#You can do it using GloVe library: #Install it: !pip install glove_python from glove import Corpus, Glove #Creating a corpus object corpus = Corpus() #Training the corpus to generate the co-occurrence matrix which is used in GloVe corpus.fit(lines, window=10) glove = Glove(no_components=5, learning_rate=0.05) glove.fit(corpus.matrix, epochs=30, no_threads=4, verbose=True) glove.add_dictionary(corpus.dictionary) glove.save('glove.model') Save #for Fasttext from gensim.models import FastText from gensim.test.utils import common_texts # some example sentences >>> print(common_texts[0]) ['human', 'interface', 'computer'] print(len(common_texts)) 9 model = FastText(vector_size=4, window=3, min_count=1) # instantiate model.build_vocab(sentences=common_texts) model.train(sentences=common_texts, total_examples=len(common_texts), epochs=10) # train model2 = FastText(vector_size=4, window=3, min_count=1, sentences=common_texts, epochs=10) import numpy as np >>> np.allclose(model.wv['computer'], model2.wv['computer']) True from gensim.test.utils import datapath >>> corpus_file = datapath('lee_background.cor') # absolute path to corpus model3 = FastText(vector_size=4, window=3, min_count=1) model3.build_vocab(corpus_file=corpus_file) # scan over corpus to build the vocabulary >>> total_words = model3.corpus_total_words # number of words in the corpus model3.train(corpus_file=corpus_file, total_words=total_words, epochs=5) from gensim.utils import tokenize from gensim import utils >>> >>> class MyIter: def __iter__(self): path = datapath('crime-and-punishment.txt') with utils.open(path, 'r', encoding='utf-8') as fin: for line in fin: yield list(tokenize(line)) >>> >>> model4 = FastText(vector_size=4, window=3, min_count=1) model4.build_vocab(sentences=MyIter()) total_examples = model4.corpus_count model4.train(sentences=MyIter(), total_examples=total_examples, epochs=5) from gensim.test.utils import get_tmpfile >>> fname = get_tmpfile("fasttext.model") >>> model.save(fname) model = FastText.load(fname) # https://radimrehurek.com/gensim/models/fasttext.html