diff --git a/Siamese_Dual_BERT/Siamese_Dual_BERT.ipynb b/Siamese_Dual_BERT/Siamese_Dual_BERT.ipynb
new file mode 100644
index 0000000..618533b
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+++ b/Siamese_Dual_BERT/Siamese_Dual_BERT.ipynb
@@ -0,0 +1,2991 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "papermill": {
+     "duration": 6.891944,
+     "end_time": "2021-04-14T22:58:53.819082",
+     "exception": false,
+     "start_time": "2021-04-14T22:58:46.927138",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "import itertools\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm.notebook import tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import *\n",
+    "from tensorflow.keras.models import *\n",
+    "from tensorflow.keras.optimizers import *\n",
+    "from tensorflow.keras.callbacks import *\n",
+    "from tensorflow.keras import backend as K\n",
+    "\n",
+    "from transformers import TFAutoModel, AutoTokenizer, BertConfig, TFBertModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "papermill": {
+     "duration": 0.027398,
+     "end_time": "2021-04-14T22:58:53.861468",
+     "exception": false,
+     "start_time": "2021-04-14T22:58:53.834070",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues):\n",
+    "\n",
+    "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
+    "\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+    "    plt.title(title, fontsize=25)\n",
+    "    #plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=90, fontsize=15)\n",
+    "    plt.yticks(tick_marks, classes, fontsize=15)\n",
+    "\n",
+    "    fmt = '.2f'\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(j, i, format(cm[i, j], fmt),\n",
+    "                 horizontalalignment=\"center\",\n",
+    "                 color=\"white\" if cm[i, j] > thresh else \"black\", fontsize = 14)\n",
+    "\n",
+    "    plt.ylabel('True label', fontsize=20)\n",
+    "    plt.xlabel('Predicted label', fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
+    "papermill": {
+     "duration": 4.602321,
+     "end_time": "2021-04-14T22:58:58.477732",
+     "exception": false,
+     "start_time": "2021-04-14T22:58:53.875411",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(9768, 6)\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>category</th>\n",
+       "      <th>headline</th>\n",
+       "      <th>authors</th>\n",
+       "      <th>link</th>\n",
+       "      <th>short_description</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Attorney Aaron Schlossberg Insists Anti-Spanis...</td>\n",
+       "      <td>David Moye</td>\n",
+       "      <td>https://www.huffingtonpost.com/entry/aaron-sch...</td>\n",
+       "      <td>In the apology, Aaron Schlossberg claims he mo...</td>\n",
+       "      <td>2018-05-22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Protesters Throw A Fiesta To Razz Lawyer Who R...</td>\n",
+       "      <td>Mary Papenfuss</td>\n",
+       "      <td>https://www.huffingtonpost.com/entry/fiesta-pr...</td>\n",
+       "      <td>Aaron Schlossberg was treated to outrage, mari...</td>\n",
+       "      <td>2018-05-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>443</th>\n",
+       "      <td>1</td>\n",
+       "      <td>The Controversial Way Some California Schools ...</td>\n",
+       "      <td>EdSource, Editorial Partner</td>\n",
+       "      <td>https://www.huffingtonpost.com/entry/restorati...</td>\n",
+       "      <td>By David Washburn The two 9th-grade girls hear...</td>\n",
+       "      <td>2018-05-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Latino Man Insulted When Starbucks Barista Wri...</td>\n",
+       "      <td>David Moye</td>\n",
+       "      <td>https://www.huffingtonpost.com/entry/starbucks...</td>\n",
+       "      <td>The coffee giant tried to apologize with a $50...</td>\n",
+       "      <td>2018-05-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Angry White Dude's Rant About People Speaking ...</td>\n",
+       "      <td>David Moye</td>\n",
+       "      <td>https://www.huffingtonpost.com/entry/edward-su...</td>\n",
+       "      <td>The man tells an employee “Your staff is speak...</td>\n",
+       "      <td>2018-05-16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     category                                           headline  \\\n",
+       "258         0  Attorney Aaron Schlossberg Insists Anti-Spanis...   \n",
+       "351         0  Protesters Throw A Fiesta To Razz Lawyer Who R...   \n",
+       "443         1  The Controversial Way Some California Schools ...   \n",
+       "456         0  Latino Man Insulted When Starbucks Barista Wri...   \n",
+       "543         0  Angry White Dude's Rant About People Speaking ...   \n",
+       "\n",
+       "                         authors  \\\n",
+       "258                   David Moye   \n",
+       "351               Mary Papenfuss   \n",
+       "443  EdSource, Editorial Partner   \n",
+       "456                   David Moye   \n",
+       "543                   David Moye   \n",
+       "\n",
+       "                                                  link  \\\n",
+       "258  https://www.huffingtonpost.com/entry/aaron-sch...   \n",
+       "351  https://www.huffingtonpost.com/entry/fiesta-pr...   \n",
+       "443  https://www.huffingtonpost.com/entry/restorati...   \n",
+       "456  https://www.huffingtonpost.com/entry/starbucks...   \n",
+       "543  https://www.huffingtonpost.com/entry/edward-su...   \n",
+       "\n",
+       "                                     short_description       date  \n",
+       "258  In the apology, Aaron Schlossberg claims he mo... 2018-05-22  \n",
+       "351  Aaron Schlossberg was treated to outrage, mari... 2018-05-19  \n",
+       "443  By David Washburn The two 9th-grade girls hear... 2018-05-17  \n",
+       "456  The coffee giant tried to apologize with a $50... 2018-05-17  \n",
+       "543  The man tells an employee “Your staff is speak... 2018-05-16  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### READ DATA ###\n",
+    "\n",
+    "df = pd.read_json('../input/news-category-dataset/News_Category_Dataset_v2.json', lines=True)\n",
+    "df = df[df.category.isin(df.category.value_counts().tail(8).index)].copy() # select 8 low dimensional categories\n",
+    "map_label = dict(enumerate(df.category.factorize()[1]))\n",
+    "df['category'] = df.category.factorize()[0]\n",
+    "\n",
+    "print(df.shape)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "papermill": {
+     "duration": 0.037772,
+     "end_time": "2021-04-14T22:58:58.530790",
+     "exception": false,
+     "start_time": "2021-04-14T22:58:58.493018",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "### UTILITY FUNCTIONS FOR TOKENIZATIONS, MASKS AND SEGMENTS CREATION ###\n",
+    "### from: https://www.kaggle.com/akensert/bert-base-tf2-0-now-huggingface-transformer\n",
+    "\n",
+    "def set_seed(seed):\n",
+    "    \n",
+    "    tf.random.set_seed(seed)\n",
+    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
+    "    np.random.seed(seed)\n",
+    "    random.seed(seed)\n",
+    "    \n",
+    "\n",
+    "def convert_to_transformer_inputs(str1, str2, tokenizer, max_sequence_length, double=True):\n",
+    "    \n",
+    "    def return_id(str1, str2, truncation_strategy, length):\n",
+    "\n",
+    "        inputs = tokenizer.encode_plus(str1, str2,\n",
+    "            add_special_tokens=True,\n",
+    "            max_length=length,\n",
+    "            truncation_strategy=truncation_strategy)\n",
+    "        \n",
+    "        input_ids =  inputs[\"input_ids\"]\n",
+    "        input_masks = [1] * len(input_ids)\n",
+    "        input_segments = inputs[\"token_type_ids\"]\n",
+    "        \n",
+    "        padding_length = length - len(input_ids)\n",
+    "        padding_id = tokenizer.pad_token_id\n",
+    "        \n",
+    "        input_ids = input_ids + ([padding_id] * padding_length)\n",
+    "        input_masks = input_masks + ([0] * padding_length)\n",
+    "        input_segments = input_segments + ([0] * padding_length)\n",
+    "        \n",
+    "        return [input_ids, input_masks, input_segments]\n",
+    "    \n",
+    "    if double:\n",
+    "    \n",
+    "        input_ids_1, input_masks_1, input_segments_1 = return_id(\n",
+    "            str1, None, 'longest_first', max_sequence_length)\n",
+    "\n",
+    "        input_ids_2, input_masks_2, input_segments_2 = return_id(\n",
+    "            str2, None, 'longest_first', max_sequence_length)\n",
+    "\n",
+    "        return [input_ids_1, input_masks_1, input_segments_1,\n",
+    "                input_ids_2, input_masks_2, input_segments_2]\n",
+    "    \n",
+    "    else:\n",
+    "        \n",
+    "        input_ids, input_masks, input_segments = return_id(\n",
+    "            str1, str2, 'longest_first', max_sequence_length)\n",
+    "\n",
+    "        return [input_ids, input_masks, input_segments,\n",
+    "                None, None, None]        \n",
+    "\n",
+    "def compute_input_arrays(df, columns, tokenizer, max_sequence_length, double=True):\n",
+    "    \n",
+    "    input_ids_1, input_masks_1, input_segments_1 = [], [], []\n",
+    "    input_ids_2, input_masks_2, input_segments_2 = [], [], []\n",
+    "    for _, instance in tqdm(df[columns].iterrows(), total=len(df)):\n",
+    "        str1, str2 = instance[columns[0]], instance[columns[1]]\n",
+    "\n",
+    "        ids_1, masks_1, segments_1, ids_2, masks_2, segments_2 = \\\n",
+    "        convert_to_transformer_inputs(str1, str2, tokenizer, max_sequence_length, double=double)\n",
+    "        \n",
+    "        input_ids_1.append(ids_1)\n",
+    "        input_masks_1.append(masks_1)\n",
+    "        input_segments_1.append(segments_1)\n",
+    "\n",
+    "        input_ids_2.append(ids_2)\n",
+    "        input_masks_2.append(masks_2)\n",
+    "        input_segments_2.append(segments_2)\n",
+    "        \n",
+    "    if double:\n",
+    "        \n",
+    "        return [np.asarray(input_ids_1, dtype=np.int32), \n",
+    "                np.asarray(input_masks_1, dtype=np.int32), \n",
+    "                np.asarray(input_segments_1, dtype=np.int32),\n",
+    "                np.asarray(input_ids_2, dtype=np.int32), \n",
+    "                np.asarray(input_masks_2, dtype=np.int32), \n",
+    "                np.asarray(input_segments_2, dtype=np.int32)]\n",
+    "    \n",
+    "    else:\n",
+    "        \n",
+    "        return [np.asarray(input_ids_1, dtype=np.int32), \n",
+    "                np.asarray(input_masks_1, dtype=np.int32), \n",
+    "                np.asarray(input_segments_1, dtype=np.int32)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "papermill": {
+     "duration": 0.029122,
+     "end_time": "2021-04-14T22:58:58.574409",
+     "exception": false,
+     "start_time": "2021-04-14T22:58:58.545287",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(6837, 2) (2931, 2)\n",
+      "(6837,) (2931,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "### TRAIN TEST SPLIT ###\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df[['headline','short_description']], df['category'].values, \n",
+    "                                                    random_state=33, test_size = 0.3)\n",
+    "del df\n",
+    "\n",
+    "print(X_train.shape, X_test.shape)\n",
+    "print(y_train.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "papermill": {
+     "duration": 1.789212,
+     "end_time": "2021-04-14T22:59:00.379957",
+     "exception": false,
+     "start_time": "2021-04-14T22:58:58.590745",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9547d80b3bf34424826b92ff76f7c40b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a26b43784468412882912b8b7d90532f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "### IMPORT TOKENIZER ###\n",
+    "\n",
+    "MAX_SEQUENCE_LENGTH = 300\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.01748,
+     "end_time": "2021-04-14T22:59:00.424828",
+     "exception": false,
+     "start_time": "2021-04-14T22:59:00.407348",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# **SIMPLE BERT (ONE INPUT)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "papermill": {
+     "duration": 16.154021,
+     "end_time": "2021-04-14T22:59:16.595688",
+     "exception": false,
+     "start_time": "2021-04-14T22:59:00.441667",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3072815d3f2a4a3499e84b56a741fd06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=6837.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e769e04d2d554a66890476616b086e36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=2931.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "### CREATE SEQUENCES (id, mask, segments) FOR TRAIN AND TEST ###\n",
+    "\n",
+    "input_train = compute_input_arrays(X_train, ['headline','short_description'], tokenizer, MAX_SEQUENCE_LENGTH, double=False)\n",
+    "input_test = compute_input_arrays(X_test, ['headline','short_description'], tokenizer, MAX_SEQUENCE_LENGTH, double=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "papermill": {
+     "duration": 0.030082,
+     "end_time": "2021-04-14T22:59:16.645546",
+     "exception": false,
+     "start_time": "2021-04-14T22:59:16.615464",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def simple_bert():\n",
+    "    \n",
+    "    set_seed(33)\n",
+    "    \n",
+    "    opt = Adam(learning_rate=2e-5)\n",
+    "    \n",
+    "    id_ = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    mask_ = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    atn_ = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    \n",
+    "    config = BertConfig()\n",
+    "    config.output_hidden_states = False # Set to True to obtain hidden states\n",
+    "    bert_model = TFBertModel.from_pretrained('bert-base-uncased', config=config)\n",
+    "    \n",
+    "    embedding = bert_model(id_, attention_mask=mask_, token_type_ids=atn_)[0]\n",
+    "    \n",
+    "    x = GlobalAveragePooling1D()(embedding)    \n",
+    "    x = Dropout(0.2)(x)\n",
+    "    out = Dense(len(map_label), activation='softmax')(x)\n",
+    "\n",
+    "    model = Model(inputs=[id_, mask_, atn_], outputs=out)\n",
+    "    model.compile(loss='sparse_categorical_crossentropy', optimizer=opt)\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "papermill": {
+     "duration": 933.169527,
+     "end_time": "2021-04-14T23:14:49.833272",
+     "exception": false,
+     "start_time": "2021-04-14T22:59:16.663745",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5c4d68d25d0c48a58562b5f578e6874f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=536063208.0, style=ProgressStyle(descri…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Train on 6837 samples\n",
+      "Epoch 1/3\n",
+      "6837/6837 [==============================] - 311s 45ms/sample - loss: 0.7071\n",
+      "Epoch 2/3\n",
+      "6837/6837 [==============================] - 295s 43ms/sample - loss: 0.2715\n",
+      "Epoch 3/3\n",
+      "6837/6837 [==============================] - 295s 43ms/sample - loss: 0.1072\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x7f2e8b7f5bd0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = simple_bert()\n",
+    "model.fit(input_train, y_train, epochs=3, batch_size=6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "papermill": {
+     "duration": 39.715344,
+     "end_time": "2021-04-14T23:15:30.602903",
+     "exception": false,
+     "start_time": "2021-04-14T23:14:50.887559",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "### PREDICT TEST ###\n",
+    "\n",
+    "pred_test = np.argmax(model.predict(input_test), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "papermill": {
+     "duration": 1.019225,
+     "end_time": "2021-04-14T23:15:32.638207",
+     "exception": false,
+     "start_time": "2021-04-14T23:15:31.618982",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                precision    recall  f1-score   support\n",
+      "\n",
+      "ARTS & CULTURE       0.89      0.82      0.86       429\n",
+      "       COLLEGE       0.71      0.85      0.78       332\n",
+      "CULTURE & ARTS       0.89      0.84      0.87       286\n",
+      "     EDUCATION       0.83      0.63      0.72       277\n",
+      "   ENVIRONMENT       0.82      0.96      0.89       399\n",
+      "         FIFTY       0.82      0.86      0.84       416\n",
+      "     GOOD NEWS       0.80      0.77      0.78       450\n",
+      " LATINO VOICES       0.91      0.85      0.88       342\n",
+      "\n",
+      "      accuracy                           0.83      2931\n",
+      "     macro avg       0.83      0.82      0.83      2931\n",
+      "  weighted avg       0.83      0.83      0.83      2931\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report([map_label[i] for i in y_test], [map_label[i] for i in pred_test]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "papermill": {
+     "duration": 1.430999,
+     "end_time": "2021-04-14T23:15:35.209428",
+     "exception": false,
+     "start_time": "2021-04-14T23:15:33.778429",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cnf_matrix = confusion_matrix([map_label[i] for i in y_test], \n",
+    "                              [map_label[i] for i in pred_test])\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plot_confusion_matrix(cnf_matrix, classes=list(map_label.values()))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 0.978528,
+     "end_time": "2021-04-14T23:15:37.165887",
+     "exception": false,
+     "start_time": "2021-04-14T23:15:36.187359",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# **DUAL BERT (TWO INPUT)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "papermill": {
+     "duration": 18.533315,
+     "end_time": "2021-04-14T23:15:56.675934",
+     "exception": false,
+     "start_time": "2021-04-14T23:15:38.142619",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46e54182dfe44ad18e11588883ffd72a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=6837.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3f439923dfb4ee2a56e1d35e64ca8dd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=2931.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "### CREATE SEQUENCES (id, mask, segments) FOR TRAIN AND TEST ###\n",
+    "\n",
+    "input_train = compute_input_arrays(X_train, ['headline','short_description'], tokenizer, MAX_SEQUENCE_LENGTH)\n",
+    "input_test = compute_input_arrays(X_test, ['headline','short_description'], tokenizer, MAX_SEQUENCE_LENGTH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "papermill": {
+     "duration": 1.393606,
+     "end_time": "2021-04-14T23:15:59.142024",
+     "exception": false,
+     "start_time": "2021-04-14T23:15:57.748418",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def dual_bert():\n",
+    "    \n",
+    "    set_seed(33)\n",
+    "    \n",
+    "    opt = Adam(learning_rate=2e-5)\n",
+    "    \n",
+    "    id1 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    id2 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    \n",
+    "    mask1 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    mask2 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    \n",
+    "    atn1 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    atn2 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    \n",
+    "    config = BertConfig() \n",
+    "    config.output_hidden_states = False # Set to True to obtain hidden states\n",
+    "    bert_model1 = TFBertModel.from_pretrained('bert-base-uncased', config=config)\n",
+    "    bert_model2 = TFBertModel.from_pretrained('bert-base-uncased', config=config)\n",
+    "    \n",
+    "    embedding1 = bert_model1(id1, attention_mask=mask1, token_type_ids=atn1)[0]\n",
+    "    embedding2 = bert_model2(id2, attention_mask=mask2, token_type_ids=atn2)[0]\n",
+    "    \n",
+    "    x1 = GlobalAveragePooling1D()(embedding1)\n",
+    "    x2 = GlobalAveragePooling1D()(embedding2)\n",
+    "    \n",
+    "    x = Concatenate()([x1, x2])\n",
+    "    x = Dense(64, activation='relu')(x)\n",
+    "    x = Dropout(0.2)(x)\n",
+    "    out = Dense(len(map_label), activation='softmax')(x)\n",
+    "\n",
+    "    model = Model(inputs=[id1, mask1, atn1, id2, mask2, atn2], outputs=out)\n",
+    "    model.compile(loss='sparse_categorical_crossentropy', optimizer=opt)\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "papermill": {
+     "duration": 1722.981337,
+     "end_time": "2021-04-14T23:44:43.098655",
+     "exception": false,
+     "start_time": "2021-04-14T23:16:00.117318",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 6837 samples\n",
+      "Epoch 1/3\n",
+      "6837/6837 [==============================] - 593s 87ms/sample - loss: 0.7904\n",
+      "Epoch 2/3\n",
+      "6837/6837 [==============================] - 560s 82ms/sample - loss: 0.3198\n",
+      "Epoch 3/3\n",
+      "6837/6837 [==============================] - 560s 82ms/sample - loss: 0.1341\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x7f2e69d9f310>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = dual_bert()\n",
+    "model.fit(input_train, y_train, epochs=3, batch_size=6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "papermill": {
+     "duration": 76.528347,
+     "end_time": "2021-04-14T23:46:01.604933",
+     "exception": false,
+     "start_time": "2021-04-14T23:44:45.076586",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "### PREDICT TEST ###\n",
+    "\n",
+    "pred_test = np.argmax(model.predict(input_test), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "papermill": {
+     "duration": 1.982288,
+     "end_time": "2021-04-14T23:46:05.990713",
+     "exception": false,
+     "start_time": "2021-04-14T23:46:04.008425",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                precision    recall  f1-score   support\n",
+      "\n",
+      "ARTS & CULTURE       0.80      0.90      0.85       429\n",
+      "       COLLEGE       0.73      0.83      0.77       332\n",
+      "CULTURE & ARTS       0.91      0.84      0.87       286\n",
+      "     EDUCATION       0.83      0.64      0.72       277\n",
+      "   ENVIRONMENT       0.86      0.96      0.91       399\n",
+      "         FIFTY       0.83      0.86      0.84       416\n",
+      "     GOOD NEWS       0.84      0.73      0.78       450\n",
+      " LATINO VOICES       0.88      0.85      0.86       342\n",
+      "\n",
+      "      accuracy                           0.83      2931\n",
+      "     macro avg       0.83      0.82      0.83      2931\n",
+      "  weighted avg       0.83      0.83      0.83      2931\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report([map_label[i] for i in y_test], [map_label[i] for i in pred_test]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "papermill": {
+     "duration": 2.28804,
+     "end_time": "2021-04-14T23:46:10.239381",
+     "exception": false,
+     "start_time": "2021-04-14T23:46:07.951341",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cnf_matrix = confusion_matrix([map_label[i] for i in y_test], \n",
+    "                              [map_label[i] for i in pred_test])\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plot_confusion_matrix(cnf_matrix, classes=list(map_label.values()))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "papermill": {
+     "duration": 1.968644,
+     "end_time": "2021-04-14T23:46:14.458594",
+     "exception": false,
+     "start_time": "2021-04-14T23:46:12.489950",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# **SIAMESE BERT (TWO INPUT)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "papermill": {
+     "duration": 1.950003,
+     "end_time": "2021-04-14T23:46:18.395610",
+     "exception": false,
+     "start_time": "2021-04-14T23:46:16.445607",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def siamese_bert():\n",
+    "    \n",
+    "    set_seed(33)\n",
+    "    \n",
+    "    opt = Adam(learning_rate=2e-5)\n",
+    "    \n",
+    "    id1 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    id2 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    \n",
+    "    mask1 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    mask2 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    \n",
+    "    atn1 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    atn2 = Input((MAX_SEQUENCE_LENGTH,), dtype=tf.int32)\n",
+    "    \n",
+    "    config = BertConfig()\n",
+    "    config.output_hidden_states = False # Set to True to obtain hidden states\n",
+    "    bert_model = TFBertModel.from_pretrained('bert-base-uncased', config=config)\n",
+    "    \n",
+    "    embedding1 = bert_model(id1, attention_mask=mask1, token_type_ids=atn1)[0]\n",
+    "    embedding2 = bert_model(id2, attention_mask=mask2, token_type_ids=atn2)[0]\n",
+    "    \n",
+    "    x1 = GlobalAveragePooling1D()(embedding1)\n",
+    "    x2 = GlobalAveragePooling1D()(embedding2)\n",
+    "    \n",
+    "    x = Concatenate()([x1, x2])\n",
+    "    x = Dense(64, activation='relu')(x)\n",
+    "    x = Dropout(0.2)(x)\n",
+    "    out = Dense(len(map_label), activation='softmax')(x)\n",
+    "\n",
+    "    model = Model(inputs=[id1, mask1, atn1, id2, mask2, atn2], outputs=out)\n",
+    "    model.compile(loss='sparse_categorical_crossentropy', optimizer=opt)\n",
+    "    \n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "papermill": {
+     "duration": 1682.114267,
+     "end_time": "2021-04-15T00:14:22.473371",
+     "exception": false,
+     "start_time": "2021-04-14T23:46:20.359104",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 6837 samples\n",
+      "Epoch 1/3\n",
+      "6837/6837 [==============================] - 578s 84ms/sample - loss: 0.8184\n",
+      "Epoch 2/3\n",
+      "6837/6837 [==============================] - 547s 80ms/sample - loss: 0.3680\n",
+      "Epoch 3/3\n",
+      "6837/6837 [==============================] - 547s 80ms/sample - loss: 0.1916\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x7f2e9dd9ee50>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = siamese_bert()\n",
+    "model.fit(input_train, y_train, epochs=3, batch_size=6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "papermill": {
+     "duration": 77.120746,
+     "end_time": "2021-04-15T00:15:42.721322",
+     "exception": false,
+     "start_time": "2021-04-15T00:14:25.600576",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "### PREDICT TEST ###\n",
+    "\n",
+    "pred_test = np.argmax(model.predict(input_test), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "papermill": {
+     "duration": 2.964217,
+     "end_time": "2021-04-15T00:15:49.014465",
+     "exception": false,
+     "start_time": "2021-04-15T00:15:46.050248",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                precision    recall  f1-score   support\n",
+      "\n",
+      "ARTS & CULTURE       0.79      0.89      0.84       429\n",
+      "       COLLEGE       0.77      0.82      0.79       332\n",
+      "CULTURE & ARTS       0.91      0.76      0.83       286\n",
+      "     EDUCATION       0.75      0.73      0.74       277\n",
+      "   ENVIRONMENT       0.85      0.96      0.90       399\n",
+      "         FIFTY       0.80      0.88      0.84       416\n",
+      "     GOOD NEWS       0.86      0.72      0.78       450\n",
+      " LATINO VOICES       0.92      0.85      0.88       342\n",
+      "\n",
+      "      accuracy                           0.83      2931\n",
+      "     macro avg       0.83      0.82      0.82      2931\n",
+      "  weighted avg       0.83      0.83      0.83      2931\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report([map_label[i] for i in y_test], [map_label[i] for i in pred_test]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "papermill": {
+     "duration": 3.518968,
+     "end_time": "2021-04-15T00:15:55.431429",
+     "exception": false,
+     "start_time": "2021-04-15T00:15:51.912461",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cnf_matrix = confusion_matrix([map_label[i] for i in y_test], \n",
+    "                              [map_label[i] for i in pred_test])\n",
+    "\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plot_confusion_matrix(cnf_matrix, classes=list(map_label.values()))\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "papermill": {
+   "duration": 4641.04413,
+   "end_time": "2021-04-15T00:16:04.340475",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2021-04-14T22:58:43.296345",
+   "version": "1.2.1"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "056ad0ee594147d0b0ebdb6aa3dde3b5": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "1.2.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
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