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 at 8:25

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.