I've been seeing a lot of models mention training on massive datasets lately, but what exactly happens during the preprocessing stage in practice? Like, what specific steps are involved in data cleaning, labeling, tokenization, and how do these operations actually impact the final performance? Are there any universal best practices to follow?
What's the data processing workflow behind training large models?
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Data preprocessing is indeed the most critical part of training large models, and I can really relate to this having used some lightweight NLP models in my own smart home project. The first step, data cleaning, is often overlooked but has a huge impact—for example, anomalies or garbled characters in appliance sensor logs. If cleaning isn’t thorough, tokenization can completely break down, and the model might even treat garbage like "\x00" as a valid token, polluting the trained parameters. Back then, I spent a whole week just dealing with UTF-8 encoding issues.
The details of annotation and tokenization are no joke either. Take Chinese, for instance—without a unified tokenization standard, "智能家居" (smart home) could be split into ["智能", "家居"] or ["智能家", "居"]. The first preserves semantic integrity, while the second destroys context entirely. I tested both Jieba and Pkuseg tokenizers and found that Pkuseg had a 15% higher entity recognition accuracy on my IoT command dataset. At the end of the day, it all comes down to dataset compatibility. One best practice you can’t skip is running small-scale tests on annotation quality first—make sure the overlap between manual annotations and model splits is above 80% before scaling up. Anyone who’s hit this wall already knows this, so newbies don’t have to learn the hard way.