Open ddofer opened 2 years ago
As written, that assumes multiclass, no? I.E it would make duplicates per label, and in addition you'd need to loop over everything per the number of labels and drop duplicates (input_examples) by the label?
Works for any number of labels. Assumes that you have for every label at least 2 train examples
So, one should use SentenceLabelDataset, with each sentence X label added as a seperate example? (Won't that mean the same sentence can be a "negative" for itself? e.g. if it appears multiple times for each label, seperately, then it can be a negative for other labels/instances of itself)?
Thanks!
Sentences with the same label are considered as positives, sentences with different labels as negative
Are there any appropriate setups or losses in sentence-transformers for pretraining sentence embeddings in cases where I have labels as targets? (I want to finetune the actual embeddings, not just a seperate classifier layer, as I want to analyze the "supervised" embeddings as well. The data is fundamentally that of a single text column, but I can always duplicate part of it to get a 2 sentence format if needed). Thanks!