diff --git a/build.sh b/build.sh
old mode 100644
new mode 100755
index 9d2a4fd5..44ec10a4
--- a/build.sh
+++ b/build.sh
@@ -14,11 +14,16 @@ fi
 echo "Downloading NLTK stopwords and punctuation packages..."
 python3 -c "import nltk; nltk.download('stopwords'); nltk.download('punkt')"
 
-if [ ! -e 'src/bin/bayes_model.pkl' ]; then
+if [ ! -e 'src/bin/bayes_model_sk.pkl' ]; then
     echo "Training Bayes model..."
     python3 ./src/scripts/train_bayes.py
 fi
 
+if [ ! -e 'src/bin/logreg_model_sk.pkl' ]; then
+    echo "Training LogReg model..."
+    python3 ./src/scripts/train_logreg.py
+fi
+
 if [ ! -d 'src/bin/rnn' ]; then
     echo "Training RNN model..."
     python3 ./src/scripts/train_rnn.py
diff --git a/src/classifier.py b/src/classifier.py
index a7c52584..bf80e226 100644
--- a/src/classifier.py
+++ b/src/classifier.py
@@ -10,6 +10,9 @@
 with open("src/bin/bayes_model_sk.pkl", "rb") as f:
     bayes_model = pickle.load(f)
 
+with open("src/bin/logreg_model_sk.pkl", "rb") as f:
+    logreg_model = pickle.load(f)
+
 with open("src/bin/vectorizer.pkl", "rb") as f:
     vectorizer = pickle.load(f)
 
@@ -25,6 +28,15 @@ def predict_bayes(text: str) -> dict[str, float]:
     }
 
 
+def predict_logreg(text: str) -> dict[str, float]:
+    features = vectorizer.transform([text])
+    probs = logreg_model.predict_proba(features)[0]
+    return {
+        "male": probs[1],
+        "female": probs[0],
+    }
+
+
 def predict_rnn(text: str) -> dict[str, float]:
     pred_arr = rnn_model.predict(np.array([text]))
     pred = float(pred_arr[0, 0])
@@ -32,7 +44,7 @@ def predict_rnn(text: str) -> dict[str, float]:
     return {"male": pred, "female": 1 - pred}
 
 
-pred_funcs = {"bayes": predict_bayes, "rnn": predict_rnn}
+pred_funcs = {"bayes": predict_bayes, "rnn": predict_rnn, "logreg": predict_logreg}
 
 
 def predict(text: str, classifier: str = "bayes") -> dict[str, str | dict[str, float]]:
diff --git a/src/scripts/train_logreg.py b/src/scripts/train_logreg.py
new file mode 100644
index 00000000..b645f351
--- /dev/null
+++ b/src/scripts/train_logreg.py
@@ -0,0 +1,64 @@
+import nltk
+import numpy as np
+import os
+import pickle
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.linear_model import LogisticRegressionCV
+
+MODEL_PATH = "src/bin/logreg_model_sk.pkl"
+VECTORIZER_PATH = "src/bin/vectorizer.pkl"
+
+STOPWORDS = nltk.corpus.stopwords.words("norwegian")
+np.random.seed(42)
+
+
+def read_file(path: str) -> str:
+    with open(path) as f:
+        content = f.read()
+    return content
+
+
+def _load_corpus() -> tuple[list[str] | np.ndarray]:
+    dir_f = "corpus/data/train/F/"
+    dir_m = "corpus/data/train/M/"
+    files_f = [dir_f + i for i in os.listdir(dir_f)]
+    files_m = [dir_m + i for i in os.listdir(dir_m)]
+    labels_f = np.full(len(files_f), "F")
+    labels_m = np.full(len(files_m), "M")
+
+    raw_data = list(map(read_file, files_f + files_m))
+    labels = np.concatenate((labels_f, labels_m))
+    return raw_data, labels
+
+
+def _save_model(model: LogisticRegressionCV, path: str) -> None:
+    with open(path, "wb+") as f:
+        pickle.dump(model, f)
+
+
+def train() -> None:
+    raw_data, labels = _load_corpus()
+
+    vectorizer = CountVectorizer(
+        stop_words=STOPWORDS,
+        ngram_range=[1, 3],  # for usage of trigrams and bigrams
+        max_features=5000,
+    )
+
+    features = vectorizer.fit_transform(raw_data)
+    # shuffle data in case it is not permutation invariant
+    perms = np.random.permutation(len(labels))
+    # Does CSRMatrix handle this?
+    features = features[perms]
+    labels = labels[perms]
+
+    clf = LogisticRegressionCV(multi_class="multinomial", cv=5, max_iter=5000)
+
+    clf.fit(features, labels)
+
+    _save_model(clf, MODEL_PATH)
+    _save_model(vectorizer, VECTORIZER_PATH)
+
+
+if __name__ == "__main__":
+    train()
diff --git a/src/scripts/validation.py b/src/scripts/validation.py
index be7e84fe..819e3c41 100644
--- a/src/scripts/validation.py
+++ b/src/scripts/validation.py
@@ -38,11 +38,31 @@ def predict_on_str(text: str) -> int:
     return lambda x: [predict_on_str(s) for s in x]
 
 
+def get_predict_logreg() -> list[int]:
+    model_path = "src/bin/logreg_model_sk.pkl"
+    vectorizer_path = "src/bin/vectorizer.pkl"
+
+    with open(model_path, "rb") as f:
+        bayes_model = pickle.load(f)
+
+    with open(vectorizer_path, "rb") as f:
+        vectorizer = pickle.load(f)
+
+    def predict_on_str(text: str) -> int:
+        text = text.decode("UTF8")
+        features = vectorizer.transform([text])
+        pred = bayes_model.predict(features)
+        return 0 if pred[0] == "F" else 1
+
+    return lambda x: [predict_on_str(s) for s in x]
+
+
 if __name__ == "__main__":
     dev_set, test_set = get_dev_test()
 
     predict_rnn = get_predict_rnn("src/bin/rnn")
     predict_bayes = get_predict_bayes()
+    predict_logreg = get_predict_logreg()
 
     for name, (X, y) in [("dev", dev_set), ("test", test_set)]:
         X = X.numpy()
@@ -53,3 +73,7 @@ def predict_on_str(text: str) -> int:
         y_pred = predict_bayes(X)
         print("Results for", name, "with bayes:")
         print(classification_report(y, y_pred))
+
+        y_pred = predict_logreg(X)
+        print("Results for", name, "with logreg:")
+        print(classification_report(y, y_pred))