T5 for closed book QA

How can I use T5 for abstractive QA, I don’t want to work on a SQUAD-like dataset, but rather get answers from general questions. Is there a prefix for this kind of QA for T5?

Thank you in advance!

Hi,

For open-domain question answering, no prefix is required. Google released several checkpoints (which you can find on our hub, such as this one) from their paper, you can use them as follows:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-small-ssm-nq")
t5_tok = AutoTokenizer.from_pretrained("google/t5-small-ssm-nq")

input_ids = t5_tok("When was Franklin D. Roosevelt born?", return_tensors="pt").input_ids
gen_output = t5_qa_model.generate(input_ids)[0]

print(t5_tok.decode(gen_output, skip_special_tokens=True))