Emotion Classifier

This model classifies text into emotional categories based on the MELD (Multimodal EmotionLines Dataset) dataset. It can detect 7 emotions: anger, disgust, fear, joy, neutral, sadness, and surprise.

Model Details

Model Type: Fine-tuned transformer-based text classification model
Base Model: RoBERTa
Training Dataset: MELD (Multimodal EmotionLines Dataset)
Number of Parameters: ~125M
Sequence Length: 128 tokens
Training Approach: Fine-tuned with cross-validation

Intended Use

This model is designed to classify text into emotional categories. It can be used for:

Sentiment analysis in customer feedback
Emotion detection in conversations
User experience research
Content moderation
Game development for adaptive emotional responses

Limitations

The model was trained on scripted dialogues from TV shows, which may not fully represent natural conversations
Short texts may be harder to classify accurately
Cultural and contextual nuances might not be captured
The model may reflect biases present in the training data

Performance

Accuracy: [Insert your model's accuracy]
F1 Score: [Insert your model's F1 score]
Training Dataset Size: ~13,000 utterances

API Usage

import requests

API_URL = "https://api-inference.huggingface.co/models/YourUsername/emotion-classifier-meld"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({"inputs": "I'm feeling excited!"})
print(output)

Ethical Considerations

This model should be used responsibly. Consider the following ethical guidelines:

Do not use this model to manipulate people's emotions
Be transparent when using emotion detection in user-facing applications
Do not make high-stakes decisions based solely on this model's outputs
Consider privacy implications when analyzing personal communications

Citation

If you use this model, please cite the MELD dataset:

@inproceedings{poria-etal-2019-meld,
    title = "{MELD}: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations",
    author = "Poria, Soujanya  and
      Hazarika, Devamanyu  and
      Majumder, Navonil  and
      Naik, Gautam  and
      Cambria, Erik  and
      Mihalcea, Rada",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    pages = "527--536"
}