thutasann/mm_sentiment_model_v1

MM Sentiment Intensity Model v1

This is a fine-tuned XLM-RoBERTa model for Myanmar sentiment analysis. It classifies text into 5 intensity levels, ranging from Very Negative to Very Positive.

Model Description

The model was trained on a custom Myanmar dataset specifically curated for sentiment detection. It utilizes a custom syllable breaking preprocessing step to handle Myanmar Unicode text effectively.

• —Developed by: [Thuta Sann]
• —Model Type: Text Classification
• —Language: Myanmar (Burmese)
• —Base Model: facebookai/xlm-roberta-base

Classification Labels

How to Use

To use this model, you must apply the Syllable Breaking logic before passing text to the model.

import re
from transformers import pipeline

def myanmar_sylbreak(line):
    pat = re.compile(r"((?<!္)[က-အ](?![်္])|[a-zA-Z0-9ဣဤဥဦဧဩဪဿ၌၍၏၀-၉၊။!-/:-@[-`{-~\s])")
    return pat.sub(r" \1", line).strip()

classifier = pipeline("text-classification", model="thutasann/mm_sentiment_model_v1")

raw_text = "ဒီနေ့ ရာသီဥတု အရမ်းသာယာတယ်"
segmented_text = myanmar_sylbreak(raw_text)
result = classifier(segmented_text)
print(result)

Training Data

This model was trained on the Myanmar Sentiment Intensity Dataset v1, which is based on research from the Myanmar NLP community.

Acknowledgments

• —Base model by FacebookAI (XLM-RoBERTa).
• —Syllable breaking logic based on sylbreak.
• —Dataset inspiration from chuuhtetnaing/myanmar-text-segmentation-dataset.