word-embedding

I found "unk" token in the glove vector file glove.6B.50d.txt downloaded from https://nlp.stanford.edu/projects/glove/ . Its value is as follows: unk -0.79149 0.86617 0.11998 0.00092287 0.2776 -0.49185 0.50195 0.00060792 -0.25845 0.17865 0.2535 0.76572 0.50664 0.4025 -0.0021388 -0.28397 -0.50324 0.30449 0.51779 0.01509 -0.35031 -1.1278 0.33253 -0.3525 0.041326 1.0863 0.03391 0.33564 0.49745 -0.070131 -1.2192 -0.48512 -0.038512 -0.13554 -0.1638 0.52321 -0.31318 -0.1655 0.11909 -0.15115 -0.15621 -0.62655 -0.62336 -0.4215 0.41873 -0.92472 1.1049 -0.29996 -0.0063003 0.3954 Is it a token to be used

I'm currently working with a Keras model which has a embedding layer as first layer. In order to visualize the relationships and similarity of words between each other I need a function that returns the mapping of words and vectors of every element in the vocabulary (e.g. 'love' - [0.21, 0.56, ..., 0.65, 0.10]). Is there any way to do it? You can get the word embeddings by using the get_weights() method of the embedding layer (i.e. essentially the weights of an embedding layer are the embedding vectors): # if you have access to the embedding layer explicitly embeddings = emebdding_layer.get

When I try to create a word2vec model (skipgram with negative sampling) I received 3 files as output as follows. word2vec (File) word2vec.syn1nef.npy (NPY file) word2vec.wv.syn0.npy (NPY file) I am just worried why this happens as for my previous test examples in word2vec I only received one model(no npy files). Please help me. Models with larger internal vector-arrays can't be saved via Python 'pickle' to a single file, so beyond a certain threshold, the gensim save() method will store subsidiary arrays in separate files, using the more-efficient raw format of numpy arrays ( .npy format). You

tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None) I cannot understand the duty of this function. Is it like a lookup table? Which means to return the parameters corresponding to each id (in ids)? For instance, in the skip-gram model if we use tf.nn.embedding_lookup(embeddings, train_inputs) , then for each train_input it finds the correspond embedding? Rafał Józefowicz embedding_lookup function retrieves rows of the params tensor. The behavior is similar to using indexing with arrays in numpy. E.g. matrix = np.random.random([1024, 64]) # 64-dimensional embeddings ids =

I am currently developing a text classification tool using Keras. It works (it works fine and I got up to 98.7 validation accuracy) but I can't wrap my head around about how exactly 1D-convolution layer works with text data. What hyper-parameters should I use? I have the following sentences (input data): Maximum words in the sentence: 951 (if it's less - the paddings are added) Vocabulary size: ~32000 Amount of sentences (for training): 9800 embedding_vecor_length: 32 (how many relations each word has in word embeddings) batch_size: 37 (it doesn't matter for this question) Number of labels

Assuming that I want to update a pre-trained word-embedding matrix during training, is there a way to update only a subset of the word embedding matrix? I have looked into the Tensorflow API page and found this: # Create an optimizer. opt = GradientDescentOptimizer(learning_rate=0.1) # Compute the gradients for a list of variables. grads_and_vars = opt.compute_gradients(loss, <list of variables>) # grads_and_vars is a list of tuples (gradient, variable). Do whatever you # need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1])) for gv in grads