From 170fbd3e3c934ff4d5a8ba9bec258ee8da0add40 Mon Sep 17 00:00:00 2001 From: SebastianCB Date: Sun, 4 Dec 2022 12:06:46 -0500 Subject: [PATCH] Feat: Updated README.md --- README.md | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index d5f6fd6..808968f 100644 --- a/README.md +++ b/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.