Ensembled_functional_API_sarcasm_classification

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"bpBile8xPdIA"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18248,"status":"ok","timestamp":1670620183059,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"wuq_C7s66FL8","outputId":"5ad83554-c22d-4f91-a597-d3ce15ec071f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n","\u001b[K     |████████████████████████████████| 5.8 MB 30.6 MB/s \n","\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.8.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) 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huggingface-hub-0.11.1 tokenizers-0.13.2 transformers-4.25.1\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting datasets\n","  Downloading datasets-2.7.1-py3-none-any.whl (451 kB)\n","\u001b[K     |████████████████████████████████| 451 kB 32.3 MB/s \n","\u001b[?25hCollecting multiprocess\n","  Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n","\u001b[K     |████████████████████████████████| 132 kB 60.3 MB/s \n","\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2022.11.0)\n","Requirement already satisfied: huggingface-hub<1.0.0,>=0.2.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.11.1)\n","Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n","Collecting responses<0.19\n","  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n","Requirement already satisfied: 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(2022.9.24)\n","Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n","  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n","\u001b[K     |████████████████████████████████| 127 kB 109.8 MB/s \n","\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.6)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n","Installing collected packages: urllib3, xxhash, responses, multiprocess, datasets\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","Successfully installed datasets-2.7.1 multiprocess-0.70.14 responses-0.18.0 urllib3-1.25.11 xxhash-3.1.0\n"]}],"source":["!pip install transformers\n","!pip install datasets"]},{"cell_type":"markdown","metadata":{"id":"7mWggpW86JSy"},"source":["# **Preprocessing data**"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":4351,"status":"ok","timestamp":1670620187406,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"Znr33uHB6FOp"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import sklearn\n","from sklearn import metrics\n","import transformers\n","from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n","import json\n","import matplotlib.pyplot as plt\n","import random\n","import seaborn as sn\n","import pandas as pd\n","import re\n","import seaborn as sns\n","from sklearn.model_selection import train_test_split\n","import tokenizers\n","from datasets import load_dataset\n","from datasets import Dataset\n","\n","import json\n","import os \n","import sklearn\n","import seaborn as sbs\n","import sklearn.naive_bayes \n","import sklearn.model_selection\n","import sklearn.metrics\n","import pandas as pd"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17523,"status":"ok","timestamp":1670620204925,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"E58m1doU6FRI","outputId":"68f2bdf1-c8fb-4552-dbd3-95084751eb94"},"outputs":[{"output_type":"stream","name":"stdout","text":["(1010826, 10)\n"]}],"source":["\n","def load_json(jfile):\n","    data = []\n","    with open(jfile) as f:\n","        for line in f.readlines():\n","            j = json.loads(line)\n","            url, headline, sarcastic = j['article_link'], j['headline'], j['is_sarcastic']\n","            data.append([url, headline, sarcastic])\n","    return pd.DataFrame(data, columns=['article_link', 'headline', 'is_sarcastic'])\n","\n","\n","def load_data_reddit(path):\n","  sarcasm_data = pd.read_csv(path)\n","  print(sarcasm_data.shape)\n","  # sarcasm_data.head()\n","  sarcasm_data.drop(['author', 'subreddit', 'score', 'ups', 'downs', 'date', 'created_utc', 'parent_comment'], axis=1, inplace=True)\n","  # remove empty rows\n","  sarcasm_data.dropna(inplace=True)\n","  # sarcasm_data.head()\n","\n","  mispell_dict = {\"ain't\": \"is not\", \"cannot\": \"can not\", \"aren't\": \"are not\", \"can't\": \"can not\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\", \"didn't\": \"did not\",\n","                \"doesn't\": \"does not\",\n","                \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\", \"he'd\": \"he would\", \"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\",\n","                \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\", \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\", \"I'm\": \"I am\",\n","                \"I've\": \"I have\", \"i'd\": \"i would\", \"i'd've\": \"i would have\", \"i'll\": \"i will\", \"i'll've\": \"i will have\", \"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\",\n","                \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\", \"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\", \"mayn't\": \"may not\", \"might've\": \"might have\",\n","                \"mightn't\": \"might not\", \"mightn't've\": \"might not have\", \"must've\": \"must have\", \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\",\n","                \"needn't've\": \"need not have\", \"o'clock\": \"of the clock\", \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\",\n","                \"shan't've\": \"shall not have\", \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\",\n","                \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\", \"so's\": \"so as\", \"this's\": \"this is\", \"that'd\": \"that would\",\n","                \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\", \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\", \"they'd\": \"they would\",\n","                \"they'd've\": \"they would have\", \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\", \"wasn't\": \"was not\",\n","                \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\", \"we've\": \"we have\", \"weren't\": \"were not\",\n","                \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\", \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\",\n","                \"where'd\": \"where did\", \"where's\": \"where is\", \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\",\n","                \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"wont\": \"will not\", \"won't've\": \"will not have\", \"would've\": \"would have\",\n","                \"wouldn't\": \"would not\",\n","                \"wouldn't've\": \"would not have\", \"y'all\": \"you all\", \"y'all'd\": \"you all would\", \"y'all'd've\": \"you all would have\", \"y'all're\": \"you all are\", \"y'all've\": \"you all have\",\n","                \"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\", \"you're\": \"you are\", \"you've\": \"you have\", 'colour': 'color',\n","                'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor',\n","                'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What',\n","                'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I',\n","                'theBest': 'the best', 'howdoes': 'how does', 'Etherium': 'Ethereum',\n","                'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', \"whst\": 'what',\n","                'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization'}\n","\n","  mispell_dict = {k.lower(): v.lower() for k, v in mispell_dict.items()}\n","\n","  def preprocessing_reddit_text(s):\n","    # making our string lowercase & removing extra spaces\n","    s = str(s).lower().strip()\n","    \n","    # remove contractions.\n","    s = \" \".join([mispell_dict[word] if word in mispell_dict.keys() else word for word in s.split()])\n","    \n","    # removing \\n\n","    s = re.sub('\\n', '', s)\n","    \n","    # put spaces before & after punctuations to make words seprate. Like \"king?\" to \"king\", \"?\".\n","    s = re.sub(r\"([?!,+=—&%\\'\\\";:¿।।।|\\(\\){}\\[\\]//])\", r\" \\1 \", s)\n","    \n","    # Remove more than 2 continues spaces with 1 space.\n","    s = re.sub('[ ]{2,}', ' ', s).strip()\n","    \n","    return s\n","\n","  # apply preprocessing_text function\n","  sarcasm_data['comment'] = sarcasm_data['comment'].apply(preprocessing_reddit_text)\n","  # sarcasm_data.head()\n","\n","  sarcasm_data = sarcasm_data.dropna()\n","\n","  sarcasm_data = sarcasm_data.reset_index(drop=True)\n","  data = sarcasm_data.drop([\"label\"], axis = 1)\n","  label = sarcasm_data.drop([\"comment\"], axis = 1)\n","\n","  return data, label\n","\n","\n","def load_data_headline(path1, path2):\n","  df = load_json(path1)\n","  df2 = load_json(path2)\n","  df.is_sarcastic.value_counts(normalize=True), df.is_sarcastic.value_counts()\n","\n","  frames = [df, df2]  \n","  combine_df = pd.concat(frames)\n","\n","  combine_df.drop(['article_link'], axis=1, inplace=True)\n","  combine_df.dropna(inplace=True)\n","  \n","  mispell_dict = {\"ain't\": \"is not\", \"cannot\": \"can not\", \"aren't\": \"are not\", \"can't\": \"can not\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\", \"didn't\": \"did not\",\n","                \"doesn't\": \"does not\",\n","                \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\", \"he'd\": \"he would\", \"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\",\n","                \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\", \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\", \"I'm\": \"I am\",\n","                \"I've\": \"I have\", \"i'd\": \"i would\", \"i'd've\": \"i would have\", \"i'll\": \"i will\", \"i'll've\": \"i will have\", \"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\",\n","                \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\", \"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\", \"mayn't\": \"may not\", \"might've\": \"might have\",\n","                \"mightn't\": \"might not\", \"mightn't've\": \"might not have\", \"must've\": \"must have\", \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\",\n","                \"needn't've\": \"need not have\", \"o'clock\": \"of the clock\", \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\",\n","                \"shan't've\": \"shall not have\", \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\",\n","                \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\", \"so's\": \"so as\", \"this's\": \"this is\", \"that'd\": \"that would\",\n","                \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\", \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\", \"they'd\": \"they would\",\n","                \"they'd've\": \"they would have\", \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\", \"wasn't\": \"was not\",\n","                \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\", \"we've\": \"we have\", \"weren't\": \"were not\",\n","                \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\", \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\",\n","                \"where'd\": \"where did\", \"where's\": \"where is\", \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\",\n","                \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"wont\": \"will not\", \"won't've\": \"will not have\", \"would've\": \"would have\",\n","                \"wouldn't\": \"would not\",\n","                \"wouldn't've\": \"would not have\", \"y'all\": \"you all\", \"y'all'd\": \"you all would\", \"y'all'd've\": \"you all would have\", \"y'all're\": \"you all are\", \"y'all've\": \"you all have\",\n","                \"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\", \"you're\": \"you are\", \"you've\": \"you have\", 'colour': 'color',\n","                'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor',\n","                'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What',\n","                'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I',\n","                'theBest': 'the best', 'howdoes': 'how does', 'Etherium': 'Ethereum',\n","                'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', \"whst\": 'what',\n","                'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization'}\n","  \n","  mispell_dict = {k.lower(): v.lower() for k, v in mispell_dict.items()}\n","\n","  def preprocessing_headline_text(s):\n","    # making our string lowercase & removing extra spaces\n","    s = str(s).lower().strip()\n","    \n","    # remove contractions.\n","    s = \" \".join([mispell_dict[word] if word in mispell_dict.keys() else word for word in s.split()])\n","    \n","    # removing \\n\n","    s = re.sub('\\n', '', s)\n","    \n","    # put spaces before & after punctuations to make words seprate. Like \"king?\" to \"king\", \"?\".\n","    s = re.sub(r\"([?!,+=—&%\\'\\\";:¿।।।|\\(\\){}\\[\\]//])\", r\" \\1 \", s)\n","    \n","    # Remove more than 2 continues spaces with 1 space.\n","    s = re.sub('[ ]{2,}', ' ', s).strip()\n","    \n","    return s\n","\n","  # apply preprocessing_text function\n","  combine_df['headline'] = combine_df['headline'].apply(preprocessing_headline_text)\n","  # sarcasm_data.head()\n","\n","  combine_df = combine_df.dropna()\n","\n","  combine_df = combine_df.reset_index(drop=True)\n","  data = combine_df.drop([\"is_sarcastic\"], axis = 1)\n","  label = combine_df.drop([\"headline\"], axis = 1)\n","  \n","  return data, label\n","  \n","\n","reddit_path = \"/content/drive/MyDrive/thesis_datasets/train-balanced-sarcasm.csv\"\n","json_1_headline = '/content/drive/MyDrive/thesis_datasets/Sarcasm_Headlines_Dataset.json'\n","json_2_headline = '/content/drive/MyDrive/thesis_datasets/Sarcasm_Headlines_Dataset_v2.json'\n","\n","reddit_data, reddit_label = load_data_reddit(reddit_path)\n","headline_data, headline_label = load_data_headline(json_1_headline, json_2_headline)\n"]},{"cell_type":"code","source":["classes_dist = reddit_label['label'].value_counts().reset_index()\n","plt.figure(figsize=(15,8))\n","chart = sns.barplot(x=\"index\", y=\"label\", data=classes_dist)\n","# chart.set_xticklabels(chart.get_xticklabels(), rotation=45, horizontalalignment='right')\n","chart.set_title(\"Class distribution\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":530},"id":"gey65ssSacB-","executionInfo":{"status":"ok","timestamp":1670620204926,"user_tz":-540,"elapsed":15,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"b07683bb-f144-415f-dc13-00fec809d781"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Class distribution')"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["from wordcloud import WordCloud, STOPWORDS\n","\n","wordcloud = WordCloud(background_color='black', stopwords = STOPWORDS,\n","                max_words = 200, max_font_size = 100, \n","                random_state = 17, width=800, height=400)\n","\n","# Word cloud are nice, but not very useful"],"metadata":{"id":"sW9CMQ5bbIA8","executionInfo":{"status":"ok","timestamp":1670620205551,"user_tz":-540,"elapsed":630,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["sarcasm_data = pd.read_csv(reddit_path)\n","print(sarcasm_data.shape)\n","# sarcasm_data.head()\n","sarcasm_data.drop(['author', 'subreddit', 'score', 'ups', 'downs', 'date', 'created_utc', 'parent_comment'], axis=1, inplace=True)\n","# remove empty rows\n","sarcasm_data.dropna(inplace=True)\n","sarcasm_data.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":223},"id":"a2xRU2cmd2Me","executionInfo":{"status":"ok","timestamp":1670620209301,"user_tz":-540,"elapsed":3752,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"8715681d-8176-4495-dda7-bc8d28c3d026"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["(1010826, 10)\n"]},{"output_type":"execute_result","data":{"text/plain":["   label                                            comment\n","0      0                                         NC and NH.\n","1      0  You do know west teams play against west teams...\n","2      0  They were underdogs earlier today, but since G...\n","3      0  This meme isn't funny none of the \"new york ni...\n","4      0                    I could use one of those tools."],"text/html":["\n","  <div id=\"df-c7fa463c-e7fc-4368-a462-669befab31b6\">\n","    <div class=\"colab-df-container\">\n","      <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>label</th>\n","      <th>comment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>NC and NH.</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>You do know west teams play against west teams...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>They were underdogs earlier today, but since G...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>This meme isn't funny none of the \"new york ni...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>I could use one of those tools.</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7fa463c-e7fc-4368-a462-669befab31b6')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 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'headline']))\n","plt.imshow(wordcloud);"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":519},"id":"ZfCe2Gp5bIEk","executionInfo":{"status":"error","timestamp":1670620209302,"user_tz":-540,"elapsed":9,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"cc24c2f0-288d-459e-df76-13810114372a"},"execution_count":7,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'is_sarcastic'","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-7-258d2153da2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mwordcloud\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msarcasm_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msarcasm_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'is_sarcastic'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'headline'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwordcloud\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'is_sarcastic'"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x864 with 0 Axes>"]},"metadata":{}}]},{"cell_type":"code","source":["plt.figure(figsize=(16, 12))\n","wordcloud.generate(str(sarcasm_data.loc[sarcasm_data['is_sarcastic'] == 0, 'headline']))\n","plt.imshow(wordcloud);"],"metadata":{"id":"z1LCm_31eIz-","executionInfo":{"status":"aborted","timestamp":1670620209303,"user_tz":-540,"elapsed":8,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sarcasm_data.loc[sarcasm_data['label'] == 1, 'comment'].str.len().apply(np.log1p).hist(label='sarcastic', alpha=.5)\n","sarcasm_data.loc[sarcasm_data['label'] == 0, 'comment'].str.len().apply(np.log1p).hist(label='normal', alpha=.5)\n","plt.legend();"],"metadata":{"id":"2ape-DtOiWuB","executionInfo":{"status":"aborted","timestamp":1670620209303,"user_tz":-540,"elapsed":8,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nSxV6ZJNJeM2"},"source":["**splitting the data**"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":460,"status":"ok","timestamp":1670620506626,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"pxBHEw-s6FTa","outputId":"43f9d1d4-5da0-4bb6-9520-3baa7982646f"},"outputs":[{"output_type":"stream","name":"stdout","text":["(1010773, 1)\n","(55328, 1)\n","(1010773, 1)\n","(55328, 1)\n"]}],"source":["print(reddit_data.shape)\n","print(headline_data.shape)\n","print(reddit_label.shape)\n","print(headline_label.shape)"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1670620507089,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"uRyh6mbuH-7J"},"outputs":[],"source":["train_text, val_text, train_labels, val_labels = train_test_split(reddit_data, reddit_label, test_size=0.1)"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1670620508023,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"XCZauik0EJpE","outputId":"85733084-8403-4d50-9d34-47c9ec13e18c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["numpy.ndarray"]},"metadata":{},"execution_count":10}],"source":["y_train = train_labels[\"label\"].to_numpy()\n","y_test = val_labels[\"label\"].to_numpy()\n","type(y_train)"]},{"cell_type":"code","source":["print(\"training: \", train_text.shape)\n","print(\"testing : \", val_text.shape)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zBoAqyPsJ6SM","executionInfo":{"status":"ok","timestamp":1670620508504,"user_tz":-540,"elapsed":2,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"934180f7-b49c-425c-b591-cb1f7e14b167"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["training:  (909695, 1)\n","testing :  (101078, 1)\n"]}]},{"cell_type":"markdown","metadata":{"id":"zOztdCrUJhL6"},"source":["**Tokenizing the data**"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":305,"referenced_widgets":["66f516da9843419bba73a488590797e7","0eeee9b8cac34bfe8b12cf5e786dc5be","3fd403d505d74836a9bc4a736e15d611","ad939bde2ccd4554950a7ea48f70d2fa","62cd2ee3e1304199bdfd2767ca4af5c4","4caab5a4eb4841b1b5ab5577ee7b47c4","cd79448732e2416ba86487e2e7e9094f","3dbc38f422b74201b8bf7d1c20801442","9c47d3ec0b304b05a52fe6d388b9ca0a","7535023e735d4b5981cf83e230447ae3","47fea0703de84bee96c9c83bfa5117d3","62bfab077f034d75b4c2946eee044d4d","51845aaddc1f43f8aa5dc2aab626ef8d","dfa4063f14d54c9bb9c569d066ed18ba","3b16c940d981401b865f84ac6cc1ac90","892ed9c0e0e04be29fa6ff856b21c330","11830585b17348ab8a0603053b5b0618","a64cef0cddc34aceafd41189a4c3f494","4efb44693b7b4a6abd9e82a4ea229383","3ea58173ff1e473da8fd1384528e1ebf","26361bca567c466796e66f99b7ef8b47","23a80c57060a496fad9f64d59e977174","c4207607b8e148af8ee97d8e1db7286e","53385de35c144ae3afe8ecb3ef47eda7","5aa4034c4254451c8ceb12217aa935c8","b8be0da12a2d4ba581547c54af120c5a","29b6c7fb223c464f95d2b04996460757","d710bcb43c4f441b8c0907992a1a937d","a0e6abc6b8af4a53bb818d45fd5cf3fe","92c1232adb424d7da27d400ed40567c4","4aa729d46e3c4b0daa51ae5f5a9f2dc7","2df72da61d3947bbab7a5beb7e285d4d","c92029b996ea43fbbce22a31eedac533","9bf593b74fd84e1f9d2a93ca4723b7f4","f0695cd7eb47484da37af8ed4e20463a","33cd32b8540d4ca399ef3fa0f165d10d","ae9a0c12300c43dc8533adcce5b54e0b","54381be196724cf78d7274fca4ac494e","e2701a7411ad4b359081dc3d88af96bc","1e1b8481154b41689da0edc6cba00b57","1d6493dc10ba4671975112cc9a7e56b0","edd115f7bf7148b096e16269f8e954ae","a8a56da44e96499eb0bee15e53f2192c","60048e0ade734f5a9347a46b3defbf34","60c8047b61d24a56afd820056cbdf3b4","b2359319833d4df5b2c877dd5381040f","97076d5131ba4b209d641522212ee5ef","15b18682162f48f2a21f909ceea1d870","905126f237e54b52b187302cac3c00ec","29fff80216514cbf98540ad57d4731e8","d47d23cafc774d289036fc6438289e90","0c25af416ff54babb4ad182daf981479","e445b309610343ffad95b4868e1a64f6","174b538f457a4c5f91b8f43040331672","248e6154420142ed89290df90dcf35f8","eccfad9a573946b4ab814a534ea59e0d","c00baf90b75e4c0aaea15c2d7a2dd152","0f74c1cdc9ee43fab5f478f19a3cbf08","d780d8d2d08f44f9a59bbf239234928c","6e35bab8e3914b8fa8a5947b00c26418","ccf44f04b1ab497a9f4c48d0a1f054e9","52af8d2b6d7e4f27847717d3884aac93","a51e7d4849f84a3c9f91ffb13b7ff9c8","514d368387e04561830c54cca4792e3e","62f12f2815fa4be6af506f0e83ae0a31","f90554be647a45aa9ead732bba2d2829","cea579065429492994985306f09a76ac","742a22e2b15649c09ea544d5ceabb7c2","1b2540364181431c94b949b16e1936b6","559ae56a2c214842962dd9e71a7472b0","82c8767aa0c14a83816843c330ba98fd","d22450f05753463087387ee6b60f5445","ef555153f0784cdb952b72ce2afdee6a","0d79b0eb124d439baec5867d803f3da5","0befabf434da470387cdddd065157f77","ae00a33c611e4b688d7094059a019a57","ea20208b43fb468ab2e4cdd7ad7525a5","5501120ff0384883b5a711a9db4cb173","9467794698a7405e878778d88b467a50","d9c47160c281475a967e3e6b7682b1ad","3b874b63434b4df893864af836006a67","28635602ddaa4a94a72539b408b78304","28824996486347e3a12c45264a1fe3af","2dc1e35e83424302a116466bb404e42b","0f4920c2f69e43a0b67817aa32cd7061","74f918f081514c92be4175321a974d5a","1677722e695d483fa53455796491dac9","bc82864c20f14cc7a23861807c757915","f112485077df4b6699152f0df9f64401","8ff9709a9b6f4d8fb8d70c00fc0ee7a3","2fdeaf4420dc4507ad89100fb59d5193","02fcb25961414b5dad12c6d075ba23e5","541651eaeaa7439d823c9b0587755298","0b53f2df36e74429bbcebee4e8781884","b82799e323964fadaab21616a36ef3d4","12a0ba86c5ec4b87b6b980fa20dc9284","b4a1533b3d514069b7e74eadbafc4aca","d3351379cade45bbae7d49183ae3dc4e","a00ce39dab4b40a49b4b7d7dff2895e3"]},"executionInfo":{"elapsed":30513,"status":"ok","timestamp":1670620539667,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"uKTMExz9H-9H","outputId":"88b86a52-e0e4-42f8-9da3-df3057b8b90a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"66f516da9843419bba73a488590797e7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"62bfab077f034d75b4c2946eee044d4d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c4207607b8e148af8ee97d8e1db7286e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9bf593b74fd84e1f9d2a93ca4723b7f4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"60c8047b61d24a56afd820056cbdf3b4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eccfad9a573946b4ab814a534ea59e0d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cea579065429492994985306f09a76ac"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5501120ff0384883b5a711a9db4cb173"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f112485077df4b6699152f0df9f64401"}},"metadata":{}}],"source":["import tensorflow as tf\n","from transformers import BertTokenizer, TFBertModel\n","from transformers import DistilBertTokenizer, TFDistilBertModel\n","from transformers import RobertaTokenizer, TFRobertaModel\n","\n","tokenizer_bert = BertTokenizer.from_pretrained('bert-base-uncased')\n","tokenizer_roberta = RobertaTokenizer.from_pretrained('roberta-base')\n","tokenizer_distilbert = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n","\n","train_dd = list(train_text[\"comment\"])\n","val_dd = list(val_text[\"comment\"])\n"]},{"cell_type":"markdown","metadata":{"id":"ivQtz3j3KkES"},"source":["**Modelling Roberta**"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":393,"referenced_widgets":["e80c48a2413f46f0b18071d8ae292c71","d109089e80ce403d8d0f7736a27ab803","a9aa65910ba44084819416b4412b3dbb","ebeff3c03669409ea0487721ca59da2b","fe5b84997c544e3ba5949ab33c279a21","298d530c6c2247aab4a014ad79989de6","bc8b468e9e4d47c888ccab132d65a7dd","ec3580f186d64171bc23e64b2a639178","eff4843c08954d63ae2eccb762a7c489","c7be6070eeca4eb7a69a2a451ecd5fe7","ff7ade4160a84910a9952bae5448e891","dafdaf89e1c14703a022e80e01d2342d","8b64ba2f1b6b49aead1dec5ff94686ae","fe8937303659488893d23291809dcdc0","a4004a8d732e4e9b91b3209b6e448581","55123e275855429a9d138c7f2098292c","a0bb419b20a145ff848212d1b5a06108","62cecf54689044a8bc5e974127b9dceb","eb1c36074cba4a5f8de70f76504c4b3e","e2b99060ac4c4107942165b5beb8fb9d","8c46b30c48ac4ba5b5946f7650ef5a6d","259cd37d7eb047218859f66c1523ce7a","cd5eba09bee74059af917bf065c5bb06","e857a9d9a01f4e328e03de25c0209cf4","6f53f77ed1a64c89a08d319319c98c90","bdaaba19bb8c4bf5ad5e1386c1a3fa88","f5740c9c625348ceb620e529f614d562","db4620b626164488ae7bfcd49092922c","4e028a3e5d3d43698e7d4c5e81c8a9d6","37c19bbc280041a295522cedd16c76ab","77761712628c4def934c228ccdc75a7e","e3e33456431e489e8800162b4424eef2","772d5f73185a4863b42045533eba6db7"]},"executionInfo":{"elapsed":43369,"status":"ok","timestamp":1670620583028,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"z3oAMVIFH_At","outputId":"764721aa-4273-40b1-9563-0a4a2180b9e2"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/536M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e80c48a2413f46f0b18071d8ae292c71"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some layers from the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['mlm___cls', 'nsp___cls']\n","- This IS expected if you are initializing TFBertModel 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 TFBertModel 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 TFBertModel were initialized from the model checkpoint at bert-base-uncased.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/657M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dafdaf89e1c14703a022e80e01d2342d"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some layers from the model checkpoint at roberta-base were not used when initializing TFRobertaModel: ['lm_head']\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 roberta-base.\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"]},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/363M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd5eba09bee74059af917bf065c5bb06"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertModel: ['activation_13', 'vocab_layer_norm', 'vocab_transform', 'vocab_projector']\n","- This IS expected if you are initializing TFDistilBertModel 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 TFDistilBertModel 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 TFDistilBertModel were initialized from the model checkpoint at distilbert-base-uncased.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertModel for predictions without further training.\n"]}],"source":["import tensorflow.keras as keras \n","\n","bert_base = TFBertModel.from_pretrained(\"bert-base-uncased\")\n","roberta_base = TFRobertaModel.from_pretrained('roberta-base')\n","distilbert_base = TFDistilBertModel.from_pretrained(\"distilbert-base-uncased\")\n","\n","def bert_encode(texts, tokenizer, max_len=512):\n","    all_tokens = []\n","    all_masks = []\n","    all_segments = []\n","    \n","    for text in texts:\n","        text = tokenizer.tokenize(text)\n","        # text = tokenizer(text, padding=\"max_length\", truncation=True, max_length = max_len, return_tensors='tf')\n","            \n","        text = text[:max_len-2]\n","        input_sequence = [\"[CLS]\"] + text + [\"[SEP]\"]\n","        pad_len = max_len - len(input_sequence)\n","        \n","        tokens = tokenizer.convert_tokens_to_ids(input_sequence) + [0] * pad_len\n","        pad_masks = [1] * len(input_sequence) + [0] * pad_len\n","        segment_ids = [0] * max_len\n","        \n","        all_tokens.append(tokens)\n","        all_masks.append(pad_masks)\n","        all_segments.append(segment_ids)\n","    \n","    return np.array(all_tokens), np.array(all_masks), np.array(all_segments)\n","\n","\n","def distilbert_encode(texts, tokenizer, max_len=512):\n","    all_tokens = []\n","    \n","    for text in texts:\n","        text = tokenizer.tokenize(text)\n","            \n","        text = text[:max_len-2]\n","        input_sequence = [\"[CLS]\"] + text + [\"[SEP]\"]\n","        pad_len = max_len - len(input_sequence)\n","        \n","        tokens = tokenizer.convert_tokens_to_ids(input_sequence)\n","        tokens += [0] * pad_len\n","        pad_masks = [1] * len(input_sequence) + [0] * pad_len\n","        segment_ids = [0] * max_len\n","        \n","        all_tokens.append(tokens)\n","    \n","    return np.array(all_tokens)\n","\n","def roberta_encode(texts, tokenizer, max_len=512):\n","    all_tokens = []\n","    all_masks = []\n","    all_segments = []\n","    \n","    for text in texts:\n","        text = tokenizer.tokenize(text)\n","        # text = tokenizer(text, padding=\"max_length\", truncation=True, max_length = max_len, return_tensors='tf')\n","            \n","        text = text[:max_len-2]\n","        input_sequence = [\"[CLS]\"] + text + [\"[SEP]\"]\n","        pad_len = max_len - len(input_sequence)\n","        \n","        tokens = tokenizer.convert_tokens_to_ids(input_sequence) + [0] * pad_len\n","        pad_masks = [1] * len(input_sequence) + [0] * pad_len\n","        segment_ids = [0] * max_len\n","        \n","        all_tokens.append(tokens)\n","        all_masks.append(pad_masks)\n","        all_segments.append(segment_ids)\n","    \n","    return np.array(all_tokens), np.array(all_masks), np.array(all_segments)\n","\n","def build_bert_model(bert_layer, max_len=512):\n","    input_word_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n","    input_mask = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_mask\")\n","    segment_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"segment_ids\")\n","    \n","    bert_layer.trainable = False\n","    sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])\n","    clf_output = sequence_output[0]\n","    net = tf.keras.layers.Bidirectional(keras.layers.GRU(246, return_sequences=True))(clf_output)\n","    net = tf.keras.layers.Dropout(0.3)(net)\n","    net = tf.keras.layers.Bidirectional(keras.layers.GRU(246, return_sequences=True))(net)\n","    net = tf.keras.layers.Dropout(0.2)(net)\n","    net = tf.keras.layers.Bidirectional(keras.layers.GRU(146))(net)\n","    net = tf.keras.layers.Dropout(0.3)(net)\n","    net = tf.keras.layers.Dense(124, activation='relu')(net)\n","    out = tf.keras.layers.Dense(2, activation='softmax')(net)\n","    \n","    model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)\n","    model.compile(tf.keras.optimizers.Adam(lr=5e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","    \n","    return model\n","\n","\n","def build_distilbert_model(distilbert_layer, max_len=512):\n","    distilbert_layer.trainable = False\n","    input_word_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n","    sequence_output = distilbert_layer(input_word_ids)[0]\n","    clf_output = sequence_output\n","\n","    net = tf.keras.layers.Bidirectional(keras.layers.GRU(246, return_sequences=True))(clf_output)\n","    net = tf.keras.layers.Dropout(0.3)(net)\n","    net = tf.keras.layers.Bidirectional(keras.layers.GRU(246, return_sequences=True))(net)\n","    net = tf.keras.layers.Dropout(0.1)(net)\n","    net = tf.keras.layers.Bidirectional(keras.layers.GRU(146))(net)\n","    net = tf.keras.layers.Dropout(0.3)(net)\n","    net = tf.keras.layers.Dense(124, activation='relu')(net)\n","    # net = tf.keras.layers.Dropout(0.2)(net)\n","    out = tf.keras.layers.Dense(2, activation='softmax')(net)\n","\n","    # distilbert_layer.trainable = False\n","    # input_word_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n","    # sequence_output = distilbert_layer(input_word_ids)[0]\n","    # clf_output = sequence_output\n","\n","    # net = tf.keras.layers.Bidirectional(keras.layers.GRU(246, return_sequences=True))(clf_output)\n","    # net = tf.keras.layers.Dropout(0.3)(net)\n","    # net = tf.keras.layers.Bidirectional(keras.layers.GRU(246, return_sequences=True))(net)\n","    # net = tf.keras.layers.Dropout(0.1)(net)\n","    # net = tf.keras.layers.Bidirectional(keras.layers.GRU(146))(net)\n","    # net = tf.keras.layers.Dropout(0.3)(net)\n","    # net = tf.keras.layers.Dense(124, activation='relu')(net)\n","    # out = tf.keras.layers.Dense(2, activation='softmax')(net)\n","    \n","    # model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)\n","    model = tf.keras.models.Model(inputs=input_word_ids, outputs=out)\n","\n","\n","    model.compile(tf.keras.optimizers.Adam(lr=5e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","    \n","    return model\n","\n","\n","def build_roberta_model(roberta_layer, max_len=512):\n","    input_word_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n","    input_mask = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_mask\")\n","    segment_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"segment_ids\")\n","    \n","    roberta_layer.trainable = False\n","    sequence_output = roberta_layer([input_word_ids, input_mask, segment_ids])\n","    clf_output = sequence_output[0]\n","    net = tf.keras.layers.Bidirectional(keras.layers.GRU(246, return_sequences=True))(clf_output)\n","    net = tf.keras.layers.Dropout(0.3)(net)\n","    net = tf.keras.layers.Bidirectional(keras.layers.GRU(146))(net)\n","    net = tf.keras.layers.Dropout(0.3)(net)\n","    # net = tf.keras.layers.Conv1D(246, 1)(clf_output)\n","    # net = tf.keras.layers.Dropout(0.3)(net)\n","    # net = tf.keras.layers.Conv1D(128, 1)(net)\n","    # net = tf.keras.layers.Dropout(0.3)(net)\n","    # net = tf.keras.layers.Flatten()(net)\n","    # net = tf.keras.layers.GlobalMaxPool1D()(net)\n","    net = tf.keras.layers.Dense(124, activation='relu')(net)\n","    # net = tf.keras.layers.Dropout(0.2)(net)\n","    out = tf.keras.layers.Dense(2, activation='softmax')(net)\n","    \n","    model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)\n","    # model = tf.keras.models.Model(inputs=input_word_ids, outputs=out)\n","    model.compile(tf.keras.optimizers.Adam(lr=5e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n","    \n","    return model"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"koe0hq_aJvxa","executionInfo":{"status":"ok","timestamp":1670621306105,"user_tz":-540,"elapsed":723085,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}}},"outputs":[],"source":["max_len = 100\n","## bert encode\n","X_train_bert = bert_encode(train_dd, tokenizer_bert, max_len=max_len)\n","X_val_bert = bert_encode(val_dd, tokenizer_bert, max_len=max_len)\n","## Roberta encode\n","X_train_roberta = roberta_encode(train_dd, tokenizer_roberta, max_len=max_len)\n","X_val_roberta = roberta_encode(val_dd, tokenizer_roberta, max_len=max_len)\n","## Distilbert encode\n","X_train_distil = distilbert_encode(train_dd, tokenizer_distilbert, max_len=max_len)\n","X_val_distil = distilbert_encode(val_dd, tokenizer_distilbert, max_len=max_len)"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12701,"status":"ok","timestamp":1670621318803,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"QQ5YehpWJxNn","outputId":"7b36c627-2bae-4b10-8f6e-4c10e8380e6c"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n","  super(Adam, self).__init__(name, **kwargs)\n"]}],"source":["# build model\n","max_len = 100\n","model_bert = build_bert_model(bert_base, max_len=max_len)\n","model_roberta = build_roberta_model(roberta_base, max_len=max_len)\n","model_distilbert = build_distilbert_model(distilbert_base, max_len=max_len)\n","\n","# model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"mTWO8X27J_Y8","outputId":"3d1c38f5-74e9-4652-fb48-c236b47c360b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.5622 - accuracy: 0.7044\n","Epoch 1: val_accuracy improved from -inf to 0.72784, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5\n","28428/28428 [==============================] - 2120s 74ms/step - loss: 0.5622 - accuracy: 0.7044 - val_loss: 0.5317 - val_accuracy: 0.7278\n","Epoch 2/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.5314 - accuracy: 0.7285\n","Epoch 2: val_accuracy improved from 0.72784 to 0.74042, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5\n","28428/28428 [==============================] - 2093s 74ms/step - loss: 0.5314 - accuracy: 0.7285 - val_loss: 0.5156 - val_accuracy: 0.7404\n","Epoch 3/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.5156 - accuracy: 0.7399\n","Epoch 3: val_accuracy improved from 0.74042 to 0.74835, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5\n","28428/28428 [==============================] - 2098s 74ms/step - loss: 0.5156 - accuracy: 0.7399 - val_loss: 0.5044 - val_accuracy: 0.7484\n","Epoch 4/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.5048 - accuracy: 0.7481\n","Epoch 4: val_accuracy improved from 0.74835 to 0.75239, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5\n","28428/28428 [==============================] - 2090s 74ms/step - loss: 0.5048 - accuracy: 0.7481 - val_loss: 0.5010 - val_accuracy: 0.7524\n","Epoch 5/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4963 - accuracy: 0.7541\n","Epoch 5: val_accuracy improved from 0.75239 to 0.75454, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5\n","28428/28428 [==============================] - 2105s 74ms/step - loss: 0.4963 - accuracy: 0.7541 - val_loss: 0.4972 - val_accuracy: 0.7545\n","Epoch 6/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4881 - accuracy: 0.7598\n","Epoch 6: val_accuracy improved from 0.75454 to 0.76177, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5\n","28428/28428 [==============================] - 2106s 74ms/step - loss: 0.4881 - accuracy: 0.7598 - val_loss: 0.4882 - val_accuracy: 0.7618\n","Epoch 7/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4806 - accuracy: 0.7647\n","Epoch 7: val_accuracy improved from 0.76177 to 0.76332, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5\n","28428/28428 [==============================] - 2104s 74ms/step - loss: 0.4806 - accuracy: 0.7647 - val_loss: 0.4871 - val_accuracy: 0.7633\n","Epoch 8/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4741 - accuracy: 0.7692\n","Epoch 8: val_accuracy improved from 0.76332 to 0.76475, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5\n","28428/28428 [==============================] - 2082s 73ms/step - loss: 0.4741 - accuracy: 0.7692 - val_loss: 0.4874 - val_accuracy: 0.7647\n","Epoch 9/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4677 - accuracy: 0.7737\n","Epoch 9: val_accuracy did not improve from 0.76475\n","28428/28428 [==============================] - 2086s 73ms/step - loss: 0.4677 - accuracy: 0.7737 - val_loss: 0.4852 - val_accuracy: 0.7645\n","Epoch 10/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4615 - accuracy: 0.7771\n","Epoch 10: val_accuracy did not improve from 0.76475\n","28428/28428 [==============================] - 2099s 74ms/step - loss: 0.4615 - accuracy: 0.7771 - val_loss: 0.4851 - val_accuracy: 0.7643\n","CPU times: user 6h 25min 16s, sys: 54min 18s, total: 7h 19min 34s\n","Wall time: 5h 49min 46s\n"]}],"source":["# # training\n","\n","# %%time\n","# checkpoint = tf.keras.callbacks.ModelCheckpoint('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5', monitor='val_accuracy', save_best_only=True, verbose=1)\n","# earlystopping = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=3, verbose=1)\n","\n","# train_history = model.fit(\n","#     X_train, y_train,\n","#     validation_data=(X_val, y_test),\n","#     # validation_split=0.1,\n","#     epochs=10,\n","#     callbacks=[checkpoint, earlystopping],\n","#     batch_size=32,\n","#     verbose=1\n","# )\n","\n","# # model save weights\n","\n","# # model.save_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distil_h5/weights.h5')"]},{"cell_type":"markdown","metadata":{"id":"TKruNO5IaZkJ"},"source":["**Loading Model**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":158474,"status":"ok","timestamp":1669022754366,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"BY3tRwXMKG0e","outputId":"92cde15f-ec7e-41ed-e15a-748fb02fe3a5"},"outputs":[{"output_type":"stream","name":"stdout","text":["3159/3159 [==============================] - 155s 47ms/step\n","Validation accuracy:  0.7872534082589683\n","CPU times: user 2min 56s, sys: 24.6 s, total: 3min 21s\n","Wall time: 2min 39s\n"]}],"source":["# %%time\n","# model.load_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5')\n","# y_preds = model.predict(X_val).round().astype(int).argmax(axis=-1)\n","# print(\"Validation accuracy: \", sklearn.metrics.accuracy_score(y_test, y_preds))"]},{"cell_type":"markdown","source":["**Load 3 different models for ensembles**"],"metadata":{"id":"E-QKOiMNxUMO"}},{"cell_type":"code","execution_count":16,"metadata":{"id":"-UYVfxz_KG37","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670621341394,"user_tz":-540,"elapsed":20370,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"cdf4a867-b573-4e7f-9cd2-da9b39d5187a"},"outputs":[{"output_type":"stream","name":"stdout","text":["CPU times: user 952 ms, sys: 744 ms, total: 1.7 s\n","Wall time: 22.4 s\n"]}],"source":["%%time\n","model_bert.load_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5')\n","model_roberta.load_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/roberta/model_e10/model.h5')\n","model_distilbert.load_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"se_lBORE8Nz2"},"outputs":[],"source":["# %%time\n","# models = [model_bert, model_roberta, model_distilbert]\n","# preds = [model.predict(X_val_bert) for model in models]\n","# preds = np.array(preds)\n","# weights = [0.4, 0.4, 0.1]\n","\n","# weighted_preds = np.tensordot(preds, weights, axes = ((0), (0)))\n","# weighted_ensemble_prediction = np.argmax(weighted_preds, axis = 1)\n","\n","# weighted_accuracy = sklearn.metrics.accuracy_score(y_test, weighted_ensemble_prediction)\n","# print(\"Weighted Validation accuracy is: \", weighted_accuracy)\n"]},{"cell_type":"code","source":["%%time\n","models = [model_bert, model_roberta, model_distilbert]\n","x_vals = [X_val_bert, X_val_roberta, X_val_distil] \n","\n","preds = [model.predict(x_vals[i]) for i, model in enumerate(models)]\n","preds = np.array(preds)\n","weights = [0.5, 0.4, 0.1]\n","\n","weighted_preds = np.tensordot(preds, weights, axes = ((0), (0)))\n","weighted_ensemble_prediction = np.argmax(weighted_preds, axis = 1)\n","\n","weighted_accuracy = sklearn.metrics.accuracy_score(y_test, weighted_ensemble_prediction)\n","print(\"Weighted Validation accuracy is: \", weighted_accuracy)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6jVJALsONkjz","executionInfo":{"status":"ok","timestamp":1670623133923,"user_tz":-540,"elapsed":1783175,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"ef5085f5-bd21-48dc-ec24-5c67c7a09141"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["3159/3159 [==============================] - 719s 226ms/step\n","3159/3159 [==============================] - 679s 214ms/step\n","3159/3159 [==============================] - 382s 120ms/step\n","Weighted Validation accuracy is:  0.793298244919765\n","CPU times: user 10min 53s, sys: 1min 53s, total: 12min 46s\n","Wall time: 29min 42s\n"]}]},{"cell_type":"code","execution_count":18,"metadata":{"id":"SzQLw25r0GAX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670623178714,"user_tz":-540,"elapsed":489,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"47bc63f9-0c6e-4ffd-bfc2-a451d4260537"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[43117,  7672],\n","       [13221, 37068]])"]},"metadata":{},"execution_count":18}],"source":["# Confusion matrix\n","\n","from sklearn.metrics import confusion_matrix\n","cm = confusion_matrix(y_test, weighted_ensemble_prediction)\n","cm"]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","print(classification_report(y_test, weighted_ensemble_prediction, target_names = ['Not Sarcastic','Sarcastic']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EVpcdR_my0bh","executionInfo":{"status":"ok","timestamp":1670623186896,"user_tz":-540,"elapsed":2,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"3378ff21-90da-4176-a773-531a6860a2d4"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["               precision    recall  f1-score   support\n","\n","Not Sarcastic       0.77      0.85      0.80     50789\n","    Sarcastic       0.83      0.74      0.78     50289\n","\n","     accuracy                           0.79    101078\n","    macro avg       0.80      0.79      0.79    101078\n"," weighted avg       0.80      0.79      0.79    101078\n","\n"]}]},{"cell_type":"code","source":["import seaborn as sns\n","\n","plt.figure(figsize = (15,10))\n","sns.heatmap(cm, cmap= \"Blues\", linecolor = 'black' , linewidth = 1 , annot = True, fmt='')\n","plt.xlabel(\"Predicted\")\n","plt.ylabel(\"Actual\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":623},"id":"AROSf5JJy0gS","executionInfo":{"status":"ok","timestamp":1670623193983,"user_tz":-540,"elapsed":4,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"f1b3d33c-65f0-49a2-a6f1-0651123ec6c1"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(114.0, 0.5, 'Actual')"]},"metadata":{},"execution_count":20},{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x720 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"OKd8pFz2y0jq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"I4IRU_rs0aUY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"CczXoQRs0hrk"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Testing models on new dataset.** "],"metadata":{"id":"YS7gIMS-9jJD"}},{"cell_type":"code","source":["headline_data, headline_label "],"metadata":{"id":"qMU4wupHCp-F"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["headline_label[\"is_sarcastic\"].head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TZdtuS07Hge-","executionInfo":{"status":"ok","timestamp":1670169433039,"user_tz":-540,"elapsed":14,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"7472e793-e9a9-45c0-e0a0-801b4c3e7308"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    0\n","1    0\n","2    1\n","3    1\n","4    0\n","Name: is_sarcastic, dtype: int64"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["x_headline = headline_data[\"headline\"].to_numpy()\n","y_headline = headline_label[\"is_sarcastic\"].to_numpy()"],"metadata":{"id":"qv2mkm99HS0B"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# import tensorflow as tf\n","# from transformers import BertTokenizer, TFBertModel\n","# from transformers import DistilBertTokenizer, TFDistilBertModel\n","# from transformers import RobertaTokenizer, TFRobertaModel\n","\n","# tokenizer_bert = BertTokenizer.from_pretrained('bert-base-uncased')\n","# tokenizer_roberta = RobertaTokenizer.from_pretrained('roberta-base')\n","# tokenizer_distilbert = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n","\n","max_len = 100\n","## bert encode\n","test_bert = bert_encode(x_headline, tokenizer_bert, max_len=max_len)\n","# X_val_bert = bert_encode(val_dd, tokenizer_bert, max_len=max_len)\n","## Roberta encode\n","test_roberta = roberta_encode(x_headline, tokenizer_roberta, max_len=max_len)\n","# X_val_roberta = roberta_encode(val_dd, tokenizer_roberta, max_len=max_len)\n","## Distilbert encode\n","test_distil = distilbert_encode(x_headline, tokenizer_distilbert, max_len=max_len)\n","# X_val_distil = distilbert_encode(val_dd, tokenizer_distilbert, max_len=max_len)"],"metadata":{"id":"9Pm8CrFKMYU0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# build  model\n","max_len = 100\n","model_bert = build_bert_model(bert_base, max_len=max_len)\n","model_roberta = build_roberta_model(roberta_base, max_len=max_len)\n","model_distilbert = build_distilbert_model(distilbert_base, max_len=max_len)\n","\n","# model.summary()"],"metadata":{"id":"kdQK4_Y5NSJX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Loading saved models\n","\n","%%time\n","model_bert.load_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/BERT/model_e10/model.h5')\n","model_roberta.load_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/roberta/model_e10/model.h5')\n","model_distilbert.load_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5')"],"metadata":{"id":"29AfffOLNayM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670169508395,"user_tz":-540,"elapsed":24509,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"eff14968-c28a-4934-829e-b6c338ee866a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["CPU times: user 931 ms, sys: 745 ms, total: 1.68 s\n","Wall time: 27 s\n"]}]},{"cell_type":"code","source":["%%time\n","models = [model_bert, model_roberta, model_distilbert]\n","x_vals = [test_bert, test_roberta, test_distil] \n","\n","preds = [model.predict(x_vals[i]) for i, model in enumerate(models)]\n","preds = np.array(preds)\n","weights = [0.5, 0.4, 0.1]\n","\n","weighted_preds = np.tensordot(preds, weights, axes = ((0), (0)))\n","weighted_ensemble_prediction = np.argmax(weighted_preds, axis = 1)\n","\n","weighted_accuracy = sklearn.metrics.accuracy_score(y_headline, weighted_ensemble_prediction)\n","print(\"Weighted Validation accuracy is: \", weighted_accuracy)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IHDCP-hE1dYn","executionInfo":{"status":"ok","timestamp":1670170436954,"user_tz":-540,"elapsed":928570,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"e7fd33c3-9d08-48dd-e089-055318b0d342"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1729/1729 [==============================] - 370s 212ms/step\n","1729/1729 [==============================] - 355s 204ms/step\n","1729/1729 [==============================] - 202s 116ms/step\n","Weighted Validation accuracy is:  0.4958971949103528\n","CPU times: user 11min 43s, sys: 4min 13s, total: 15min 57s\n","Wall time: 15min 28s\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"dL3bl4u52kSJ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Prediction on 1 sentence**"],"metadata":{"id":"QdkZj__WIjIH"}},{"cell_type":"code","source":["mispell_dict = {\"ain't\": \"is not\", \"cannot\": \"can not\", \"aren't\": \"are not\", \"can't\": \"can not\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\", \"didn't\": \"did not\",\n","                \"doesn't\": \"does not\",\n","                \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\", \"he'd\": \"he would\", \"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\",\n","                \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\", \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\", \"I'm\": \"I am\",\n","                \"I've\": \"I have\", \"i'd\": \"i would\", \"i'd've\": \"i would have\", \"i'll\": \"i will\", \"i'll've\": \"i will have\", \"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\",\n","                \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\", \"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\", \"mayn't\": \"may not\", \"might've\": \"might have\",\n","                \"mightn't\": \"might not\", \"mightn't've\": \"might not have\", \"must've\": \"must have\", \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\",\n","                \"needn't've\": \"need not have\", \"o'clock\": \"of the clock\", \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\",\n","                \"shan't've\": \"shall not have\", \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\",\n","                \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\", \"so's\": \"so as\", \"this's\": \"this is\", \"that'd\": \"that would\",\n","                \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\", \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\", \"they'd\": \"they would\",\n","                \"they'd've\": \"they would have\", \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\", \"wasn't\": \"was not\",\n","                \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\", \"we've\": \"we have\", \"weren't\": \"were not\",\n","                \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\", \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\",\n","                \"where'd\": \"where did\", \"where's\": \"where is\", \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\",\n","                \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"wont\": \"will not\", \"won't've\": \"will not have\", \"would've\": \"would have\",\n","                \"wouldn't\": \"would not\",\n","                \"wouldn't've\": \"would not have\", \"y'all\": \"you all\", \"y'all'd\": \"you all would\", \"y'all'd've\": \"you all would have\", \"y'all're\": \"you all are\", \"y'all've\": \"you all have\",\n","                \"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\", \"you're\": \"you are\", \"you've\": \"you have\", 'colour': 'color',\n","                'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor',\n","                'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What',\n","                'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I',\n","                'theBest': 'the best', 'howdoes': 'how does', 'Etherium': 'Ethereum',\n","                'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', \"whst\": 'what',\n","                'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization'}\n","  \n","mispell_dict = {k.lower(): v.lower() for k, v in mispell_dict.items()}\n","\n","def preprocessing_new_text(s):\n","  # making our string lowercase & removing extra spaces\n","  s = str(s).lower().strip()\n","  \n","  # remove contractions.\n","  s = \" \".join([mispell_dict[word] if word in mispell_dict.keys() else word for word in s.split()])\n","  \n","  # removing \\n\n","  s = re.sub('\\n', '', s)\n","  \n","  # put spaces before & after punctuations to make words seprate. Like \"king?\" to \"king\", \"?\".\n","  s = re.sub(r\"([?!,+=—&%\\'\\\";:¿।।।|\\(\\){}\\[\\]//])\", r\" \\1 \", s)\n","  \n","  # Remove more than 2 continues spaces with 1 space.\n","  s = re.sub('[ ]{2,}', ' ', s).strip()\n","  \n","  return s"],"metadata":{"id":"oAZ_LdFTM0mf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sentence = \"Isn't it great that, your girlfriend dumped you?\"\n","sentence = preprocessing_new_text(sentence)\n","print(sentence)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3UN8VL9-Igwz","executionInfo":{"status":"ok","timestamp":1670125996768,"user_tz":-540,"elapsed":435,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"8b2c6242-3c25-4e1b-8478-b14f79dc4c56"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["is not it great that , your girlfriend dumped you ?\n"]}]},{"cell_type":"code","source":["max_len = 100\n","test2_bert = bert_encode(sentence, tokenizer_bert, max_len=max_len)\n","test2_roberta = roberta_encode(sentence, tokenizer_roberta, max_len=max_len)\n","test2_distil = distilbert_encode(sentence, tokenizer_distilbert, max_len=max_len)"],"metadata":{"id":"CdZW7ExINQkq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test2_distil[0].shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EWMYP-wZQUY5","executionInfo":{"status":"ok","timestamp":1670126173083,"user_tz":-540,"elapsed":424,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"8ff5ce84-b772-4954-fb31-0868ce0c7784"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(100,)"]},"metadata":{},"execution_count":82}]},{"cell_type":"code","source":["%%time\n","models = [model_bert, model_roberta, model_distilbert]\n","x_vals = [test2_bert, test2_roberta, test2_distil] \n","\n","preds = [model.predict(x_vals[i]) for i, model in enumerate(models)]\n","preds = np.array(preds)\n","weights = [0.5, 0.4, 0.1]\n","\n","weighted_preds = np.tensordot(preds, weights, axes = ((0), (0)))\n","weighted_ensemble_prediction = np.argmax(weighted_preds, axis = 1)\n","\n","# weighted_accuracy = sklearn.metrics.accuracy_score(y_headline, weighted_ensemble_prediction)\n","# print(\"Weighted Validation accuracy is: \", weighted_accuracy)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rd8nEAT5Igzh","executionInfo":{"status":"ok","timestamp":1670126004229,"user_tz":-540,"elapsed":1458,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"34834f90-a6b9-49a1-f275-895c7dceaa27"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2/2 [==============================] - 0s 155ms/step\n","2/2 [==============================] - 0s 146ms/step\n","2/2 [==============================] - 0s 88ms/step\n","CPU times: user 513 ms, sys: 101 ms, total: 615 ms\n","Wall time: 1.16 s\n"]}]},{"cell_type":"code","source":["weighted_ensemble_prediction.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nGzT9eTYIg3B","executionInfo":{"status":"ok","timestamp":1670126100293,"user_tz":-540,"elapsed":567,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"1925bbc0-91b1-4245-aeb2-df32101c97bb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(51,)"]},"metadata":{},"execution_count":78}]},{"cell_type":"code","source":["sentence = \"sun rises from the east\"\n","sentence = preprocessing_text(sentence)\n","print(sentence)\n","\n","sentence = tokenizer.texts_to_sequences([sentence])\n","sentence = pad_sequences(sentence, maxlen = MAX_LEN)\n","sentence"],"metadata":{"id":"8MYM5avmHrY8"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d5gklw0Gk3Vq"},"outputs":[],"source":["print(classification_report(y_test, pred, target_names = ['Not Sarcastic','Sarcastic']))"]},{"cell_type":"code","source":["plt.figure(figsize = (10,10))\n","sns.heatmap(cm,cmap= \"Blues\", linecolor = 'black' , linewidth = 1 , annot = True, fmt='')\n","plt.xlabel(\"Predicted\")\n","plt.ylabel(\"Actual\")"],"metadata":{"id":"FMIRlnRVzn_B"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0m9sG1eba4YA"},"outputs":[],"source":["sentence = \"sun rises from the east\"\n","sentence = preprocessing_text(sentence)\n","print(sentence)\n","\n","sentence = tokenizer.texts_to_sequences([sentence])\n","sentence = pad_sequences(sentence, maxlen = MAX_LEN)\n","sentence"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4w3r6Ru2a4bK"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZTBbt5i5yab8"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X8a4b5WhzgAW"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2SZ-Gw-wyQyV"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0R-5V4QhjnEx"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":328},"executionInfo":{"elapsed":11195,"status":"error","timestamp":1668500364772,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"-Ir185RFa4jG","outputId":"d7b2395a-7623-4a0e-926b-0a9a7d365bc9"},"outputs":[{"ename":"TypeError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-21-6f7daa9f9863>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/weights/\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/saving/saved_model/load.py\u001b[0m in \u001b[0;36mcommon_spec\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m   1149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m       \u001b[0;31m# Please file a bug if you are being hindered by this error.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1151\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'No common supertype of {x} and {y}.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1152\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: No common supertype of TensorSpec(shape=(None, None), dtype=tf.int64, name=None) and TensorSpec(shape=(None, 100), dtype=tf.int32, name='input_ids/attention_mask')."]}],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_KUOt2yo6FVs"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q_3GYKOK6FYS"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KvRaymJu6Fay"},"outputs":[],"source":["from sklearn.metrics import confusion_matrix\n","import seaborn as sns\n","def plot_cm(y_true, y_pred, figsize=(15, 15)):\n","    cm = confusion_matrix(y_true, y_pred, labels=np.unique(y_true))\n","    cm_sum = np.sum(cm, axis=1, keepdims=True)\n","    cm_perc = cm / cm_sum.astype(float) * 100\n","    annot = np.empty_like(cm).astype(str)\n","    nrows, ncols = cm.shape\n","    for i in range(nrows):\n","        for j in range(ncols):\n","            c = cm[i, j]\n","            p = cm_perc[i, j]\n","            if i == j:\n","                s = cm_sum[i]\n","                annot[i, j] = '%.1f%%\\n%d/%d' % (p, c, s)\n","            elif c == 0:\n","                annot[i, j] = ''\n","            else:\n","                annot[i, j] = '%.1f%%\\n%d' % (p, c)\n","    cm = pd.DataFrame(cm, index=np.unique(y_true), columns=np.unique(y_true))\n","    cm.index.name = 'Actual'\n","    cm.columns.name = 'Predicted'\n","    fig, ax = plt.subplots(figsize=figsize)\n","    sns.heatmap(cm, cmap= \"YlGnBu\", annot=annot, fmt='', ax=ax)\n","df_eval = pd.DataFrame({'y_true': y_valid, 'y_preds': y_preds})\n","df_eval['y_true'] = (df_eval['y_true'].apply(label_int2str))\n","df_eval['y_preds'] = (df_eval['y_preds'].apply(label_int2str))\n","plot_cm(df_eval['y_true'], df_eval['y_preds'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nNw1S0Jm6Fc9"},"outputs":[],"source":["# For word cloud\n","\n","from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n","\n","# Wordcloud \n","# Create stopword list:\n","stopwords = set(STOPWORDS)\n","stopwords.update([\"one\", \"first\", \"will\", \"want\", \"give\"])\n","\n","\n","texts = \" \".join(text for text in df.headline)\n","print (\"There are {} words in the combination of headlines.\".format(len(texts)))\n","# Create and generate a word cloud image:\n","wordcloud = WordCloud(max_font_size=50, max_words=100,\n","                      stopwords=stopwords,\n","                      background_color=\"white\").generate(texts)\n","\n","# Display the generated image:\n","plt.figure(figsize = (10, 8))\n","plt.imshow(wordcloud, interpolation='bilinear')\n","plt.axis(\"off\")\n","plt.show()\n","\n","# Save the image in the img folder:\n","wordcloud.to_file(\"first_review.png\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fTSzj1F06FfP"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lQSiPFAd6FhN"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"URohonso6FjJ"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lmXecIhs6Fm9"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7EUOOSRn6Fo8"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p6Gq50fr6Fsb"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e8WzNWhjb011"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OWkoZSuAb05D"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6R4oWfbtb07q"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4UtIQhMRb0_I"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V96zQzWrb1Ep"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WoKXCCqOb1Ik"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ko6_zmyTb1L7"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[],"mount_file_id":"1xvFmRnjbpgwtQapOrseH4g0wpHNqpO9H","authorship_tag":"ABX9TyNKhbbBNXcRWOeqt1F49YCt"},"gpuClass":"premium","kernelspec":{"display_name":"Python 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