Continue pretraining on a released model
I’m utilizing the e5 base model to generate document vectors for a similarity task. Due to a limited amount of labeled data, I delved into unsupervised pretraining with TSDAE. Unfortunately, my pretrained model yields worse results than the standard e5 model without any fine-tuning. Is there something flawed in my approach?pre_model = 'intfloat/multilingual-e5-base'
word_embedding_model = models.Transformer(pre_model)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), 'cls')
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])

train_sentences = df['Text'].tolist()

train_dataset = datasets.DenoisingAutoEncoderDataset(train_sentences)

train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True)

for epoch in range(pre_epochs):
    train_loss = LoggingDenoisingAutoEncoderLoss(model, decoder_name_or_path=pre_model,tie_encoder_decoder=False)
    model.fit(
        train_objectives=[(train_dataloader,train_loss)],
        epochs=1, 
        weight_decay=0,
        scheduler='constantlr',
        optimizer_params={'lr': pre_lr},
        show_progress_bar=True
    )

model.save(pretrain_save_path)Or does pretraining typically replace all the current weights?