diff --git a/apply_model/java/.gitignore b/apply_model/java/.gitignore
new file mode 100644
index 0000000..7ce0c93
--- /dev/null
+++ b/apply_model/java/.gitignore
@@ -0,0 +1,137 @@
+# Created by https://www.toptal.com/developers/gitignore/api/java,intellij+all,maven
+# Edit at https://www.toptal.com/developers/gitignore?templates=java,intellij+all,maven
+
+### Intellij+all ###
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# AWS User-specific
+.idea/**/aws.xml
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# SonarLint plugin
+.idea/sonarlint/
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser
+
+### Intellij+all Patch ###
+# Ignore everything but code style settings and run configurations
+# that are supposed to be shared within teams.
+
+.idea/*
+
+!.idea/codeStyles
+!.idea/runConfigurations
+
+### Java ###
+# Compiled class file
+*.class
+
+# Log file
+*.log
+
+# BlueJ files
+*.ctxt
+
+# Mobile Tools for Java (J2ME)
+.mtj.tmp/
+
+# Package Files #
+*.jar
+*.war
+*.nar
+*.ear
+*.zip
+*.tar.gz
+*.rar
+
+# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
+hs_err_pid*
+replay_pid*
+
+### Maven ###
+target/
+pom.xml.tag
+pom.xml.releaseBackup
+pom.xml.versionsBackup
+pom.xml.next
+release.properties
+dependency-reduced-pom.xml
+buildNumber.properties
+.mvn/timing.properties
+# https://github.com/takari/maven-wrapper#usage-without-binary-jar
+.mvn/wrapper/maven-wrapper.jar
+
+# Eclipse m2e generated files
+# Eclipse Core
+.project
+# JDT-specific (Eclipse Java Development Tools)
+.classpath
+
+# End of https://www.toptal.com/developers/gitignore/api/java,intellij+all,maven
\ No newline at end of file
diff --git a/apply_model/java/pom.xml b/apply_model/java/pom.xml
index c3e912f..ec88642 100644
--- a/apply_model/java/pom.xml
+++ b/apply_model/java/pom.xml
@@ -12,7 +12,16 @@
         <dependency>
             <groupId>ai.catboost</groupId>
             <artifactId>catboost-prediction</artifactId>
-            <version>0.2.4</version>
+            <version>1.1</version>
+        </dependency>
+        <dependency>
+            <groupId>ai.catboost</groupId>
+            <artifactId>catboost-common</artifactId>
+            <version>1.1</version>
         </dependency>
     </dependencies>
+    <properties>
+        <maven.compiler.source>11</maven.compiler.source>
+        <maven.compiler.target>11</maven.compiler.target>
+    </properties>
 </project>
diff --git a/apply_model/java/src/main/java/CatBoost4jPredictionTutorial.java b/apply_model/java/src/main/java/CatBoost4jPredictionTutorial.java
index f65a55e..b505fa3 100644
--- a/apply_model/java/src/main/java/CatBoost4jPredictionTutorial.java
+++ b/apply_model/java/src/main/java/CatBoost4jPredictionTutorial.java
@@ -4,12 +4,14 @@
 
 import java.io.IOException;
 
+import static java.util.Objects.requireNonNull;
+
 public class CatBoost4jPredictionTutorial {
     private CatBoostModel adultModel = null;
 
     public CatBoost4jPredictionTutorial() throws CatBoostError, IOException {
         // Load "adult.cbm" model that we trained withing Jupyter Notebook
-        adultModel = CatBoostModel.loadModel(ClassLoader.getSystemResourceAsStream("models/adult.cbm"));
+        adultModel = CatBoostModel.loadModel(requireNonNull(ClassLoader.getSystemResourceAsStream("models/adult.cbm")));
 
         // You can also try to load your own model just comment out the line above and uncomment two lines below while
         // replacing "foo/bar" with path to your model that classifies data from UCI Adult Dataset.
@@ -35,7 +37,7 @@ public void playWithModelForAdultDataset() throws CatBoostError {
 
         System.out.print("Adult dataset model metainformation\n");
 
-        System.out.print(String.format("tree count: %d\n", adultModel.getTreeCount()));
+        System.out.printf("tree count: %d\n", adultModel.getTreeCount());
 
         // In our case we were solving a binary classification problem (weather person makes over 50K a year), so the
         // dimension of the prediction will be 1, it will return probability of the object to belong to the positive
@@ -46,16 +48,16 @@ public void playWithModelForAdultDataset() throws CatBoostError {
         //
         // For most of cases prediction dimension will be 1 (for regression and for ranking), it can be N for cases of
         // multiclassification, where N is a number of classes.
-        System.out.print(String.format("prediction dimension: %d\n",adultModel.getPredictionDimension()));
+        System.out.printf("prediction dimension: %d\n",adultModel.getPredictionDimension());
 
         // Take a note, number of numeric features used by the model may be less than number of numeric features
         // that were present in a training dataset. This may happen if, for example, when traing dataset contained
         // constant features, they do not carry any information for classifier, so training process will ignore them.
-        System.out.print(String.format("used numeric feature count: %d\n", adultModel.getUsedNumericFeatureCount()));
+        System.out.printf("used numeric feature count: %d\n", adultModel.getUsedNumericFeatureCount());
 
         // Number of categoric features used by the classifier may also be less than number of categoric feature present
         // in training dataset, for the same reasons as for numeric features.
-        System.out.print(String.format("used categoric feature count: %d\n", adultModel.getUsedCategoricFeatureCount()));
+        System.out.printf("used categoric feature count: %d\n", adultModel.getUsedCategoricFeatureCount());
 
         // Ok now lets try to use our model for prediction. We'll look at the test part of Adult dataset. You will need
         // to download it [1] from UCI repository. Look for "adult.test", "adult.name" will also be useful because it
@@ -91,7 +93,7 @@ public void playWithModelForAdultDataset() throws CatBoostError {
         // CatBoost doesn't compute "probability", to turn CatBoost prediction into a probability we'll need to apply
         // sigmoid function.
         final double personAMakesOver50KProbability = sigmoid(personAPrediction.get(0, 0));
-        System.out.print(String.format("Person A make over 50K a year with probability %f\n", personAMakesOver50KProbability));
+        System.out.printf("Person A make over 50K a year with probability %f\n", personAMakesOver50KProbability);
 
         // When we were training CatBoost we used a default classification threshold for AUC which is equal to 0.5,
         // this means that our formula is optimized for this threashold, though we may change threshold to optimize some
@@ -99,7 +101,7 @@ public void playWithModelForAdultDataset() throws CatBoostError {
         final double classificationThreshold = 0.5;
 
         final boolean personAMakesOver50K = personAMakesOver50KProbability > classificationThreshold;
-        System.out.print(String.format("Person A %s\n", answer(personAMakesOver50K)));
+        System.out.printf("Person A %s\n", answer(personAMakesOver50K));
 
         // Now lets find an example with missing features and income greater than 50K a year. At line 40 of "adult.test"
         // we can find following line:
@@ -124,8 +126,8 @@ public void playWithModelForAdultDataset() throws CatBoostError {
         final CatBoostPredictions personBPrediction = adultModel.predict(personBNumericFeatures, personBCategoricFeatures);
         final double personBMakeOver50KProbability = sigmoid(personBPrediction.get(0, 0));
         final boolean personBMakesOver50K = personBMakeOver50KProbability > classificationThreshold;
-        System.out.print(String.format("Person B make over 50K a year with probability %f\n", personBMakeOver50KProbability));
-        System.out.print(String.format("Person B %s\n", answer(personBMakesOver50K)));
+        System.out.printf("Person B make over 50K a year with probability %f\n", personBMakeOver50KProbability);
+        System.out.printf("Person B %s\n", answer(personBMakesOver50K));
 
         // There is also a batch interface for model application, e.g. you can apply model to multiple objects at once.
         //
@@ -151,10 +153,10 @@ public void playWithModelForAdultDataset() throws CatBoostError {
         System.out.print("Using batch interface\n");
 
         // Predictions should be same as above
-        System.out.print(String.format("Person A make over 50K a year with probability %f\n", personsABMakeOver50KProbabilities[0]));
-        System.out.print(String.format("Person A %s\n", answer(personsABMakeOver50K[0])));
-        System.out.print(String.format("Person B make over 50K a year with probability %f\n", personsABMakeOver50KProbabilities[1]));
-        System.out.print(String.format("Person B %s\n", answer(personsABMakeOver50K[1])));
+        System.out.printf("Person A make over 50K a year with probability %f\n", personsABMakeOver50KProbabilities[0]);
+        System.out.printf("Person A %s\n", answer(personsABMakeOver50K[0]));
+        System.out.printf("Person B make over 50K a year with probability %f\n", personsABMakeOver50KProbabilities[1]);
+        System.out.printf("Person B %s\n", answer(personsABMakeOver50K[1]));
 
         // TODO(yazevnul): add examples with hashed categorical features when `CatBoostModel.hashCategoricalFeatures`
         // will become public.