I have a dataset where one feature is text and 4 more features. Sentence-Bert vectorizer transforms text data into tensors. I can use these sparse matrices directly with a machine learning classifier. Can I replace the text column with tensors? Also how can I train the model. Below is the code I used to transform the text into vectors.

model = SentenceTransformer('sentence-transformers/LaBSE')
sentence_embeddings = model.encode(X_train['tweet'], convert_to_tensor=True, show_progress_bar=True)
sentence_embeddings1 = model.encode(X_test['tweet'], convert_to_tensor=True, show_progress_bar=True)
  • 1
    $\begingroup$ one can linearize the tensors into a big 1d array of numbers and concat this array to other features and train $\endgroup$
    – Nikos M.
    Oct 15, 2021 at 8:25


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.