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--- |
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base_model: poom-sci/WangchanBERTa-finetuned-sentiment |
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datasets: |
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- pythainlp/wisesight_sentiment |
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language: |
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- th |
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library_name: transformers |
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license: apache-2.0 |
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pipeline_tag: text-classification |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: sentiment-thai-text-model |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# sentiment-thai-text-model |
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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. |
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## Model description |
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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. |
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## Intended uses & limitations |
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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: |
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Social media sentiment analysis |
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Product or service reviews sentiment classification |
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Customer feedback processing |
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Limitations: |
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Language: The model is specialized for Thai text and may not perform well with other languages. |
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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. |
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Ambiguity: Handling of highly ambiguous or sarcastic sentences may still be challenging. |
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## Training and evaluation data |
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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: |
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Positive: Indicates that the text expresses positive sentiment. |
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Neutral: Indicates that the text is neutral or objective in sentiment. |
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Negative: Indicates that the text expresses negative sentiment. |
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More details on the dataset used can be provided upon request. |
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## Training procedure |
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The model was trained using the following hyperparameters: |
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Learning rate: 2e-05 |
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Batch size: 32 for both training and evaluation |
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Seed: 42 (for reproducibility) |
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Optimizer: Adam (with betas=(0.9, 0.999) and epsilon=1e-08) |
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Scheduler: Linear learning rate scheduler |
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Number of epochs: 2 |
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The training used a combination of cross-entropy loss for multi-class classification and early stopping based on evaluation metrics. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
