# Will be based on # ConstructiveLoss function. # # https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/quora_duplicate_questions/training_OnlineContrastiveLoss.py from torch.utils.data import DataLoader from sentence_transformers import losses, util from sentence_transformers import LoggingHandler, SentenceTransformer, evaluation from sentence_transformers.readers import InputExample import logging from datetime import datetime import csv import os from zipfile import ZipFile import random #### Just some code to print debug information to stdout logging.basicConfig(format='%(asctime)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO, handlers=[LoggingHandler()]) logger = logging.getLogger(__name__) #### /print debug information to stdout #As base model, we use DistilBERT-base that was pre-trained on NLI and STSb data model = SentenceTransformer('sentence-transformers/paraphrase-albert-base-v2') num_epochs = 10 train_batch_size = 10 #As distance metric, we use cosine distance (cosine_distance = 1-cosine_similarity) distance_metric = losses.SiameseDistanceMetric.COSINE_DISTANCE #Negative pairs should have a distance of at least 0.5 margin = 0.5 dataset_path = "data_set_training.csv" model_save_path = 'output/training_OnlineConstrativeLoss-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S") os.makedirs(model_save_path, exist_ok=True) ######### Read train data ########## # Read train data train_samples = [] with open(dataset_path, encoding='utf8') as fIn: reader = csv.DictReader(fIn, delimiter='|', quoting=csv.QUOTE_NONE) for row in reader: sample = InputExample(texts=[row['address1'], row['address2']], label=int(row['are_same'])) train_samples.append(sample) train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size) train_loss = losses.OnlineContrastiveLoss(model=model, distance_metric=distance_metric, margin=margin) ################### Development Evaluators ################## # We add 3 evaluators, that evaluate the model on Duplicate Questions pair classification, # Duplicate Questions Mining, and Duplicate Questions Information Retrieval #evaluators = [] ###### Classification ###### # Given (quesiton1, question2), is this a duplicate or not? # The evaluator will compute the embeddings for both questions and then compute # a cosine similarity. If the similarity is above a threshold, we have a duplicate. dev_sentences1 = [] dev_sentences2 = [] dev_labels = [] with open( "dev_set_training.csv", encoding='utf8') as fIn: reader = csv.DictReader(fIn, delimiter='|', quoting=csv.QUOTE_NONE) for row in reader: dev_sentences1.append(row['address1']) dev_sentences2.append(row['address2']) dev_labels.append(int(row['are_same'])) binary_acc_evaluator = evaluation.BinaryClassificationEvaluator(dev_sentences1, dev_sentences2, dev_labels) #evaluators.append(binary_acc_evaluator) ###### Duplicate Questions Mining ###### # Given a large corpus of questions, identify all duplicates in that corpus. # For faster processing, we limit the development corpus to only 10,000 sentences. #max_dev_samples = 10000 #dev_sentences = {} #dev_duplicates = [] #with open("dev_corpus.csv", encoding='utf8') as fIn: # reader = csv.DictReader(fIn, delimiter='|', quoting=csv.QUOTE_NONE) # for row in reader: # dev_sentences[row['qid']] = row['question'] # # if len(dev_sentences) >= max_dev_samples: # break # #with open(os.path.join(dataset_path, "duplicate-mining/dev_duplicates.tsv"), encoding='utf8') as fIn: # reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE) # for row in reader: # if row['qid1'] in dev_sentences and row['qid2'] in dev_sentences: # dev_duplicates.append([row['qid1'], row['qid2']]) # # ## The ParaphraseMiningEvaluator computes the cosine similarity between all sentences and ## extracts a list with the pairs that have the highest similarity. Given the duplicate ## information in dev_duplicates, it then computes and F1 score how well our duplicate mining worked #paraphrase_mining_evaluator = evaluation.ParaphraseMiningEvaluator(dev_sentences, dev_duplicates, name='dev') #evaluators.append(paraphrase_mining_evaluator) # # ####### Duplicate Questions Information Retrieval ###### ## Given a question and a large corpus of thousands questions, find the most relevant (i.e. duplicate) question ## in that corpus. # ## For faster processing, we limit the development corpus to only 10,000 sentences. #max_corpus_size = 100000 # #ir_queries = {} #Our queries (qid => question) #ir_needed_qids = set() #QIDs we need in the corpus #ir_corpus = {} #Our corpus (qid => question) #ir_relevant_docs = {} #Mapping of relevant documents for a given query (qid => set([relevant_question_ids]) # #with open(os.path.join(dataset_path, 'information-retrieval/dev-queries.tsv'), encoding='utf8') as fIn: # next(fIn) #Skip header # for line in fIn: # qid, query, duplicate_ids = line.strip().split('\t') # duplicate_ids = duplicate_ids.split(',') # ir_queries[qid] = query # ir_relevant_docs[qid] = set(duplicate_ids) # # for qid in duplicate_ids: # ir_needed_qids.add(qid) # ## First get all needed relevant documents (i.e., we must ensure, that the relevant questions are actually in the corpus #distraction_questions = {} #with open(os.path.join(dataset_path, 'information-retrieval/corpus.tsv'), encoding='utf8') as fIn: # next(fIn) #Skip header # for line in fIn: # qid, question = line.strip().split('\t') # # if qid in ir_needed_qids: # ir_corpus[qid] = question # else: # distraction_questions[qid] = question # ## Now, also add some irrelevant questions to fill our corpus #other_qid_list = list(distraction_questions.keys()) #random.shuffle(other_qid_list) # #for qid in other_qid_list[0:max(0, max_corpus_size-len(ir_corpus))]: # ir_corpus[qid] = distraction_questions[qid] # ##Given queries, a corpus and a mapping with relevant documents, the InformationRetrievalEvaluator computes different IR ## metrices. For our use case MRR@k and Accuracy@k are relevant. #ir_evaluator = evaluation.InformationRetrievalEvaluator(ir_queries, ir_corpus, ir_relevant_docs) # #evaluators.append(ir_evaluator) # ## Create a SequentialEvaluator. This SequentialEvaluator runs all three evaluators in a sequential order. ## We optimize the model with respect to the score from the last evaluator (scores[-1]) #seq_evaluator = evaluation.SequentialEvaluator(evaluators, main_score_function=lambda scores: scores[-1]) # # #logger.info("Evaluate model without training") #seq_evaluator(model, epoch=0, steps=0, output_path=model_save_path) # Train the model model.fit(train_objectives=[(train_dataloader, train_loss)], evaluator=binary_acc_evaluator, epochs=num_epochs, warmup_steps=5, output_path=model_save_path )