examples/unsupervised_learning/CT/train_stsb_ct.py

import torch
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers import SentenceTransformer, LoggingHandler, models, util, InputExample
from sentence_transformers import losses
import os
import gzip
import csv
from datetime import datetime
import logging

#### 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

## Training parameters
model_name = 'distilbert-base-uncased'
batch_size = 16
pos_neg_ratio = 8   # batch_size must be devisible by pos_neg_ratio
epochs = 1
max_seq_length = 75

# Save path to store our model
model_save_path = 'output/train_stsb_ct-{}-{}'.format(model_name, datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))


################# Train sentences #################
# We use 1 Million sentences from Wikipedia to train our model
wikipedia_dataset_path = 'data/wiki1m_for_simcse.txt'
if not os.path.exists(wikipedia_dataset_path):
    util.http_get('https://huggingface.co/datasets/princeton-nlp/datasets-for-simcse/resolve/main/wiki1m_for_simcse.txt', wikipedia_dataset_path)

# train_sentences are simply your list of sentences
train_sentences = []
with open(wikipedia_dataset_path, 'r', encoding='utf8') as fIn:
    for line in fIn:
        line = line.strip()
        if len(line) >= 10:
            train_sentences.append(line)

################# Download and load STSb #################
data_folder = 'data/stsbenchmark'
sts_dataset_path = f'{data_folder}/stsbenchmark.tsv.gz'

if not os.path.exists(sts_dataset_path):
    util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path)


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)

dev_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')

################# Intialize an SBERT model #################
word_embedding_model = models.Transformer(model_name, max_seq_length=max_seq_length)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])


# For ContrastiveTension we need a special data loader to construct batches with the desired properties
train_dataloader =  losses.ContrastiveTensionDataLoader(train_sentences, batch_size=batch_size, pos_neg_ratio=pos_neg_ratio)

# As loss, we losses.ContrastiveTensionLoss
train_loss = losses.ContrastiveTensionLoss(model)


model.fit(
    train_objectives=[(train_dataloader, train_loss)],
    evaluator=dev_evaluator,
    epochs=1,
    evaluation_steps=1000,
    weight_decay=0,
    warmup_steps=0,
    optimizer_class=torch.optim.RMSprop,
    optimizer_params={'lr': 1e-5},
    output_path=model_save_path,
    use_amp=False    #Set to True, if your GPU has optimized FP16 cores
)

########### Load the model and evaluate on test set

model = SentenceTransformer(model_save_path)
test_evaluator(model)