diff --git a/examples/kge-transe.ipynb b/examples/kge-transe.ipynb
new file mode 100644
index 000000000..33ca8a3b7
--- /dev/null
+++ b/examples/kge-transe.ipynb
@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b68543d5e71ceeb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from graphdatascience import GraphDataScience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e685a47b61f968ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NEO4J_URI = \"bolt://localhost:7687\"\n",
+    "NEO4J_DB = \"neo4j\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.environ.get(\"NEO4J_USER\") and os.environ.get(\"NEO4J_PASSWORD\"):\n",
+    "    NEO4J_AUTH = (\n",
+    "        os.environ.get(\"NEO4J_USER\"),\n",
+    "        os.environ.get(\"NEO4J_PASSWORD\"),\n",
+    "    )\n",
+    "gds = GraphDataScience(NEO4J_URI, auth=NEO4J_AUTH, database=NEO4J_DB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a14f06aebe1ed34c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = gds.run_cypher(\n",
+    "    \"\"\"\n",
+    "        CREATE\n",
+    "         (dan:Person {name: 'Dan'}),\n",
+    "         (annie:Person {name: 'Annie'}),\n",
+    "         (matt:Person {name: 'Matt'}),\n",
+    "         (jeff:Person {name: 'Jeff'}),\n",
+    "         (brie:Person {name: 'Brie'}),\n",
+    "         (elsa:Person {name: 'Elsa'}),\n",
+    "\n",
+    "         (cookies:Product {name: 'Cookies'}),\n",
+    "         (tomatoes:Product {name: 'Tomatoes'}),\n",
+    "         (cucumber:Product {name: 'Cucumber'}),\n",
+    "         (celery:Product {name: 'Celery'}),\n",
+    "         (kale:Product {name: 'Kale'}),\n",
+    "         (milk:Product {name: 'Milk'}),\n",
+    "         (chocolate:Product {name: 'Chocolate'}),\n",
+    "\n",
+    "         (dan)-[:BUYS {amount: 1.2}]->(cookies),\n",
+    "         (dan)-[:BUYS {amount: 3.2}]->(milk),\n",
+    "         (dan)-[:BUYS {amount: 2.2}]->(chocolate),\n",
+    "\n",
+    "         (annie)-[:BUYS {amount: 1.2}]->(cucumber),\n",
+    "         (annie)-[:BUYS {amount: 3.2}]->(milk),\n",
+    "         (annie)-[:BUYS {amount: 3.2}]->(tomatoes),\n",
+    "\n",
+    "         (matt)-[:BUYS {amount: 3}]->(tomatoes),\n",
+    "         (matt)-[:BUYS {amount: 2}]->(kale),\n",
+    "         (matt)-[:BUYS {amount: 1}]->(cucumber),\n",
+    "\n",
+    "         (jeff)-[:BUYS {amount: 3}]->(cookies),\n",
+    "         (jeff)-[:BUYS {amount: 2}]->(milk),\n",
+    "\n",
+    "         (brie)-[:BUYS {amount: 1}]->(tomatoes),\n",
+    "         (brie)-[:BUYS {amount: 2}]->(milk),\n",
+    "         (brie)-[:BUYS {amount: 2}]->(kale),\n",
+    "         (brie)-[:BUYS {amount: 3}]->(cucumber),\n",
+    "         (brie)-[:BUYS {amount: 0.3}]->(celery),\n",
+    "\n",
+    "         (elsa)-[:BUYS {amount: 3}]->(chocolate),\n",
+    "         (elsa)-[:BUYS {amount: 3}]->(milk)\n",
+    "    \"\"\"\n",
+    ")\n",
+    "node_projection = [\"Person\", \"Product\"]\n",
+    "relationship_projection = {\"BUYS\": {\"orientation\": \"UNDIRECTED\", \"properties\": \"amount\"}}\n",
+    "G, result = gds.graph.project(\"purchases222\", node_projection, relationship_projection)\n",
+    "print(f\"The projection took {result['projectMillis']} ms\")\n",
+    "print(f\"Graph '{G.name()}' node count: {G.node_count()}\")\n",
+    "print(f\"Graph '{G.name()}' node labels: {G.node_labels()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e049480efa34e8ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gds.model.transe.train(\n",
+    "    G,\n",
+    "    proportions=[0.8, 0.1, 0.1],\n",
+    "    embedding_dimension=50,\n",
+    "    batch_size=512,\n",
+    "    epochs=100,\n",
+    "    optimizer=\"Adam\",\n",
+    "    optimizer_kwargs={\"lr\": 0.01, \"weight_decay\": 5e-4},\n",
+    "    # loss\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/graphdatascience/model/model_proc_runner.py b/graphdatascience/model/model_proc_runner.py
index 20e5dd6e3..d06782526 100644
--- a/graphdatascience/model/model_proc_runner.py
+++ b/graphdatascience/model/model_proc_runner.py
@@ -1,3 +1,4 @@
+import json
 from typing import Any, Dict, List, Optional, Tuple
 
 from pandas import DataFrame, Series
@@ -45,6 +46,34 @@ def create(
             relationship_type_embeddings,
         )
 
+    @compatible_with("train", min_inclusive=ServerVersion(2, 5, 0))
+    @client_only_endpoint("gds.model.transe")
+    def train(self,
+              G: Graph,
+              proportions: list,
+              embedding_dimension: int,
+              batch_size: int,
+              epochs: int,
+              optimizer: str,
+              optimizer_kwargs: dict,
+              # loss: str
+              ) -> int:
+        config = {'scoring_function': 'TransE',
+                  'proportions': proportions,
+                  'embedding_dimension': embedding_dimension,
+                  'num_epochs': epochs,
+                  'graph_name': G.name(),
+                  'batch_size': batch_size,
+                  'optimizer': optimizer,
+                  'optimizer_kwargs': optimizer_kwargs,
+                  # 'loss': loss,
+                  }
+        config_path = "/tmp/kge-train-config-dump.json"
+        print('Dumped to ' + config_path)
+        config_file = open(config_path, "w")
+
+        json.dump(config, config_file)
+        return 0
 
 class ModelProcRunner(ModelResolver):
     @client_only_endpoint("gds.model")