import streamlit as st import pysbd from transformers import pipeline from sentence_transformers import CrossEncoder from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline class QuestionAnswering: def __init__(self): model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering" self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelWithLMHead.from_pretrained(model_name) self.sentence_segmenter = pysbd.Segmenter(language='en',clean=False) self.passage_retreival_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') self.qa_model = pipeline("question-answering",'a-ware/bart-squadv2') def fetch_answers(self, question, document): document_paragraphs = document.splitlines() query_paragraph_list = [(question, para) for para in document_paragraphs if len(para.strip()) > 0 ] scores = self.passage_retreival_model.predict(query_paragraph_list) top_5_indices = scores.argsort()[-3:] top_5_query_paragraph_list = [query_paragraph_list[i] for i in top_5_indices ] top_5_query_paragraph_list.reverse() top_5_query_paragraph_answer_list = "" count = 1 for query, passage in top_5_query_paragraph_list: passage_sentences = self.sentence_segmenter.segment(passage) answer = self.qa_model(question = query, context = passage)['answer'] evidence_sentence = "" for i in range(len(passage_sentences)): if answer.startswith('.') or answer.startswith(':'): answer = answer[1:].strip() if answer in passage_sentences[i]: evidence_sentence = evidence_sentence + " " + passage_sentences[i] model_input = f"question: {query} context: {evidence_sentence}" encoded_input = self.tokenizer([model_input], return_tensors='pt', max_length=512, truncation=True) output = self.model.generate(input_ids = encoded_input.input_ids, attention_mask = encoded_input.attention_mask) output_answer = self.tokenizer.decode(output[0], skip_special_tokens=True) result_str = ""+str(count)+": "+ output_answer +"\n" result_str = result_str + " "+ evidence_sentence + "\n\n" top_5_query_paragraph_answer_list += result_str count+=1 return top_5_query_paragraph_answer_list