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import math
from sentence_transformers import models, losses, datasets
from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import sys
import os
import gzip
import csv
from MultiDatasetDataLoader import MultiDatasetDataLoader
#### 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
model_name = 'distilroberta-base'
num_epochs = 1
sts_dataset_path = 'data-eval/stsbenchmark.tsv.gz'
batch_size_pairs = 384
batch_size_triplets = 256
max_seq_length = 128
use_amp = True #Set to False, if you use a CPU or your GPU does not support FP16 operations
evaluation_steps = 500
warmup_steps = 500
#####
if not os.path.exists(sts_dataset_path):
util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path)
# Save path of the model
model_save_path = 'output/training_paraphrases_'+model_name.replace("/", "-")+'-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
## SentenceTransformer model
word_embedding_model = models.Transformer(model_name, max_seq_length=max_seq_length)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
datasets = []
for filepath in sys.argv[1:]:
dataset = []
with_guid = 'with-guid' in filepath #Some datasets have a guid in the first column
with gzip.open(filepath, 'rt', encoding='utf8') as fIn:
for line in fIn:
splits = line.strip().split("\t")
if with_guid:
guid = splits[0]
texts = splits[1:]
else:
guid = None
texts = splits
dataset.append(InputExample(texts=texts, guid=guid))
datasets.append(dataset)
train_dataloader = MultiDatasetDataLoader(datasets, batch_size_pairs=batch_size_pairs, batch_size_triplets=batch_size_triplets)
# Our training loss
train_loss = losses.MultipleNegativesRankingLoss(model)
#Read STSbenchmark dataset and use it as development set
logging.info("Read STSbenchmark dev dataset")
dev_samples = []
with gzip.open(sts_dataset_path, 'rt', encoding='utf8') as fIn:
reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE)
for row in reader:
if row['split'] == 'dev':
score = float(row['score']) / 5.0 #Normalize score to range 0 ... 1
dev_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=score))
dev_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
# Configure the training
logging.info("Warmup-steps: {}".format(warmup_steps))
# Train the model
model.fit(train_objectives=[(train_dataloader, train_loss)],
evaluator=dev_evaluator,
epochs=num_epochs,
evaluation_steps=evaluation_steps,
warmup_steps=warmup_steps,
output_path=model_save_path,
use_amp=use_amp,
checkpoint_path=model_save_path,
checkpoint_save_steps=1000,
checkpoint_save_total_limit=3
)
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