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README.md

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+---
+language: en
+tags:
+- text-classification
+- emotional-support
+- empathy
+- mental-health
+license: mit
+datasets:
+- esconv
+---
+
+# Emotional Support Strategy Classifier
+
+This model is a fine-tuned RoBERTa-base model for classifying emotional support conversation strategies.
+
+## Model Description
+
+- **Base Model**: roberta-base
+- **Task**: Multi-class text classification
+- **Training Data**: ESConv (Emotional Support Conversation) dataset
+- **Number of Labels**: 8
+
+## Labels
+
+The model classifies text into 8 emotional support strategies:
+
+0. Affirmation and Reassurance
+1. Information
+2. Others
+3. Providing Suggestions
+4. Question
+5. Reflection of feelings
+6. Restatement or Paraphrasing
+7. Self-disclosure
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "RyanDDD/empathy-strategy-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example prediction
+text = "I understand how you feel. It's completely normal to feel this way."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+outputs = model(**inputs)
+predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+predicted_class = torch.argmax(predictions, dim=-1).item()
+
+print(f"Predicted strategy: {model.config.id2label[predicted_class]}")
+```
+
+## Training
+
+Fine-tuned on the ESConv dataset using the Hugging Face Transformers library.
+
+## Citation
+
+If you use this model, please cite the ESConv dataset:
+
+```bibtex
+@inproceedings{liu2021towards,
+  title={Towards Emotional Support Dialog Systems},
+  author={Liu, Siyang and Zheng, Chujie and Demasi, Orianna and Sabour, Sahand and Li, Yu and Yu, Zhou and Jiang, Yong and Huang, Minlie},
+  booktitle={Proceedings of ACL},
+  year={2021}
+}
+```