I’m getting the following prompt when calling model.train() from gensim word2vec The only solutions I found on my search for an answer point to the itarable vs iterator difference, and at this point, I tried everything I could to solve this on my own, currently, my code looks like this: The corpus variable is a list containing sentences, and each
Tag: word2vec
Word2Vec + LSTM Good Training and Validation but Poor on Test
currently I’am training my Word2Vec + LSTM for Twitter sentiment analysis. I use the pre-trained GoogleNewsVectorNegative300 word embedding. The reason I used the pre-trained GoogleNewsVectorNegative300 because the performance much worse when I trained my own Word2Vec using own dataset. The problem is why my training process had validation acc and loss stuck at 0.88 and 0.34 respectively. Then, my confussion
Retrieve n-grams with word2vec
I have a list of texts. I turn each text into a token list. For example if one of the texts is ‘I am studying word2vec’ the respective token list will be (assuming I consider n-grams with n = 1, 2, 3) [‘I’, ‘am’, ‘studying ‘, ‘word2vec, ‘I am’, ‘am studying’, ‘studying word2vec’, ‘I am studying’, ‘am studying word2vec’]. Is
Modifying .trainables.syn1neg[i] with previously trained vectors in Gensim word2vec
My issue is the following. In my code I’m modifying the .wv[word] before training but after .build_vocab(), which is fairly straight forward. Just instead of the vectors in there add mine for every word. Where setIntersection is just a set of common words between gensim word2vec and RandomIndexing trained. Same size of 300 in both. Now I want to also
Sort dictionary python by value (word2vec)
I want to sort my dict by value, but if I apply this code it doesn’t work (it print only my key-value pairs without any kind of sorting). If I change key=lambda x: x[1] to x[0] it correctly sort by key, so I don’t understand what I’m doing wrong. My code: Answer You’re trying to sort sets, and Python isn’t
How to find accuracy, precision, recall, f1 score for my word2vec model?
I am working on a project to find similarity among products. The model splits the excel data sheet into 90% training / 10% validation. When I check manually for validation the model works pretty well. But I am having trouble with the evaluation process. How should I find accuracy, precision, recall and F1 score to understand how well my model
TensorFlow ‘module’ object has no attribute ‘global_variables_initializer’
I’m new to Tensorflow I’m running a Deep learning Assignment from Udacity on iPython notebook. link And it has an error. Please help! How can I fix this? Thank you. Answer In older versions, it was called tf.initialize_all_variables.
Using NearestNeighbors and word2vec to detect sentence similarity
I have calculated a word2vec model using python and gensim in my corpus. Then I calculated the mean word2vec vector for each sentence (averaging all the vectors for all the words in the sentence) and stored it in a pandas data frame. The columns of the pandas data frame df are: sentence Book title (the book where the sentence comes