[leobg]

If I have 1,000 labeled examples for a classification task, I’ll expand that into a training dataset using augmentation, and then finetune a small model like RoBERTa. It’s fast, cheap, accurate — and predictable.

Others have had success with SetFit as the training framework and Ettin as the base model.

[coder68]

oh this seems like an interesting idea, what tactics do you use for augmentation? For my own use-case, I think I could reorder semantic chunks, or maybe randomly delete pieces, but curious what tactics you use!

I have also considered training a small language model for synthetic data generation.

[leobg]

Yes, exactly. You want to randomize the parts that are irrelevant. For example, if you're classifying news articles, you may want to shorten them anyway. A human would be able to tell what category an article belongs to without reading the whole thing - so may do a combination of URL, headline, beginning, middle, and/or end. And if you do that, it's easy to turn one training example into 10 or more. You just vary the length of the individual parts.