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scdong/
distilbert-response-type

Name: distilbert-response-type
Rating: 2.5 (7 reviews)

safetensors

distilbert

mit

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🧠 DistilBERT Response Type Classifier

This is a fine-tuned DistilBERT model designed to classify patient messages into one of four mental health support categories:

advice
information
question
validation

It is used as part of the Mental Health Counselor Assistant app to help generate helpful, therapeutic responses.

💼 Use Case

Given a short text input from a patient, this model predicts the most appropriate type of response a mental health counselor might provide.

Example:

python

from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast
import torch

model = DistilBertForSequenceClassification.from_pretrained("scdong/distilbert-response-type")
tokenizer = DistilBertTokenizerFast.from_pretrained("scdong/distilbert-response-type")

text = "I just feel so overwhelmed lately"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

predicted_label = torch.argmax(logits, dim=1).item()
print(predicted_label)  # Maps to: 0=advice, 1=information, 2=question, 3=validation

The model is used to route text to custom prompt templates like:

Advice prompt: “You are a licensed counselor. What supportive advice would you give to someone who said: {msg}?”
Validation prompt: “You are an empathetic therapist. Validate the client’s emotions in response to: {msg}”

📁 Files

This repo includes:

config.json — model architecture config
model.safetensors — trained model weights
tokenizer_config.json, tokenizer.json, vocab.txt — tokenizer files
special_tokens_map.json — optional token mappings
training_args.bin — training metadata (optional)

🧪 Training Details

The model was fine-tuned using a balanced dataset labeled with response types based on:

The final model was validated on a held-out test set and integrated into the counselor assistant tool.

📜 License

This model is released under an open license for research and educational purposes. Please use responsibly and do not deploy for unsupervised clinical use.