Submitted by AImSamy t3_zl5kmi in MachineLearning
At edenai we're trying to compete specialized AI models VS OpenAI's GPT3 in different applications (kw extraction, sentiment analysis, NER ...etc). So we need to find the best prompt for doing NER with GPT3 but not verry succesfully. We need at least a list of words and their types (class).
Does anyone have an idea ?
The respond in JSON is amazing. I didn't know we could do that. Thanks I'll try that.
Well, SpaCy does it. Maybe they can tell.
spaCy has an NLP framework to train NER models, but it does not use NLG to do so.
I believe the question was about data, and I replied to it. That SpaCy developers knows where to find data for training.
Spacy uses gpt3?
No, but they train their models for detecting NER, so they are likely to know where such data can be found. They apply their own models and BERT, if you are curious about the models.
Generative models are not really the most effective approach for NER. Using another example as someone else provided:
What are the colors in the following sentence: "The white man like his burgers medium rare. He doesn't mind getting the red blood on his new green shirt." Return as JSON.
The colors in the sentence "The white man like his burgers medium rare. He doesn't mind getting the red blood on his new green shirt." are:
White
Red
Green
Here is the same information in JSON format:
Copy code
{ "colors": [ "White", "Red", "Green" ] }
I hope this helps! Let me know if you have any other questions.
A properly trained NER would not have made the mistake of labeling a racial token as a color.
You call it a mistake, but I (as a human, yes, really) would have included it in the list.
Yeah, I would include it too.
Really? Is that convention or just a common design choice? Is it because white people are not literally white, just relatively white?
Since I'm getting the down vote love here let me add some context to this. A human reader would see "white man" to mean Caucasian, not a man that is either dressed in all white clothing or has their skin painted white or has little melatonin. Annotating white in this context when training an NER would not make sense contextually if the goal is to identify color entities; labeling "white-skinned/light-skinned" would make sense as a color annotation.
A Finnish accountant during tax season and a Finnish-American surfer in Hawaii probably have different levels of melatonin in their skin but are both "white" (racially).
Nah, this is a super ambiguous example that even humans don't agree on. Maybe try something like "buffalo buffalo buffalo" :) or some word like "the tortoise leapfrogged the hare" what animals were involved in the race? Should be 2 and not 3.
Doesn't mean specialized ners aren't better tho, just that this white man example ain't a good test.
HateRedditCantQuitit t1_j049e1g wrote
I just sent this to chatgpt, and it worked fine:
​
>What are the locations present in the following sentence?
>
>“I flew from SF to NY today, with a layover in Blorpington.”
>
>Please respond in a JSON list of the form
>
>```
>
>{
>
> “locations”: […]
>
>}
>
>```