BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling

TL;DR

This paper introduces perplexity sampling, a novel data selection method that significantly reduces resources needed for pre-training Spanish language models while maintaining or improving performance.

Contribution

The paper proposes perplexity sampling, a new data-centric approach that halves training steps and uses only 20% of data, enabling efficient Spanish language model pre-training.

Findings

Models are comparable to state-of-the-art.

Perplexity sampling reduces training data and steps.

Achieves better results on certain tasks.

Abstract

The pre-training of large language models usually requires massive amounts of resources, both in terms of computation and data. Frequently used web sources such as Common Crawl might contain enough noise to make this pre-training sub-optimal. In this work, we experiment with different sampling methods from the Spanish version of mC4, and present a novel data-centric technique which we name $\textit{perplexity sampling}$ that enables the pre-training of language models in roughly half the amount of steps and using one fifth of the data. The resulting models are comparable to the current state-of-the-art, and even achieve better results for certain tasks. Our work is proof of the versatility of Transformers, and paves the way for small teams to train their models on a limited budget. Our models are available at this $\href{https://huggingface.co/bertin-project}{URL}$.