SimCSE

Gao et al. present in SimCSE a simple method to train sentence embeddings without having training data.

The idea is to encode the same sentence twice. Due to the used dropout in transformer models, both sentence embeddings will be at slightly different positions. The distance between these two embeddings will be minimized, while the distance to other embeddings of the other sentences in the same batch will be maximized (they serve as negative examples).

Usage with SentenceTransformers

SentenceTransformers implements the MultipleNegativesRankingLoss, which makes training with SimCSE trivial:

from sentence_transformers import SentenceTransformer, InputExample
from sentence_transformers import models, losses
from torch.utils.data import DataLoader

# Define your sentence transformer model using CLS pooling
model_name = "distilroberta-base"
word_embedding_model = models.Transformer(model_name, max_seq_length=32)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])

# Define a list with sentences (1k - 100k sentences)
train_sentences = [
    "Your set of sentences",
    "Model will automatically add the noise",
    "And re-construct it",
    "You should provide at least 1k sentences",
]

# Convert train sentences to sentence pairs
train_data = [InputExample(texts=[s, s]) for s in train_sentences]

# DataLoader to batch your data
train_dataloader = DataLoader(train_data, batch_size=128, shuffle=True)

# Use the denoising auto-encoder loss
train_loss = losses.MultipleNegativesRankingLoss(model)

# Call the fit method
model.fit(
    train_objectives=[(train_dataloader, train_loss)], epochs=1, show_progress_bar=True
)

model.save("output/simcse-model")

SimCSE from Sentences File

train_simcse_from_file.py loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.

SimCSE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.

Training Examples

• train_askubuntu_simcse.py - Shows the example how to train with SimCSE on the AskUbuntu Questions dataset.

• train_stsb_simcse.py - This script uses 1 million sentences and evaluates SimCSE on the STSbenchmark dataset.

Ablation Study

We use the evaluation setup proposed in our TSDAE paper.

Using mean pooling, with max_seq_length=32 and batch_size=128

Base Model	AskUbuntu Test-Performance (MAP)
distilbert-base-uncased	53.59
bert-base-uncased	54.89
distilroberta-base	56.16
roberta-base	55.89

Using mean pooling, with max_seq_length=32 and distilroberta-base model.

Batch Size	AskUbuntu Test-Performance (MAP)
128	56.16
256	56.63
512	56.69

Using max_seq_length=32, distilroberta-base model, and 512 batch size.

Pooling Mode	AskUbuntu Test-Performance (MAP)
Mean pooling	56.69
CLS pooling	56.56
Max pooling	52.91

Note: This is a re-implementation of SimCSE within sentence-transformers. For the official CT code, see: princeton-nlp/SimCSE