How would you use a quality classifier to filter a 200-...

To filter a massive corpus, define positive examples as high-quality instruction-style data and negative examples as lightly filtered web text. Train a fastText model, which is fast, interpretable, and lightweight. Use this model to score the corpus in parallel CPU jobs and apply a threshold to achieve a target retention rate. For example, a 1.4-1.6% retention rate at a 0.5 threshold can yield approximately 3.8 trillion tokens, which is sufficient for training a 7B model.