diff --git a/lab06/jenkinsfile-evaluate b/lab06/jenkinsfile-evaluate
new file mode 100644
index 0000000..9d44104
--- /dev/null
+++ b/lab06/jenkinsfile-evaluate
@@ -0,0 +1,39 @@
+pipeline {
+   agent {
+      dockerfile true
+   }
+   parameters{
+      buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'WHICH_BUILD'
+        )
+   }
+   stages {
+      stage('checkout') {
+            steps {
+               copyArtifacts fingerprintArtifacts: true, projectName: 's440058-create-dataset', selector: buildParameter('WHICH_BUILD')
+            }
+        }
+      stage('Docker'){
+         steps{
+            sh 'python3 "./pytorch-example-evaluate.py" > eval-acc-result.txt'
+         }
+      }
+      stage('archiveArtifacts') {
+			steps{
+				archiveArtifacts 'eval-acc-result.txt'
+			}
+		}
+    }
+    post {
+        success {
+            mail body: 'SUCCESS TRAINING', subject: 's440058', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+
+        failure {
+            mail body: 'FAILURE TRAINING', subject: 's440058', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+
+   }
+}
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diff --git a/pytorch-example-evaluate.py b/pytorch-example-evaluate.py
new file mode 100644
index 0000000..b5a50c4
--- /dev/null
+++ b/pytorch-example-evaluate.py
@@ -0,0 +1,47 @@
+from sklearn.model_selection import train_test_split
+import torch
+import torch.nn as nn
+import pandas as pd
+import numpy as np
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, TensorDataset, random_split
+from sklearn import preprocessing
+import sys
+
+class LogisticRegressionModel(torch.nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super(LogisticRegressionModel, self).__init__()
+        self.linear = nn.Linear(input_dim, output_dim)
+        self.sigmoid = nn.Sigmoid()
+    def forward(self, x):
+        out = self.linear(x)
+        return self.sigmoid(out)
+
+results = pd.read_csv('diabetes2.csv')
+
+results.dropna()
+
+data_train, data_valid, data_test = np.split(results.sample(frac=1), [int(.6*len(results)), int(.8*len(results))])
+columns_to_train   =  ['Glucose', 'BloodPressure', 'Insulin', 'Age']
+
+x_train = data_train[columns_to_train].astype(np.float32)
+y_train = data_train['Outcome'].astype(np.float32)
+
+x_test = data_test[columns_to_train].astype(np.float32)
+y_test = data_test['Outcome'].astype(np.float32)
+
+fTrain = torch.from_numpy(x_train.values)
+tTrain = torch.from_numpy(y_train.values.reshape(460,1))
+
+fTest= torch.from_numpy(x_test.values)
+tTest = torch.from_numpy(y_test.values)
+
+input_dim = 4
+output_dim = 1
+
+model = LogisticRegressionModel(input_dim, output_dim)
+
+pred = model(xTest)
+accuracy = accuracy_score(fTest, np.argmax(pred.detach(), axis = 1))
+
+print(f'Accuracy: {accuracy}')
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