

## Training procedure

The model was trained using the following hyperparameters:

Learning rate: 2e-05
Batch size: 32 for both training and evaluation
Seed: 42 (for reproducibility)
Optimizer: Adam (with betas=(0.9, 0.999) and epsilon=1e-08)
Scheduler: Linear learning rate scheduler
Number of epochs: 5
The training used a combination of cross-entropy loss for multi-class classification and early stopping based on evaluation metrics.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1