Not very good answers
When I try to feed in this context from a long txt file lets say below:
[New_Spark_Questions.txt](https://github.com/huggingface/transformers/files/6273496/New_Spark_Questions.txt)
and I feed in a question from that txt file: Would police or the FBI ever be able to access DNA or other information collected?
it is not giving me a very good answer: I get Answer: 'help speed up the progress of autism research' with score 0.4306815266609192. How do we see all the cores available, how does the model decide which answer is the best?


The text you are providing is probably too long for the model. Most Transformer models accept a sequence length of 512 tokens.

Which model did you use?<|||||>I just used the general q-a pipeline:
from google.colab import files
uploaded = files.upload() 
filename = "New_Spark_Questions.txt"
new_file = uploaded[filename].decode("utf-8")
!pip3 install sentencepiece
!pip3 install git+https://github.com/huggingface/transformers
question = "Why should I take part in SPARK?" 
from transformers import pipeline
qa = pipeline("question-answering")
answer = qa(question=question, context=new_file)
print(f"Question: {question}")
print(f"Answer: '{answer['answer']}' with score {answer['score']}")
How does the q-a pipeline decide the score?
Also how do we use a model like Bert, XLNet, etc. on a q-a pipeline
Does the input in the q-a pipeline have to be a dictionary?<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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