Open quansun opened 3 years ago
In case it helps, I made this article that explains how to use GPT-3 for NER.
Here is a full prompt example:
Extract job titles from the following sentences.
Sentence: John Doe has been working for Microsoft for 20 years as a Linux Engineer.
Job title: Linux Engineer
###
Sentence: John Doe has been working for Microsoft for 20 years and he loved it.
Job title: none
###
Sentence: Marc Simoncini | Director | Meetic
Job title: Director
###
Sentence: Franck Riboud was born on 7 November 1955 in Lyon. He is the son of Antoine Riboud, who transformed the former European glassmaker BSN Group into a leading player in the food industry. He is the CEO at Danone.
Job title: CEO
###
Sentence: Damien is the CTO of Platform.sh, he was previously the CTO of Commerce Guys, a leading ecommerce provider.
Job title:
Basically with GPT-3 sandbox, you could create examples like this:
gpt.add_example(Example("""Sentence: John Doe has been working for Microsoft for 20 years as a Linux Engineer.
Job title:""",
"Linux Engineer"))
Hi, thanks for developing such a great tool.
Just wondering if you could add an example for training GPT-3 for Named Entity Recognition (NER) tasks?
I'm not sure how I can use the add_example function to specify the answer to a question for NER tasks:
... gpt.add_example(Example('Tom was born in 1942', '[(Tom, Name), (1942, Year)]')) ...
Thanks!