| | --- |
| | 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: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # 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 |
