Sentence Embeddings From Fine-Tuned BERTForSequenceClassification
Hey everyone,I have a binary classification task for a set of documents, and I’d like to visualize these documents from their embeddings. I’ve previously used the sentence-transformers library to do this, but I wanted to see if it was possible to improve these embeddings by fine-tuning my own BERT model to the particular task rather than just using a pre-trained model. I read through someguidesanddiscussionsonline, and it seems like I should be able to use the embedding for the CLS token from the last hidden state layer as a sentence embedding. However, when I pull those values from the hidden_states of the fine-tuned BERTForSequenceClassification model, every embedding is the same.This is the code I’m using to fine-tune the pre-trained model:model = BertForSequenceClassification.from_pretrained("bert-base-cased",
                                                      num_labels=1,
                                                      output_attentions=False,
                                                      output_hidden_states=True)
model.to(device)
model.train()
optim = AdamW(model.parameters(), lr=5e-5)

for epoch in range(3):
    for batch in dataloader_train:
        optim.zero_grad()
        input_ids = batch[0].to(device)
        attention_mask = batch[1].to(device)
        labels = batch[2].to(device)
        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs[0]
        loss.backward()
        optim.step()

model.eval()And this is the code I’m using to pull the embeddings:def embeddings(model, dataloader_val):
    model.cuda()
    embeddings = np.zeros((0, 768))
    
    for batch in dataloader_val:
        
        batch = tuple(b.to(device) for b in batch)
        
        inputs = {'input_ids':      batch[0],
                  'attention_mask': batch[1],
                 }

        with torch.no_grad():        
            outputs = model(**inputs)
        embeddings = np.concatenate((embeddings, outputs[1][0][:,0,:].cpu().numpy()), axis=0)
    
    return embeddingsAny ideas or thoughts on why the embeddings for all of the CLS tokens would be the same?
2021-09-29T21:04:01Z
One way around this problem that I was thinking of was to train via the sequence classification task and then load the trained model as a normal BertModel and used the normalpooler_output. Has anyone tried something like this?