Update README.md

README.md

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 ## Model description
 
-The following model starts with read_uniform, that generates a fictional dataset based in the choice of an uniform distributed random rating for one of the simulated users of the recommender-system that is being designed in this research project.
-  read_unfirom is also used as a benchmark for the model. read_lda_topics it's based in absolute values from LDA, although presenting an low score return. Our third dataset is read_lda_ratings uses a relative approach, not paying that much attention to quantity
-  ranking the opportunities. 
+The surprise library provides 11 classifier models that try to predict the classification of training data based on several different collaborative-filtering techniques.
+The models provided with a brief explanation in English are mentioned below, for more information please refer to the package [documentation](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html).
 
-![architeru](https://github.com/marcosdib/S2Query/Classification_Architecture_model.png)
+random_pred.NormalPredictor: Algorithm predicting a random rating based on the distribution of the training set, which is assumed to be normal.
 
+baseline_only.BaselineOnly: Algorithm predicting the baseline estimate for given user and item.
+
+knns.KNNBasic: A basic collaborative filtering algorithm.
+
+knns.KNNWithMeans: A basic collaborative filtering algorithm, taking into account the mean ratings of each user.
+
+knns.KNNWithZScore: A basic collaborative filtering algorithm, taking into account the z-score normalization of each user.
+
+knns.KNNBaseline: A basic collaborative filtering algorithm taking into account a baseline rating.
+
+matrix_factorization.SVD: The famous SVD algorithm, as popularized by Simon Funk during the Netflix Prize.
+
+matrix_factorization.SVDpp: The SVD++ algorithm, an extension of SVD taking into account implicit ratings.
+
+matrix_factorization.NMF: A collaborative filtering algorithm based on Non-negative Matrix Factorization.
+
+slope_one.SlopeOne: A simple yet accurate collaborative filtering algorithm.
+
+co_clustering.CoClustering: A collaborative filtering algorithm based on co-clustering.
+
+Every model was used and evaluated. When faced with each other different methods presented different error estimatives. 
 
 ## Intended uses
 You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to