Feat: Updated README.md

README.md

@@ -61,9 +61,9 @@ Para guardar el modelo
 model.save('word2vec.model')
 ```
 
-Para distancia de coseno entre dos textos convertidos a arreglos.
+Para encontrar la similitud de coseno entre dos textos preprocesados:
 ```
-coseno = model.wv.wmdistance(first_array, second_array)
+coseno = model.wv.n_similarity(corpus_a, corpus_b)
 ```
 
 ## Citations
@@ -72,11 +72,3 @@ Ofir Pele and Michael Werman "A linear time histogram metric for improved SIFT m
 Ofir Pele and Michael Werman "Fast and robust earth mover's distances" <https://ieeexplore.ieee.org/document/5459199/>_
 
 Matt Kusner et al. "From Word Embeddings To Document Distances" <http://proceedings.mlr.press/v37/kusnerb15.pdf>
-
-#### **Model:** 
-
-https://zenodo.org/record/1410403#.Y1fknbbMJD8
-
-Aitor Almeida, & Aritz Bilbao. (2018). Spanish 3B words Word2Vec Embeddings (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1410403
-
-Bilbao-Jayo, A., & Almeida, A. (2018). Automatic political discourse analysis with multi-scale convolutional neural networks and contextual data. International Journal of Distributed Sensor Networks, 14(11), 1550147718811827.