Vishnu2517/Zoom_feedback

EdTech Feedback Validation Model

Model Description

This model is designed to validate user feedback in EdTech applications by determining whether a given feedback text aligns with a selected reason. It uses a BERT-based architecture for text pair classification.

Intended Uses & Limitations

Primary Use Case

• Validating user feedback in educational technology applications
• Ensuring feedback text aligns with predefined reason categories
• Improving user experience by providing accurate feedback categorization

Limitations

• Trained on English text only
• Requires both feedback text and reason text as input
• Binary classification (aligned/not aligned)

Training and Evaluation Data

The model was trained on a custom dataset containing:

• Training samples: 2,061 feedback-reason pairs
• Evaluation samples: 9,000 feedback-reason pairs
• All training samples were positive (aligned) examples
• Evaluation set contains both positive and negative examples

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "your-username/edtech-feedback-validation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example usage
text = "this is an amazing app for online classes!"
reason = "good app for conducting online classes"

# Tokenize inputs
inputs = tokenizer(text, reason, return_tensors="pt", padding=True, truncation=True)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.softmax(outputs.logits, dim=1)
    prediction = torch.argmax(probabilities, dim=1).item()
    confidence = probabilities[0][prediction].item()
    
print(f"Prediction: {prediction} (Aligned: {prediction == 1})")
print(f"Confidence: {confidence:.3f}")

Model Architecture

• Base Model: BERT (bert-base-uncased)
• Task: Text Pair Classification
• Output: Binary classification (0: Not Aligned, 1: Aligned)
• Training Framework: PyTorch

License

This model is released under the MIT License.