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| # -*- coding: utf-8 -*- | |
| """Job-coach-testing-site-zeroshot.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/github/vanderbilt-data-science/job-coach-question-answering/blob/111-create-a-gradio-hf-space-to-test-whether-specific-information-is-in-a-coaching-document/job-coach-testing-zeroshot.ipynb | |
| """ | |
| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForQuestionAnswering | |
| from transformers import pipeline | |
| classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| question_answerer = pipeline("question-answering", model = "bert-large-uncased-whole-word-masking-finetuned-squad") | |
| def zshot(context, queries): | |
| answered=[] | |
| res=[] | |
| notAnswered=[] | |
| queries=queries.split("?") | |
| queries.pop(-1) | |
| for query in queries: | |
| query.strip() | |
| result = question_answerer(question = query, context=context) | |
| answered.append([query,result['answer']]) | |
| for item in answered: | |
| result = ([text, classifier(text, item[0])]) | |
| if result[1]['scores'][0] > 0.01: | |
| res.append(item[0] +"? Answer: " + item[1]) | |
| else: | |
| notAnswered.append(item[0]) | |
| result1=''', | |
| '''.join(res) | |
| result1='''Information Included in the Document: | |
| '''+ result1 | |
| result2=''', | |
| '''.join(notAnswered) | |
| result2=''' | |
| Information not included in the document, on no clearly stated: | |
| '''+ result2 | |
| return result1 + result2 | |
| app = gr.Interface( | |
| zshot, | |
| inputs=[ | |
| gr.Textbox(label="Context", value=""), | |
| gr.Textbox(label="Queries", value=""), | |
| ], | |
| outputs=["text"], | |
| ) | |
| app.launch() | |
| """#### Examples""" |