Emotion Text Classifier

🚀 Fine-tuned DistilRoBERTa-base for Emotion Classification 🤬🤢😀😐😭😲

This fine-tuned DistilRoBERTa-base model is designed for emotion classification, enabling accurate emotion prediction from text data.

✨ Features

• Sentiment Analysis: The DistilRoBERTa-base model is capable of performing sentiment analysis.
• Emotion Classification: It classifies text into 6 Ekman emotions (anger, disgust, fear, joy, sadness, surprise) and a neutral class.
• Fine-tuning: Fine-tuned on transcripts from the Friends show for classifying emotions in dialogue from Netflix shows or movies.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="michellejieli/emotion_text_classifier")
classifier("I love this!")

Output:
[{'label': 'joy', 'score': 0.9887555241584778}]

📚 Documentation

Model Description

DistilRoBERTa-base is a transformer model that performs sentiment analysis. The model was fine-tuned on transcripts from the Friends show to classify emotions from text data, especially dialogue from Netflix shows or movies. It predicts 6 Ekman emotions and a neutral class, including anger, disgust, fear, joy, neutrality, sadness, and surprise.

The model is a fine-tuned version of Emotion English DistilRoBERTa-base and DistilRoBERTa-base. It was initially trained on the following table from Emotion English DistilRoBERTa-base:

It was fine-tuned on:

📄 License

No license information is provided in the original document.

📞 Contact

Please reach out to michelleli1999@gmail.com if you have any questions or feedback.

📚 Reference

Jochen Hartmann, "Emotion English DistilRoBERTa-base". https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/, 2022.
Ashritha R Murthy and K M Anil Kumar 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1110 012009