🏅Telegram Khmer Chatbot + Open Source Code🏅

This will never happen if Khmer NLTK Module doesn't exist, thanks 🙏

🎯TODO

• Tokenization and Bag of Words Creation
• Data Preprocessing
• Neural Network Model
• Training the Model
• Model Evaluation
• Telegram Bot Integration
• Make it more inteligent and flexible

💪Installation

pip install numpy

pip install khmer-nltk

pip install python-telegram-bot

pip install torch

🏹 Quick tour

[Official Blog Post Website Url]

To get the quick way of using this code package, please install requirement.txt and read the comment in code header before run that code script.

Some Mention

• Check folder directory
• create virtual environment with "myEnv" folder
• install with requrements version module
• Check Bot Api
• Check Bot Username
• Make sure u aready istall library I mentioned. (if u have any issue, U can contact me by Telegram Directly @SOYTET)

🔯 Demo

Run the ChatApp Scripts to Excute Telegram integration and Bot response after training finish

Python ./App/ChatApp.py

While Running ChatApp.py Script, so u can make some chat with Telegram Bot which u integrate with your own

សួស្ដីបង

✍️ Citation

@misc{Chatbot,
  author = {SOY TET},
  title = {Khmer Telegram Traditional Chatbot},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository}
}

Used in:

• stopes: A library for preparing data for machine translation research
• LASER Language-Agnostic SEntence Representations
• Pretrained Models and Evaluation Data for the Khmer Language
• Multilingual Open Text 1.0: Public Domain News in 44 Languages
• ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System
• Shared Task on Cross-lingual Open-Retrieval QA
• No Language Left Behind: Scaling Human-Centered Machine Translation
• Wordless
• A Simple and Fast Strategy for Handling Rare Words in Neural Machine Translation

👨‍🎓 References

• NLP: Text Segmentation Using Conditional Random Fields
• Khmer Word Segmentation Using Conditional Random Fields
• Word Segmentation of Khmer Text Using Conditional Random Fields