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from sentence_transformers import SentenceTransformer, LoggingHandler
from sentence_transformers import models, util, datasets, evaluation, losses
import logging
import os
import gzip
from torch.utils.data import DataLoader
from datetime import datetime
import sys
#### 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()])
#### /print debug information to stdout
################# Download AskUbuntu and extract training corpus #################
askubuntu_folder = 'data/askubuntu'
result_folder = 'output/askubuntu-tsdae-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
batch_size = 8
## Download the AskUbuntu dataset from https://github.com/taolei87/askubuntu
for filename in ['text_tokenized.txt.gz', 'dev.txt', 'test.txt', 'train_random.txt']:
filepath = os.path.join(askubuntu_folder, filename)
if not os.path.exists(filepath):
util.http_get('https://github.com/taolei87/askubuntu/raw/master/'+filename, filepath)
# Read the corpus
corpus = {}
dev_test_ids = set()
with gzip.open(os.path.join(askubuntu_folder, 'text_tokenized.txt.gz'), 'rt', encoding='utf8') as fIn:
for line in fIn:
splits = line.strip().split("\t")
id = splits[0]
title = splits[1]
corpus[id] = title
# Read dev & test dataset
def read_eval_dataset(filepath):
dataset = []
with open(filepath) as fIn:
for line in fIn:
query_id, relevant_id, candidate_ids, bm25_scores = line.strip().split("\t")
if len(relevant_id) == 0: #Skip examples without relevant entries
continue
relevant_id = relevant_id.split(" ")
candidate_ids = candidate_ids.split(" ")
negative_ids = set(candidate_ids) - set(relevant_id)
dataset.append({
'query': corpus[query_id],
'positive': [corpus[pid] for pid in relevant_id],
'negative': [corpus[pid] for pid in negative_ids]
})
dev_test_ids.add(query_id)
dev_test_ids.update(candidate_ids)
return dataset
dev_dataset = read_eval_dataset(os.path.join(askubuntu_folder, 'dev.txt'))
test_dataset = read_eval_dataset(os.path.join(askubuntu_folder, 'test.txt'))
## Now we need a list of train sentences.
## In this example we simply use all sentences that don't appear in the train/dev set
train_sentences = []
for id, sentence in corpus.items():
if id not in dev_test_ids:
train_sentences.append(sentence)
logging.info("{} train sentences".format(len(train_sentences)))
################# Intialize an SBERT model #################
model_name = sys.argv[1] if len(sys.argv) >= 2 else 'bert-base-uncased'
word_embedding_model = models.Transformer(model_name)
# Apply **cls** pooling to get one fixed sized sentence vector
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), 'cls')
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
################# Train and evaluate the model (it needs about 1 hour for one epoch of AskUbuntu) #################
# We wrap our training sentences in the DenoisingAutoEncoderDataset to add deletion noise on the fly
train_dataset = datasets.DenoisingAutoEncoderDataset(train_sentences)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
train_loss = losses.DenoisingAutoEncoderLoss(model, decoder_name_or_path=model_name, tie_encoder_decoder=True)
# Create a dev evaluator
dev_evaluator = evaluation.RerankingEvaluator(dev_dataset, name='AskUbuntu dev')
logging.info("Dev performance before training")
dev_evaluator(model)
total_steps = 20000
logging.info("Start training")
model.fit(
train_objectives=[(train_dataloader, train_loss)],
evaluator=dev_evaluator,
evaluation_steps=1000,
epochs=1,
steps_per_epoch=total_steps,
weight_decay=0,
scheduler='constantlr',
optimizer_params={'lr': 3e-5},
output_path=result_folder,
show_progress_bar=True
)
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