sentiment-thai-text-model
This model is a fine-tuned version of
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
