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"""
Tests that the pretrained models produce the correct scores on the STSbenchmark dataset
"""
import csv
import gzip
import os
import unittest
from torch.utils.data import DataLoader
import logging
from sentence_transformers import CrossEncoder, util, LoggingHandler
from sentence_transformers.readers import InputExample
from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator
class CrossEncoderTest(unittest.TestCase):
def setUp(self):
sts_dataset_path = 'datasets/stsbenchmark.tsv.gz'
if not os.path.exists(sts_dataset_path):
util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path)
#Read STSB
self.stsb_train_samples = []
self.dev_samples = []
self.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':
self.dev_samples.append(inp_example)
elif row['split'] == 'test':
self.test_samples.append(inp_example)
else:
self.stsb_train_samples.append(inp_example)
def evaluate_stsb_test(self, model, expected_score):
evaluator = CECorrelationEvaluator.from_input_examples(self.test_samples, name='sts-test')
score = evaluator(model)*100
print("STS-Test Performance: {:.2f} vs. exp: {:.2f}".format(score, expected_score))
assert score > expected_score or abs(score-expected_score) < 0.1
def test_pretrained_stsb(self):
model = CrossEncoder("cross-encoder/stsb-distilroberta-base")
self.evaluate_stsb_test(model, 87.92)
def test_train_stsb(self):
model = CrossEncoder('distilroberta-base', num_labels=1)
train_dataloader = DataLoader(self.stsb_train_samples, shuffle=True, batch_size=16)
model.fit(train_dataloader=train_dataloader,
epochs=1,
warmup_steps=int(len(train_dataloader)*0.1))
self.evaluate_stsb_test(model, 75)
if "__main__" == __name__:
unittest.main() |