Question-Answering over documents
Hello,
Does anyone use this model as a Reader in a QnA pipeline over document collection? I see I can use it with a
'''
Context:
XXXXX
Q:XXXXX
A:
'''
This kind of promt works and I can fetch context via embedding retrieval. Is there any other way I can train it with the document corpus ?
Best Regards,
Todor
Hi, reading comprehension task should be doable, but the result should depend on the length type of the context. To improve accuracy, it is recommended to include a small number of examples :)
Hey,
@juewang
you really are amazing and helpful in helping out, I have been following you in the discussions here.
I need guidance in my project if you could help.
I want to tune the model (if it is possible either with the Trainer or other way) with some dataset of specific domain and then I should be able to ask the questions. Now, here I can see that we also need to pass the context from which we need to generate the answer.
Is there a way that doesn't require the context to be supplied? Just ask question and the model can answer? Like the close book answering procedure? If yes, could you direct me in that direction please.
Thanks,
Aayush
Hey, @juewang you really are amazing and helpful in helping out, I have been following you in the discussions here.
I need guidance in my project if you could help.I want to tune the model (if it is possible either with the Trainer or other way) with some dataset of specific domain and then I should be able to ask the questions. Now, here I can see that we also need to pass the context from which we need to generate the answer.
Is there a way that doesn't require the context to be supplied? Just ask question and the model can answer? Like the close book answering procedure? If yes, could you direct me in that direction please.
Thanks,
Aayush
Hello! Sure, closed-book answering is possible! We have recently released OpenChatKit, along with its training code base, which is designed for training chatbot models but can also be used to fine-tune GPT-JT (with dist_prefixlm_train.py). You shall prepare your dataset in jsonl format and perform fine-tuning.