import os import random from typing import cast import time import torch import transformers from datasets import DatasetDict, load_dataset from dotenv import load_dotenv from src.evaluation import evaluate from src.readers.dpr_reader import DprReader from src.retrievers.es_retriever import ESRetriever from src.retrievers.faiss_retriever import FaissRetriever from src.utils.log import get_logger from src.utils.preprocessing import result_to_reader_input logger = get_logger() load_dotenv() transformers.logging.set_verbosity_error() if __name__ == '__main__': dataset_name = "GroNLP/ik-nlp-22_slp" paragraphs = load_dataset(dataset_name, "paragraphs") questions = cast(DatasetDict, load_dataset(dataset_name, "questions")) questions_test = questions["test"] # logger.info(questions) dataset_paragraphs = cast(DatasetDict, load_dataset( "GroNLP/ik-nlp-22_slp", "paragraphs")) # Initialize retriever retriever = FaissRetriever(dataset_paragraphs) #retriever = ESRetriever(dataset_paragraphs) # Retrieve example # random.seed(111) random_index = random.randint(0, len(questions_test["question"])-1) example_q = questions_test["question"][random_index] example_a = questions_test["answer"][random_index] scores, result = retriever.retrieve(example_q) reader_input = result_to_reader_input(result) # Initialize reader reader = DprReader() answers = reader.read(example_q, reader_input) # Calculate softmaxed scores for readable output sm = torch.nn.Softmax(dim=0) document_scores = sm(torch.Tensor( [pred.relevance_score for pred in answers])) span_scores = sm(torch.Tensor( [pred.span_score for pred in answers])) print(example_q) for answer_i, answer in enumerate(answers): print(f"[{answer_i + 1}]: {answer.text}") print(f"\tDocument {answer.doc_id}", end='') print(f"\t(score {document_scores[answer_i] * 100:.02f})") print(f"\tSpan {answer.start_index}-{answer.end_index}", end='') print(f"\t(score {span_scores[answer_i] * 100:.02f})") print() # Newline # print(f"Example q: {example_q} answer: {result['text'][0]}") # for i, score in enumerate(scores): # print(f"Result {i+1} (score: {score:.02f}):") # print(result['text'][i]) # Determine best answer we want to evaluate highest, highest_index = 0, 0 for i, value in enumerate(span_scores): if value + document_scores[i] > highest: highest = value + document_scores[i] highest_index = i # Retrieve exact match and F1-score exact_match, f1_score = evaluate( example_a, answers[highest_index].text) print(f"Gold answer: {example_a}\n" f"Predicted answer: {answers[highest_index].text}\n" f"Exact match: {exact_match:.02f}\n" f"F1-score: {f1_score:.02f}") # Calculate overall performance # total_f1 = 0 # total_exact = 0 # total_len = len(questions_test["question"]) # start_time = time.time() # for i, question in enumerate(questions_test["question"]): # print(question) # answer = questions_test["answer"][i] # print(answer) # # scores, result = retriever.retrieve(question) # reader_input = result_to_reader_input(result) # answers = reader.read(question, reader_input) # # document_scores = sm(torch.Tensor( # [pred.relevance_score for pred in answers])) # span_scores = sm(torch.Tensor( # [pred.span_score for pred in answers])) # # highest, highest_index = 0, 0 # for j, value in enumerate(span_scores): # if value + document_scores[j] > highest: # highest = value + document_scores[j] # highest_index = j # print(answers[highest_index]) # exact_match, f1_score = evaluate(answer, answers[highest_index].text) # total_f1 += f1_score # total_exact += exact_match # print(f"Total time:", round(time.time() - start_time, 2), "seconds.") # print(total_f1) # print(total_exact) # print(total_f1/total_len)