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Fine-tuning AI models on web data faces challenges with boilerplate and multilingual deduplication

By PulseAugur Editorial · [1 sources] ·

A user encountered significant issues when attempting to fine-tune a model using self-collected web-scraped data. Problems included boilerplate contamination, where website elements like navigation menus and footers were incorporated into training examples, leading to poorly structured outputs. This was addressed by using more precise DOM targeting and employing rotating residential proxies to ensure cleaner HTML. The order of deduplication also proved critical, impacting the model's tendency to repeat phrases. A persistent challenge remains multilingual near-duplicate detection, with current methods like MinHash proving insufficient across different languages. AI

IMPACT Highlights practical difficulties in data collection and cleaning for AI model fine-tuning, particularly with multilingual corpora.

RANK_REASON User-generated content discussing technical challenges in AI model training.

Read on r/MachineLearning →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Fine-tuning AI models on web data faces challenges with boilerplate and multilingual deduplication

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User-generated content discussing technical challenges in AI model training.
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Symeoon ·

    What actually broke when I tried to fine-tune on web-scraped data and what fixed it [D]

    <!-- SC_OFF --><div class="md"><p>Fine tuning on self collected web data felt straightforward until it wasn’t. Here’s what actually caused problems:</p> <p>Boilerplate contamination was the silent killer. Navigation menus, cookie banners, and footer text were leaking into trainin…

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