OpenSQZ/Qwen2.5-1.5B-classifier

Qwen2.5 Text Quality Classifier

Fine-tuned Qwen2.5-1.5B and Qwen2.5-3B models for automated text quality assessment. Predicts quality scores on a 0-1 scale focusing on educational value and mathematical intelligence.

Model Details

• Base Models: Qwen2.5-1.5B / Qwen2.5-3B
• Task: Text Quality Regression
• Languages: English, Chinese
• Training Data: OpenSQZ/Classifiers-Data
• Loss Function: MSE Loss

Performance

Model	Test MSE Loss
Qwen2.5-1.5B	0.00226
Qwen2.5-3B	0.00209

Quick Start

Installation

pip install transformers torch

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "OpenSQZ/Qwen2.5-1.5B-Classifier"  # or Qwen2.5-3B-Quality-Classifier
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Predict quality score
text = "Linear algebra is fundamental to understanding vector spaces and matrix operations in mathematics."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=8192)

with torch.no_grad():
    outputs = model(**inputs)
    score = torch.sigmoid(outputs.logits).item()

print(f"Quality Score: {score:.3f}")  # Output: Quality Score: 0.847

Quality Score Interpretation

Score Range	Quality Level	Use Case
0.8 - 1.0	Excellent	Premium training data
0.6 - 0.8	Good	Standard training data
0.4 - 0.6	Average	Conditional use
0.0 - 0.4	Poor	Filter out

Model Selection

• 1.5B Model: Faster inference, good for real-time applications
• 3B Model: Higher accuracy, better for batch processing

Limitations

• Optimized for educational and mathematical content
• May not generalize well to creative or subjective content
• Scores should be used as guidance, not absolute judgments

Citation

@model{qwen25_quality_classifier_2025,
  title={Qwen2.5 Text Quality Classifier},
  author={Chao Li, Yifan Zhang},
  year={2025},
  publisher={OpenSQZ}
}

License

Apache 2.0