File size: 3,530 Bytes
2359bda |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
"""
This examples trains a CrossEncoder for the STSbenchmark task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continious labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python training_stsbenchmark.py
"""
from torch.utils.data import DataLoader
import math
from sentence_transformers import LoggingHandler, util
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator
from sentence_transformers import InputExample
import logging
from datetime import datetime
import sys
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()])
logger = logging.getLogger(__name__)
#### /print debug information to stdout
#Check if dataset exsist. If not, download and extract it
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)
#Define our Cross-Encoder
train_batch_size = 16
num_epochs = 4
model_save_path = 'output/training_stsbenchmark-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
#We use distilroberta-base as base model and set num_labels=1, which predicts a continous score between 0 and 1
model = CrossEncoder('distilroberta-base', num_labels=1)
# Read STSb dataset
logger.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
if row['split'] == 'dev':
dev_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=score))
elif row['split'] == 'test':
test_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=score))
else:
#As we want to get symmetric scores, i.e. CrossEncoder(A,B) = CrossEncoder(B,A), we pass both combinations to the train set
train_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=score))
train_samples.append(InputExample(texts=[row['sentence2'], row['sentence1']], label=score))
# We wrap train_samples (which is a List[InputExample]) into a pytorch DataLoader
train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
# We add an evaluator, which evaluates the performance during training
evaluator = CECorrelationEvaluator.from_input_examples(dev_samples, name='sts-dev')
# Configure the training
warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1) #10% of train data for warm-up
logger.info("Warmup-steps: {}".format(warmup_steps))
# Train the model
model.fit(train_dataloader=train_dataloader,
evaluator=evaluator,
epochs=num_epochs,
warmup_steps=warmup_steps,
output_path=model_save_path)
##### Load model and eval on test set
model = CrossEncoder(model_save_path)
evaluator = CECorrelationEvaluator.from_input_examples(test_samples, name='sts-test')
evaluator(model) |