functional_API_sarcasm_classification_BERT_combined_2data

{"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":17261,"status":"ok","timestamp":1670618888898,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"wuq_C7s66FL8","outputId":"f8cb1c70-369d-46cb-8ef7-fa96982470ff"},"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 4.1 MB/s \n","\u001b[?25hCollecting huggingface-hub<1.0,>=0.10.0\n","  Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n","\u001b[K     |████████████████████████████████| 182 kB 92.1 MB/s \n","\u001b[?25hRequirement already satisfied: 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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","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\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 69.5 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"]}],"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":3826,"status":"ok","timestamp":1670618892720,"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","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":17964,"status":"ok","timestamp":1670618910681,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"E58m1doU6FRI","outputId":"c8d74779-fb8b-4a15-fa2e-b750ecd18605"},"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":"markdown","metadata":{"id":"nSxV6ZJNJeM2"},"source":["**splitting the data**"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1670618910681,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"pxBHEw-s6FTa","outputId":"56e33d25-22df-4147-b5b4-2a027f8f9a53"},"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":5,"metadata":{"executionInfo":{"elapsed":555,"status":"ok","timestamp":1670618911228,"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":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670618911229,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"XCZauik0EJpE","outputId":"6a4cb016-ca23-485e-a6f1-1357ca94b516"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["numpy.ndarray"]},"metadata":{},"execution_count":6}],"source":["y_train = train_labels[\"label\"].to_numpy()\n","y_test = val_labels[\"label\"].to_numpy()\n","type(y_train)"]},{"cell_type":"markdown","metadata":{"id":"zOztdCrUJhL6"},"source":["**Tokenizing the data**"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["c6e857e1358a44e3be91f434cf7cd993","421edeb2726f47658501a2417ac85e46","a27f53987d194560b2fb84c3f17b1189","d79255132ec7431aa8a03a4b84b26c7a","b85557703f254028859a811215da5770","f09e7ab52c2142d59301a43df16bd31d","f6fbad0807474909ba2fa52e08aec210","1eea61660258492da7a5611c1ae3e0f7","a95a611817d1438b8bb0d9cdd14db8db","eac91592f7814611aca7fbfb6ea052c0","d6f6d000d0fb441bb918480cae45e801","519dff3adfaf484facee9a183d95f7c4","ac9c43816bb142c2b287e5916b1d6d6d","9cb110f84f6c433ba5837935c34f6da3","0057990f4b514d1abd2ddfb7ebd07d5c","2c4e605e5bc8477cb6c85e3b8ba95ce1","bb997f736cfb43f2a51caa1413ebc93d","378b7979ff604b8d87c1349d562c2722","8ebed8d7fd6c479788e5c143a62b5101","12df3bb664d74ca6964c9a1a0cca0692","b700d222675349289ac1d476f42bd5cd","f9efb91d6e83405fb0100f0f223c3c27","6e754175b534438a8044a4e66db90b79","7feacac800204bfabbc9eff6db54c5d7","efe39c5db99b45239a8317d80f217472","e675fcdd75af4b54b297469ac089ab4f","fed090b513b74c50b4d1b6dabacc1a7d","d297a9d6d38246fd8664053188fe3ce1","d3e2f8d174ee4194a4b578e12c34bca8","21933542ae724aec8147e11a10fa6d26","4a6ecf68478545d4a51488c35e992207","3d7fb3791b324a8093bf05fc830ad3a9","bf08d49674e6430c8d8ef3991ffddab5"]},"executionInfo":{"elapsed":8705,"status":"ok","timestamp":1670618919932,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"uKTMExz9H-9H","outputId":"746b105c-4a07-4be8-c8fb-eed3ced61d2a"},"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":"c6e857e1358a44e3be91f434cf7cd993"}},"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":"519dff3adfaf484facee9a183d95f7c4"}},"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":"6e754175b534438a8044a4e66db90b79"}},"metadata":{}}],"source":["import tensorflow as tf\n","from transformers import BertTokenizer, TFBertModel\n","tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\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","# ))"]},{"cell_type":"markdown","metadata":{"id":"ivQtz3j3KkES"},"source":["**Modelling Roberta**"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156,"referenced_widgets":["dbe9caa5970d4ed9ac389aed5d43fb60","bcb471593e114e7ab9e66d22ef4eac29","ce49422e870a4df2a24476f40aa5ce4d","eed740200b854d40bb1ffcf7952de707","875fad684f4046c1a366fa7572d82b8b","3673d748a55646b19976bee1f566b990","b3da0c4ae7394f8b9be2c5c8dcaff730","2d5f691e190045c08532f189caefbfa2","43f682f069284572989a48251e9c1a63","f7f2c34505f34cb78f9fe38428d320e5","08f109590bfa4e9c83c499a309744a39"]},"executionInfo":{"elapsed":18320,"status":"ok","timestamp":1670618938249,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"z3oAMVIFH_At","outputId":"980135d5-9d40-46ac-9c16-e3941f362ec9"},"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":"dbe9caa5970d4ed9ac389aed5d43fb60"}},"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"]}],"source":["import tensorflow.keras as keras \n","\n","encoder = TFBertModel.from_pretrained(\"bert-base-uncased\")\n","\n","# encoder = 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 build_model(bert_layer, max_len=512):\n","# def build_model(distilbert_layer, max_len=100):\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","    clf_output = sequence_output[0]\n","\n","\n","    # pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])\n","    # bert_layer = bert_layer\n","    # bert_layer.trainable = False\n","    # clf_output = bert_layer[:, 0, :]\n","    # clf_output = bert_layer[0]\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(0.2)(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.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\n"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":333046,"status":"ok","timestamp":1670619271287,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"koe0hq_aJvxa"},"outputs":[],"source":["max_len = 100\n","\n","X_train = bert_encode(train_dd, tokenizer, max_len=max_len)\n","X_val = bert_encode(val_dd, tokenizer, max_len=max_len)"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6170,"status":"ok","timestamp":1670619277455,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"QQ5YehpWJxNn","outputId":"87b614c7-ae92-4fba-93e6-dd3ea40a47f7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_word_ids (InputLayer)    [(None, 100)]        0           []                               \n","                                                                                                  \n"," input_mask (InputLayer)        [(None, 100)]        0           []                               \n","                                                                                                  \n"," segment_ids (InputLayer)       [(None, 100)]        0           []                               \n","                                                                                                  \n"," tf_bert_model (TFBertModel)    TFBaseModelOutputWi  109482240   ['input_word_ids[0][0]',         \n","                                thPoolingAndCrossAt               'input_mask[0][0]',             \n","                                tentions(last_hidde               'segment_ids[0][0]']            \n","                                n_state=(None, 100,                                               \n","                                 768),                                                            \n","                                 pooler_output=(Non                                               \n","                                e, 768),                                                          \n","                                 past_key_values=No                                               \n","                                ne, hidden_states=N                                               \n","                                one, attentions=Non                                               \n","                                e, cross_attentions                                               \n","                                =None)                                                            \n","                                                                                                  \n"," bidirectional (Bidirectional)  (None, 100, 492)     1499616     ['tf_bert_model[0][0]']          \n","                                                                                                  \n"," dropout_37 (Dropout)           (None, 100, 492)     0           ['bidirectional[0][0]']          \n","                                                                                                  \n"," bidirectional_1 (Bidirectional  (None, 100, 492)    1092240     ['dropout_37[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," dropout_38 (Dropout)           (None, 100, 492)     0           ['bidirectional_1[0][0]']        \n","                                                                                                  \n"," bidirectional_2 (Bidirectional  (None, 292)         560640      ['dropout_38[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," dropout_39 (Dropout)           (None, 292)          0           ['bidirectional_2[0][0]']        \n","                                                                                                  \n"," dense (Dense)                  (None, 124)          36332       ['dropout_39[0][0]']             \n","                                                                                                  \n"," dense_1 (Dense)                (None, 2)            250         ['dense[0][0]']                  \n","                                                                                                  \n","==================================================================================================\n","Total params: 112,671,318\n","Trainable params: 3,189,078\n","Non-trainable params: 109,482,240\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"]}],"source":["# build model\n","\n","model = build_model(encoder, max_len=max_len)\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",")"]},{"cell_type":"markdown","metadata":{"id":"TKruNO5IaZkJ"},"source":["**Loading Model**"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":728477,"status":"ok","timestamp":1670620008342,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"BY3tRwXMKG0e","outputId":"3951f6da-ed01-49f6-ee9e-17357ade39ab"},"outputs":[{"output_type":"stream","name":"stdout","text":["3159/3159 [==============================] - 722s 227ms/step\n","Validation accuracy:  0.7864322602346703\n","CPU times: user 4min 10s, sys: 44.2 s, total: 4min 54s\n","Wall time: 12min 10s\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":"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":"oK2LnS0KtohT","executionInfo":{"status":"ok","timestamp":1670620052756,"user_tz":-540,"elapsed":421,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"10f5e4f8-bbaf-49c3-a143-adf789a1e456"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[42112,  8515],\n","       [13072, 37379]])"]},"metadata":{},"execution_count":12}]},{"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":"hxBMr6b2tojm","executionInfo":{"status":"ok","timestamp":1670620053171,"user_tz":-540,"elapsed":2,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"dec824fa-dc0b-4f53-c6d5-40a053f0b728"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["               precision    recall  f1-score   support\n","\n","Not Sarcastic       0.76      0.83      0.80     50627\n","    Sarcastic       0.81      0.74      0.78     50451\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":"lM8Lzfnhtolu","executionInfo":{"status":"ok","timestamp":1670620054452,"user_tz":-540,"elapsed":871,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"}},"outputId":"5f7d7eef-8dea-4673-c339-f8e3e8243f4c"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(114.0, 0.5, 'Actual')"]},"metadata":{},"execution_count":14},{"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":"uQ1yWmsWtooZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"cXyav0zMtorM"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-UYVfxz_KG37"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"se_lBORE8Nz2"},"outputs":[],"source":["# hugging face model save\n","\n","# text_classification_model.save_weights('/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distil_h5/weights.h5')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26021,"status":"ok","timestamp":1668608536910,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"SzQLw25r0GAX","outputId":"767acd61-e0fd-4d8a-f3e5-0ee037c2dd9c"},"outputs":[{"name":"stderr","output_type":"stream","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"]}],"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()"]},{"cell_type":"markdown","metadata":{"id":"3gzhQn7jhewL"},"source":["**Load model**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d5gklw0Gk3Vq"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0m9sG1eba4YA"},"outputs":[],"source":["# model = BertModel.from_pretrained(\"./test/saved_model/\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4w3r6Ru2a4bK"},"outputs":[],"source":["from transformers import AutoModelForSequenceClassification\n","\n","path = \"/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/weights2\"\n","imported = tf.saved_model.load(path)\n","# model = AutoModelForSequenceClassification.from_pretrained(path)\n","\n","DEFAULT_FUNCTION_KEY = 'serving_default'\n","# loaded = tf.saved_model.load(saved_model_path)\n","inference_func = imported.signatures[DEFAULT_FUNCTION_KEY]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":432},"executionInfo":{"elapsed":316,"status":"error","timestamp":1668759374517,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"ZTBbt5i5yab8","outputId":"b9fa0924-b6ca-4dbc-9f9c-505bc3228440"},"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/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1615\u001b[0m             return self._call_with_flat_signature(args, kwargs,\n\u001b[0;32m-> 1616\u001b[0;31m                                                   cancellation_manager)\n\u001b[0m\u001b[1;32m   1617\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_with_flat_signature\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1639\u001b[0m       raise TypeError(\n\u001b[0;32m-> 1640\u001b[0;31m           \u001b[0;34mf\"{self._flat_signature_summary()} takes {self._num_positional_args} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1641\u001b[0m           f\"positional arguments, got {len(args)}.\")\n","\u001b[0;31mTypeError\u001b[0m: signature_wrapper(attention_mask, input_ids) takes 0 positional arguments, got 1.","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-14-ba12eb67e017>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minference_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\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[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1600\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mthe\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mdo\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0ms\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m     \"\"\"\n\u001b[0;32m-> 1602\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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   1603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1604\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1616\u001b[0m                                                   cancellation_manager)\n\u001b[1;32m   1617\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1618\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mstructured_err\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1620\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_with_flat_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1610\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[1;32m   1611\u001b[0m           return self._call_with_structured_signature(args, kwargs,\n\u001b[0;32m-> 1612\u001b[0;31m                                                       cancellation_manager)\n\u001b[0m\u001b[1;32m   1613\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstructured_err\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1614\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/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_with_structured_signature\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1687\u001b[0m     args, kwargs, filtered_flat_args = (\n\u001b[1;32m   1688\u001b[0m         self._function_spec.canonicalize_function_inputs(*args, **kwargs))\n\u001b[0;32m-> 1689\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structured_signature_check_missing_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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   1690\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structured_signature_check_unexpected_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1691\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structured_signature_check_arg_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_structured_signature_check_missing_args\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1706\u001b[0m         \u001b[0mmissing_arguments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmissing_arguments\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m       raise TypeError(f\"{self._structured_signature_summary()} missing \"\n\u001b[0m\u001b[1;32m   1709\u001b[0m                       \u001b[0;34m\"required arguments: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1710\u001b[0m                       f\"{', '.join(sorted(missing_arguments))}.\")\n","\u001b[0;31mTypeError\u001b[0m: signature_wrapper(*, attention_mask, input_ids) missing required arguments: attention_mask, input_ids."]}],"source":["# predict_dataset = eval_dataset.map(lambda image, label: image)\n","\n","for batch in val_dataset.take(1):\n","  print(inference_func(batch))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1668759538400,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"X8a4b5WhzgAW","outputId":"2af0778b-c3f5-4653-d2b4-021f2df7a899"},"outputs":[{"name":"stdout","output_type":"stream","text":["({'input_ids': <tf.Tensor: shape=(100,), dtype=int32, numpy=\n","array([  101,  2129,  8108,  2017, 10214,  2000,  2031,  3348,  2074,\n","        2138,  1996,  2060,  2711,  2003,  1037,  2450,  1010,  2008,\n","        2003,  3348,  2923,   102,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0,     0,     0,     0,     0,     0,     0,     0,     0,\n","           0], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(100,), dtype=int32, numpy=\n","array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)>}, <tf.Tensor: shape=(), dtype=int32, numpy=1>)\n"]}],"source":["for i in val_dataset.take(1):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2SZ-Gw-wyQyV"},"outputs":[],"source":["sDEFAULT_FUNCTION_KEY = 'serving_default'\n","loaded = tf.saved_model.load(saved_model_path)\n","inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":432},"executionInfo":{"elapsed":333,"status":"error","timestamp":1668759568794,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"0R-5V4QhjnEx","outputId":"7ab225aa-3b2d-4ae7-a925-957c8d76de6e"},"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/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1615\u001b[0m             return self._call_with_flat_signature(args, kwargs,\n\u001b[0;32m-> 1616\u001b[0;31m                                                   cancellation_manager)\n\u001b[0m\u001b[1;32m   1617\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_with_flat_signature\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1639\u001b[0m       raise TypeError(\n\u001b[0;32m-> 1640\u001b[0;31m           \u001b[0;34mf\"{self._flat_signature_summary()} takes {self._num_positional_args} \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1641\u001b[0m           f\"positional arguments, got {len(args)}.\")\n","\u001b[0;31mTypeError\u001b[0m: signature_wrapper(attention_mask, input_ids) takes 0 positional arguments, got 1.","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-22-c8cbb63bd6a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minference_func\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;36m32\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[0mff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\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\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1600\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mthe\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mdo\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0ms\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m     \"\"\"\n\u001b[0;32m-> 1602\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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   1603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1604\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1616\u001b[0m                                                   cancellation_manager)\n\u001b[1;32m   1617\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1618\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mstructured_err\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1620\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_with_flat_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1610\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[1;32m   1611\u001b[0m           return self._call_with_structured_signature(args, kwargs,\n\u001b[0;32m-> 1612\u001b[0;31m                                                       cancellation_manager)\n\u001b[0m\u001b[1;32m   1613\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstructured_err\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1614\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/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_with_structured_signature\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1687\u001b[0m     args, kwargs, filtered_flat_args = (\n\u001b[1;32m   1688\u001b[0m         self._function_spec.canonicalize_function_inputs(*args, **kwargs))\n\u001b[0;32m-> 1689\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structured_signature_check_missing_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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   1690\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structured_signature_check_unexpected_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1691\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_structured_signature_check_arg_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_structured_signature_check_missing_args\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1706\u001b[0m         \u001b[0mmissing_arguments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmissing_arguments\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1708\u001b[0;31m       raise TypeError(f\"{self._structured_signature_summary()} missing \"\n\u001b[0m\u001b[1;32m   1709\u001b[0m                       \u001b[0;34m\"required arguments: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1710\u001b[0m                       f\"{', '.join(sorted(missing_arguments))}.\")\n","\u001b[0;31mTypeError\u001b[0m: signature_wrapper(*, attention_mask, input_ids) missing required arguments: attention_mask, input_ids."]}],"source":["ff = inference_func(next(iter(val_dataset.batch(32)))).numpy()\n","ff.argmax(axis=-1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"elapsed":1508,"status":"error","timestamp":1668501055076,"user":{"displayName":"Furqan Ali","userId":"08783436873128206665"},"user_tz":-540},"id":"gp-NdK4oa4fm","outputId":"e5747139-46dd-4294-c734-cfe2c1b60f7f"},"outputs":[{"ename":"AttributeError","evalue":"ignored","output_type":"error","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'"]}],"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)))"]},{"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":["path = \"/content/drive/MyDrive/Colab Notebooks/Thesis and paper_work/distilbert/weights/\"\n","model = tf.keras.models.load_model(path)"]},{"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":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nNw1S0Jm6Fc9"},"outputs":[],"source":[]},{"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":"1nXGlSvHY4Qusg4foOTj5F2Fe3RdU3GAv","authorship_tag":"ABX9TyOhugmnQP1ZtewsYB75/pX3"},"gpuClass":"premium","kernelspec":{"display_name":"Python 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