Tr_sent1, te_sent1, tr_sent2, te_sent2, tr_rel, te_rel = train_test_split(input_text1, input_text2, similarity, test_size=0. Tokenizer = Tokenizer(num_words=max_words, lower=True, split=' ', oov_token="UNK") Below is my code for pre-processing: input_text1 = I also fix the max_sentence_length (maximum number of sentences in a text) and max_sequence_length (max words within a sentence). Next the encoded sequences are sent to a custom attention layer that returns a 2D tensor. The purpose is to derive knowledge from different levels of a document structure.For this, I first split the text into a list of sentences, then tokenize. It make use of a seq2seq model RNN for sentence predictions. I am trying to encode words within a sentence for each text. My input is a pair of long form texts and target is 0 or 1. My task is to build a supervised model for text similarity using Keras.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |