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:], dtype='float32')embeddings_index[word] = coefs
f.close()embedding_dim = 200embedding_matrix = np.zeros((max_words, embedding_dim))
for word, i in word_index.items():if i < max_words:embedding_vector = embeddings_index.get(word)if embedding_vector is not None:embedding_matrix[i] = embedding_vectormodel = Sequential()
model.add(Embedding(max_words, embedding_dim, input_length=maxlen))
.
.
model.layers[0].set_weights([embedding_matrix])
model.layers[0].trainable = False
model.compile(----)
model.fit(-----)

I am very new to scikit-learn, from what I have seen to make an model in sklearn you do:

lr = LogisticRegression()
lr.fit(X_train, y_train)
lr.predict(x_test)

So, my question is how do I use pre-trained Glove with this model? where do I pass the pre-trained glove embedding_matrix

Thank you very much and I really appreciate your help.

You can simply use the Zeugma library.

You can install it with pip install zeugma, then create and train your model with the following lines of code (assuming corpus_train and corpus_test are lists of strings):

from sklearn.linear_model import LogisticRegresion
from zeugma.embeddings import EmbeddingTransformerglove = EmbeddingTransformer('glove')
x_train = glove.transform(corpus_train)model = LogisticRegression()
model.fit(x_train, y_train)x_test = glove.transform(corpus_test)
model.predict(x_test)

You can also use different pre-trained embeddings (complete list here) or train your own (see Zeugma's documentation for how to do this).