I need multiply the weigths of terms in TFIDF matrix by the word-embeddings of word2vec matrix but I can’t do it because each matrix have a different number of terms. **I am using the same corpus for get both matrix, I don’t know why each matrix have a different number of terms
.**

My problem is that I have a matrix TFIDF with the shape `(56096, 15500)`

(corresponding to: number of terms, number of documents) and matrix Word2vec with the shape `(300, 56184)`

(corresponding to : number of word-embeddings, number of terms).

And I need the same numbers of terms in both matrix.

I use this code for get the matrix of word-embeddings Word2vec:

```
def w2vec_gensim(norm_corpus):
wpt = nltk.WordPunctTokenizer()
tokenized_corpus = [wpt.tokenize(document) for document in norm_corpus]
# Set values for various parameters
feature_size = 300
# Word vector dimensionality
window_context = 10
# Context window size
min_word_count = 1
# Minimum word count
sample = 1e-3
# Downsample setting for frequent words
w2v_model = word2vec.Word2Vec(tokenized_corpus, size=feature_size, window=window_context, min_count = min_word_count, sample=sample, iter=100)
words = list(w2v_model.wv.vocab)
vectors=[]
for w in words:
vectors.append(w2v_model[w].tolist())
embedding_matrix= np.array(vectors)
embedding_matrix= embedding_matrix.T
print(embedding_matrix.shape)
return embedding_matrix
```

And this code for get the TFIDF matrix:

```
tv = TfidfVectorizer(min_df=0., max_df=1., norm='l2', use_idf=True, smooth_idf=True)
def matriz_tf_idf(datos, tv):
tv_matrix = tv.fit_transform(datos)
tv_matrix = tv_matrix.toarray()
tv_matrix = tv_matrix.T
return tv_matrix
```

And I need the same number of terms in each matrix. For example, if I have 56096 terms in TFIDF, I need the same number in embeddings matrix, I mean matrix TFIDF with the shape `(56096, 1550)`

and matrix of embeddings Word2vec with the shape `(300, 56096)`

. How I can get the same number of terms in both matrix?

Because I can’t delete without more data, due to I need the multiplication to make sense because my goal is to get the embeddings from the documents.

Thank you very much in advance.