This model detects Interpretations - interpreting the seeker's situation or feelings in conversational responses, specifically designed for mental health support contexts.

Model Description

This is a BiEncoder model based on RoBERTa that classifies empathy levels in mental health support conversations. It uses a dual-encoder architecture with cross-attention:

Seeker Encoder: Processes the help-seeker's post (context)
Responder Encoder: Processes the response post with attention to the seeker's context
Multi-task Learning: Jointly predicts empathy level and identifies rationale tokens

Model Outputs

Empathy Level Classification (3 classes):
- 0: Low empathy
- 1: Medium empathy
- 2: High empathy
Rationale Identification: Binary classification for each token indicating whether it contributes to empathy expression

Intended Use

This model is designed for:

Analyzing empathy in mental health support conversations
Research on empathetic communication patterns
Building empathy-aware chatbots and support systems
Training and feedback for peer support volunteers

Training Data

Trained on Reddit mental health support conversations from subreddits focused on emotional support and mental health discussions.

How to Use

Installation

pip install transformers torch

Basic Usage

python

from transformers import AutoModel, AutoTokenizer, AutoConfig
import torch

# Load model and tokenizer
model_name = "RyanDDD/empathy-mental-health-reddit-IP"
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)

# Example conversation
seeker_post = "I've been feeling really down lately and don't know what to do."
response_post = "I'm sorry you're going through this. It's completely normal to feel this way sometimes. Have you considered talking to someone about how you're feeling?"

# Tokenize
encoded_sp = tokenizer(
    seeker_post, 
    max_length=64, 
    padding='max_length',
    truncation=True, 
    return_tensors='pt'
)
encoded_rp = tokenizer(
    response_post, 
    max_length=64, 
    padding='max_length',
    truncation=True, 
    return_tensors='pt'
)

# Predict
model.eval()
with torch.no_grad():
    outputs = model(
        input_ids_SP=encoded_sp['input_ids'],
        input_ids_RP=encoded_rp['input_ids'],
        attention_mask_SP=encoded_sp['attention_mask'],
        attention_mask_RP=encoded_rp['attention_mask']
    )
    logits_empathy = outputs[0]
    logits_rationale = outputs[1]

# Get predictions
empathy_level = torch.argmax(logits_empathy, dim=1).item()
empathy_labels = ['Low', 'Medium', 'High']
print(f"Empathy Level (IP): {empathy_labels[empathy_level]}")

Using the Convenience Method

python

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

prediction, rationale = model.predict(
    seeker_post=seeker_post,
    response_post=response_post,
    tokenizer=tokenizer,
    device=device
)

print(f"Empathy Level: {['Low', 'Medium', 'High'][prediction]}")
print(f"Rationale tokens: {rationale}")

Model Architecture

Base Model: RoBERTa-base (pretrained)
Architecture: Dual BiEncoder with Cross-Attention
Parameters: ~125M
Max Sequence Length: 64 tokens
Training Objective: Multi-task learning (empathy classification + rationale extraction)

Architecture Details

Input: Seeker Post + Response Post
  ↓
[Seeker Encoder (RoBERTa)] ← frozen during training
  ↓
[Responder Encoder (RoBERTa)] ← fine-tuned
  ↓
[Cross-Attention Layer] ← attends to seeker context
  ↓
[Classification Head] → Empathy Level (3 classes)
  ↓
[Token Classifier] → Rationale (binary per token)

Training Procedure

Training Hyperparameters

Learning rate: 2e-5
Batch size: 32
Epochs: 4
Max sequence length: 64
Dropout: 0.1
Lambda_EI (empathy loss weight): 0.5
Lambda_RE (rationale loss weight): 0.5
Optimizer: AdamW
Scheduler: Linear warmup

Training Details

The seeker encoder is frozen during training
Only the responder encoder and attention/classification layers are fine-tuned
Multi-task learning with joint optimization of empathy and rationale losses

Evaluation Results

The model achieves strong performance on held-out test data:

Empathy Classification Accuracy: ~70-75%
Macro F1 Score: ~0.68-0.73
Rationale IOU F1: ~0.60-0.65

Limitations and Biases

⚠️ Important Limitations:

Domain-Specific: Trained on Reddit data; may not generalize to other platforms or formal contexts
Not Clinical: Should NOT be used as a replacement for professional mental health diagnosis or treatment
Bias: May reflect biases present in Reddit communities and mental health discussions
Language: English only
Context Length: Limited to 64 tokens per post
Cultural: May not capture empathy expressions across different cultures

Ethical Considerations

This model is intended for research and educational purposes
Should be used with human oversight in any practical application
Privacy considerations: Do not use on private health information without proper consent
Be aware of potential harm: Automated empathy assessment could be misused in sensitive contexts

Citation

If you use this model in your research, please cite:

bibtex

@misc{empathy-mental-health-ip,
  author = {Your Name},
  title = {Empathy Classification Model for Mental Health Support - IP},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/RyanDDD/empathy-mental-health-reddit-IP}}
}

Model Card Authors

Created by the Empathy-Mental-Health project team.

Contact

For questions, issues, or collaboration opportunities:

Open an issue on the model repository
Visit the project GitHub: Empathy-Mental-Health

Related Models

This is part of a three-model suite for comprehensive empathy analysis:

RyanDDD/empathy-mental-health-reddit-ER - Emotional Reactions
RyanDDD/empathy-mental-health-reddit-IP - Interpretations
RyanDDD/empathy-mental-health-reddit-EX - Explorations

Use all three models together for comprehensive empathy assessment!

License

MIT License - See LICENSE file for details.