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# Benchmarks: NT, Why is blood important?
#model_name = "deepset/roberta-base-squad2" # 180
#model_name = "deepset/deberta-v3-large-squad2" # est. 4X
model_name = "deepset/tinyroberta-squad2" # 86
#model_name = "deepset/minilm-uncased-squad2" # 96
#model_name = "deepset/electra-base-squad2" # 185 (nice wordy results)
# Install Dependences
# Use my Conda qna environment, then you're all set
# !pip install transformers
# !pip install ipywidgets
# !pip install gradio # see setup for installing gradio
import gradio as gr
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
def question_answer(context_filename, question):
"""Produce a NLP response based on the input text filename and question."""
with open(context_filename) as f:
context = f.read()
nlp_input = {'question': question, 'context': context}
result = nlp(nlp_input)
return result['answer']
demo = gr.Interface(
fn=question_answer,
#inputs=gr.inputs.Textbox(lines=2, placeholder='Enter your question'),
inputs=[
gr.Dropdown([
'spiderman.txt',
'world-john.txt',
'world-romans.txt',
'world-nt.txt',
'world-ot.txt']), # 'lotr01.txt'
"text"
],
outputs="textbox")
demo.launch(share=False)
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