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# 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 | |
) |