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install transformers\n","!pip install datasets"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wuq_C7s66FL8","executionInfo":{"status":"ok","timestamp":1670617773416,"user_tz":-540,"elapsed":17837,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"b97fbe3d-5f25-450f-c049-d7cf195874cd"},"execution_count":1,"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 32.8 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n","Collecting huggingface-hub<1.0,>=0.10.0\n","  Downloading 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(3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2022.9.24)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\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 93.2 MB/s \n","\u001b[?25hRequirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.10.0)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.6)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/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"]}]},{"cell_type":"markdown","source":["# **Preprocessing data**"],"metadata":{"id":"7mWggpW86JSy"}},{"cell_type":"code","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","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"],"metadata":{"id":"Znr33uHB6FOp","executionInfo":{"status":"ok","timestamp":1670617777190,"user_tz":-540,"elapsed":3777,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E58m1doU6FRI","executionInfo":{"status":"ok","timestamp":1670617794020,"user_tz":-540,"elapsed":16840,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"c0f9b19f-1417-407a-f83e-77961b7f4cc2"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["(1010826, 10)\n"]}]},{"cell_type":"markdown","source":["**splitting the data**"],"metadata":{"id":"nSxV6ZJNJeM2"}},{"cell_type":"code","source":["print(reddit_data.shape)\n","print(headline_data.shape)\n","print(reddit_label.shape)\n","print(headline_label.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pxBHEw-s6FTa","executionInfo":{"status":"ok","timestamp":1670617794021,"user_tz":-540,"elapsed":8,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"80cf58e7-7088-42fd-e3f2-09f5f5444a7a"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["(1010773, 1)\n","(55328, 1)\n","(1010773, 1)\n","(55328, 1)\n"]}]},{"cell_type":"code","source":["train_text, val_text, train_labels, val_labels = train_test_split(reddit_data, reddit_label, test_size=0.1)"],"metadata":{"id":"uRyh6mbuH-7J","executionInfo":{"status":"ok","timestamp":1670617794021,"user_tz":-540,"elapsed":6,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["\n","y_train =train_labels[\"label\"].to_numpy()\n","y_test = val_labels[\"label\"].to_numpy()\n","type(y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lRYABiqiM-vP","executionInfo":{"status":"ok","timestamp":1670617794021,"user_tz":-540,"elapsed":6,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"70123ad1-4801-4e2e-e7cd-f9a9b270d448"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["numpy.ndarray"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","source":["**Tokenizing the data**"],"metadata":{"id":"zOztdCrUJhL6"}},{"cell_type":"code","source":["# from transformers import RobertaTokenizer, TFRobertaModel\n","import tensorflow as tf\n","from transformers import DistilBertTokenizer, TFDistilBertModel\n","\n","tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n","# tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n","\n","train_dd = list(train_text[\"comment\"])\n","val_dd = list(val_text[\"comment\"])\n","# train_label = list(train_labels[\"label\"])\n","# val_label = list(val_labels[\"label\"])\n","\n","# train_encodings = tokenizer(train_dd, padding=\"max_length\", truncation=True, max_length = 100, return_tensors='tf')\n","# val_encodings = tokenizer(val_dd, padding=\"max_length\", truncation=True, max_length = 100, return_tensors='tf')\n","\n","# train_dataset = tf.data.Dataset.from_tensor_slices((\n","#     dict(train_encodings),\n","#     train_label\n","# ))\n","\n","# val_dataset = tf.data.Dataset.from_tensor_slices((\n","#     dict(val_encodings),\n","#     val_label\n","# ))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["948b704c06314c3393be46492b794f45","bc1f12921be94d638381b3e1e9790df7","40d2a8398fd84ab4aff220829d984cc2","1aac5553a50c437b977886bdbe865384","e4337c706a8d4989a4f8d23df808687f","4e3aa40e0d8b400d84a4ac8eadd55d2c","22262c3e536c4b7eb036019a2e2ad66e","b8347df08df44926ab6b35d56b59aa1e","6d9fb4f84deb4b0ba736c34e8cc963e0","34a88ce7e25c4fefb566a3adc7c4b6ed","c56df3229f7340b4842094ae682dc5cd","c0887566381842eaa04e2aa0a6ef086f","2f82e739afc4444cafdc6d225daae1fd","39c6a5ecd0e94ff1b6b69652b38a30b1","5de703b2050e45258e00a7e3aa3d0db5","1486136d7934482ea7aed82464f3fc7e","8deae203fd8843238f750ce834d92c69","72b3d067b854465f9d1d890da7da3ee8","32d5d0a7a3814759b82ab71e1c3b4707","c53ef5d8f2f6482b97b0b96d2f55cf0d","a9683189319a438daf35364c418faf9b","9d62ae2fb70c42159f97f360068ff71b","912a429e4cda48cf8d9329938b93f202","ab61975341b342eab0002cc5ec64c2f5","a34a0567fade4966a3e773e0b4e5cb47","971a3236112242a6a8561e8dbe2e21cd","c85b954ca400496aa11c6dbc25ba8f4b","53b4e801036842de82d0721f5ff92b42","5f21321955f84a19a7d26bfcb6a54b82","6ff93386c00e4075a43395ad67d40c36","aa310a6893ca4fd692d0d8caa755fd04","95650a6e72fb4d4a8f9111d82f35d524","cfaef0c84e7b4e878f1bf6940f05d8ac"]},"id":"uKTMExz9H-9H","executionInfo":{"status":"ok","timestamp":1670617805012,"user_tz":-540,"elapsed":10994,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"a8bf1286-3570-4ca4-f5af-55a39476735e"},"execution_count":7,"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":"948b704c06314c3393be46492b794f45"}},"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":"c0887566381842eaa04e2aa0a6ef086f"}},"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":"912a429e4cda48cf8d9329938b93f202"}},"metadata":{}}]},{"cell_type":"markdown","source":["**Modelling DistilBert**"],"metadata":{"id":"ivQtz3j3KkES"}},{"cell_type":"markdown","source":["# **funtional API**"],"metadata":{"id":"yWpgdxhX2Alx"}},{"cell_type":"code","source":["import tensorflow.keras as keras \n","\n","encoder = TFDistilBertModel.from_pretrained(\"distilbert-base-uncased\")\n","# encoder.trainable = False\n","\n","# def distilbert_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 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","\n","def build_model(distilbert_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","    distilbert_layer.trainable = False\n","    \n","    # sequence_output = distilbert_layer([input_word_ids, input_mask, segment_ids])\n","    # distilbert_layer.trainable = False\n","    # last_hidden_states = sequence_output.last_hidden_state\n","    # print(sequence_output)\n","    # clf_output = last_hidden_states\n","    # clf_output = sequence_output[:, 0, :]\n","    # clf_output = sequence_output[0]\n","    # print(clf_output)\n","\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","    # print(sequence_output.shape)\n","    # clf_output = sequence_output[:, 0, :]\n","    # print(sequence_output.shape)\n","    clf_output = sequence_output\n","    # print(clf_output.shape)\n","    # clf_output = sequence_output[0]\n","    # print(clf_output.shape)\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","    # 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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156,"referenced_widgets":["b74b3923f41a4122a1cd8c192e5a5e7b","393ca186fa684b07ab6970e7e613ae0e","39a99a5f5798474c9b10223d6d2c5df1","2505947f6cd441ab8d41ebd1a3062ef9","b3ae031ad57946bc918e917ecffaaf89","6e80145e0665461c86bfe441675c00f5","b125922becd344b491afb7abe7032ae7","cd7f47e6262d4f96b4cb1806897aff04","176ff7802dd84096883864bb2ee524f8","bcdd6803e3724b8e83733032f9546640","39f257fb1b144fa1ac87a0b927b8a1ed"]},"id":"MxtybTQr1_hi","executionInfo":{"status":"ok","timestamp":1670617819073,"user_tz":-540,"elapsed":14069,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"8854c467-2b10-4e06-99c7-29f6cc729a86"},"execution_count":8,"outputs":[{"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":"b74b3923f41a4122a1cd8c192e5a5e7b"}},"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_projector', 'vocab_layer_norm', 'vocab_transform']\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"]}]},{"cell_type":"code","source":["max_len = 100\n","\n","X_train = distilbert_encode(train_dd, tokenizer, max_len=max_len)\n","X_val = distilbert_encode(val_dd, tokenizer, max_len=max_len)"],"metadata":{"id":"BOo7xo0u1_jk","executionInfo":{"status":"ok","timestamp":1670618115486,"user_tz":-540,"elapsed":296422,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# build model\n","max_len = 100\n","model = build_model(encoder, max_len=max_len)\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"89WIQJ4G5zKi","executionInfo":{"status":"ok","timestamp":1670618119588,"user_tz":-540,"elapsed":4111,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"8880208d-5390-4377-85b8-a43bacf41a9b"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_word_ids (InputLayer)  [(None, 100)]            0         \n","                                                                 \n"," tf_distil_bert_model (TFDis  TFBaseModelOutput(last_h  66362880 \n"," tilBertModel)               idden_state=(None, 100,             \n","                             768),                               \n","                              hidden_states=None, att            \n","                             entions=None)                       \n","                                                                 \n"," bidirectional (Bidirectiona  (None, 100, 492)         1499616   \n"," l)                                                              \n","                                                                 \n"," dropout_19 (Dropout)        (None, 100, 492)          0         \n","                                                                 \n"," bidirectional_1 (Bidirectio  (None, 100, 492)         1092240   \n"," nal)                                                            \n","                                                                 \n"," dropout_20 (Dropout)        (None, 100, 492)          0         \n","                                                                 \n"," bidirectional_2 (Bidirectio  (None, 292)              560640    \n"," nal)                                                            \n","                                                                 \n"," dropout_21 (Dropout)        (None, 292)               0         \n","                                                                 \n"," dense (Dense)               (None, 124)               36332     \n","                                                                 \n"," dense_1 (Dense)             (None, 2)                 250       \n","                                                                 \n","=================================================================\n","Total params: 69,551,958\n","Trainable params: 3,189,078\n","Non-trainable params: 66,362,880\n","_________________________________________________________________\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/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"]}]},{"cell_type":"code","source":["# training\n","\n","%%time\n","checkpoint = tf.keras.callbacks.ModelCheckpoint('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/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",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ifRLPMTw1_m6","outputId":"74aa5876-4324-400b-be15-8be948788e2f","executionInfo":{"status":"ok","timestamp":1669134585205,"user_tz":-540,"elapsed":15329484,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.5749 - accuracy: 0.6934\n","Epoch 1: val_accuracy improved from -inf to 0.72082, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1547s 54ms/step - loss: 0.5749 - accuracy: 0.6934 - val_loss: 0.5418 - val_accuracy: 0.7208\n","Epoch 2/10\n","28427/28428 [============================>.] - ETA: 0s - loss: 0.5396 - accuracy: 0.7222\n","Epoch 2: val_accuracy improved from 0.72082 to 0.73827, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1529s 54ms/step - loss: 0.5396 - accuracy: 0.7222 - val_loss: 0.5204 - val_accuracy: 0.7383\n","Epoch 3/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.5236 - accuracy: 0.7345\n","Epoch 3: val_accuracy improved from 0.73827 to 0.74575, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1526s 54ms/step - loss: 0.5236 - accuracy: 0.7345 - val_loss: 0.5111 - val_accuracy: 0.7458\n","Epoch 4/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.5124 - accuracy: 0.7427\n","Epoch 4: val_accuracy improved from 0.74575 to 0.75205, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1537s 54ms/step - loss: 0.5124 - accuracy: 0.7427 - val_loss: 0.5027 - val_accuracy: 0.7521\n","Epoch 5/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.5035 - accuracy: 0.7486\n","Epoch 5: val_accuracy improved from 0.75205 to 0.75327, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1533s 54ms/step - loss: 0.5035 - accuracy: 0.7486 - val_loss: 0.5024 - val_accuracy: 0.7533\n","Epoch 6/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4956 - accuracy: 0.7540\n","Epoch 6: val_accuracy improved from 0.75327 to 0.75359, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1535s 54ms/step - loss: 0.4956 - accuracy: 0.7540 - val_loss: 0.4977 - val_accuracy: 0.7536\n","Epoch 7/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4884 - accuracy: 0.7591\n","Epoch 7: val_accuracy improved from 0.75359 to 0.75827, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1533s 54ms/step - loss: 0.4884 - accuracy: 0.7591 - val_loss: 0.4914 - val_accuracy: 0.7583\n","Epoch 8/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4814 - accuracy: 0.7644\n","Epoch 8: val_accuracy improved from 0.75827 to 0.75933, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1532s 54ms/step - loss: 0.4814 - accuracy: 0.7644 - val_loss: 0.4921 - val_accuracy: 0.7593\n","Epoch 9/10\n","28428/28428 [==============================] - ETA: 0s - loss: 0.4747 - accuracy: 0.7686\n","Epoch 9: val_accuracy improved from 0.75933 to 0.75964, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1530s 54ms/step - loss: 0.4747 - accuracy: 0.7686 - val_loss: 0.4933 - val_accuracy: 0.7596\n","Epoch 10/10\n","28427/28428 [============================>.] - ETA: 0s - loss: 0.4678 - accuracy: 0.7730\n","Epoch 10: val_accuracy improved from 0.75964 to 0.76176, saving model to /content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/model.h5\n","28428/28428 [==============================] - 1526s 54ms/step - loss: 0.4678 - accuracy: 0.7730 - val_loss: 0.4888 - val_accuracy: 0.7618\n","CPU times: user 4h 52min 20s, sys: 35min 14s, total: 5h 27min 35s\n","Wall time: 4h 15min 28s\n"]}]},{"cell_type":"code","source":["# working way of saving model\n","\n","# model.save_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e5/model.h5')"],"metadata":{"id":"se_lBORE8Nz2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%%time\n","model.load_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/model_e10_updated/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))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cvB8C88Q9Hv9","executionInfo":{"status":"ok","timestamp":1670618551281,"user_tz":-540,"elapsed":392789,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"f8cd3d9b-550a-47e1-c197-639162fd0561"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["3159/3159 [==============================] - 388s 121ms/step\n","Validation accuracy:  0.7862343932408635\n","CPU times: user 2min 55s, sys: 22.3 s, total: 3min 17s\n","Wall time: 6min 35s\n"]}]},{"cell_type":"code","source":["# Confusion matrix\n","\n","from sklearn.metrics import confusion_matrix\n","cm = confusion_matrix(y_test, y_preds)\n","cm"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5TYWLgAGBdJ9","executionInfo":{"status":"ok","timestamp":1670618702804,"user_tz":-540,"elapsed":1056,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"d143e192-b195-4c5a-ad47-c102d3a436b0"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[40624,  9807],\n","       [11800, 38847]])"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","print(classification_report(y_test, y_preds, target_names = ['Not Sarcastic','Sarcastic']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DlvrlHbPmmA5","executionInfo":{"status":"ok","timestamp":1670618674604,"user_tz":-540,"elapsed":637,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"ef9e96f7-d919-4325-abd1-e8512966a90c"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["               precision    recall  f1-score   support\n","\n","Not Sarcastic       0.77      0.81      0.79     50431\n","    Sarcastic       0.80      0.77      0.78     50647\n","\n","     accuracy                           0.79    101078\n","    macro avg       0.79      0.79      0.79    101078\n"," weighted avg       0.79      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":"X7ZfibNbmmDT","executionInfo":{"status":"ok","timestamp":1670618703313,"user_tz":-540,"elapsed":4,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"dc10131c-0953-474e-c1a9-f46f23f2687f"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(114.0, 0.5, 'Actual')"]},"metadata":{},"execution_count":17},{"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":"hVlO1EvPmmFo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"cMQWzRARmmJG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# y_preds = model.predict(X_val[0])\n","xxx = np.expand_dims(X_val[0], axis=0)\n","print(xxx.shape)\n","# y_preds = model.predict(xxx)\n","# y_preds = model.predict(xxx).round().astype(int).argmax(axis=-1)\n","y_preds.argmax(axis=-1)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FDfpmJgM-0TZ","executionInfo":{"status":"ok","timestamp":1668930597104,"user_tz":-540,"elapsed":265,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"38253c78-2169-422b-97b7-59bb9699c87d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 100)\n","1/1 [==============================] - 0s 34ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["array([1])"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","source":["# Save the entire model\n","# text_classification_model.save('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilBert_model_e5/')\n","\n","# it will work\n","model.save(\"/content/full_model.h5\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SzQLw25r0GAX","executionInfo":{"status":"ok","timestamp":1668608536910,"user_tz":-540,"elapsed":26021,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"767acd61-e0fd-4d8a-f3e5-0ee037c2dd9c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.regularization.dropout.Dropout object at 0x7f01f63e3c50>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.regularization.dropout.Dropout object at 0x7f0207a45ad0>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.regularization.dropout.Dropout object at 0x7f02048f2b90>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.regularization.dropout.Dropout object at 0x7f02048e05d0>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.regularization.dropout.Dropout object at 0x7f02048c92d0>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.regularization.dropout.Dropout object at 0x7f0204a2ef50>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.rnn.bidirectional.Bidirectional object at 0x7f0207b35c10>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.rnn.gru.GRU object at 0x7f0207b35e50>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.rnn.gru.GRU object at 0x7f0207b3c7d0>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.rnn.gru.GRUCell object at 0x7f0207b35f10>, because it is not built.\n","WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.rnn.gru.GRUCell object at 0x7f0207b3c950>, because it is not built.\n","WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, transformer_layer_call_fn while saving (showing 5 of 166). These functions will not be directly callable after loading.\n"]}]},{"cell_type":"markdown","source":["**Load model**"],"metadata":{"id":"3gzhQn7jhewL"}},{"cell_type":"code","source":[],"metadata":{"id":"d5gklw0Gk3Vq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# model = BertModel.from_pretrained(\"./test/saved_model/\")"],"metadata":{"id":"0m9sG1eba4YA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# imported.predict_on_batch(next(iter(val_dataset.batch(16)))).numpy()\n","# ff = new_model(next(iter(val_dataset.batch(16)))).numpy()\n","# ff.argmax(axis=-1)\n","\n","# next(iter(val_dataset.batch(16)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"id":"gp-NdK4oa4fm","executionInfo":{"status":"error","timestamp":1668501055076,"user_tz":-540,"elapsed":1508,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"e5747139-46dd-4294-c734-cfe2c1b60f7f"},"execution_count":null,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-29-268ddd4147d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimported\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\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[0mnumpy\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      2\u001b[0m \u001b[0;31m# ff = new_model(next(iter(val_dataset.batch(16)))).numpy()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# ff.argmax(axis=-1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# next(iter(val_dataset.batch(16)))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: '_UserObject' object has no attribute 'predict_on_batch'"]}]},{"cell_type":"code","source":["path = \"/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/weights/\"\n","model = tf.keras.models.load_model(path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":345},"id":"-Ir185RFa4jG","executionInfo":{"status":"error","timestamp":1668500364772,"user_tz":-540,"elapsed":11195,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"d7b2395a-7623-4a0e-926b-0a9a7d365bc9"},"execution_count":null,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","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')."]}]},{"cell_type":"code","source":[],"metadata":{"id":"_KUOt2yo6FVs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"q_3GYKOK6FYS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"KvRaymJu6Fay"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"nNw1S0Jm6Fc9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"fTSzj1F06FfP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"lQSiPFAd6FhN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"URohonso6FjJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"lmXecIhs6Fm9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"7EUOOSRn6Fo8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"p6Gq50fr6Fsb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"e8WzNWhjb011"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"OWkoZSuAb05D"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"6R4oWfbtb07q"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"4UtIQhMRb0_I"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"V96zQzWrb1Ep"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"WoKXCCqOb1Ik"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Ko6_zmyTb1L7"},"execution_count":null,"outputs":[]}]}