I need a hand, I would like to train/make a model to do Sentiment analysis of a context/situation, given a sentence/action.
ex.
Context: There are $0.50 available. You have to choose between two possible actions:
● BOOST THE OTHER PARTICIPANT: In which case, you get 0 cents and the other participant gets $0.50;
● DON’T BOOST THE OTHER PARTICIPANT: In which case, you get $0.50 and the other participant gets 0 cents.
Action: BOOST THE OTHER PARTICIPANT
And I would like from the model a single Sentiment score of this action (but evaluated in the general context).
I have tried various BERT models but they do not satisfy me, perhaps I have simply tried the wrong models.
You should try gpt2 models they are good in case of contextual understanding. But the task you are describing will require a lot of somewhere between 50k-100k samples with,5-7 epoch ( Cause gpt2 was trained on very less data it takes more epoch to understand data)
what?
You can fine tune gpt2 models on text right?
So why don’t you use your dataset rows and create a text structure like this:" Text: {Your_text_here} (helps model understand that this portion have ended.) Sentiment: {ideal_ sentiment_here.} <|eos|>(this can be used as ending genration word.
