diff --git a/.gitignore b/.gitignore
index d68c297..7519f64 100644
--- a/.gitignore
+++ b/.gitignore
@@ -138,9 +138,11 @@ output
 #Block data
 data/*
 !data/FB15k/
+!data/FakeNewsNet
 
 #Block embeddings
 embeddings/*
+fn_embeddings/*
 
 # misc
 .DS_Store
diff --git a/FakeNewsDetection.ipynb b/FakeNewsDetection.ipynb
new file mode 100644
index 0000000..8f84352
--- /dev/null
+++ b/FakeNewsDetection.ipynb
@@ -0,0 +1,1648 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Detecting Fake News using Graph Neural Networksin Knowledge Graphs \n",
+    "This notebook will be used to valuate the classification prerformance of our model. Be sure to have trained a model using the specific fake news dataset to ensure all entities have been \"observed\" and assigned an embedding. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LIAR\n",
+    "## Step 1: Import triple extracted dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/people/person/employment_history./business/employment_tenure/title\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text_id</th>\n",
+       "      <th>head</th>\n",
+       "      <th>relation</th>\n",
+       "      <th>tail</th>\n",
+       "      <th>label</th>\n",
+       "      <th>fb_head</th>\n",
+       "      <th>fb_tail</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9524.json</td>\n",
+       "      <td>Scott Walker</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>governor</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m/04tzmt</td>\n",
+       "      <td>/m/05pd99q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     text_id          head                                           relation  \\\n",
+       "0  9524.json  Scott Walker  people/person/employment_history./business/emp...   \n",
+       "\n",
+       "       tail  label    fb_head     fb_tail  \n",
+       "0  governor  False  /m/04tzmt  /m/05pd99q  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "#load test data\n",
+    "LIAR = pd.read_csv(\n",
+    "        \"./data/LIAR/test.csv\",\n",
+    "        sep=\";\",\n",
+    "        header=0,\n",
+    "        engine=\"python\",\n",
+    "        usecols = ['text_id', 'head', 'relation', 'tail', 'fb_head', 'fb_tail', 'label']\n",
+    "\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "heads = LIAR[\"fb_head\"]\n",
+    "tails = LIAR[\"fb_tail\"]\n",
+    "relations = LIAR[\"relation\"]\n",
+    "\n",
+    "relations = [\"/\" + rel for rel in relations]\n",
+    "\n",
+    "print(relations[0])\n",
+    "\n",
+    "\n",
+    "LIAR.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. unique entities (original): 14951\n",
+      "no. unique entities in dataset: 155\n",
+      "no. unseen entities in dataset: 108\n",
+      "no. unique entities (updated): 15059\n"
+     ]
+    }
+   ],
+   "source": [
+    "from os import path\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "#construct entity encoder with original and new entity ids\n",
+    "entity_id = pd.read_csv('data/FB15k/entities.txt', sep='\\t', header=None, names=['entity', 'id'], engine='python')\n",
+    "model_entities = entity_id['entity'].values\n",
+    "print('no. unique entities (original):', len(model_entities))\n",
+    "\n",
+    "# add unseen entities from fake news dataset\n",
+    "dataset_entities = np.concatenate([np.array(heads), np.array(tails)])\n",
+    "dataset_entities =  np.unique(dataset_entities)\n",
+    "print('no. unique entities in dataset:', len(dataset_entities))\n",
+    "new_entities = np.array([ent for ent in dataset_entities if not ent in model_entities])\n",
+    "print('no. unseen entities in dataset:', len(new_entities))\n",
+    "\n",
+    "all_entities = np.concatenate((model_entities, new_entities), axis=0)\n",
+    "print('no. unique entities (updated):', len(all_entities))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "# fit encoder\n",
+    "le_entity = LabelEncoder()\n",
+    "le_entity.classes_ = np.load(path.join('fn_embeddings','le_entity_classes_LIAR.npy'), allow_pickle=True)\n",
+    "#load relation encoder\n",
+    "le_relation = LabelEncoder()\n",
+    "le_relation.classes_ = np.load(path.join('fn_embeddings','le_relation_classes_LIAR.npy'), allow_pickle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "le_relation test:  [1033]\n",
+      "[1033]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_rel = '/american_football/football_team/current_roster./sports/sports_team_roster/position'\n",
+    "test_rel2 = '/people/person/employment_history./business/employment_tenure/title'\n",
+    "print('le_relation test: ', le_relation.transform([test_rel2]))\n",
+    "\n",
+    "print(le_relation.transform([relations[3]]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#create torch_geometric Data object\n",
+    "#edge_index = torch.tensor([le_heads, le_tails], dtype=torch.long)\n",
+    "#edge_attributes = torch.tensor(le_relation.transform(relations), dtype=torch.long)\n",
+    "#dataset = Data(x=le_entity.transform(all_entities), edge_type=edge_attributes, edge_index=edge_index)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "le_heads = le_entity.transform(heads)\n",
+    "le_tails = le_entity.transform(tails)\n",
+    "edge_index = torch.tensor([le_heads, le_tails], dtype=torch.long)\n",
+    "\n",
+    "le_relations = le_relation.transform(relations)\n",
+    "edge_type = torch.tensor(le_relations, dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. true: 79\n",
+      "no. false: 122\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
+       "        0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
+       "        0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
+       "        0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n",
+       "        0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
+       "        0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
+       "        1, 0, 0, 0, 0, 0, 1, 0, 0])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create and 'encode' lables\n",
+    "raw_labels = np.array(LIAR[\"label\"])\n",
+    "false = 0\n",
+    "true = 0\n",
+    "for l in raw_labels:\n",
+    "    if l == False:\n",
+    "        false += 1\n",
+    "    else:\n",
+    "        true += 1\n",
+    "print('no. true:', true)\n",
+    "print('no. false:', false)\n",
+    "labels = torch.tensor([0 if l == False else 1 for l in raw_labels], dtype=torch.int64)\n",
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Classify using Multi-Relational GraphStar model\n",
+    "Use model pretrained with the entities of this specific fake news dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use env37test kernel\n",
+    "import torch\n",
+    "model = torch.load(\"output/FB15k_237_CPU_60epochs.pkl\", map_location=torch.device('cpu'))\n",
+    "\n",
+    "#model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([15167, 256])"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = model.z\n",
+    "z.size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ap: 0.40816418350539074 , auc: 0.49927370823822376\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "pred = model.lp_score(z, edge_index, edge_type)\n",
+    "lp_auc, lp_ap = model.lp_test(pred, labels)\n",
+    "\n",
+    "print('ap:', lp_ap, ', auc:', lp_auc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7f48189a10>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import precision_score, confusion_matrix, ConfusionMatrixDisplay\n",
+    "\n",
+    "pred_classes = torch.tensor([0 if p < 0 else 1 for p in pred], dtype=torch.int64)\n",
+    "\n",
+    "cm = confusion_matrix(labels, pred_classes)\n",
+    "\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['false', 'true']) #usikker på rekkefølgen her\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text_id</th>\n",
+       "      <th>head</th>\n",
+       "      <th>relation</th>\n",
+       "      <th>tail</th>\n",
+       "      <th>label</th>\n",
+       "      <th>fb_head</th>\n",
+       "      <th>fb_tail</th>\n",
+       "      <th>predicted_label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>2615.json</td>\n",
+       "      <td>Lee Fisher</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>Lieutenant governor</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m/034q8v</td>\n",
+       "      <td>/m/0fkzq</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>592.json</td>\n",
+       "      <td>United States Senate</td>\n",
+       "      <td>organization/organization/headquarters./locati...</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m/07t58</td>\n",
+       "      <td>/m/09c7w0</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>12745.json</td>\n",
+       "      <td>Barack Obama</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>President of the United States</td>\n",
+       "      <td>True</td>\n",
+       "      <td>/m/02mjmr</td>\n",
+       "      <td>/m/060d2</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>6386.json</td>\n",
+       "      <td>Jon Runyan</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>Member of Congress</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m/04q2pt</td>\n",
+       "      <td>/m/01gkgk</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>8676.json</td>\n",
+       "      <td>Rick Scott</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>governor</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m/0btx2g</td>\n",
+       "      <td>/m/05pd99q</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>8853.json</td>\n",
+       "      <td>Barack Obama</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>President of the United States</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m/02mjmr</td>\n",
+       "      <td>/m/060d2</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>10208.json</td>\n",
+       "      <td>Barack Obama</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>President of the United States</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m/02mjmr</td>\n",
+       "      <td>/m/060d2</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>11409.json</td>\n",
+       "      <td>Charlie Dent</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>Rep</td>\n",
+       "      <td>False</td>\n",
+       "      <td>/m/04fcm3</td>\n",
+       "      <td>/m/07r8g3</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>12994.json</td>\n",
+       "      <td>Bill Clinton</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>Secretary</td>\n",
+       "      <td>True</td>\n",
+       "      <td>/m/0157m</td>\n",
+       "      <td>/m/01fcnx</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>9891.json</td>\n",
+       "      <td>Lorne Michaels</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>Executive producer</td>\n",
+       "      <td>True</td>\n",
+       "      <td>/m/0p_2r</td>\n",
+       "      <td>Executive producer</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       text_id                  head  \\\n",
+       "50   2615.json            Lee Fisher   \n",
+       "51    592.json  United States Senate   \n",
+       "52  12745.json          Barack Obama   \n",
+       "53   6386.json            Jon Runyan   \n",
+       "54   8676.json            Rick Scott   \n",
+       "55   8853.json          Barack Obama   \n",
+       "56  10208.json          Barack Obama   \n",
+       "57  11409.json          Charlie Dent   \n",
+       "58  12994.json          Bill Clinton   \n",
+       "59   9891.json        Lorne Michaels   \n",
+       "\n",
+       "                                             relation  \\\n",
+       "50  people/person/employment_history./business/emp...   \n",
+       "51  organization/organization/headquarters./locati...   \n",
+       "52  people/person/employment_history./business/emp...   \n",
+       "53  people/person/employment_history./business/emp...   \n",
+       "54  people/person/employment_history./business/emp...   \n",
+       "55  people/person/employment_history./business/emp...   \n",
+       "56  people/person/employment_history./business/emp...   \n",
+       "57  people/person/employment_history./business/emp...   \n",
+       "58  people/person/employment_history./business/emp...   \n",
+       "59  people/person/employment_history./business/emp...   \n",
+       "\n",
+       "                              tail  label    fb_head             fb_tail  \\\n",
+       "50             Lieutenant governor  False  /m/034q8v            /m/0fkzq   \n",
+       "51                   United States  False   /m/07t58           /m/09c7w0   \n",
+       "52  President of the United States   True  /m/02mjmr            /m/060d2   \n",
+       "53              Member of Congress  False  /m/04q2pt           /m/01gkgk   \n",
+       "54                        governor  False  /m/0btx2g          /m/05pd99q   \n",
+       "55  President of the United States  False  /m/02mjmr            /m/060d2   \n",
+       "56  President of the United States  False  /m/02mjmr            /m/060d2   \n",
+       "57                             Rep  False  /m/04fcm3           /m/07r8g3   \n",
+       "58                       Secretary   True   /m/0157m           /m/01fcnx   \n",
+       "59              Executive producer   True   /m/0p_2r  Executive producer   \n",
+       "\n",
+       "    predicted_label  \n",
+       "50             True  \n",
+       "51            False  \n",
+       "52             True  \n",
+       "53             True  \n",
+       "54             True  \n",
+       "55             True  \n",
+       "56             True  \n",
+       "57             True  \n",
+       "58             True  \n",
+       "59             True  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predicted_raw_labels = [False if p == 0 else True for p in pred_classes]\n",
+    "predicted_raw_labels\n",
+    "LIAR['predicted_label'] = predicted_raw_labels\n",
+    "\n",
+    "LIAR[50:60]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Dataset exploration\n",
+    "We suspect some issues with classification performance stem from the quality of the LIAR RDF dataset. To futher understand the limitations of our model, we will try to explore what properties of the dataset make classification hard.  \n",
+    "\n",
+    "### Conflicting labels\n",
+    "Identical triples may have been extracted from both true and false documents, meaning they will exist in our dataset with conflicting labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. triples:  201\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([['/m/04tzmt',\n",
+       "        '/people/person/employment_history./business/employment_tenure/title',\n",
+       "        '/m/05pd99q']], dtype=object)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create array of triples [[h,r,t], [h,r,t], [h,r,t], ...]\n",
+    "triples = np.vstack((heads, relations, tails)).T\n",
+    "print('no. triples: ', len(triples))\n",
+    "triples[:1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. unique triples: 134\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('no. unique triples:', len(set([triple[0] + triple[1] + triple[2] for triple in triples])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "triple_dict = {}\n",
+    "conflicting_triples = []\n",
+    "for i in range(len(triples)):\n",
+    "    triple = tuple(triples[i])\n",
+    "    if triple in triple_dict:    \n",
+    "        if triple_dict[triple] != labels[i]:\n",
+    "            conflicting_triples.append(triple)\n",
+    "    else:\n",
+    "        triple_dict[triple] = labels[i]\n",
+    "conflicting_triples = set(conflicting_triples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/m/0f8t6k /people/person/employment_history./business/employment_tenure/title /m/05pd99q\n",
+      "/m/0271_s /people/person/employment_history./business/employment_tenure/title /m/05pd99q\n",
+      "/m/02mjmr /people/person/employment_history./business/employment_tenure/title /m/048zv9l\n",
+      "/m/0157m /people/person/employment_history./business/employment_tenure/title /m/060d2\n",
+      "/m/04tzmt /people/person/employment_history./business/employment_tenure/title /m/05pd99q\n",
+      "/m/02mjmr /people/person/employment_history./business/employment_tenure/title /m/060d2\n",
+      "/m/01fwm3 /organization/role/leaders./organization/leadership/person /m/07t58\n",
+      "\n",
+      "no. uniqe triples associated with both labels: 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "for triple in conflicting_triples:\n",
+    "    print(triple[0], triple[1], triple[2])\n",
+    "\n",
+    "print('\\nno. uniqe triples associated with both labels:', len(conflicting_triples))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### option 1: removing triples \n",
+    "remove all triples included in label conflict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. triples in cleaned dataset: 143\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_triples = []\n",
+    "cleaned_labels = []\n",
+    "for i in range(len(triples)):\n",
+    "    triple = triples[i]\n",
+    "    triple = tuple(triple)\n",
+    "    if triple not in conflicting_triples:\n",
+    "        cleaned_triples.append(triple)\n",
+    "        cleaned_labels.append(labels[i])\n",
+    "print('no. triples in cleaned dataset:', len(cleaned_triples))\n",
+    "\n",
+    "cleaned_triples = np.array(cleaned_triples)\n",
+    "cleaned_labels = torch.tensor(cleaned_labels, dtype=torch.int64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = np.vsplit(cleaned_triples.T, 3)\n",
+    "\n",
+    "cleaned_heads = t[0].flatten()\n",
+    "cleaned_relations = t[1].flatten()\n",
+    "cleaned_tails = t[2].flatten()\n",
+    "\n",
+    "\n",
+    "cleaned_le_heads = le_entity.transform(cleaned_heads)\n",
+    "cleaned_le_tails = le_entity.transform(cleaned_tails)\n",
+    "cleaned_edge_index = torch.tensor([cleaned_le_heads, cleaned_le_tails], dtype=torch.long)\n",
+    "\n",
+    "cleaned_le_relations = le_relation.transform(cleaned_relations)\n",
+    "cleaned_edge_type = torch.tensor(cleaned_le_relations, dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ap: 0.40124318478274923 , auc: 0.47045454545454546\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_pred = model.lp_score(z, cleaned_edge_index, cleaned_edge_type)\n",
+    "lp_auc, lp_ap = model.lp_test(cleaned_pred, cleaned_labels)\n",
+    "\n",
+    "print('ap:', lp_ap, ', auc:', lp_auc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 15.7264,  20.5741,  13.8190,  14.3914,   7.0685,  15.9150,   6.6974,\n",
+       "         15.0100,  13.5818,  13.0623, -10.5703,  15.8250,  15.0079,  15.0079,\n",
+       "         21.1873,  15.0079,  13.4696,   6.5500,  15.8451,   6.5895,  15.8620,\n",
+       "          9.0786,  15.7282,  13.8719,  16.3676,  16.3170,  16.1365,   0.1115,\n",
+       "         14.6679,  20.6207,  15.8791,  15.8339,  -2.6281,  13.5036,  20.5372,\n",
+       "         15.7612,  15.7553,  11.8745,  27.8678,  14.4203,  15.8517,  15.8877,\n",
+       "         14.9755,   4.3363,  17.0142,  15.8395,  15.7869,   7.7396,  15.8315,\n",
+       "         11.5955,  15.8912,  15.8711,  26.0412,   7.2123,   0.4710,  14.4164,\n",
+       "         15.8731,  15.8581,  16.4693,   6.4843,   6.3987,  15.8082,  15.7058,\n",
+       "          6.4677,   7.9754,   7.9754,  -1.7726,   6.0192,  -1.7726,  -1.7726,\n",
+       "         14.2982,   7.8923,   7.8923,  15.7562,  15.7310,  14.8623,  13.4865,\n",
+       "         15.6548,  15.6548,   3.4196,  28.1789,  15.8985,  20.5741,   6.7694,\n",
+       "         14.5086,  15.8689,  15.7915,   6.3969,  27.6602,  15.8466,  15.8466,\n",
+       "         10.2443,  16.4087,  16.4087,  -1.1832,   5.2127,  16.3929,  15.3436,\n",
+       "         15.8921,  16.5208,  15.8947,  15.7964,  15.6279,  20.5090,  15.8354,\n",
+       "          6.4793,  11.1237,  14.5073,  -2.6281,  12.3574,  16.4336,  15.7818,\n",
+       "         15.0437,  16.5219,  15.8464,  15.7287,  16.6142,  15.9446,  16.5642,\n",
+       "         16.3135,  16.3135,  15.7272,  10.5168,  14.3288,   4.3319,  15.6679,\n",
+       "         14.4164,  15.6818,  15.6818,  15.7869,  15.8637,  16.4949,  16.0811,\n",
+       "         13.8916,  16.0811,  16.5949,  15.8468,  15.8237,  -1.3501,  15.8692,\n",
+       "         -1.4290,  15.8763,   2.9085], grad_fn=<SqueezeBackward1>)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1])"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_pred_classes = torch.tensor([0 if p < 0 else 1 for p in cleaned_pred], dtype=torch.int64)\n",
+    "cleaned_pred_classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3880597014925373"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(cleaned_labels, cleaned_pred_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6, 82],\n",
+       "       [ 3, 52]])"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_cm = confusion_matrix(cleaned_labels, cleaned_pred_classes)\n",
+    "cleaned_cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7f4e4136d0>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cleaned_cm, display_labels=['false', 'true']) #usikker på rekkefølgen her\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### option 2: Manually change triple label of conflicting "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dataset similarities to fb15k\n",
+    "Many entities from the fake news datasets have not been seen in the original FB15k knowledge graph. How does this affetc the performance of the classification?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. unique entities in fake news dataset: 155\n",
+      "no. new entities in fake news dataset: 108\n"
+     ]
+    }
+   ],
+   "source": [
+    "#\n",
+    "print('no. unique entities in fake news dataset:', len(dataset_entities))\n",
+    "print('no. new entities in fake news dataset:', len(new_entities))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that only 30% of entities in the fake news dataset are in the original FB15k knowledge graph.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. triples with known entities: 61\n"
+     ]
+    }
+   ],
+   "source": [
+    "known_triples = []\n",
+    "known_labels = []\n",
+    "for i in range(len(triples)):\n",
+    "    triple = triples[i]\n",
+    "    if triple[0] not in new_entities and triple[2] not in new_entities:\n",
+    "        known_triples.append(tuple(triple))\n",
+    "        known_labels.append(labels[i])\n",
+    "print('no. triples with known entities:', len(known_triples))\n",
+    "\n",
+    "known_triples = np.array(known_triples)\n",
+    "known_labels = torch.tensor(known_labels, dtype=torch.int64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kt = np.vsplit(known_triples.T, 3)\n",
+    "\n",
+    "known_heads = kt[0].flatten()\n",
+    "known_relations = kt[1].flatten()\n",
+    "known_tails = kt[2].flatten()\n",
+    "\n",
+    "\n",
+    "known_le_heads = le_entity.transform(known_heads)\n",
+    "known_le_tails = le_entity.transform(known_tails)\n",
+    "known_edge_index = torch.tensor([known_le_heads, known_le_tails], dtype=torch.long)\n",
+    "\n",
+    "known_le_relations = le_relation.transform(known_relations)\n",
+    "known_edge_type = torch.tensor(known_le_relations, dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[  137,  4792,   466,  4792,  4792,  4792,  4792, 10187,  4792,  4792,\n",
+       "         10782,  4792,  4792, 10190,  4792,  4792,  4792,  4792,   466,  4792,\n",
+       "          4792,  4792, 11775,  4792,  4792,  4792,  4792,  4792,  4792,  4792,\n",
+       "          6589,  4792,  4792,  4792,  4792,  4792,  4792,  4792, 11187,  4792,\n",
+       "          4792,  5627,  4792,  4792,  4792,  4792,  4792,  4792,  4792,  4792,\n",
+       "          4792,  4792,  4792,  4792, 10807,  7185, 10807, 11999,  4792,  4792,\n",
+       "          4792],\n",
+       "        [10338,  9034,  9034,  9034,  9034,  9034,   819, 10807,  9034,  9034,\n",
+       "          9034,  9034,  9034, 10807,  9034,  9034,  9034,  9034,  9034,  4583,\n",
+       "          9034,  9034, 11945,  9034,  9034,  9034,  9034,  9034,  9034,  6589,\n",
+       "          6817,  6589,  6589,  9034,  9034,  9034,  9034,  9034, 10807,  9034,\n",
+       "          9034, 11226,  9034, 11944,  9034,  9034,  9034,  9034,  9034,  9034,\n",
+       "          9034,  9034,  9034,  9034,  7185,  9034,  7185,  9034,  9034,  9034,\n",
+       "         10807]])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "known_edge_index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3673469387755102"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "known_pred = model.lp_score(z, known_edge_index, known_edge_type)\n",
+    "known_pred_classes = torch.tensor([0 if p < 0 else 1 for p in known_pred], dtype=torch.int64)\n",
+    "\n",
+    "precision_score(known_labels, known_pred_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How about performance on triples with only unseen entities:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. triples with known entities: 76\n"
+     ]
+    }
+   ],
+   "source": [
+    "unknown_triples = []\n",
+    "unknown_labels = []\n",
+    "for i in range(len(triples)):\n",
+    "    triple = triples[i]\n",
+    "    if triple[0] in new_entities and triple[2] in new_entities:\n",
+    "        unknown_triples.append(tuple(triple))\n",
+    "        unknown_labels.append(labels[i])\n",
+    "print('no. triples with known entities:', len(unknown_triples))\n",
+    "\n",
+    "unknown_triples = np.array(unknown_triples)\n",
+    "unknown_labels = torch.tensor(unknown_labels, dtype=torch.int64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ukt = np.vsplit(unknown_triples.T, 3)\n",
+    "\n",
+    "unknown_heads = ukt[0].flatten()\n",
+    "unknown_relations = ukt[1].flatten()\n",
+    "unknown_tails = ukt[2].flatten()\n",
+    "\n",
+    "\n",
+    "unknown_le_heads = le_entity.transform(unknown_heads)\n",
+    "unknown_le_tails = le_entity.transform(unknown_tails)\n",
+    "unknown_edge_index = torch.tensor([unknown_le_heads, unknown_le_tails], dtype=torch.long)\n",
+    "\n",
+    "unknown_le_relations = le_relation.transform(unknown_relations)\n",
+    "unknown_edge_type = torch.tensor(unknown_le_relations, dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.42105263157894735"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unknown_pred = model.lp_score(z, unknown_edge_index, unknown_edge_type)\n",
+    "unknown_pred_classes = torch.tensor([0 if p < 0 else 1 for p in unknown_pred], dtype=torch.int64)\n",
+    "\n",
+    "precision_score(unknown_labels, unknown_pred_classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fake News Net\n",
+    "## Step 1: Import triple extracted dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>head</th>\n",
+       "      <th>relation</th>\n",
+       "      <th>tail</th>\n",
+       "      <th>fb_head</th>\n",
+       "      <th>fb_tail</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>text_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>William H. Seward</td>\n",
+       "      <td>organization/role/leaders./organization/leader...</td>\n",
+       "      <td>Republican Party</td>\n",
+       "      <td>/m/0k_2z</td>\n",
+       "      <td>/m/085srz</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>John Tyler</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>President of the United States</td>\n",
+       "      <td>/m/042dk</td>\n",
+       "      <td>/m/02mjmr</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>Donald Trump</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>President of the United States</td>\n",
+       "      <td>/m/0cqt90</td>\n",
+       "      <td>/m/02mjmr</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>Hillary Clinton</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>President of the United States</td>\n",
+       "      <td>/m/0d06m5</td>\n",
+       "      <td>/m/02mjmr</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>Donald Trump</td>\n",
+       "      <td>people/person/employment_history./business/emp...</td>\n",
+       "      <td>King</td>\n",
+       "      <td>/m/0cqt90</td>\n",
+       "      <td>/m/03w9bnr</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      head                                           relation  \\\n",
+       "text_id                                                                         \n",
+       "415      William H. Seward  organization/role/leaders./organization/leader...   \n",
+       "415             John Tyler  people/person/employment_history./business/emp...   \n",
+       "415           Donald Trump  people/person/employment_history./business/emp...   \n",
+       "415        Hillary Clinton  people/person/employment_history./business/emp...   \n",
+       "416           Donald Trump  people/person/employment_history./business/emp...   \n",
+       "\n",
+       "                                   tail    fb_head     fb_tail  label  \n",
+       "text_id                                                                \n",
+       "415                    Republican Party   /m/0k_2z   /m/085srz   True  \n",
+       "415      President of the United States   /m/042dk   /m/02mjmr   True  \n",
+       "415      President of the United States  /m/0cqt90   /m/02mjmr   True  \n",
+       "415      President of the United States  /m/0d06m5   /m/02mjmr   True  \n",
+       "416                                King  /m/0cqt90  /m/03w9bnr   True  "
+      ]
+     },
+     "execution_count": 237,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#load test data\n",
+    "FNN = pd.read_csv(\n",
+    "        \"./data/FakeNewsNet/train.csv\",\n",
+    "        sep=\",\",\n",
+    "        header=0,\n",
+    "        engine=\"python\",\n",
+    "        index_col=0\n",
+    "    )\n",
+    "\n",
+    "FNN = FNN.set_index('text_id')\n",
+    "\n",
+    "FNN.tail(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/organization/role/leaders./organization/leadership/person'"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heads = FNN[\"fb_head\"]\n",
+    "tails = FNN[\"fb_tail\"]\n",
+    "relations = FNN[\"relation\"]\n",
+    "\n",
+    "relations = [\"/\" + rel for rel in relations]\n",
+    "\n",
+    "relations[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. unique entities (original): 14951\n",
+      "no. unique entities in dataset: 682\n",
+      "no. unseen entities in dataset: 542\n",
+      "no. unique entities (updated): 15493\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "#construct entity encoder with original and new entity ids\n",
+    "entity_id = pd.read_csv('data/FB15k/entities.txt', sep='\\t', header=None, names=['entity', 'id'], engine='python')\n",
+    "model_entities = entity_id['entity'].values\n",
+    "print('no. unique entities (original):', len(model_entities))\n",
+    "\n",
+    "# add unseen entities from fake news dataset\n",
+    "dataset_entities = np.concatenate([np.array(heads), np.array(tails)])\n",
+    "dataset_entities =  np.unique(dataset_entities)\n",
+    "print('no. unique entities in dataset:', len(dataset_entities))\n",
+    "new_entities = np.array([ent for ent in dataset_entities if not ent in model_entities])\n",
+    "print('no. unseen entities in dataset:', len(new_entities))\n",
+    "\n",
+    "all_entities = np.concatenate((model_entities, new_entities), axis=0)\n",
+    "print('no. unique entities (updated):', len(all_entities))\n",
+    "\n",
+    "print('/m/01fnkt' in new_entities)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fit encoder\n",
+    "le_entity_fnn = LabelEncoder()\n",
+    "le_entity_fnn.classes_ = np.load(path.join('fn_embeddings','le_entity_classes.npy'), allow_pickle=True)\n",
+    "#load relation encoder\n",
+    "le_relation_fnn = LabelEncoder()\n",
+    "le_relation_fnn.classes_ = np.load(path.join('fn_embeddings','le_relation_classes.npy'), allow_pickle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1577])"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "le_entity_fnn.transform(['/m/01fnkt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "le_heads = le_entity_fnn.transform(heads)\n",
+    "le_tails = le_entity_fnn.transform(tails)\n",
+    "edge_index = torch.tensor([le_heads, le_tails], dtype=torch.long)\n",
+    "\n",
+    "le_relations = le_relation_fnn.transform(relations)\n",
+    "edge_type = torch.tensor(le_relations, dtype=torch.long)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no. true: 589\n",
+      "no. false: 411\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create and 'encode' lables\n",
+    "raw_labels = np.array(FNN[\"label\"])\n",
+    "false = 0\n",
+    "true = 0\n",
+    "for l in raw_labels:\n",
+    "    if l == False:\n",
+    "        false += 1\n",
+    "    else:\n",
+    "        true += 1\n",
+    "print('no. true:', true)\n",
+    "print('no. false:', false)\n",
+    "labels = torch.tensor([0 if l == False else 1 for l in raw_labels], dtype=torch.int64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([16035, 256])"
+      ]
+     },
+     "execution_count": 252,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = torch.load(\"output/FB15k_237_CPU_20epochs.pkl\", map_location=torch.device('cpu'))\n",
+    "\n",
+    "model.z.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred = model.lp_score(model.z, edge_index, edge_type)\n",
+    "pred_classes = torch.tensor([0 if p < 0 else 1 for p in pred], dtype=torch.int64)\n",
+    "\n",
+    "cm = confusion_matrix(labels, pred_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7efec8c490>"
+      ]
+     },
+     "execution_count": 254,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['false', 'true']) #usikker på rekkefølgen her\n",
+    "disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Triple validity prediction vs its documents label: 0.5933333333333334\n"
+     ]
+    }
+   ],
+   "source": [
+    "triple_classification_acc = precision_score(labels, pred_classes)\n",
+    "\n",
+    "print('Triple validity prediction vs its documents label:', triple_classification_acc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Document classification\n",
+    "The fist strategy will be to assign the lable 'False' to a document as long as one false triple was observed in it.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(280,)"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text_pred_dict = {}\n",
+    "for i in range(edge_index.shape[1]):\n",
+    "    text_id = FNN.index[i]\n",
+    "    pred_class = pred_classes[i]\n",
+    "    if text_id in text_pred_dict:\n",
+    "        if pred_class == 0:\n",
+    "            text_pred_dict[text_id] = pred_class\n",
+    "    else:\n",
+    "        text_pred_dict[text_id] = pred_class\n",
+    "        \n",
+    "pred_text_labels = np.array(list(text_pred_dict.values())).flatten()\n",
+    "pred_text_labels.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentage of true documents: 0.5321428571428571\n"
+     ]
+    }
+   ],
+   "source": [
+    "text_labels = FNN.groupby('text_id').mean().values.flatten()\n",
+    "\n",
+    "print('Percentage of true documents:', text_labels.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5228758169934641"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "precision_score(text_labels, pred_text_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7f7b8de110>"
+      ]
+     },
+     "execution_count": 251,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "text_cm = confusion_matrix(text_labels, pred_text_labels)\n",
+    "text_disp = ConfusionMatrixDisplay(confusion_matrix=text_cm, display_labels=['false', 'true']) #usikker på rekkefølgen her\n",
+    "text_disp.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env37test",
+   "language": "python",
+   "name": "env37test"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/FakeNewsNet/gs_train.csv b/data/FakeNewsNet/gs_train.csv
new file mode 100644
index 0000000..0c369dd
--- /dev/null
+++ b/data/FakeNewsNet/gs_train.csv
@@ -0,0 +1,113 @@
+text_id,head,relation,tail,fb_head,fb_relation,fb_tail,label
+280,Jefferson Davis monument,located in,New Orleans,Jefferson Davis monument,/location/location/containedby,/m/0f2tj,FALSE
+280,Jefferson Davis,president of,Confederate States of America,/m/043q0,/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office,/m/020d5,FALSE
+280,Mitch Landrieu,officeholder in,New Orleans,/m/05xdp5,/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office,/m/0f2tj,FALSE
+280,Mitch Landrieu,title,Mayor,/m/05xdp5,/people/person/employment_history./business/employment_tenure/title,/m/0pqc5,FALSE
+280,Barack Obama,role,US president,/m/02mjmr,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/060d2,FALSE
+280,Barack Obama,ethnicity,African American,/m/02mjmr,/people/person/ethnicity,/m/0x67,FALSE
+280,Beaureguard BillyBob Johnson,supports,Confederate States of America,Beaureguard BillyBob Johnson,/base/popstra/organization/supporter./base/popstra/support/supporter,/m/020d5,FALSE
+280,Fox news,reports on ,Black Lives Matter,/m/02z_b,/base/newsevents/news_reporting_organisation/news_reports./base/newsevents/news_report/event,/m/012hdhkj,FALSE
+282,Andrew Jackson,role,US president,/m/0rlz,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/060d2,FALSE
+282,American Civil War,reports on ?,Donald Trump,/m/0cqt90,/base/newsevents/news_reporting_organisation/news_reports./base/newsevents/news_report/event,/m/0kbq,FALSE
+282,Donald Trump,member of,Party of Lincoln (republican party),/m/0cqt90,/government/politician/party./government/political_party_tenure/party,/m/07wbk,FALSE
+282,Andrew Jackson,asociated with,Democratic Party,/m/0rlz,/government/politician/party./government/political_party_tenure/party,/m/0d075m,FALSE
+282,Donald Trump,fan of,Kentucky Fried Chicken,/m/0cqt90,/food/diet_follower/follows_diet,/m/09b6t,FALSE
+282,The Red Shtick,intern,Dave Robicheaux,The Red Shtick,/business/employer/employees./business/employment_tenure/person,/m/04lcmzy,FALSE
+282,American Civil War,involved person,Colonel Sanders,/m/0kbq,/military/military_conflict/military_personnel_involved,/m/09b78,FALSE
+282,Colonel Sanders,is,hero,/m/09b78,"/people/profession/specializations , /people/person/profession",/m/03kll,FALSE
+286,Donald Trump,role,US president,/m/0cqt90,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/060d2,FALSE
+286,United States House Committee on Oversight and Government Reform,chairman ,Jason Chaffetz,United States House Committee on Oversight and Government Reform,/organization/role/leaders./organization/leadership/person,/m/047blr4,FALSE
+286,FBI,director (former),James Comey,/m/02_1m,/organization/role/leaders./organization/leadership/person,/m/06r04p,FALSE
+286,Hillary Clinton,commits,treason,/m/0d06m5,/base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of,/m/07ppd,FALSE
+288,Freedom Crossroads,employee,Louis Leweigh,Freedom Crossroads,/business/employer/employees./business/employment_tenure/person,Louis Leweigh,FALSE
+288,Louis Leweigh,occupation,Correspondent,Louis Leweigh,/people/person/profession,/m/02k13d,FALSE
+288,Monica Lewinsky,affair,Bill Clinton,/m/0509p,/celebrities/celebrity/sexual_relationships./celebrities/romantic_relationship/celebrity,/m/0157m,FALSE
+288,Bill Clinton,involved person,Monica Lewinsky,/m/0157m,/people/cause_of_death/people,/m/0509p,FALSE
+288,Hillary Clinton,involved person,Monica Lewinsky,/m/0d06m5,/people/cause_of_death/people,/m/0509p,FALSE
+288,Monica Lewinsky,murdered in,burglary,/m/0509p,/people/deceased_person/cause_of_death,/m/016x3s,FALSE
+295,public servant (government official),people with role,Maxine Waters,/m/035y33,/people/profession/people_with_this_profession,/m/024tyk,FALSE
+295,Donald Trump,role,US president,/m/0cqt90,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/060d2,FALSE
+295,Donald Trump,asociated with,Russia,/m/0cqt90,/base/popstra/celebrity/endorsements./base/popstra/paid_support/company,/m/06bnz,FALSE
+295,Maxine Waters,medical condition,Dementia,/m/024tyk,/medicine/notable_person_with_medical_condition/condition,/m/09klv,FALSE
+295,Maxine Waters,asociated with,Democratic Party,/m/024tyk,/government/politician/party./government/political_party_tenure/party,/m/0d075m,FALSE
+295,Maxine Waters,part of,United States Congress,/m/024tyk,/people/appointed_role/appointment./people/appointment/appointed_by,/m/07t31,FALSE
+295,Maxine Waters,ethnicity,African American,/m/024tyk,/people/person/ethnicity,/m/0x67,FALSE
+295,1tch,employee,Teddy Stick,1tch,/business/employer/employees./business/employment_tenure/person,Teddy Stick,FALSE
+300,Great Recession,affected negatively,the poor,/m/03qk63k,/influence/influence_node/influenced,the poor,TRUE
+303,Jean Shepard,member of,Grand Ole Opry,/m/01npnk3,/organization/membership_organization/members./organization/organization_membership/member,/m/0gg86,TRUE
+303,Jean Shepard,inducted,Country Music Hall of Fame,/m/01npnk3,/award/hall_of_fame_inductee/hall_of_fame_inductions./award/hall_of_fame_induction/hall_of_fame,/m/0ggkm,TRUE
+303,Country Music Hall of Fame,hall_of_fame_dicipline,music,/m/0ggkm,/award/hall_of_fame/discipline,/m/04rlf,TRUE
+303,Jean Shepard,medical condition,Parkinsons,/m/01npnk3,/medicine/notable_person_with_medical_condition/condition,/m/0g02vk,TRUE
+303,Jean Shepard,born in,Oklahoma,/m/01npnk3,/people/person/place_of_birth,/m/05mph,TRUE
+303,Jean Shepard,lived in,"Visalia, California",/m/01npnk3,/people/person/places_lived./people/place_lived/location,/m/0r80l,TRUE
+303,Jean Shepard,part of band,Melody Ranch Girls,/m/01npnk3,/music/musical_group/member./music/group_membership/member,Melody Ranch Girls,TRUE
+303,Hank Thompson,music genre,Country Music,/m/01k3fvw,/music/artist/genre,/m/01lyv,TRUE
+303,singer,people with profession,Jean Shepard,/m/09l65,/people/profession/people_with_this_profession,/m/01npnk3,TRUE
+303,bassist,musical profession,Jean Shepard,/m/0gbbt,/people/profession/people_with_this_profession,/m/01npnk3,TRUE
+307,Tim Kaine,associated with,Democratic Party,/m/053f8h,/government/politician/party./government/political_party_tenure/party,/m/0d075m,TRUE
+307,Hillary Clinton,appoint,Tim Kaine,/m/0d06m5,/people/appointer/appointment_made./people/appointment/appointee,/m/053f8h,TRUE
+307,Tim Kaine,role,Senator,/m/053f8h,/people/person/employment_history./business/employment_tenure/title,/m/048zv9l,TRUE
+307,Tim Kaine,lives in ,Virginia,/m/053f8h,/people/person/places_lived./people/place_lived/location,/m/07z1m,TRUE
+307,Virginia,officeholder,Tim Kaine,/m/07z1m,/government/political_district/representatives./government/government_position_held/office_holder,/m/053f8h,TRUE
+307,Mike Pence,asociated with ,Donald Trump,/m/022r9r,/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend,/m/0cqt90,TRUE
+308,Hofstra University,located in,New York,/m/02607j,/location/location/containedby,/m/02_286,TRUE
+308,Not Who We Are campaign,campain at location,Hofstra University,Not Who We Are campaign,/location/location/containedby,/m/02607j,TRUE
+308,Donald Trump,is part of ,racism,/m/0cqt90,/base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of,/m/06d4h,TRUE
+308,Not Who We Are campaign,manager of,Josh Hendler,Not Who We Are campaign,/organization/role/leaders./organization/leadership/person,Josh Hendler,TRUE
+308,Rob Bielunas,student at,Hofstra University,Rob Bielunas,/people/person/education./education/education/institution,/m/02607j,TRUE
+308,Donald Trump,role,presidential candidate,/m/0cqt90,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/066q5c,TRUE
+328,Donald Trump,is part of,racism,/m/0cqt90,/base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of,/m/06d4h,TRUE
+328,Barack Obama,role,US president,/m/02mjmr,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/060d2,TRUE
+328,Barack Obama,born in,Hawaii,/m/02mjmr,/people/person/place_of_birth,/m/03gh4,TRUE
+328,Lester Holt,profession,moderator,/m/06jvj7,/people/person/profession,moderator,TRUE
+328,Donald Trump,role,presidential candidate,/m/0cqt90,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/066q5c,TRUE
+328,Donald Trump,associated with,Republican Party,/m/0cqt90,/government/politician/party./government/political_party_tenure/party,/m/07wbk,TRUE
+328,Donald Trump,role,presidential candidate,/m/0cqt90,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/066q5c,TRUE
+328,Hillary Clinton,role,presidential candidate,/m/0d06m5,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/066q5c,TRUE
+2,HIVAIDs drug,cause,death,/m/01cw8w,/medicine/disease/causes,/m/0294j,FALSE
+2,Bill Clinton,associates with,Ranbaxy,/m/0157m,/base/popstra/celebrity/endorsements./base/popstra/paid_support/company,/m/0243xw,FALSE
+2,Bill Clinton,Chairman,Clinton Health Access Initiative,/m/0157m,/organization/organization/board_members./organization/organization_board_membership/member,Clinton Health Access Initiative,FALSE
+2,Bill Clinton,involved with,corruption,/m/0157m,/base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of,/m/09pngm,FALSE
+3,Donald Trump,encourage ,manslaugther,/m/0cqt90,/influence/influence_node/influenced,/m/0x2gl,FALSE
+3,Donald Trump,ethnicity,Oompa Loompa,/m/0cqt90,/fictional_universe/fictional_character/species,Oompa Loompa,FALSE
+3,Donald Trump,supports,concentration camps,/m/0cqt90,/base/popstra/celebrity/supporter./base/popstra/support/supported_organization,/m/01b5_d,FALSE
+8,Hillary Clinton,collaboration,CNN,/m/0d06m5,/influence/influence_node/influenced_by,/m/0gsgr,FALSE
+8,Hillary Clinton,medical condition,Parkinsons disease,/m/0d06m5,/medicine/notable_person_with_medical_condition/condition,/m/0g02vk,FALSE
+8,Hillary Clinton,involved with,corruption,/m/0d06m5,/influence/influence_node/influenced,/m/09pngm,FALSE
+8,Democratic Party,supports,slavery,/m/0d075m,/base/popstra/celebrity/supporter./base/popstra/support/supported_organization,/m/06z49,FALSE
+8,Republican Party,supports,emancipation,/m/0d075m,/base/popstra/celebrity/supporter./base/popstra/support/supported_organization,/m/0dtbg9,FALSE
+8,Bill Clinton,is,sexual predator,/m/0157m,/base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of,/m/01fdjk,FALSE
+8,Hillary Clinton,is,racism,/m/0d06m5,/base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of,/m/06d4h,FALSE
+11,Barack Obama,created,ISIS,/m/02mjmr,/organization/organization_founder/organizations_founded,/m/027x630,FALSE
+11,Europe,supports,ISIS,/m/02j9z,/base/popstra/celebrity/supporter./base/popstra/support/supported_organization,/m/027x630,FALSE
+11,The United States,supports,ISIS,/m/09c7w0,/base/popstra/celebrity/supporter./base/popstra/support/supported_organization,/m/027x630,FALSE
+14,ISIS,creator,Barack Obama,/m/027x630,/organization/organization/founders,/m/02mjmr,FALSE
+14,Barack Obama,appear at,Coachella,/m/02mjmr,/projects/project_role/projects./projects/project_participation/participant,/m/02h71z,FALSE
+14,Michelle Obama,medical condition,sleep apnea,/m/025s5v9,/medicine/notable_person_with_medical_condition/condition,/m/071d3,FALSE
+14,Barack Obama,profession,terrorism,/m/02mjmr,/people/person/profession,/m/07jq_,FALSE
+14,Barack Obama,place of birth,Kenya,/m/02mjmr,/people/person/place_of_birth,/m/019rg5,FALSE
+14,Barack Obama,religion,Islam,/m/02mjmr,/people/person/religion,/m/0flw86,FALSE
+14,Barack Obama,guilty of,crime of possessing illegal drugs,/m/02mjmr,/base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of,/m/05cxz3,FALSE
+14,Hillary Clinton,guilty of,crime of possessing illegal drugs,/m/0d06m5,/base/fight/crime_type/people_convicted_of_this_crime./base/crime/criminal_conviction/guilty_of,/m/05cxz3,FALSE
+119,Megan Rapinoe,profession,soccer player,/m/05mz656,/people/person/profession,/m/0pcq81q,TRUE
+119,Megan Rapinoe,supports,BLM,/m/05mz656,/base/popstra/celebrity/supporter./base/popstra/support/supported_organization,/m/012hdhkj,TRUE
+119,Colin Kaepernick,profession,american football player,/m/03cm6b3,/people/person/profession,/m/02h665k,TRUE
+119,Colin Kaepernick,supports,BLM,/m/03cm6b3,/base/popstra/celebrity/supporter./base/popstra/support/supported_organization,/m/012hdhkj,TRUE
+138,Barry Marshall,nobel prize in,medicine,/m/023fbk,/base/nobelprizes/nobel_subject_area/nobel_awards./base/nobelprizes/nobel_honor/nobel_prize_winner,/m/04sh3,TRUE
+138,Jonas Salk,pioneer,vaccine,/m/0g60g,/law/inventor/inventions,/m/07__7,TRUE
+138,Michael Oreskes,profession,News Director,Michael Oreskes,/people/person/profession,/m/014l7h,TRUE
+173,Joa Arpaio,profession,Sheriff,/m/0fzdz7,/people/person/profession,m/0mb31,TRUE
+173,Donald Trump,associated with,GOP (republican party),/m/0cqt90,/government/politician/party./government/political_party_tenure/party,/m/07wbk,TRUE
+173,Donald Trump,role,presidential candidate,/m/0cqt90,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/066q5c,TRUE
+173,Barack Obama,born in,Hawaii,/m/02mjmr,/people/person/place_of_birth,/m/03gh4,TRUE
+174,Benjamin Netanyahu,profession,prime minister,/m/0fm2h,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/060bp,TRUE
+174,Benjamin Netanyahu,place of birth,Israel,/m/0fm2h,/people/person/place_of_birth,/m/03spz,TRUE
+174,Hillary Clinton,associated with,Democratic Party,/m/0d06m5,/government/politician/party./government/political_party_tenure/party,/m/0d075m,TRUE
+174,Hillary Clinton,role,presidential candidate,/m/0d06m5,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/066q5c,TRUE
+178,Ben LaBolt,press secretary,Obamas 2012 reelection campaign,Ben LaBolt,/projects/project_role/projects./projects/project_participation/participant,/m/0gk_h41,TRUE
+178,Josh Ernest,press secretary,Obamas 2012 reelection campaign,/m/0l8ps43,/projects/project_role/projects./projects/project_participation/participant,/m/081sq,TRUE
+178,Hillary Clinton,associated with,Democratic Party,/m/0d06m5,/government/politician/party./government/political_party_tenure/party,/m/0d075m,TRUE
+178,Hillary Clinton,role,presidential candidate,/m/0d06m5,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/066q5c,TRUE
+178,Barack Obama,profession,president,/m/02mjmr,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/060d2,TRUE
+178,Donald Trump,associated with,Republican Party,/m/0cqt90,/government/politician/party./government/political_party_tenure/party,/m/07wbk,TRUE
+178,Donald Trump,role,presidential candidate,/m/0cqt90,/government/politician/government_positions_held./government/government_position_held/office_position_or_title,/m/066q5c,TRUE
\ No newline at end of file
diff --git a/data/FakeNewsNet/train.csv b/data/FakeNewsNet/train.csv
new file mode 100644
index 0000000..aa2b1f6
--- /dev/null
+++ b/data/FakeNewsNet/train.csv
@@ -0,0 +1,1001 @@
+,text_id,head,relation,tail,fb_head,fb_tail,label
+0,0,Bill Clinton,organization/role/leaders./organization/leadership/person,NPR,/m/0157m,/m/0c0sl,False
+1,1,Bill Clinton,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/0157m,/m/02mjmr,False
+2,1,Marsha Blackburn,people/person/employment_history./business/employment_tenure/title,Rep,/m/01fnkt,/m/02qkv0r,False
+3,1,Roger Bate,organization/role/leaders./organization/leadership/person,American Enterprise Institute,/m/0b23zg,/m/0p8q4,False
+4,1,Hillary Clinton,people/person/employment_history./business/employment_tenure/title,Candidate,/m/0d06m5,/m/07r9r0,False
+5,1,Rod Rosenstein,organization/role/leaders./organization/leadership/person,Rod Rosenstein,/m/04y7p0_,/m/04y7p0_,False
+6,1,Rod Rosenstein,people/person/employment_history./business/employment_tenure/title,Attorney,/m/04y7p0_,/m/04gc2,False
+7,1,Barack Obama,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/0h7q0rq,/m/02mjmr,False
+8,3,Barack Obama,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/0h7q0rq,/m/02mjmr,False
+9,3,HES,organization/role/leaders./organization/leadership/person,Getty+ImagesJoe+Raedle,HES,Getty+ImagesJoe+Raedle,False
+10,5,Barack Obama,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/0h7q0rq,/m/02mjmr,False
+11,5,Barack Obama,organization/role/leaders./organization/leadership/person,United Nations,/m/0h7q0rq,/m/07t65,False
+12,5,Barack Obama,people/person/employment_history./business/employment_tenure/title,Alex+Pfeiffer,/m/0h7q0rq,Alex+Pfeiffer,False
+13,5,Barack Obama,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/0h7q0rq,/m/02mjmr,False
+14,8,Barack Obama,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/0h7q0rq,/m/02mjmr,False
+15,8,Bill Clinton,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/0157m,/m/02mjmr,False
+16,10,Barack Obama,organization/role/leaders./organization/leadership/person,Islamic State of Iraq and the Levant,/m/0h7q0rq,/m/0h7q0rq,False
+17,11,Bashar al-Assad,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/01_vwx,/m/02mjmr,False
+18,11,Nafeez Ahmed,people/person/employment_history./business/employment_tenure/title,Journalist,/m/0cb1tz,/m/0d8qb,False
+19,11,Islamic State of Iraq and the Levant,organization/organization/headquarters./location/mailing_address/country,Iraq,/m/0h7q0rq,/m/0v74,False
+20,13,United States Secret Service,organization/role/leaders./organization/leadership/person,Hillary Clinton,/m/0fynw,/m/0d06m5,False
+21,13,Hillary Clinton,organization/role/leaders./organization/leadership/person,United States Secret Service,/m/0d06m5,/m/0fynw,False
+22,14,Barack Obama,people/person/places_lived./people/place_lived/location,The West Wing,/m/0h7q0rq,/m/0g60z,False
+23,14,Barack Obama,people/person/places_lived./people/place_lived/location,The West Wing,/m/0h7q0rq,/m/0g60z,False
+24,14,United States Secret Service,organization/role/leaders./organization/leadership/person,Barack Obama,/m/0fynw,/m/0h7q0rq,False
+25,14,Islamic State of Iraq and the Levant,organization/role/leaders./organization/leadership/person,Barack Obama,/m/0h7q0rq,/m/0h7q0rq,False
+26,14,Jodie Foster,people/person/places_lived./people/place_lived/location,Nell,/m/0chw_,/m/03m7gdr,False
+27,15,Barack Obama,people/person/employment_history./business/employment_tenure/title,President of the United States,/m/0h7q0rq,/m/02mjmr,False
+28,15,Bill Clinton,people/person/employment_history./business/employment_tenure/title,Secretary,/m/0157m,/m/06slz5,False
+29,16,Raymond T. Odierno,people/person/employment_history./business/employment_tenure/title,Chief of Staff of the United States Army,/m/0268g3w,/m/02g5lp,False
+30,17,Ted Cruz,people/person/employment_history./business/employment_tenure/title,Senator,/m/07j6ty,/m/04c_3b,False
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diff --git a/data/FakeNewsNet/train_both.csv b/data/FakeNewsNet/train_both.csv
new file mode 100644
index 0000000..2126487
--- /dev/null
+++ b/data/FakeNewsNet/train_both.csv
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+735,/m/0l8ps43,/projects/project_role/projects./projects/project_participation/participant,/m/081sq
diff --git a/module/graph_star.py b/module/graph_star.py
index 3d5bbeb..7f8130b 100644
--- a/module/graph_star.py
+++ b/module/graph_star.py
@@ -167,7 +167,7 @@ def __init__(
             )
         self.rl = nn.Linear(num_relations, hid)
         self.RW = nn.Parameter(relation_embeddings)
-        self.LP_loss = nn.BCEWithLogitsLoss()
+        self.LP_loss = nn.BCELoss()
 
     def forward(self, x, edge_index, batch, star=None, y=None, edge_type=None):
         if self.training:
@@ -237,7 +237,7 @@ def lp_score(self, z, edge_index, edge_type):
         z = F.dropout(z, 0.5, training=self.training)
         pred = self.relation_score_function(
             z[edge_index[0]].unsqueeze(1),
-            self.RW[edge_type].unsqueeze(1),
+            self.RW[edge_type],
             z[edge_index[1]].unsqueeze(1),
         )
         return pred
@@ -276,9 +276,17 @@ def add_self_loop_edge(self, edge_index, edge_type, x_size):
         )
         return edge_index, edge_type
 
+    def DistMult2(self, head, relation, tail):
+        score = head @ torch.diag_embed(relation,0,1) @ torch.transpose(tail,-2,-1)
+        score = score.view(-1,1).squeeze(1)
+        return torch.sigmoid(score)
+    
     def DistMult(self, head, relation, tail):
-        score = head * relation * tail
-        return score.sum(dim=2).squeeze(1)
+        score = head * relation.unsqueeze(1) * tail
+        score = score.sum(dim=2).squeeze(1)
+        #experimental
+        return torch.sigmoid(score)
+
 
     def updateZ(self, z):
         self.z = z
@@ -310,6 +318,8 @@ def lp_log_ranks(
             relation = torch.full((z.size(0),), pos_edge_type[i], dtype=dt, device=dev)
             # Currently distmult function score
             pred = self.lp_score(z, head_pred_ei, relation)
+            # Round to 3 decimalplaces
+            pred = torch.round(pred * 10**4) / (10**4)
             # score of actual triple
             target = pred[pos_edge_index[0][i]]
 
@@ -333,6 +343,7 @@ def lp_log_ranks(
 
         # tail batch < h, r, ?>
         # for all triples
+        
         for i in tqdm(range(pos_edge_index.size(1)), desc="tail prediction"):
 
             # For the head from this triple (pos_edge_index[0][i]) to all tails
@@ -349,6 +360,9 @@ def lp_log_ranks(
             relation = torch.full((z.size(0),), pos_edge_type[i], dtype=dt, device=dev)
             # Currently distmult function score
             pred = self.lp_score(z, tail_pred_ei, relation)
+
+            # Round to 3 decimalplaces
+            pred = torch.round(pred * 10**4) / (10**4)
             # score of actual triple
             target = pred[pos_edge_index[1][i]]
 
@@ -373,8 +387,8 @@ def lp_log_ranks(
         ranks = np.array(ranks)
 
         res = {
-            "MRR": (1 / ranks).sum() / len(ranks),
-            "MR": (ranks).sum() / len(ranks),
+            "MRR": ((1 / ranks).sum().float() / len(ranks)).item(),
+            "MR": ((ranks).sum().float() / len(ranks)).item(),
             "HIT@1": (ranks <= 1).sum() / len(ranks),
             "HIT@3": (ranks <= 3).sum() / len(ranks),
             "HIT@10": (ranks <= 10).sum() / len(ranks),
diff --git a/run_fn_detection.py b/run_fn_detection.py
new file mode 100644
index 0000000..8685e4f
--- /dev/null
+++ b/run_fn_detection.py
@@ -0,0 +1,205 @@
+import sys
+import ssl
+from os import path, mkdir
+import numpy as np
+import pandas as pd
+import torch
+import trainer
+import utils.gsn_argparse as gap
+import utils.label_encode_dataset as led
+import utils.create_node_embedding as cne
+import utils.create_relation_embedding as cre
+import utils.misc as misc
+from gensim.models import KeyedVectors
+from sklearn.preprocessing import LabelEncoder
+from torch_geometric.utils import structured_negative_sampling
+
+ssl._create_default_https_context = ssl._create_unverified_context
+
+
+def train_val_test_split(dataset, train, val, test):
+    # edge_indexes
+    dataset.train_pos_edge_index = train.edge_index
+    dataset.val_pos_edge_index = val.edge_index
+    dataset.test_pos_edge_index = test.edge_index
+
+    # relations
+    dataset.train_edge_type = train.edge_type
+    dataset.val_edge_type = val.edge_type
+    dataset.test_edge_type = test.edge_type
+
+    # negatives
+    # nb! add symmetry before
+    anti_symmetric_train = torch.flip(dataset.train_pos_edge_index,[0])
+    symmetric_train = torch.cat([dataset.train_pos_edge_index, anti_symmetric_train], dim=1)
+
+    neg_train = structured_negative_sampling(symmetric_train)
+    dataset.train_neg_edge_index = torch.tensor(
+        [list(neg_train[0]), list(neg_train[2])], dtype=torch.long
+    ).narrow(1,0,anti_symmetric_train.size(1))
+    
+    anti_symmetric_val = torch.flip(dataset.val_pos_edge_index,[0])
+    symmetric_val = torch.cat([dataset.val_pos_edge_index, anti_symmetric_val], dim=1)
+
+    neg_val = structured_negative_sampling(symmetric_val)
+    dataset.val_neg_edge_index = torch.tensor(
+        [list(neg_val[0]), list(neg_val[2])], dtype=torch.long
+    ).narrow(1,0,anti_symmetric_val.size(1))
+
+    anti_symmetric_test = torch.flip(dataset.test_pos_edge_index,[0])
+    symmetric_test = torch.cat([dataset.test_pos_edge_index, anti_symmetric_test], dim=1)
+    neg_test = structured_negative_sampling(symmetric_test)
+    dataset.test_neg_edge_index = torch.tensor(
+        [list(neg_test[0]), list(neg_test[2])], dtype=torch.long
+    ).narrow(1,0,anti_symmetric_test.size(1))
+    return dataset
+
+
+def load_data(news_dataset, kg_dataset, dataset_name, hidden=64, node_embedding_size=16, embedding_path="fn_embeddings"):
+    print("Loading open KG: " + kg_dataset + "...")
+    columns = {
+        "FB15k": ["head", "tail", "relation"],
+        "FB15k_237": ["head", "relation", "tail"],
+    }
+
+    train = pd.read_csv(
+        "./data/" + kg_dataset + "/train.txt",
+        sep="\t",
+        header=None,
+        names=columns[kg_dataset],
+        engine="python",
+    )
+    valid = pd.read_csv(
+        "./data/" + kg_dataset + "/valid.txt",
+        sep="\t",
+        header=None,
+        names=columns[kg_dataset],
+        engine="python",
+    )
+    test = pd.read_csv(
+        "./data/" + kg_dataset + "/test.txt",
+        sep="\t",
+        header=None,
+        names=columns[kg_dataset],
+        engine="python",
+    )
+
+    print("Loading fake news dataset: " + news_dataset + "...")
+    fn_test = pd.read_csv(
+        "./data/" + news_dataset + "/train_both.csv",
+        sep=",",
+        header=0,
+        engine="python",
+        index_col=0,
+    )
+
+    # create model folder
+    save_folder = "model_" + misc.get_now()
+    print("creating model folder: " + save_folder + "...")
+    mkdir(save_folder)
+
+    # needed for embedding all nodes across datasets (will use edge_index to split datasets)
+    all_data = pd.concat([train, valid])
+    all_data = pd.concat([all_data, test])
+    all_data.drop_duplicates(inplace=True)
+
+    entity_id = pd.read_csv('data/FB15k/entities.txt', sep='\t', header=None, names=['entity', 'id'], engine='python')
+    entities = entity_id['entity'].values
+
+    news_dataset_entities = np.concatenate([fn_test["fb_head"], fn_test["fb_tail"]])
+    news_dataset_entities = np.unique(news_dataset_entities)
+    new_entities = np.array([ent for ent in news_dataset_entities if ent not in entities])
+    entities = np.concatenate([entities, new_entities])
+    entity_ids = torch.arange(0, len(entities), 1, dtype=torch.float)
+    
+    relation_id = pd.read_csv('data/FB15k/relations.txt', sep='\t', header=None, names=['relation', 'id'], engine='python')
+    relations = relation_id['relation'].values
+
+
+    # fit entity and relation encoder
+    le_entity = LabelEncoder()
+    le_entity.fit(entities)
+    le_relation = LabelEncoder()
+    le_relation.fit(relations)
+
+
+    np.save(path.join(save_folder, "le_relation_classes_" + dataset_name + ".npy"), le_relation.classes_)
+    np.save(path.join(save_folder, "le_entity_classes_" + dataset_name + ".npy"), le_entity.classes_)
+
+    train = led.label_encode_dataset(le_entity, le_relation, train, entity_ids)
+    valid = led.label_encode_dataset(le_entity, le_relation, valid, entity_ids)
+    test = led.label_encode_dataset(le_entity, le_relation, test, entity_ids)
+
+    all_data = led.label_encode_dataset(le_entity, le_relation, all_data, entity_ids)
+    news_dataset_x = torch.tensor(le_entity.transform(new_entities), dtype=torch.float)
+    all_data.x = torch.cat([all_data.x, news_dataset_x])
+    print('all_data.x.size: ', all_data.x.size())
+    # create node embeddings if none exists
+    cne.create_node_embedding(
+        all_data, dataset_name, dimensions=node_embedding_size, workers=4, path=save_folder
+    )
+    cre.create_relation_embedding(relations, le_relation, dataset_name, dimensions=hidden, path=save_folder)
+    embedded_nodes = KeyedVectors.load_word2vec_format(
+        "{}/node_embedding_{}_{}.kv".format(save_folder, dataset_name, str(node_embedding_size))
+    )
+    
+    embedded_relations = KeyedVectors.load_word2vec_format(
+        "{}/relation_embedding_le_{}_{}.bin".format(save_folder, dataset_name, str(hidden)),
+        binary=True,
+    )
+    # need to sort to get correct indexing
+    sorted_embedding = []
+    for i in range(0, len(embedded_nodes.vectors)):
+        sorted_embedding.append(embedded_nodes.get_vector(str(i)))  # error her
+    all_data.x = torch.tensor(sorted_embedding, dtype=torch.float)
+
+    sorted_embedding = []
+    for i in range(0, len(embedded_relations.vectors)):
+        sorted_embedding.append(embedded_relations.get_vector(str(i)))
+    embedded_relations = torch.tensor(sorted_embedding, dtype=torch.float)
+
+    all_data.batch = torch.zeros((1, all_data.num_nodes), dtype=torch.int64).view(-1)
+
+    num_features = all_data.x.shape[-1]
+    num_relations = len(np.unique(relations))
+
+    data = train_val_test_split(all_data, train, valid, test)
+
+    data.edge_index = torch.cat(
+        [data.train_pos_edge_index, data.val_pos_edge_index, data.test_pos_edge_index],
+        dim=1,
+    )
+    data.edge_type = torch.cat(
+        [train.edge_type, valid.edge_type, test.edge_type], dim=0
+    )
+
+    return data, num_features, num_relations, embedded_relations, save_folder
+
+
+def main(_args):
+    print(
+        "\033[1;32m"
+        + "@@@@@@@@@@@@@@@@ Fake News Detection through Multi-Relational Graph Star @@@@@@@@@@@@@@@@"
+        + "\033[0m"
+    )
+    args = gap.parser.parse_args(_args)
+    dataset_name = args.dataset + '_' + args.news_dataset
+    data, num_features, num_relations, embedded_relations, save_folder = load_data(
+        hidden=args.hidden, kg_dataset=args.dataset, news_dataset=args.news_dataset, dataset_name=dataset_name
+    )
+
+    embedded_relations.to(args.device)    
+    trainer.trainer(
+        args,
+        dataset_name,
+        data,
+        num_features=num_features,
+        num_relations=num_relations,
+        relation_embeddings=embedded_relations,
+        num_epoch=args.epochs,
+        save_folder=save_folder
+    )
+    
+
+if __name__ == "__main__":
+    main(sys.argv[1:])
diff --git a/trainer.py b/trainer.py
index a1b6814..dfec798 100644
--- a/trainer.py
+++ b/trainer.py
@@ -99,12 +99,13 @@ def trainer(
     dataset,
     relation_embeddings,
     num_relations,
+    save_folder,
     num_features=0,
     epochs_per_test=1,
     epoch_per_val=1,
     num_epoch=200,
     save_per_epoch=100,
-    cal_mrr_score=True,
+    cal_mrr_score=True
 ):
 
     # GPU cuDNN auto tuner
@@ -143,11 +144,6 @@ def trainer(
     tw.init_writer(DATASET_NAME)
     tw.write_text("model/info", gap.args2string(args, sort_dict=True))
 
-    # Create directory, if it doesn't already exists
-    out_path = "output"
-    if not path.exists(out_path):
-        mkdir(out_path)
-
     optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.l2)
     scheduler = ReduceLROnPlateau(
         optimizer,
@@ -219,7 +215,7 @@ def trainer(
         scheduler.step(val_loss)
         scheduler.step(test_loss)
 
-        if epoch % save_per_epoch == 0:
-            torch.save(model, path.join(out_path, DATASET_NAME + ".pkl"))
+        if epoch == num_epoch or epoch % save_per_epoch == 0:
+            torch.save(model, path.join(save_folder, DATASET_NAME + "_" + str(num_epoch) + 'epochs' + ".pkl"))
 
     tw.writer.close()
diff --git a/utils/gsn_argparse.py b/utils/gsn_argparse.py
index 42e9c72..f0e13f9 100644
--- a/utils/gsn_argparse.py
+++ b/utils/gsn_argparse.py
@@ -97,4 +97,5 @@ def tempDevice(x):
     "--relation_score_function", type=str, default="DistMult", help="DistMult"
 )
 parser.add_argument("--dataset", type=str, default="FB15k_237")
+parser.add_argument("--news_dataset", type=str, default="LIAR")
 parser.add_argument("--epochs", type=int, default=2000)
diff --git a/utils/label_encode_dataset.py b/utils/label_encode_dataset.py
index e9b9bbd..971a482 100644
--- a/utils/label_encode_dataset.py
+++ b/utils/label_encode_dataset.py
@@ -1,9 +1,8 @@
 from torch_geometric.data import Data
-import numpy as np
 import torch
 
 
-def label_encode_dataset(le_entity, le_relation, data):
+def label_encode_dataset(le_entity, le_relation, data, entity_ids):
     head = data["head"].values
     tail = data["tail"].values
     relations = data["relation"].values
@@ -15,8 +14,5 @@ def label_encode_dataset(le_entity, le_relation, data):
 
     edge_attributes = torch.tensor(relations, dtype=torch.long)
     edge_index = torch.tensor([heads, tails], dtype=torch.long)
-    unique_entities = torch.tensor(
-        np.unique(edge_index.reshape(edge_index.shape[-1] * 2, 1)), dtype=torch.float
-    )
 
-    return Data(x=unique_entities, edge_type=edge_attributes, edge_index=edge_index)
+    return Data(x=entity_ids, edge_type=edge_attributes, edge_index=edge_index)
diff --git a/utils/misc.py b/utils/misc.py
new file mode 100644
index 0000000..48cba8c
--- /dev/null
+++ b/utils/misc.py
@@ -0,0 +1,7 @@
+from datetime import datetime as dtime
+import numpy as np
+def get_now():
+    now = dtime.now()
+    date = str(now.date())
+    time = str(now.time()).split(".")[0]
+    return date + "_" + time + str(np.random.randint(0,100))
\ No newline at end of file
diff --git a/utils/tensorboard_writer.py b/utils/tensorboard_writer.py
index 0624ebc..eabfbdf 100644
--- a/utils/tensorboard_writer.py
+++ b/utils/tensorboard_writer.py
@@ -53,7 +53,7 @@ def log_epoch(
 
 
 def write_text(path, text):
-    writer.add_text(path, text, epochs)
+    writer.add_text(path, text, steps)
 
 
 def init_writer(name):