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"""
This script trains sentence transformers with a triplet loss function.
As corpus, we use the wikipedia sections dataset that was describd by Dor et al., 2018, Learning Thematic Similarity Metric Using Triplet Networks.
"""
from sentence_transformers import SentenceTransformer, InputExample, LoggingHandler, losses, models, util
from torch.utils.data import DataLoader
from sentence_transformers.evaluation import TripletEvaluator
from datetime import datetime
from zipfile import ZipFile
import csv
import logging
import os
logging.basicConfig(format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO,
handlers=[LoggingHandler()])
logger = logging.getLogger(__name__)
#You can specify any huggingface/transformers pre-trained model here, for example, bert-base-uncased, roberta-base, xlm-roberta-base
model_name = 'distilbert-base-uncased'
dataset_path = 'datasets/wikipedia-sections'
if not os.path.exists(dataset_path):
os.makedirs(dataset_path, exist_ok=True)
filepath = os.path.join(dataset_path, 'wikipedia-sections-triplets.zip')
util.http_get('https://sbert.net/datasets/wikipedia-sections-triplets.zip', filepath)
with ZipFile(filepath, 'r') as zip:
zip.extractall(dataset_path)
### Create a torch.DataLoader that passes training batch instances to our model
train_batch_size = 16
output_path = "output/training-wikipedia-sections-"+model_name+"-"+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
num_epochs = 1
### Configure sentence transformers for training and train on the provided dataset
# Use Huggingface/transformers model (like BERT, RoBERTa, XLNet, XLM-R) for mapping tokens to embeddings
word_embedding_model = models.Transformer(model_name)
# Apply mean pooling to get one fixed sized sentence vector
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
pooling_mode_mean_tokens=True,
pooling_mode_cls_token=False,
pooling_mode_max_tokens=False)
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
logger.info("Read Triplet train dataset")
train_examples = []
with open(os.path.join(dataset_path, 'train.csv'), encoding="utf-8") as fIn:
reader = csv.DictReader(fIn, delimiter=',', quoting=csv.QUOTE_MINIMAL)
for row in reader:
train_examples.append(InputExample(texts=[row['Sentence1'], row['Sentence2'], row['Sentence3']], label=0))
train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=train_batch_size)
train_loss = losses.TripletLoss(model=model)
logger.info("Read Wikipedia Triplet dev dataset")
dev_examples = []
with open(os.path.join(dataset_path, 'validation.csv'), encoding="utf-8") as fIn:
reader = csv.DictReader(fIn, delimiter=',', quoting=csv.QUOTE_MINIMAL)
for row in reader:
dev_examples.append(InputExample(texts=[row['Sentence1'], row['Sentence2'], row['Sentence3']]))
if len(dev_examples) >= 1000:
break
evaluator = TripletEvaluator.from_input_examples(dev_examples, name='dev')
warmup_steps = int(len(train_dataloader) * num_epochs * 0.1) #10% of train data
# Train the model
model.fit(train_objectives=[(train_dataloader, train_loss)],
evaluator=evaluator,
epochs=num_epochs,
evaluation_steps=1000,
warmup_steps=warmup_steps,
output_path=output_path)
##############################################################################
#
# Load the stored model and evaluate its performance on STS benchmark dataset
#
##############################################################################
logger.info("Read test examples")
test_examples = []
with open(os.path.join(dataset_path, 'test.csv'), encoding="utf-8") as fIn:
reader = csv.DictReader(fIn, delimiter=',', quoting=csv.QUOTE_MINIMAL)
for row in reader:
test_examples.append(InputExample(texts=[row['Sentence1'], row['Sentence2'], row['Sentence3']]))
model = SentenceTransformer(output_path)
test_evaluator = TripletEvaluator.from_input_examples(test_examples, name='test')
test_evaluator(model, output_path=output_path)
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