embedding

问题 I am training a model that takes tokenized strings which are then passed through an embedding layer and an LSTM thereafter. However, there seems to be an error in the input, as it does not pass through the embedding layer. class DrugModel(nn.Module): def __init__(self, input_dim, output_dim, hidden_dim, drug_embed_dim, lstm_layer, lstm_dropout, bi_lstm, linear_dropout, char_vocab_size, char_embed_dim, char_dropout, dist_fn, learning_rate, binary, is_mlp, weight_decay, is_graph, g_layer, g

问题 I am training a model that takes tokenized strings which are then passed through an embedding layer and an LSTM thereafter. However, there seems to be an error in the input, as it does not pass through the embedding layer. class DrugModel(nn.Module): def __init__(self, input_dim, output_dim, hidden_dim, drug_embed_dim, lstm_layer, lstm_dropout, bi_lstm, linear_dropout, char_vocab_size, char_embed_dim, char_dropout, dist_fn, learning_rate, binary, is_mlp, weight_decay, is_graph, g_layer, g

问题 I'm using the module bert-for-tf2 in order to wrap BERT model as Keras layer in Tensorflow 2.0 I've followed your guide for implementing BERT model as Keras layer. I'm trying to extract embeddings from a sentence; in my case, the sentence is "Hello" I have a question about the output of the model prediction; I've written this model: model_word_embedding = tf.keras.Sequential([ tf.keras.layers.Input(shape=(4,), dtype='int32', name='input_ids'), bert_layer ]) model_word_embedding .build(input

问题 I'm trying to use the FastText Python API https://pypi.python.org/pypi/fasttext Although, from what I've read, this API can't load the newer .bin model files at https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md as suggested in https://github.com/salestock/fastText.py/issues/115 I've tried everything that is suggested at that issue, and furthermore https://github.com/Kyubyong/wordvectors doesn't have the .bin for English, otherwise the problem would be solved. Does

问题 I know that BERT has total vocabulary size of 30522 which contains some words and subwords. I want to get the initial input embeddings of BERT. So, my requirement is to get the table of size [30522, 768] to which I can index by token id to get its embeddings. Where can I get this table? 回答1: The BertModels have get_input_embeddings(): import torch from transformers import BertModel, BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') bert = BertModel.from_pretrained(

问题 After reading the tutorial at gensim's docs, I do not understand what is the correct way of generating new embeddings from a trained model. So far I have trained gensim's fast text embeddings like this: from gensim.models.fasttext import FastText as FT_gensim model_gensim = FT_gensim(size=100) # build the vocabulary model_gensim.build_vocab(corpus_file=corpus_file) # train the model model_gensim.train( corpus_file=corpus_file, epochs=model_gensim.epochs, total_examples=model_gensim.corpus

问题 After reading the tutorial at gensim's docs, I do not understand what is the correct way of generating new embeddings from a trained model. So far I have trained gensim's fast text embeddings like this: from gensim.models.fasttext import FastText as FT_gensim model_gensim = FT_gensim(size=100) # build the vocabulary model_gensim.build_vocab(corpus_file=corpus_file) # train the model model_gensim.train( corpus_file=corpus_file, epochs=model_gensim.epochs, total_examples=model_gensim.corpus

来源： https://stackoverflow.com/questions/63779875/sentences-embedding-using-word2vec

来源： https://stackoverflow.com/questions/63779875/sentences-embedding-using-word2vec

来源： https://stackoverflow.com/questions/63779875/sentences-embedding-using-word2vec