Deverta V3 Japanese Large Anticipation

🚀 DeBERTa Emotion Predictor

This package provides a DeBERTa-based model for predicting emotions in Japanese text. It uses fine-tuned DeBERTa models to estimate emotions in Japanese text. You can easily obtain the predicted emotion labels and the confidence of the positive class for each text using models for eight emotions (Joy, Sadness, Anticipation, Surprise, Anger, Fear, Disgust, Trust).

🚀 Quick Start

✨ Features

• Eight Emotion Prediction: Utilize fine-tuned models for each emotion to perform emotion prediction on text.
• Flexible Input Formats: Accept single texts, text lists, or pandas Series as input and return results in DataFrame format.
• Efficient Inference: Designed to load the model to the GPU only when necessary to reduce GPU memory usage.

📦 Installation

Use pip to install the package:

pip install deberta-emotion-predictor

In addition, the following dependencies are required:

pip install torch 
pip install transformers
pip install pandas 

Note: To use the GPU, you need to install the NVIDIA GPU driver. Please refer to other materials for this.

💻 Usage Examples

Basic Usage

from deberta_emotion_predictor import DeBERTaEmotionPredictor
predictor = DeBERTaEmotionPredictor()
result = predictor.predict_emotions("今日はとても嬉しい！")
predictor.show_emotions(result)

Important Note: Since eight types of DeBERTa models are downloaded from Hugging-face, the first startup takes a long time. It will be faster from the second execution onwards.

Advanced Usage

You can also input a DataFrame:

import pandas as pd
from deberta_emotion_predictor import DeBERTaEmotionPredictor

# model_dir points to the location of the language model and tokenizer
predictor = DeBERTaEmotionPredictor()

# Sample texts (list format)
sample_texts = [
    "そうだ 京都、行こう。",
    "がんばるひとの、がんばらない時間。",
    "わたしらしくをあたらしく",
    "ピースはここにある。",
    "結婚しなくても幸せになれるこの時代に、私は、あなたと結婚したいのです。",
    "これからの地球のために一肌、脱ぎました。",
    "自分は、きっと想像以上だ。",
    "ハローしあわせ。",
    "日本を、1枚で。"
]
res_df = predictor.predict_emotions(sample_texts)

predictor.show_emotions(res_df)

Directory Structure

deberta_emotion_predictor/         
├── README.md                      # This description file
├── deberta_emotion_predictor.py   # Implementation of the DeBERTaEmotionPredictor class
│   └── tokenizer_DeBERTa_v3_large/ # Tokenizer
├── setup.py
├── pyproject.toml
├── README.md
├── LICENSE
└── usage.py                       

Required Environment

• Python 3.6 or higher
• PyTorch
• transformers
• pandas

📄 License

This project is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

Copyright (c) 2025 Yoichi Takenaka

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/4.0/.

This project is based on:

• DeBERTa (https://huggingface.co/microsoft/deberta-v3-large), licensed under the MIT License.
• DeBERTa Japanese Model (https://huggingface.co/globis-university/deberta-v3-japanese-large), licensed under the CC BY-SA 4.0 License.

Any modifications or derivative works must also be distributed under the same CC BY-SA 4.0 License.