diff --git a/A_Naive_Bayes_Classifier_of_Spatial_Prepositions.ipynb b/A_Naive_Bayes_Classifier_of_Spatial_Prepositions.ipynb
index bf7d818..56d6fcb 100644
--- a/A_Naive_Bayes_Classifier_of_Spatial_Prepositions.ipynb
+++ b/A_Naive_Bayes_Classifier_of_Spatial_Prepositions.ipynb
@@ -5,7 +5,7 @@
     "colab": {
       "private_outputs": true,
       "provenance": [],
-      "authorship_tag": "ABX9TyOtAws7g+Ww5md7+Ng1v7T2",
+      "authorship_tag": "ABX9TyO/HriD8rlWF7QYjff37k8Q",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -380,7 +380,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Split corpus into Train (80%) and Test (20%)\n",
+        "# Splits corpus into Train (80%) and Test (20%)\n",
         "\n",
         "from sklearn.model_selection import train_test_split\n",
         "\n",
@@ -417,10 +417,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Hora de classificar o conjunto de treinamento\n",
-        "\n",
-        "model = MultinomialNB()  # Cria um objeto do tipo \"Classificador Bayesiano Multinomial\"\n",
-        "model.fit(X_train, y_train)  # Busca ajustar os dados às etiquetas"
+        "model = MultinomialNB()  # Creates MNB model\n",
+        "model.fit(X_train, y_train)  # Adjests data according to the tags"
       ],
       "metadata": {
         "id": "tSQ06050QRWB"
@@ -442,7 +440,7 @@
     {
       "cell_type": "code",
       "source": [
-        "predictions[:3]  # Conferindo..."
+        "predictions[:3]  "
       ],
       "metadata": {
         "id": "uHz4gI5LQW1P"
@@ -453,8 +451,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Cálculo das métricas com sklearn\n",
-        "\n",
+        "# Using Scikit-learn\n",
         "from sklearn.metrics import classification_report\n",
         "\n",
         "print(classification_report(y_test, predictions))"
@@ -514,8 +511,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Probabilidades (prévia) das classes\n",
-        "# Muito útil, pois mostra o viés\n",
+        "# Probabilitues (prior) of classes\n",
+        "# Shows bias\n",
         "\n",
         "model.class_log_prior_"
       ],
@@ -539,7 +536,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# N. of atributes\n",
+        "# N. of attributes\n",
         "\n",
         "model.n_features_in_"
       ],
@@ -552,8 +549,8 @@
     {
       "cell_type": "code",
       "source": [
-        "# Probabilidade posterior de cada atributo (logarítmica)\n",
-        "# A primeira lista corresponde à classe 0 (Negativo) e, a segunda, à classe 1 (Positivo)\n",
+        "# Post probability of each attribute (logarithmic)\n",
+        "# The first list corresponds to class 0 (Negative) and the second to class 1 (Positive)\n",
         "\n",
         "model.feature_log_prob_"
       ],
@@ -566,7 +563,7 @@
     {
       "cell_type": "code",
       "source": [
-        "# Nomes dos atributos indexados alfabeticamente\n",
+        "# Alphabetically indexed attribute names\n",
         "\n",
         "vetor.get_feature_names_out()"
       ],
@@ -606,10 +603,7 @@
     {
       "cell_type": "code",
       "source": [
-        "df['Ratio SPATIAL/NON-SPATIAL'] = df['SPATIAL'] / df['NON-SPATIAL'] \n",
-        "\n",
-        "# O DataFrame resultante poderá ser ordenado por \"espacialidade\" \n",
-        "# (como são números negativos, quanto menor a razão acima, mais \"epacial\")\n",
+        "df['Ratio SPATIAL/NON-SPATIAL'] = df['SPATIAL'] / df['NON-SPATIAL'] #The smaller the ratio, the more \"spatial\" is the term.\n",
         "df"
       ],
       "metadata": {