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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) | |