word-embedding

来源： https://stackoverflow.com/questions/63062583/building-a-recommendation-system-using-word2vec

问题 I have used keras to use pre-trained word embeddings but I am not quite sure how to do it on scikit-learn model. I need to do this in sklearn as well because I am using vecstack to ensemble both keras sequential model and sklearn model. This is what I have done for keras model: glove_dir = '/home/Documents/Glove' embeddings_index = {} f = open(os.path.join(glove_dir, 'glove.6B.200d.txt'), 'r', encoding='utf-8') for line in f: values = line.split() word = values[0] coefs = np.asarray(values[1:

问题 If I have a text string to be vectorized, how should I handle numbers inside it? Or if I feed a Neural Network with numbers and words, how can I keep the numbers as numbers? I am planning on making a dictionary of all my words (as suggested here). In this case all strings will become arrays of numbers. How should I handle characters that are numbers? how to output a vector that does not mix the word index with the number character? Does converting numbers to strings weakens the information i

问题 In the paper describing BERT, there is this paragraph about WordPiece Embeddings. We use WordPiece embeddings (Wu et al., 2016) with a 30,000 token vocabulary. The first token of every sequence is always a special classification token ([CLS]). The final hidden state corresponding to this token is used as the aggregate sequence representation for classification tasks. Sentence pairs are packed together into a single sequence. We differentiate the sentences in two ways. First, we separate them

问题 I have seen that NLP models such as BERT utilize WordPiece for tokenization. In WordPiece, we split the tokens like playing to play and ##ing . It is mentioned that it covers a wider spectrum of Out-Of-Vocabulary (OOV) words. Can someone please help me explain how WordPiece tokenization is actually done, and how it handles effectively helps to rare/OOV words? 回答1: WordPiece and BPE are two similar and commonly used techniques to segment words into subword-level in NLP tasks. In both cases,

问题 I have sentences that I vectorize using sentence_vector() method of BiobertEmbedding python module (https://pypi.org/project/biobert-embedding/). For some group of sentences I have no problem but for some others I have the following error message : File "/home/nobunaga/.local/lib/python3.6/site-packages/biobert_embedding/embedding.py", line 133, in sentence_vector encoded_layers = self.eval_fwdprop_biobert(tokenized_text) File "/home/nobunaga/.local/lib/python3.6/site-packages/biobert

问题 I have sentences that I vectorize using sentence_vector() method of BiobertEmbedding python module (https://pypi.org/project/biobert-embedding/). For some group of sentences I have no problem but for some others I have the following error message : File "/home/nobunaga/.local/lib/python3.6/site-packages/biobert_embedding/embedding.py", line 133, in sentence_vector encoded_layers = self.eval_fwdprop_biobert(tokenized_text) File "/home/nobunaga/.local/lib/python3.6/site-packages/biobert

问题 I started experimenting with word embeddings, and I found some results which I don't know how to interpret. I first used an English corpus for both training and testing and afterwards, I used the English corpus for training and a small French corpus for testing (all corpora have been annotated for the same binary classification task). In both cases, I used the pre-trained on tweets Glove embeddings. As the results in the case where I also used the French corpus improved (by almost 5%,

问题 I have checked the PyTorch tutorial and questions similar to this one on Stackoverflow. I get confused; does the embedding in pytorch (Embedding) make the similar words closer to each other? And do I just need to give to it all the sentences? Or it is just a lookup table and I need to code the model? 回答1: nn.Embedding holds a Tensor of dimension (vocab_size, vector_size) , i.e. of the size of the vocabulary x the dimension of each vector embedding, and a method that does the lookup. When you

问题 I have checked the PyTorch tutorial and questions similar to this one on Stackoverflow. I get confused; does the embedding in pytorch (Embedding) make the similar words closer to each other? And do I just need to give to it all the sentences? Or it is just a lookup table and I need to code the model? 回答1: nn.Embedding holds a Tensor of dimension (vocab_size, vector_size) , i.e. of the size of the vocabulary x the dimension of each vector embedding, and a method that does the lookup. When you