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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].
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.
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).
**Speed Benchmark**
============================= ======================== ==============
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
============================= ======================== ==============
For a comprehensive performance benchmark, the following notebooks are available:
- `PyThaiNLP basic function and pythainlp.wangchanberta CPU at Google
Colab`_
- `pythainlp.wangchanberta GPU`_
.. _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
Modules
-------
.. autoclass:: NamedEntityRecognition
:members:
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.
.. autoclass:: ThaiNameTagger
:members:
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.
.. autofunction:: segment
:noindex:
The `segment` function is a subword tokenization tool that breaks down text into subword units, offering a foundation for further text processing and analysis.
References
----------
[^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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