Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models
Paper • 2504.14194 • Published
id stringlengths 20 20 | score int64 1 5 | normalized_score float64 0.2 1 | content stringlengths 212 2.96M | sub_path stringclasses 1
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BkiUdoY4dbghMG6xpZYO | 5 | 1 | \section*{Acknowledgements}
We wish to thank Dr. Andrea Geralico and Dr. Damiano Tommasini for the support with the numerical analysis and Prof. Salvatore Capozziello for useful discussions. This work was partially supported by DGAPA-UNAM, Grant No. 113514, and CONACyT, Grant No. 166391.
| train/arxiv |
BkiUdoU5qsBC8zW3y8KN | 5 | 1 | \section{Introduction}
\begin{figure}
\centering
\includegraphics[trim={0.2cm 0cm 0.4cm 0cm},clip,width=0.3\textwidth]{freq_bias.pdf}
\vspace{-2mm}
\caption{Typical popularity distribution of items depicting the long tail.\label{fig:freq_bias}}
\vspace{-4mm}
\end{figure}
The goal of session-based recommendation (... | train/arxiv |
BkiUaXnxK5YsWTJsNBiq | 5 | 1 | \section{Introduction}
The classical theorem of B\'ezout~\cite{Bezout} bounds the number of solutions
to a system of polynomials by the product of their degrees.
While this B\'ezout bound is sharp for generic systems of polynomial equations,
that is no longer the case when the equations possess additional structure.
F... | train/arxiv |
BkiUbbM5qsNCPSsgaKqk | 5 | 1 | \section{Introduction}
Since the pioneering Bell paper \cite{Bell} the Bell inequalities became the subject of intensive study (for a review see \cite{Liang},\cite{Brunner}). Their importance stems from the fact that their violation at the quantum level provides the evidence that the quantum theory cannot be viewed as ... | train/arxiv |
BkiUaF825V5hcGj03HSJ | 5 | 1 | \section{Introduction}
\label{sec:intro}
Research on wave energy converters (WECs) has concentrated traditionally on systems of small floating bodies, like offshore heaving buoys (see \cite{EV76}--\cite{ FA02}). However, the seminal theories on WECs that originated from this first scientific approach to wave energy ext... | train/arxiv |
BkiUdfPxaKPQoka4R7Xr | 5 | 1 | "\\section{Introduction}\n\nDuring the last ten years, much have been done about classification of s(...TRUNCATED) | train/arxiv |
BkiUfGk5qsFCiE6Ue_hi | 5 | 1 | "\\section*{Data Analysis Recipes:\\\\\n Products of multivariate Gaussians\\\\\n in Bayesian infe(...TRUNCATED) | train/arxiv |
BkiUbCk5qWTD6essZG9s | 5 | 1 | "\\section{Introduction}\\label{sec:Introduction}\n The pioneering works of Feynman \\cite{feynman19(...TRUNCATED) | train/arxiv |
BkiUfp7xaKgQZUmnaXGu | 5 | 1 | "\\section{INTRODUCTION}\n\nMagnetically induced ferroelectricity or electric control of magnetic or(...TRUNCATED) | train/arxiv |
BkiUdak5qX_Bpe9RFdgN | 5 | 1 | "\\section{Introduction}\n\\label{intro}\n\nMost of the known exoplanets orbit late-F, G, or early-K(...TRUNCATED) | train/arxiv |
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Top 30B token SlimPajama Subset selected by the Readability rater
This repository contains the dataset described in the paper Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models.
Code: https://github.com/opendatalab/Meta-rater
Dataset Description
This dataset contains the top 30B tokens from the SlimPajama-627B corpus, selected using the Readability dimension of the PRRC (Professionalism, Readability, Reasoning, Cleanliness) framework. Each document in this subset is scored and filtered by a ModernBERT-based rater fine-tuned to assess the clarity, coherence, and ease of understanding of the text.
- Source: SlimPajama-627B Annotated Dataset
- Selection: Top 30B tokens by PRRC-Readability score
- Quality metric: Readability (0–5 scale, see below)
- Annotation coverage: 100% of selected subset
Dataset Statistics
- Total tokens: 30B (subset of SlimPajama-627B)
- Selection method: Top-ranked by PRRC-Readability ModernBERT rater
- Domains: Same as SlimPajama (CommonCrawl, C4, GitHub, Books, ArXiv, Wikipedia, StackExchange)
- Annotation: Each document has a readability score (0–5)
Readability Quality Metric
Readability evaluates the clarity, coherence, and ease of understanding of the text. Higher scores indicate content that is clear, well-structured, and easy to follow, while lower scores reflect text that is difficult to comprehend due to poor structure, grammar, or vocabulary.
- 0–1: Significant issues with clarity or coherence; difficult to read
- 2–3: Generally clear but with some sections that are hard to understand
- 4–5: Very clear, coherent, and easy to read
Scores are assigned by a ModernBERT model fine-tuned on Llama-3.3-70B-Instruct annotations, as described in the Meta-rater paper.
Annotation Process
- Initial annotation: Llama-3.3-70B-Instruct rated 500k+ SlimPajama samples for readability
- Model training: ModernBERT fine-tuned on these annotations
- Scoring: All SlimPajama documents scored by ModernBERT; top 30B tokens selected
Citation
If you use this dataset, 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
This dataset is released under the same license as the original SlimPajama dataset. See the original SlimPajama repository for details.
Contact
- Project Lead: Ren Ma (maren@pjlab.org.cn)
- Corresponding Author: Conghui He (heconghui@pjlab.org.cn)
- Issues: GitHub Issues
Made with ❤️ by the OpenDataLab team
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