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--- |
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license: unknown |
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base_model: |
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- airesearch/wangchanberta-base-att-spm-uncased |
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--- |
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# AmbatronBERTa |
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AmbatronBERTa is a Thai language model fine-tuned specifically for text classification tasks, built upon the WangchanBERTa architecture. |
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## Model Description |
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AmbatronBERTa is designed to handle the complexities of the Thai language. It has been fine-tuned on a dataset of over 3,000 research papers to improve classification accuracy. Leveraging the transformer-based WangchanBERTa, it efficiently captures the nuances of Thai text, making it suitable for classifying documents across multiple fields. |
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## Developers |
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AmbatronBERTa was developed by students at **King Mongkut's University of Technology North Bangkok**: |
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- **Peerawat Banpahan** |
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- **Waris Thongpho** |
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## Use Cases |
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AmbatronBERTa can be applied to a wide range of tasks, such as: |
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- **Research Classification:** Categorizing academic papers into relevant topics. |
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- **Document Organization:** Classifying articles, blogs, and other documents by themes. |
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- **Sentiment Analysis:** Analyzing sentiment in Thai-language texts across various contexts. |
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## How to Use |
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To use AmbatronBERTa with the `transformers` library: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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# Load the tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("Peerawat2024/AmbatronBERTa") |
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model = AutoModelForSequenceClassification.from_pretrained("Peerawat2024/AmbatronBERTa") |
