training_nli.py

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


model_name = "skt_kobert_model_"

train_batch_size = 16

model_save_path = 'output/training_nli_'

word_embedding_model = models.Transformer(model_name, isKor=True)

# 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])

logging.info("Read AllNLI train dataset")

label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
train_samples = []

with open('./KorNLUDatasets/KorNLI/snli_1.0_train.ko.tsv', "rt", encoding="utf-8") as fIn:
    lines = fIn.readlines()
    for line in lines:
        s1, s2, label = line.split('\t')
        label = label2int[label.strip()]
        train_samples.append(InputExample(texts=[s1, s2], label=label))

train_dataset = SentencesDataset(train_samples, model=model)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size)
train_loss = losses.SoftmaxLoss(model=model, sentence_embedding_dimension=model.get_sentence_embedding_dimension(), num_labels=len(label2int))


#Read STSbenchmark dataset and use it as development set
logging.info("Read STSbenchmark dev dataset")
dev_samples = []

with open('./KorNLUDatasets/KorSTS/tune_dev.tsv', 'rt', encoding='utf-8') as fIn:
    lines = fIn.readlines()
    for line in lines:
        s1, s2, score = line.split('\t')
        score = score.strip()
        score = float(score) / 5.0
        dev_samples.append(InputExample(texts= [s1,s2], label=score))

dev_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, batch_size=train_batch_size, name='sts-dev')

num_epochs = 1

warmup_steps = math.ceil(len(train_dataset) * num_epochs / train_batch_size * 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=dev_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
#
##############################################################################

test_samples = []
with open('./KorNLUDatasets/KorSTS/tune_test.tsv', 'rt', encoding='utf-8') as fIn:
    lines = fIn.readlines()
    for line in lines:
        s1, s2, score = line.split('\t')
        score = score.strip()
        score = float(score) / 5.0
        test_samples.append(InputExample(texts=[s1,s2], label=score))

print("\n\n\n")
print("======================TEST===================")
print("\n\n\n")
model = SentenceTransformer(model_save_path)
print(f"model save path > {model_save_path}")
test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, batch_size=train_batch_size, name='sts-test')
test_evaluator(model, output_path=model_save_path)