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"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python training_nli.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 CESoftmaxAccuracyEvaluator
from sentence_transformers.readers import InputExample
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()])
logger = logging.getLogger(__name__)
#### /print debug information to stdout
#As dataset, we use SNLI + MultiNLI
#Check if dataset exsist. If not, download and extract it
nli_dataset_path = 'datasets/AllNLI.tsv.gz'
if not os.path.exists(nli_dataset_path):
util.http_get('https://sbert.net/datasets/AllNLI.tsv.gz', nli_dataset_path)
# Read the AllNLI.tsv.gz file and create the training dataset
logger.info("Read AllNLI train dataset")
label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
train_samples = []
dev_samples = []
with gzip.open(nli_dataset_path, 'rt', encoding='utf8') as fIn:
reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE)
for row in reader:
label_id = label2int[row['label']]
if row['split'] == 'train':
train_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=label_id))
else:
dev_samples.append(InputExample(texts=[row['sentence1'], row['sentence2']], label=label_id))
train_batch_size = 16
num_epochs = 4
model_save_path = 'output/training_allnli-'+datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
#Define our CrossEncoder model. We use distilroberta-base as basis and setup it up to predict 3 labels
model = CrossEncoder('distilroberta-base', num_labels=len(label2int))
#We wrap train_samples, which is a list ot InputExample, in a pytorch DataLoader
train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
#During training, we use CESoftmaxAccuracyEvaluator to measure the accuracy on the dev set.
evaluator = CESoftmaxAccuracyEvaluator.from_input_examples(dev_samples, name='AllNLI-dev')
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,
evaluation_steps=10000,
warmup_steps=warmup_steps,
output_path=model_save_path)
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