| .. currentmodule:: pythainlp.wangchanberta |
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| pythainlp.wangchanberta |
| ======================= |
| The `pythainlp.wangchanberta` module is built upon the WangchanBERTa base model, specifically the `wangchanberta-base-att-spm-uncased` model, as detailed in the paper by Lowphansirikul et al. [^Lowphansirikul_2021]. |
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| This base model is utilized for various natural language processing tasks in the Thai language, including named entity recognition, part-of-speech tagging, and subword tokenization. |
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| If you intend to fine-tune the model or explore its capabilities further, please refer to the [thai2transformers repository](https://github.com/vistec-AI/thai2transformers). |
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| **Speed Benchmark** |
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| ============================= ======================== ============== |
| Function Named Entity Recognition Part of Speech |
| ============================= ======================== ============== |
| PyThaiNLP basic function 89.7 ms 312 ms |
| pythainlp.wangchanberta (CPU) 9.64 s 9.65 s |
| pythainlp.wangchanberta (GPU) 8.02 s 8 s |
| ============================= ======================== ============== |
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| For a comprehensive performance benchmark, the following notebooks are available: |
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| - `PyThaiNLP basic function and pythainlp.wangchanberta CPU at Google |
| Colab`_ |
| - `pythainlp.wangchanberta GPU`_ |
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| .. _PyThaiNLP basic function and pythainlp.wangchanberta CPU at Google Colab: https://colab.research.google.com/drive/1ymTVB1UESXAyZlSpjknCb72xpdcZ86Db?usp=sharing |
| .. _pythainlp.wangchanberta GPU: https://colab.research.google.com/drive/1AtkFT1HMGL2GO7O2tM_hi_7mExKwmhMw?usp=sharing |
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| Modules |
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| .. autoclass:: NamedEntityRecognition |
| :members: |
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| The `NamedEntityRecognition` class is a fundamental component for identifying named entities in Thai text. It allows you to extract entities such as names, locations, and organizations from text data. |
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| .. autoclass:: ThaiNameTagger |
| :members: |
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| The `ThaiNameTagger` class is designed for tagging Thai names within text. This is essential for tasks such as entity recognition, information extraction, and text classification. |
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| .. autofunction:: segment |
| :noindex: |
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| The `segment` function is a subword tokenization tool that breaks down text into subword units, offering a foundation for further text processing and analysis. |
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| References |
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| [^Lowphansirikul_2021] Lowphansirikul L, Polpanumas C, Jantrakulchai N, Nutanong S. WangchanBERTa: Pretraining transformer-based Thai Language Models. [ArXiv:2101.09635](http://arxiv.org/abs/2101.09635) [Internet]. 2021 Jan 23 [cited 2021 Feb 27]. |
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