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nlp-with-disaster-tweets-using-tf-and-transformers.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4f2c4c9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)\u001b[0m\u001b[31m\n",
+      "\u001b[0m\u001b[31mERROR: No matching distribution found for tensorflow\u001b[0m\u001b[31m\n",
+      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "0019a2a2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:50:45.178585Z",
+     "iopub.status.busy": "2023-08-29T18:50:45.177755Z",
+     "iopub.status.idle": "2023-08-29T18:50:54.583521Z",
+     "shell.execute_reply": "2023-08-29T18:50:54.582507Z"
+    },
+    "papermill": {
+     "duration": 9.415037,
+     "end_time": "2023-08-29T18:50:54.585938",
+     "exception": false,
+     "start_time": "2023-08-29T18:50:45.170901",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tensorflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m/Users/vikranthbakkashetty/Desktop/capstone/nlp-with-disaster-tweets-using-tf-and-transformers.ipynb Cell 2\u001b[0m line \u001b[0;36m9\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/vikranthbakkashetty/Desktop/capstone/nlp-with-disaster-tweets-using-tf-and-transformers.ipynb#W0sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/vikranthbakkashetty/Desktop/capstone/nlp-with-disaster-tweets-using-tf-and-transformers.ipynb#W0sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/vikranthbakkashetty/Desktop/capstone/nlp-with-disaster-tweets-using-tf-and-transformers.ipynb#W0sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mtf\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/vikranthbakkashetty/Desktop/capstone/nlp-with-disaster-tweets-using-tf-and-transformers.ipynb#W0sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtransformers\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/vikranthbakkashetty/Desktop/capstone/nlp-with-disaster-tweets-using-tf-and-transformers.ipynb#W0sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtransformers\u001b[39;00m \u001b[39mimport\u001b[39;00m BertTokenizer\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import re\n",
+    "import string\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "import transformers\n",
+    "from transformers import BertTokenizer\n",
+    "from transformers import TFAutoModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "512019f0",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:50:54.598833Z",
+     "iopub.status.busy": "2023-08-29T18:50:54.598222Z",
+     "iopub.status.idle": "2023-08-29T18:50:54.663019Z",
+     "shell.execute_reply": "2023-08-29T18:50:54.662110Z"
+    },
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "papermill": {
+     "duration": 0.07344,
+     "end_time": "2023-08-29T18:50:54.665205",
+     "exception": false,
+     "start_time": "2023-08-29T18:50:54.591765",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train = pd.read_csv('/Users/vikranthbakkashetty/Desktop/capstone/train.csv')\n",
+    "test = pd.read_csv('/Users/vikranthbakkashetty/Desktop/capstone/test.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "eb1c0bad",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:50:54.677416Z",
+     "iopub.status.busy": "2023-08-29T18:50:54.677139Z",
+     "iopub.status.idle": "2023-08-29T18:50:54.693178Z",
+     "shell.execute_reply": "2023-08-29T18:50:54.692064Z"
+    },
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+     "outputs_hidden": false
+    },
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+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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>id</th>\n",
+       "      <th>keyword</th>\n",
+       "      <th>location</th>\n",
+       "      <th>text</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id keyword location                                               text  \\\n",
+       "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
+       "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
+       "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
+       "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
+       "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
+       "\n",
+       "   target  \n",
+       "0       1  \n",
+       "1       1  \n",
+       "2       1  \n",
+       "3       1  \n",
+       "4       1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "21032b6b",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
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+     "shell.execute_reply": "2023-08-29T18:50:54.721922Z"
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+     "start_time": "2023-08-29T18:50:54.701445",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train[\"length\"] = train[\"text\"].apply(lambda x : len(x))\n",
+    "test[\"length\"] = test[\"text\"].apply(lambda x : len(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "781fb8f9",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:50:54.737804Z",
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+    },
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+    "papermill": {
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+     "start_time": "2023-08-29T18:50:54.730424",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    7613.000000\n",
+       "mean      101.037436\n",
+       "std        33.781325\n",
+       "min         7.000000\n",
+       "25%        78.000000\n",
+       "50%       107.000000\n",
+       "75%       133.000000\n",
+       "max       157.000000\n",
+       "Name: length, dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[\"length\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6018ce8b",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:50:54.764622Z",
+     "iopub.status.busy": "2023-08-29T18:50:54.764345Z",
+     "iopub.status.idle": "2023-08-29T18:50:54.815089Z",
+     "shell.execute_reply": "2023-08-29T18:50:54.814242Z"
+    },
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "papermill": {
+     "duration": 0.059238,
+     "end_time": "2023-08-29T18:50:54.817058",
+     "exception": false,
+     "start_time": "2023-08-29T18:50:54.757820",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "train['text'] = train['text'].apply(lambda x: \" \".join([word.lower() for word in str(x).split()]))\n",
+    "test['text'] = test['text'].apply(lambda x: \" \".join([word.lower() for word in str(x).split()]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2a1b9c59",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:50:54.829628Z",
+     "iopub.status.busy": "2023-08-29T18:50:54.829362Z",
+     "iopub.status.idle": "2023-08-29T18:50:55.349167Z",
+     "shell.execute_reply": "2023-08-29T18:50:55.348259Z"
+    },
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "papermill": {
+     "duration": 0.528831,
+     "end_time": "2023-08-29T18:50:55.351560",
+     "exception": false,
+     "start_time": "2023-08-29T18:50:54.822729",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "def remove_html(text):\n",
+    "    html=re.compile(r'<.*?>')\n",
+    "    return html.sub(r'',text)\n",
+    "\n",
+    "def remove_emoji(text):\n",
+    "    emoji_pattern = re.compile(\"[\"\n",
+    "                               u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
+    "                               u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
+    "                               u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
+    "                               u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
+    "                               u\"\\U00002702-\\U000027B0\"\n",
+    "                               u\"\\U000024C2-\\U0001F251\"\n",
+    "                               \"]+\", flags=re.UNICODE)\n",
+    "    return emoji_pattern.sub(r'', text)\n",
+    "\n",
+    "def decontraction(text):\n",
+    "    text = re.sub(r\"won\\'t\", \" will not\", text)\n",
+    "    text = re.sub(r\"won\\'t've\", \" will not have\", text)\n",
+    "    text = re.sub(r\"can\\'t\", \" can not\", text)\n",
+    "    text = re.sub(r\"don\\'t\", \" do not\", text)\n",
+    "\n",
+    "    text = re.sub(r\"can\\'t've\", \" can not have\", text)\n",
+    "    text = re.sub(r\"ma\\'am\", \" madam\", text)\n",
+    "    text = re.sub(r\"let\\'s\", \" let us\", text)\n",
+    "    text = re.sub(r\"ain\\'t\", \" am not\", text)\n",
+    "    text = re.sub(r\"shan\\'t\", \" shall not\", text)\n",
+    "    text = re.sub(r\"sha\\n't\", \" shall not\", text)\n",
+    "    text = re.sub(r\"o\\'clock\", \" of the clock\", text)\n",
+    "    text = re.sub(r\"y\\'all\", \" you all\", text)\n",
+    "\n",
+    "    text = re.sub(r\"n\\'t\", \" not\", text)\n",
+    "    text = re.sub(r\"n\\'t've\", \" not have\", text)\n",
+    "    text = re.sub(r\"\\'re\", \" are\", text)\n",
+    "    text = re.sub(r\"\\'s\", \" is\", text)\n",
+    "    text = re.sub(r\"\\'d\", \" would\", text)\n",
+    "    text = re.sub(r\"\\'d've\", \" would have\", text)\n",
+    "    text = re.sub(r\"\\'ll\", \" will\", text)\n",
+    "    text = re.sub(r\"\\'ll've\", \" will have\", text)\n",
+    "    text = re.sub(r\"\\'t\", \" not\", text)\n",
+    "    text = re.sub(r\"\\'ve\", \" have\", text)\n",
+    "    text = re.sub(r\"\\'m\", \" am\", text)\n",
+    "    text = re.sub(r\"\\'re\", \" are\", text)\n",
+    "    return text\n",
+    "\n",
+    "def clean(tweet):\n",
+    "\n",
+    "    tweet = re.sub(r\"https?:\\/\\/t.co\\/[A-Za-z0-9]+\", \"\", tweet)\n",
+    "\n",
+    "    Special = '@#!?+&*[]-%:/()$=><|{}^'\n",
+    "    for s in Special:\n",
+    "        tweet = tweet.replace(s, \"\")\n",
+    "\n",
+    "    return tweet\n",
+    "\n",
+    "train['text'] = train['text'].apply(lambda s : clean(s))\n",
+    "test['text'] = test['text'].apply(lambda s : clean(s))\n",
+    "train['text'] = train['text'].apply(lambda s : remove_html(s))\n",
+    "test['text'] = test['text'].apply(lambda s : remove_html(s))\n",
+    "train['text'] = train['text'].apply(lambda s : remove_emoji(s))\n",
+    "test['text'] = test['text'].apply(lambda s : remove_emoji(s))\n",
+    "train['text'] = train['text'].apply(lambda s : decontraction(s))\n",
+    "test['text'] = test['text'].apply(lambda s : decontraction(s))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1a48e087",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:50:55.364193Z",
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+     "iopub.status.idle": "2023-08-29T18:50:55.373965Z",
+     "shell.execute_reply": "2023-08-29T18:50:55.373043Z"
+    },
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "papermill": {
+     "duration": 0.01879,
+     "end_time": "2023-08-29T18:50:55.376120",
+     "exception": false,
+     "start_time": "2023-08-29T18:50:55.357330",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "      <th></th>\n",
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+       "      <td>1</td>\n",
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+       "      <th>1</th>\n",
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+       "      <td>forest fire near la ronge sask. canada</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>all residents asked to  ishelter in place' are...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13,000 people receive wildfires evacuation ord...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>just got sent this photo from ruby alaska as s...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id keyword location                                               text  \\\n",
+       "0   1     NaN      NaN  our deeds are the reason of this earthquake ma...   \n",
+       "1   4     NaN      NaN             forest fire near la ronge sask. canada   \n",
+       "2   5     NaN      NaN  all residents asked to  ishelter in place' are...   \n",
+       "3   6     NaN      NaN  13,000 people receive wildfires evacuation ord...   \n",
+       "4   7     NaN      NaN  just got sent this photo from ruby alaska as s...   \n",
+       "\n",
+       "   target  length  \n",
+       "0       1      69  \n",
+       "1       1      38  \n",
+       "2       1     133  \n",
+       "3       1      65  \n",
+       "4       1      88  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "27aea799",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:50:55.389812Z",
+     "iopub.status.busy": "2023-08-29T18:50:55.389213Z",
+     "iopub.status.idle": "2023-08-29T18:51:01.488247Z",
+     "shell.execute_reply": "2023-08-29T18:51:01.487251Z"
+    },
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "papermill": {
+     "duration": 6.108293,
+     "end_time": "2023-08-29T18:51:01.490690",
+     "exception": false,
+     "start_time": "2023-08-29T18:50:55.382397",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58c34d917a494030a444b373a32813e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)lve/main/config.json:   0%|          | 0.00/747 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61bff88240994d8ab483df233aae7633",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86da6a938b714ad6a2812b4f285e142b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d92634aa02a404f986e545a6f8f4c75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "seq_len = 256\n",
+    "batch_size = 16\n",
+    "num_samples = len(train)\n",
+    "model_name = 'cardiffnlp/twitter-roberta-base-sentiment'\n",
+    "\n",
+    "tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\n",
+    "\n",
+    "train_tokens = tokenizer(\n",
+    "    train['text'].tolist(),\n",
+    "    max_length=seq_len,\n",
+    "    truncation=True,\n",
+    "    padding='max_length',\n",
+    "    add_special_tokens=True,\n",
+    "    return_tensors='np'\n",
+    ")\n",
+    "#one hot encoding\n",
+    "y_train = train['target'].values\n",
+    "labels = np.zeros((num_samples, y_train.max() + 1))\n",
+    "labels[np.arange(num_samples), y_train] = 1\n",
+    "\n",
+    "dataset = tf.data.Dataset.from_tensor_slices(\n",
+    "    (\n",
+    "        train_tokens['input_ids'],\n",
+    "        train_tokens['attention_mask'],\n",
+    "        labels\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "def map_func(input_ids, masks, labels):\n",
+    "    return {\n",
+    "        'input_ids': input_ids,\n",
+    "        'attention_mask': masks\n",
+    "    }, labels\n",
+    "\n",
+    "dataset = dataset.map(map_func)\n",
+    "dataset = dataset.shuffle(10000).batch(batch_size=batch_size, drop_remainder=True)\n",
+    "\n",
+    "split = 0.7\n",
+    "size = int((train_tokens['input_ids'].shape[0] // batch_size) * split)\n",
+    "\n",
+    "train_ds = dataset.take(size)\n",
+    "val_ds = dataset.skip(size)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "07c3184c",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T18:51:01.507599Z",
+     "iopub.status.busy": "2023-08-29T18:51:01.505873Z",
+     "iopub.status.idle": "2023-08-29T19:43:48.159755Z",
+     "shell.execute_reply": "2023-08-29T19:43:48.158701Z"
+    },
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "papermill": {
+     "duration": 3166.664367,
+     "end_time": "2023-08-29T19:43:48.162103",
+     "exception": false,
+     "start_time": "2023-08-29T18:51:01.497736",
+     "status": "completed"
+    },
+    "pycharm": {
+     "is_executing": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72b50f841f5e4724bceaab5e4c8752a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading tf_model.h5:   0%|          | 0.00/501M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some layers from the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment were not used when initializing TFRobertaModel: ['classifier']\n",
+      "- This IS expected if you are initializing TFRobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing TFRobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "All the layers of TFRobertaModel were initialized from the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment.\n",
+      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "332/332 [==============================] - 341s 907ms/step - loss: 0.4342 - binary_accuracy: 0.8061 - val_loss: 0.3402 - val_binary_accuracy: 0.8645\n",
+      "Epoch 2/10\n",
+      "332/332 [==============================] - 298s 896ms/step - loss: 0.3578 - binary_accuracy: 0.8558 - val_loss: 0.2811 - val_binary_accuracy: 0.8872\n",
+      "Epoch 3/10\n",
+      "332/332 [==============================] - 298s 896ms/step - loss: 0.3082 - binary_accuracy: 0.8790 - val_loss: 0.2332 - val_binary_accuracy: 0.9108\n",
+      "Epoch 4/10\n",
+      "332/332 [==============================] - 298s 899ms/step - loss: 0.2663 - binary_accuracy: 0.8998 - val_loss: 0.2078 - val_binary_accuracy: 0.9191\n",
+      "Epoch 5/10\n",
+      "332/332 [==============================] - 298s 897ms/step - loss: 0.2294 - binary_accuracy: 0.9136 - val_loss: 0.1747 - val_binary_accuracy: 0.9301\n",
+      "Epoch 6/10\n",
+      "332/332 [==============================] - 298s 896ms/step - loss: 0.1958 - binary_accuracy: 0.9279 - val_loss: 0.1173 - val_binary_accuracy: 0.9532\n",
+      "Epoch 7/10\n",
+      "332/332 [==============================] - 297s 895ms/step - loss: 0.1554 - binary_accuracy: 0.9424 - val_loss: 0.0952 - val_binary_accuracy: 0.9628\n",
+      "Epoch 8/10\n",
+      "332/332 [==============================] - 298s 897ms/step - loss: 0.1321 - binary_accuracy: 0.9539 - val_loss: 0.0786 - val_binary_accuracy: 0.9694\n",
+      "Epoch 9/10\n",
+      "332/332 [==============================] - 298s 896ms/step - loss: 0.1162 - binary_accuracy: 0.9550 - val_loss: 0.0516 - val_binary_accuracy: 0.9786\n",
+      "Epoch 10/10\n",
+      "332/332 [==============================] - 298s 897ms/step - loss: 0.0936 - binary_accuracy: 0.9646 - val_loss: 0.0570 - val_binary_accuracy: 0.9812\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = TFAutoModel.from_pretrained(model_name)\n",
+    "\n",
+    "\n",
+    "input_ids = tf.keras.layers.Input(shape=(seq_len,), name='input_ids', dtype='int32')\n",
+    "mask = tf.keras.layers.Input(shape=(seq_len,), name='attention_mask', dtype='int32')\n",
+    "\n",
+    "\n",
+    "embeddings = model(input_ids, attention_mask=mask)[0]\n",
+    "embeddings = embeddings[:, 0, :]\n",
+    "\n",
+    "x = tf.keras.layers.Dense(512, activation='relu')(embeddings)\n",
+    "\n",
+    "y = tf.keras.layers.Dense(2, activation='softmax', name='outputs')(x)\n",
+    "\n",
+    "bert_model = tf.keras.Model(inputs=[input_ids, mask], outputs=y)\n",
+    "\n",
+    "\n",
+    "\n",
+    "optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5)\n",
+    "loss = tf.keras.losses.CategoricalCrossentropy()\n",
+    "acc = tf.keras.metrics.BinaryAccuracy()\n",
+    "\n",
+    "bert_model.compile(optimizer=optimizer, loss=loss, metrics=[acc])\n",
+    "\n",
+    "history = bert_model.fit(\n",
+    "    train_ds,\n",
+    "    validation_data=val_ds,\n",
+    "    epochs=10,\n",
+    "    batch_size=batch_size\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3c66aa92",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T19:43:48.693104Z",
+     "iopub.status.busy": "2023-08-29T19:43:48.692717Z",
+     "iopub.status.idle": "2023-08-29T19:44:26.368159Z",
+     "shell.execute_reply": "2023-08-29T19:44:26.367152Z"
+    },
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "papermill": {
+     "duration": 37.943319,
+     "end_time": "2023-08-29T19:44:26.370343",
+     "exception": false,
+     "start_time": "2023-08-29T19:43:48.427024",
+     "status": "completed"
+    },
+    "pycharm": {
+     "is_executing": true
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "143/143 [==============================] - 38s 262ms/step - loss: 0.0539 - binary_accuracy: 0.9812\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[0.05391732603311539, 0.9812062978744507]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bert_model.evaluate(val_ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1a608ce0",
+   "metadata": {
+    "collapsed": false,
+    "execution": {
+     "iopub.execute_input": "2023-08-29T19:44:26.926665Z",
+     "iopub.status.busy": "2023-08-29T19:44:26.926239Z",
+     "iopub.status.idle": "2023-08-29T19:45:52.189263Z",
+     "shell.execute_reply": "2023-08-29T19:45:52.188149Z"
+    },
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "papermill": {
+     "duration": 85.535301,
+     "end_time": "2023-08-29T19:45:52.192145",
+     "exception": false,
+     "start_time": "2023-08-29T19:44:26.656844",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def prep_data(text):\n",
+    "    tokens = tokenizer(\n",
+    "        text, max_length=256, truncation=True, \n",
+    "        padding='max_length', \n",
+    "        add_special_tokens=True, \n",
+    "        return_tensors='tf'\n",
+    "    )\n",
+    "    return {\n",
+    "        'input_ids': tokens['input_ids'], \n",
+    "        'attention_mask': tokens['attention_mask']\n",
+    "    }\n",
+    "\n",
+    "test['target'] = None\n",
+    "\n",
+    "for i, row in test.iterrows():\n",
+    "    tokens = prep_data(row['text'])\n",
+    "    probs = bert_model.predict_on_batch(tokens)\n",
+    "    pred = np.argmax(probs)\n",
+    "    test.at[i, 'target'] = pred\n",
+    "    \n",
+    "test['target'] = test['target'].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "05adafd8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-08-29T19:45:52.735409Z",
+     "iopub.status.busy": "2023-08-29T19:45:52.735062Z",
+     "iopub.status.idle": "2023-08-29T19:45:52.746165Z",
+     "shell.execute_reply": "2023-08-29T19:45:52.745272Z"
+    },
+    "papermill": {
+     "duration": 0.283108,
+     "end_time": "2023-08-29T19:45:52.748077",
+     "exception": false,
+     "start_time": "2023-08-29T19:45:52.464969",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>id</th>\n",
+       "      <th>keyword</th>\n",
+       "      <th>location</th>\n",
+       "      <th>text</th>\n",
+       "      <th>length</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>just happened a terrible car crash</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>heard about earthquake is different cities, st...</td>\n",
+       "      <td>64</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
+       "      <td>96</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>apocalypse lighting. spokane wildfires</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>typhoon soudelor kills 28 in china and taiwan</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id keyword location                                               text  \\\n",
+       "0   0     NaN      NaN                 just happened a terrible car crash   \n",
+       "1   2     NaN      NaN  heard about earthquake is different cities, st...   \n",
+       "2   3     NaN      NaN  there is a forest fire at spot pond, geese are...   \n",
+       "3   9     NaN      NaN             apocalypse lighting. spokane wildfires   \n",
+       "4  11     NaN      NaN      typhoon soudelor kills 28 in china and taiwan   \n",
+       "\n",
+       "   length  target  \n",
+       "0      34       1  \n",
+       "1      64       1  \n",
+       "2      96       1  \n",
+       "3      40       1  \n",
+       "4      45       1  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "17f55338",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-08-29T19:45:53.556239Z",
+     "iopub.status.busy": "2023-08-29T19:45:53.555787Z",
+     "iopub.status.idle": "2023-08-29T19:45:53.589116Z",
+     "shell.execute_reply": "2023-08-29T19:45:53.588238Z"
+    },
+    "papermill": {
+     "duration": 0.430644,
+     "end_time": "2023-08-29T19:45:53.591735",
+     "exception": false,
+     "start_time": "2023-08-29T19:45:53.161091",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "sample = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')\n",
+    "sub = pd.DataFrame({'id':sample['id'].values.tolist(), 'target':test['target']})\n",
+    "sub.to_csv('submission.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6a09a06a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-08-29T19:45:54.390293Z",
+     "iopub.status.busy": "2023-08-29T19:45:54.389879Z",
+     "iopub.status.idle": "2023-08-29T19:45:54.403027Z",
+     "shell.execute_reply": "2023-08-29T19:45:54.402198Z"
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