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OpenSQZ/Qwen2.5-1.5B-classifier

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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Model Card

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

ModelTest MSE Loss
Qwen2.5-1.5B0.00226
Qwen2.5-3B0.00209

Quick Start

Installation

bash
pip install transformers torch

Usage

python
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 RangeQuality LevelUse Case
0.8 - 1.0ExcellentPremium training data
0.6 - 0.8GoodStandard training data
0.4 - 0.6AverageConditional use
0.0 - 0.4PoorFilter 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

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

License

Apache 2.0