meta-rater-1b-cleanliness

PRRC-Cleanliness Language Model (1.3B Parameters, 30B Tokens)

This repository contains the model described in the paper Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models.

Code: https://github.com/opendatalab/Meta-rater

Model Description

This is a 1.3B parameter transformer-based decoder-only language model trained from scratch on 30B tokens selected from SlimPajama dataset using the Cleanliness dimension of the PRRC framework. The training data was curated by selecting text with high cleanliness scores, focusing on well-formatted, complete, and noise-free content.

Model Details

• Architecture: Transformer decoder-only
• Parameters: 1.345B (1,345,423,360 parameters)
• Training Tokens: 30B tokens
• Context Window: 1,024 tokens
• Vocabulary Size: 32,000 (LLaMA tokenizer)
• Data Selection Method: Top-k selection based on Cleanliness scores
• Rating Model: ModernBERT-base fine-tuned for Cleanliness assessment

Architecture Specifications

• Hidden Dimension: 2,048
• Number of Layers: 24
• Attention Heads: 16
• Key-Value Heads: 16
• MLP Ratio: 8/3
• Position Encoding: RoPE (base=10,000)

Data Selection Criteria

The training data was selected using the Cleanliness rating model, which evaluates:

• Correct Formatting: Human-edited appearance without corrupted characters
• Appropriate Content: No irrelevant links, advertisements, or spam
• Content Completeness: Complete sentences and coherent structure
• Structural Integrity: Proper organization and layout
• Noise Reduction: Minimal irrelevant or distracting elements

Selected texts typically include:

• Well-formatted articles and documents
• Clean editorial content
• Professional publications
• Quality web content without artifacts
• Properly structured educational materials

Training Details

• Hardware: 32x NVIDIA A800 GPUs
• Global Batch Size: 4,194,304 tokens
• Learning Rate: 5e-5
• Optimizer: Adam (β₁=0.9, β₂=0.95, ε=1e-8)
• Training Time: ~14 hours

Performance Results

Downstream Task Performance (Average Accuracy)

• General Knowledge: 56.45% (+3.66% vs Random)

  • ARC-Easy: 56.89%
  • ARC-Challenge: 27.65%
  • SciQ: 84.80%

• Commonsense Reasoning: 44.88% (+0.94% vs Random)

  • HellaSwag: 40.34%
  • SIQA: 41.97%
  • WinoGrande: 52.33%

• Reading Comprehension: 30.72% (+0.70% vs Random)

  • RACE: 30.24%
  • OpenbookQA: 31.20%

• Overall Average: 45.68% (+1.90% vs Random)

Key Findings

• Strong General Knowledge: Significant improvement in knowledge-based tasks
• Formatting Benefits: Clean, well-structured training data improves model output quality
• Noise Reduction: Elimination of web artifacts and spam improves learning efficiency
• Structural Quality: Better understanding of proper text organization and flow

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "opendatalab/meta-rater-1b-cleanliness"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text (particularly good for clean, well-formatted content)
prompt = "Here are the key points to consider:"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_length=100,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Applications

This model is particularly well-suited for:

• Content generation requiring clean formatting
• Document creation and professional writing
• Web content development without artifacts
• Educational materials with proper structure
• Clean text processing applications
• Data preprocessing and cleaning tasks
• Quality content creation for publications

Strengths

• Generates well-formatted and clean text output
• Strong performance on knowledge-intensive tasks
• Reduced likelihood of producing noisy or corrupted text
• Better understanding of proper document structure
• Enhanced ability to maintain content organization
• Improved resistance to format-related errors

Limitations

• May prioritize format over content depth in some cases
• Could be overly conservative in text generation
• Limited context window (1,024 tokens)
• No instruction tuning or safety alignment
• May avoid creative formatting that could be beneficial

Data Quality Impact

This model demonstrates the importance of clean training data:

• Artifact Removal: Training on clean data reduces model exposure to web scraping artifacts
• Structural Learning: Well-formatted input leads to better-structured output
• Noise Resistance: Lower exposure to irrelevant content improves focus
• Professional Standards: Training on quality content improves output professionalism

Comparison with Baselines

• vs Random Baseline: +1.90% overall, with strongest gains in General Knowledge (+3.66%)
• vs Other PRRC Dimensions: Competitive performance with focus on content quality
• vs Meta-rater All (25): Demonstrates the individual contribution of data cleanliness

Quality Characteristics

This model excels at producing:

• Clean Formatting: Proper structure and organization
• Complete Content: Full sentences and coherent paragraphs
• Professional Appearance: Business and academic writing standards
• Artifact-Free Text: No web scraping remnants or corrupted characters
• Consistent Structure: Logical flow and proper segmentation

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}
}

License

Please refer to the license terms of the original SlimPajama dataset and follow applicable data licensing requirements.

Contact

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