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README.md

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+---
+language: en
+tags:
+- text-classification
+- emotion-recognition
+- sentiment-analysis
+- MELD
+- multimodal
+pipeline_tag: text-classification
+widget:
+- text: "I'm so excited about this!"
+- text: "That makes me really angry."
+- text: "I'm feeling sad today."
+license: mit
+datasets:
+- MELD
+---
+
+# 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
+
+```python
+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"
+}
+```