How can I implement text classification for the purpose of matching using GPT-3?


This Content is from Stack Overflow. Question asked by hhalaweh

I have tried fine tuning a GPT-3 model for the purpose of text classification to classify whether two names match such as ‘William Jonathan’ and ‘William J’ and the label would be yes/no, yes indicating that two names are matching and no indicating that they aren’t. I have created a large number of examples related to different scenarios such as names being spelled differently, abbreviations, missing token, etc. After fine-tuning the model on GPT-3 using examples that look like this with a jsonl format:

{"text": "Are the following two names the same?nWilliam JonathannWilliam J", "label": "Yes"}

It is not able to do binary classification, however it outputs a large number of labels next to each other, for instance:

Prompt: Are the following two names the same?nWilliam Jonathan nWilliam J

Completion: YesYesNoYesNoYesYesNoYesNoYesNoYesNoYesYes

Any idea on how I can perform binary text classification using GPT-3 on an example similar to the above?


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