| .. currentmodule:: pythainlp.wangchanberta | |
| 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]. | |