stsb-m-mt-es-distilbert-base-uncased / training_stsb_m_mt.py

Added training script and updated README

92a62c5 almost 3 years ago

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	"""
	MODIFIED: (efv) Use STSb-multi-mt Spanish
	source: https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark.py

	---

	This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch. It generates sentence embeddings
	that can be compared using cosine-similarity to measure the similarity.

	Usage:
	python training_nli.py

	OR
	python training_nli.py pretrained_transformer_model_name
	"""
	from torch.utils.data import DataLoader
	import math
	from sentence_transformers import SentenceTransformer, LoggingHandler, losses, models, util
	from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
	from sentence_transformers.readers import InputExample
	import logging
	from datetime import datetime
	import sys
	import os
	import gzip
	import csv

	from datasets import load_dataset

	#### 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



	#You can specify any huggingface/transformers pre-trained model here, for example, bert-base-uncased, roberta-base, xlm-roberta-base
	model_name = sys.argv[1] if len(sys.argv) > 1 else 'distilbert-base-uncased'

	# Read the dataset
	train_batch_size = 16
	num_epochs = 4
	model_save_path = 'output/training_stsbenchmark_'+model_name.replace("/", "-")+'-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")

	# 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])

	# Convert the dataset to a DataLoader ready for training
	logging.info("Read stsb-multi-mt train dataset")

	train_samples = []
	dev_samples = []
	test_samples = []

	def samples_from_dataset(dataset):
	samples = [InputExample(texts=[e['sentence1'], e['sentence2']], label=e['similarity_score'] / 5) \
	for e in dataset]
	return samples

	train_samples = samples_from_dataset(load_dataset("stsb_multi_mt", name="es", split="train"))
	dev_samples = samples_from_dataset(load_dataset("stsb_multi_mt", name="es", split="dev"))
	test_samples = samples_from_dataset(load_dataset("stsb_multi_mt", name="es", split="test"))

	train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
	train_loss = losses.CosineSimilarityLoss(model=model)


	logging.info("Read stsb-multi-mt dev dataset")
	evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')


	# Configure the training. We skip evaluation in this example
	warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1) #10% of train data for warm-up
	logging.info("Warmup-steps: {}".format(warmup_steps))


	## 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=model_save_path)


	##############################################################################
	#
	# Load the stored model and evaluate its performance on STS benchmark dataset
	#
	##############################################################################

	#model = SentenceTransformer(model_save_path)
	test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='stsb-multi-mt-test')
	test_evaluator(model, output_path=model_save_path)