Train a Privacy Shield in 30 Minutes: The Zero-Data Trick Inside tanaos-text-anonymizer-v1
15 days ago 高效码农Train a Privacy Shield in 30 Minutes—Inside tanaos-text-anonymizer-v1’s Zero-Data Trick ❝ Core question: How do you scrub names, addresses, phones, dates and locations from text when you have zero labeled examples? One-sentence answer: Load tanaos-text-anonymizer-v1, let the Artifex library synthesise 10 k training lines on the fly, fine-tune for ten minutes, and you get a tiny model that replaces sensitive spans with [MASKED] tokens faster than you can grep. ❞ What this article answers (and why you should care) 「Central question:」 “Can a model with only 110 M parameters really reach production-grade PII removal without any human-labeled data?” 「Short answer:」 …
FineWeb2: Adaptive Pre-Training Data Processing for Superior Multilingual LLMs
6 months ago 高效码农FineWeb2: A Game-Changer for Multilingual Large Models — A Comprehensive Guide to Adaptive Pre-Training Data Processing In the realm of large language models (LLMs), the race for superiority is intensifying, with the quality and diversity of pre-training data emerging as critical factors. FineWeb2, a groundbreaking new pre-training dataset curation pipeline developed by researchers from Hugging Face and EPFL, is set to redefine the landscape of multilingual LLMs. By leveraging a data-driven approach and innovative techniques, FineWeb2 enables the creation of high-quality pre-training corpora tailored to any language, offering a scalable solution to the challenges of multilingual model development. The Challenge …