opendatalab/meta-rater-cleanliness-rating

Cleanliness Rating Model

Model Description

This model is a fine-tuned version of ModernBERT-base designed to evaluate the Cleanliness dimension of text quality on a 5-point scale (0-5). Cleanliness measures how well-formatted, complete, and noise-free a text is, focusing on structural integrity rather than semantic content.

Model Details

• Base Model: ModernBERT-base
• Parameters: 149M
• Context Window: 4,096 tokens
• Task: Text quality rating (regression)
• Score Range: 0-5 (continuous)
• Performance: 87.88% F1 score, 92.25% accuracy

Rating Scale

The model uses a 5-point rating system based on four key criteria:

• 1: Serious formatting/structural issues that significantly affect fluency
• 2: Obvious problems that noticeably affect reading fluency
• 3: Some problems present but don't seriously impact reading fluency
• 4: Minor issues that don't affect overall readability
• 5: Perfect formatting and structure across all criteria

Evaluation Criteria

The model assesses text across four main dimensions:

1. Correct Formatting

• Text appears human-edited rather than machine-extracted
• No inappropriate or corrupted characters
• Proper text structure and layout

2. Appropriate Content

• No irrelevant links, advertisements, or spam
• Sufficient content length to extract clear structure and theme
• Content focused on the main topic

3. Completeness Content

• Complete sentences written naturally by humans
• Coherent opinions, facts, or stories rather than fragments
• Proper article structure and flow

Note: Text ending with $TRUNCATED$ is considered a manual ending flag and doesn't affect completeness scoring.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model and tokenizer
model_name = "opendatalab/meta-rater-cleanliness-rating"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example text
text = "This is a well-formatted article about renewable energy. It contains complete sentences and proper structure."

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=4096)
with torch.no_grad():
    outputs = model(**inputs)
    score = outputs.logits.squeeze().argmax(dim=0)

print(f"Cleanliness Score: {score:.2f}")

Training Details

• Training Data: 747,422 examples from SlimPajama dataset
• Annotation Model: Llama-3.3-70B-Instruct
• Training Epochs: 10
• Evaluation Split: 93,428 test examples
• Data Split: 8:1:1 (train:dev:test)

Applications

This model is particularly useful for:

• Web scraping quality control and content filtering
• Data preprocessing for machine learning datasets
• Content management systems for automated quality checks
• Data curation for language model pre-training
• Document digitization quality assessment
• Automated content moderation and filtering

Common Issues Detected

The model can identify various types of text quality problems:

• Formatting artifacts from web scraping or OCR
• Incomplete sentences or fragmented text
• Excessive links or promotional content
• Corrupted characters or encoding issues
• Poor structure with inadequate content organization
• Advertisement contamination and irrelevant insertions

What the Model Does NOT Consider

• The specific language the text is written in
• The length of the text
• Usage of placeholders for data privacy or safety
• Content topic, professionalism, or semantic meaning
• Writing style or grammatical sophistication

Use Cases by Score Range

• 4.0-5.0: High-quality content suitable for training data
• 3.0-3.9: Acceptable content with minor cleaning needed
• 2.0-2.9: Moderate issues requiring preprocessing
• 1.0-1.9: Significant problems, may need extensive cleaning
• 0.0-0.9: Poor quality, likely unsuitable for most applications

Limitations

• Designed primarily for English text
• May not detect all domain-specific formatting conventions
• Performance may vary for highly technical formats (code, mathematical notation)
• Should be used in conjunction with other quality metrics for comprehensive assessment

Citation

If you use this model in your research, please cite:

@article{zhuang2025meta,
  title={Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models},
  author={Zhuang, Xinlin and Peng, Jiahui and Ma, Ren and Wang, Yinfan and Bai, Tianyi and Wei, Xingjian and Qiu, Jiantao and Zhang, Chi and Qian, Ying and He, Conghui},
  journal={arXiv preprint arXiv:2504.14194},
  year={2025}
}

You can find more details about Meta-rater at https://github.com/opendatalab/Meta-rater.

License

This model is released under the same license as the base ModernBERT model.

Contact

For questions or issues, please contact the authors or open an issue in the repository.