sbert/train_sts_task.py

# This script is hacked and modified from https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py
# For more specified training tasks, please refer https://github.com/UKPLab/sentence-transformers/tree/master/examples/training

from torch.utils.data import DataLoader
import math
from sentence_transformers import SentenceTransformer, LoggingHandler, losses, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import os
import gzip
import csv

#### 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



def train_sts(model, training_config):
    sts_dataset_path = training_config['sts_dataset_path']
    train_batch_size = training_config['train_batch_size']
    num_epochs = training_config['num_epochs']
    model_save_path = training_config['model_save_path']
    if not os.path.exists(model_save_path):
        os.mkdir(model_save_path)
    model_save_path = os.path.join(model_save_path, 'training_stsbenchmark_continue_training-' + datetime.now().strftime(
        "%Y-%m-%d_%H-%M-%S"))


    # Convert the dataset to a DataLoader ready for training
    logging.info("Read STSbenchmark train dataset")

    train_samples = []
    dev_samples = []
    test_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:
            score = float(row['score']) / 5.0  # Normalize score to range 0 ... 1
            inp_example = InputExample(texts=[row['sentence1'], row['sentence2']], label=score)

            if row['split'] == 'dev':
                dev_samples.append(inp_example)
            elif row['split'] == 'test':
                test_samples.append(inp_example)
            else:
                train_samples.append(inp_example)



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


    # Development set: Measure correlation between cosine score and gold labels
    logging.info("Read STSbenchmark 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='sts-test')
    test_evaluator(model, output_path=model_save_path)