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---
language:
- en
tags:
- text-classification
- edtech
- feedback-validation
- bert
- pytorch
license: mit
datasets:
- custom-edtech-feedback
metrics:
- accuracy
- precision
- recall
- f1
---
# 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
```python
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.
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