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| { | |
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| "source": [ | |
| "<a href=\"https://colab.research.google.com/github/ivancorrales/colab-notebooks/blob/main/Quotes_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
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| { | |
| "cell_type": "markdown", | |
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| "id": "4P---2pd7amc" | |
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| "## Install and import libraries" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "hF1HDw_fX2t7", | |
| "outputId": "4b40c4bd-b107-40da-e78b-aaf3d591aadd" | |
| }, | |
| "source": [ | |
| "!pip install torch\n", | |
| "!pip install numpy\n", | |
| "!pip install pandas\n", | |
| "!pip install matplotlib\n", | |
| "!pip install seaborn\n", | |
| "!pip install sklearn\n", | |
| "!pip install transformers\n", | |
| "!pip install pytorch-lightning" | |
| ], | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
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| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "i68D1PC6X7Z8", | |
| "outputId": "617222a5-5ec5-4aec-c473-4d46e9db9223" | |
| }, | |
| "source": [ | |
| "# Import all libraries\n", | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import re\n", | |
| "import os\n", | |
| "\n", | |
| "# Huggingface transformers\n", | |
| "import transformers\n", | |
| "from transformers import BertModel,BertTokenizer,AdamW, get_linear_schedule_with_warmup\n", | |
| "\n", | |
| "import torch\n", | |
| "from torch import nn ,cuda\n", | |
| "from torch.utils.data import DataLoader,Dataset,RandomSampler, SequentialSampler\n", | |
| "\n", | |
| "import pytorch_lightning as pl\n", | |
| "from pytorch_lightning.callbacks import ModelCheckpoint\n", | |
| "\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n", | |
| "\n", | |
| "import seaborn as sns\n", | |
| "from pylab import rcParams\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from matplotlib import rc\n", | |
| "%matplotlib inline\n", | |
| "\n", | |
| "is_gpu_available = torch.cuda.is_available()\n", | |
| "device = torch.device(\"cuda:0\" if is_gpu_available else \"cpu\")\n", | |
| "if is_gpu_available:\n", | |
| " !nvidia-smi" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Tue Nov 2 18:14:27 2021 \n", | |
| "+-----------------------------------------------------------------------------+\n", | |
| "| NVIDIA-SMI 495.44 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", | |
| "|-------------------------------+----------------------+----------------------+\n", | |
| "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", | |
| "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", | |
| "| | | MIG M. |\n", | |
| "|===============================+======================+======================|\n", | |
| "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", | |
| "| N/A 50C P8 30W / 149W | 3MiB / 11441MiB | 0% Default |\n", | |
| "| | | N/A |\n", | |
| "+-------------------------------+----------------------+----------------------+\n", | |
| " \n", | |
| "+-----------------------------------------------------------------------------+\n", | |
| "| Processes: |\n", | |
| "| GPU GI CI PID Type Process name GPU Memory |\n", | |
| "| ID ID Usage |\n", | |
| "|=============================================================================|\n", | |
| "| No running processes found |\n", | |
| "+-----------------------------------------------------------------------------+\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "RONDkksk4Myj" | |
| }, | |
| "source": [ | |
| "## Download the dataset\n", | |
| "\n", | |
| "We will make use of a dataset that is hosted on Kaggle. We need to follow the steps below to download and use kaggle data within Google Colab:\n", | |
| "\n", | |
| "1. Sign in to https://kaggle.com/, then click on your profile picture on the top right and select “My Account” from the menu.\n", | |
| "\n", | |
| "2. Scroll down to the “API” section and click “Create New API Token”. This will download a file kaggle.json.\n", | |
| "\n", | |
| "3. Upload the downloaded kaggle.json file in the next cell.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "resources": { | |
| "http://localhost:8080/nbextensions/google.colab/files.js": { | |
| "data": 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| |
| "ok": true, | |
| "headers": [ | |
| [ | |
| "content-type", | |
| "application/javascript" | |
| ] | |
| ], | |
| "status": 200, | |
| "status_text": "" | |
| } | |
| }, | |
| "base_uri": "https://localhost:8080/", | |
| "height": 151 | |
| }, | |
| "id": "VCpfuN0YN8c6", | |
| "outputId": "74172752-5bcd-4119-aaba-3df671a19211" | |
| }, | |
| "source": [ | |
| "\"\"\"The Kaggle dataset path\"\"\"\n", | |
| "KAGGLE_DATASET ='akmittal/quotes-dataset'\n", | |
| "\n", | |
| "!pip install -q kaggle\n", | |
| "from google.colab import files\n", | |
| "files.upload()\n", | |
| "\n", | |
| "!pip install -q kaggle\n", | |
| "!mkdir -p ~/.kaggle\n", | |
| "!mv kaggle.json ~/.kaggle/\n", | |
| "!ls ~/.kaggle\n", | |
| "!chmod 600 /root/.kaggle/kaggle.json\n", | |
| "!kaggle datasets download \"{KAGGLE_DATASET}\"\n", | |
| "!mkdir /content/dataset\n", | |
| "!unzip -q /content/quotes-dataset.zip -d /content/dataset\n", | |
| "!ls /content/dataset" | |
| ], | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/html": [ | |
| "\n", | |
| " <input type=\"file\" id=\"files-a446b69f-4713-4cce-affa-6dd6f8148678\" name=\"files[]\" multiple disabled\n", | |
| " style=\"border:none\" />\n", | |
| " <output id=\"result-a446b69f-4713-4cce-affa-6dd6f8148678\">\n", | |
| " Upload widget is only available when the cell has been executed in the\n", | |
| " current browser session. Please rerun this cell to enable.\n", | |
| " </output>\n", | |
| " <script src=\"/nbextensions/google.colab/files.js\"></script> " | |
| ], | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Saving kaggle.json to kaggle.json\n", | |
| "kaggle.json\n", | |
| "Downloading quotes-dataset.zip to /content\n", | |
| " 0% 0.00/3.88M [00:00<?, ?B/s]\n", | |
| "100% 3.88M/3.88M [00:00<00:00, 63.5MB/s]\n", | |
| "quotes.json\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "gYXczrVt91Uf" | |
| }, | |
| "source": [ | |
| "## Load dataset\n", | |
| "\n", | |
| "Load the json file into a Pandas DataFrame" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| }, | |
| "id": "JdI8DW9d96KB", | |
| "outputId": "b6ec64da-395f-489e-8421-b302a3cf7731" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "\n", | |
| "df = pd.read_json('/content/dataset/quotes.json')\n", | |
| "df.head(6)" | |
| ], | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Quote</th>\n", | |
| " <th>Author</th>\n", | |
| " <th>Tags</th>\n", | |
| " <th>Popularity</th>\n", | |
| " <th>Category</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Don't cry because it's over, smile because it ...</td>\n", | |
| " <td>Dr. Seuss</td>\n", | |
| " <td>[attributed-no-source, cry, crying, experience...</td>\n", | |
| " <td>0.155666</td>\n", | |
| " <td>life</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>Don't cry because it's over, smile because it ...</td>\n", | |
| " <td>Dr. Seuss</td>\n", | |
| " <td>[attributed-no-source, cry, crying, experience...</td>\n", | |
| " <td>0.155666</td>\n", | |
| " <td>happiness</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>I'm selfish, impatient and a little insecure. ...</td>\n", | |
| " <td>Marilyn Monroe</td>\n", | |
| " <td>[attributed-no-source, best, life, love, mista...</td>\n", | |
| " <td>0.129122</td>\n", | |
| " <td>love</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>I'm selfish, impatient and a little insecure. ...</td>\n", | |
| " <td>Marilyn Monroe</td>\n", | |
| " <td>[attributed-no-source, best, life, love, mista...</td>\n", | |
| " <td>0.129122</td>\n", | |
| " <td>life</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>I'm selfish, impatient and a little insecure. ...</td>\n", | |
| " <td>Marilyn Monroe</td>\n", | |
| " <td>[attributed-no-source, best, life, love, mista...</td>\n", | |
| " <td>0.129122</td>\n", | |
| " <td>truth</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Be yourself; everyone else is already taken.</td>\n", | |
| " <td>Oscar Wilde</td>\n", | |
| " <td>[attributed-no-source, be-yourself, honesty, i...</td>\n", | |
| " <td>0.113223</td>\n", | |
| " <td>inspiration</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Quote ... Category\n", | |
| "0 Don't cry because it's over, smile because it ... ... life\n", | |
| "1 Don't cry because it's over, smile because it ... ... happiness\n", | |
| "2 I'm selfish, impatient and a little insecure. ... ... love\n", | |
| "3 I'm selfish, impatient and a little insecure. ... ... life\n", | |
| "4 I'm selfish, impatient and a little insecure. ... ... truth\n", | |
| "5 Be yourself; everyone else is already taken. ... inspiration\n", | |
| "\n", | |
| "[6 rows x 5 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "SCnCH9Hjt6n9" | |
| }, | |
| "source": [ | |
| "Clean the dataset" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| }, | |
| "id": "RNrwr6cL-clv", | |
| "outputId": "cb1fbf03-af82-43a0-deae-c9dd034c48c3" | |
| }, | |
| "source": [ | |
| "# Take only the required attributes and discard the others\n", | |
| "df = df[['Quote','Tags']]\n", | |
| "# Drop the duplicates records into our dataset\n", | |
| "df = df.drop_duplicates(['Quote'])\n", | |
| "print(f'The data frame contains {len(df)} records.')\n", | |
| "df.head(6)" | |
| ], | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "The data frame contains 36937 records.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Quote</th>\n", | |
| " <th>Tags</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Don't cry because it's over, smile because it ...</td>\n", | |
| " <td>[attributed-no-source, cry, crying, experience...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>I'm selfish, impatient and a little insecure. ...</td>\n", | |
| " <td>[attributed-no-source, best, life, love, mista...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Be yourself; everyone else is already taken.</td>\n", | |
| " <td>[attributed-no-source, be-yourself, honesty, i...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>Two things are infinite: the universe and huma...</td>\n", | |
| " <td>[attributed-no-source, human-nature, humor, in...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>Be who you are and say what you feel, because ...</td>\n", | |
| " <td>[ataraxy, be-yourself, confidence, fitting-in,...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>You've gotta dance like there's nobody watchin...</td>\n", | |
| " <td>[dance, heaven, hurt, inspirational, life, lov...</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Quote Tags\n", | |
| "0 Don't cry because it's over, smile because it ... [attributed-no-source, cry, crying, experience...\n", | |
| "2 I'm selfish, impatient and a little insecure. ... [attributed-no-source, best, life, love, mista...\n", | |
| "5 Be yourself; everyone else is already taken. [attributed-no-source, be-yourself, honesty, i...\n", | |
| "6 Two things are infinite: the universe and huma... [attributed-no-source, human-nature, humor, in...\n", | |
| "9 Be who you are and say what you feel, because ... [ataraxy, be-yourself, confidence, fitting-in,...\n", | |
| "10 You've gotta dance like there's nobody watchin... [dance, heaven, hurt, inspirational, life, lov..." | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "BFyllY5nCrlX" | |
| }, | |
| "source": [ | |
| "## Normalize dataset\n", | |
| "\n", | |
| "In this step we will normalize the tags (tolowercase, remove empty blanks). Additionally, we will work only with the 15 most used tags." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "zLfKg9TrCvOR", | |
| "outputId": "ddb42980-8faf-4982-813e-1da66f824896" | |
| }, | |
| "source": [ | |
| "df.Tags = df.Tags.transform(lambda tags: [tag.lower().strip() for tag in tags])\n", | |
| "\n", | |
| "tags = [element for list_ in df.Tags for element in list_]\n", | |
| "tags = [tag.lower().strip() for tag in tags]\n", | |
| "\n", | |
| "print(f'There are {len(tags)} tags.')" | |
| ], | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "There are 215664 tags.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "yd0r1GCsIO2I" | |
| }, | |
| "source": [ | |
| "As we can obser on the above cell, there are more than 200k tags. As this Notebook has mainly educational purposes we will discard the other tags and clean the dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "2gKO7ysqITAY", | |
| "outputId": "a8b9b874-dfc3-434b-afa2-97c8337fa4dd" | |
| }, | |
| "source": [ | |
| "classes = pd.Series(tags).value_counts()[:15].index\n", | |
| "classes = list(set(classes))\n", | |
| "classes.sort()\n", | |
| "df['Tags'] = df.Tags.transform(lambda tags: list(set(tags).intersection(classes)))\n", | |
| "df = df[df.Tags.transform(lambda tags: len(tags)>0)]\n", | |
| "\n", | |
| "print(f'We will only consider the following tags: {classes}.')\n", | |
| "print(f'The data frame contains {len(df)} records with one or more tags.')" | |
| ], | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "We will only consider the following tags: ['death', 'faith', 'god', 'happiness', 'hope', 'humor', 'inspirational', 'inspirational-quotes', 'life', 'love', 'philosophy', 'poetry', 'relationships', 'truth', 'wisdom'].\n", | |
| "The data frame contains 23632 records with one or more tags.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "13s93yV3W2JW" | |
| }, | |
| "source": [ | |
| "To work on our multi-label tag classification we will convert the field 'Tags' (with arrays of tags) into 15 columns (one per tag) with binary values." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 0 | |
| }, | |
| "id": "GoW3JPQcK__e", | |
| "outputId": "7e442436-6900-4362-988d-aa03948aac3a" | |
| }, | |
| "source": [ | |
| "from sklearn.preprocessing import MultiLabelBinarizer\n", | |
| "mlb = MultiLabelBinarizer()\n", | |
| "df = df.join(pd.DataFrame(mlb.fit_transform(df.pop('Tags')),\n", | |
| " columns=mlb.classes_,\n", | |
| " index=df.index))\n", | |
| "df.head(n=6)" | |
| ], | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Quote</th>\n", | |
| " <th>death</th>\n", | |
| " <th>faith</th>\n", | |
| " <th>god</th>\n", | |
| " <th>happiness</th>\n", | |
| " <th>hope</th>\n", | |
| " <th>humor</th>\n", | |
| " <th>inspirational</th>\n", | |
| " <th>inspirational-quotes</th>\n", | |
| " <th>life</th>\n", | |
| " <th>love</th>\n", | |
| " <th>philosophy</th>\n", | |
| " <th>poetry</th>\n", | |
| " <th>relationships</th>\n", | |
| " <th>truth</th>\n", | |
| " <th>wisdom</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>Don't cry because it's over, smile because it ...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>I'm selfish, impatient and a little insecure. ...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>Be yourself; everyone else is already taken.</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>Two things are infinite: the universe and huma...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>You've gotta dance like there's nobody watchin...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>You know you're in love when you can't fall as...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Quote death ... truth wisdom\n", | |
| "0 Don't cry because it's over, smile because it ... 0 ... 0 0\n", | |
| "2 I'm selfish, impatient and a little insecure. ... 0 ... 1 0\n", | |
| "5 Be yourself; everyone else is already taken. 0 ... 0 0\n", | |
| "6 Two things are infinite: the universe and huma... 0 ... 0 0\n", | |
| "10 You've gotta dance like there's nobody watchin... 0 ... 0 0\n", | |
| "13 You know you're in love when you can't fall as... 0 ... 0 0\n", | |
| "\n", | |
| "[6 rows x 16 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "K8WwqqMlCY3G" | |
| }, | |
| "source": [ | |
| "## Splitting dataset into train, test and validation data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "TgHtvRaSwUvw" | |
| }, | |
| "source": [ | |
| "We have more than 20k records into our dataframe. Our intention is purely educational, so let's take a subset with \"only\" 2k records to work with." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "78VsqaTvwpfz" | |
| }, | |
| "source": [ | |
| "df = df[:2000]" | |
| ], | |
| "execution_count": 9, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "bP1sQyJ_w1hF" | |
| }, | |
| "source": [ | |
| "The 70% of our records will be used for training the model and the other 30% will be distributed between test and validation." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "_ZWWpuyKCfeg", | |
| "outputId": "e425b184-1f8f-4f86-9bbc-756b97d2018e" | |
| }, | |
| "source": [ | |
| "RANDOM_STATE = 167\n", | |
| "\n", | |
| "train_data,temp_data = train_test_split(\n", | |
| " df,\n", | |
| " test_size=.3, \n", | |
| " random_state=RANDOM_STATE,\n", | |
| " shuffle=True,\n", | |
| ")\n", | |
| "\n", | |
| "\n", | |
| "test_data, val_data = train_test_split(\n", | |
| " temp_data, \n", | |
| " test_size=0.5, \n", | |
| " random_state=RANDOM_STATE, \n", | |
| " shuffle=True,\n", | |
| ")\n", | |
| "print(f' - dataset for training model: {train_data.shape[0]}.')\n", | |
| "print(f' - dataset for validate trained model: {val_data.shape[0]}.')\n", | |
| "print(f' - dataset for test the model {test_data.shape[0]}.')" | |
| ], | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " - dataset for training model: 1400.\n", | |
| " - dataset for validate trained model: 300.\n", | |
| " - dataset for test the model 300.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Zpbqr78qZIBr" | |
| }, | |
| "source": [ | |
| "## Preparing the Dataset and DataModule\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "JWghzJkXZIhS" | |
| }, | |
| "source": [ | |
| "class QuoteTagDataset (Dataset):\n", | |
| " def __init__(self, data, tokenizer, max_len):\n", | |
| " self.tokenizer = tokenizer\n", | |
| " self.data = data\n", | |
| " self.max_len = max_len\n", | |
| " \n", | |
| " def __len__(self):\n", | |
| " return len(self.data)\n", | |
| " \n", | |
| " def __getitem__(self, item_idx):\n", | |
| " item = self.data.iloc[item_idx]\n", | |
| " quote = item['Quote']\n", | |
| " labels = item[classes]\n", | |
| "\n", | |
| " inputs = self.tokenizer.encode_plus(\n", | |
| " quote,\n", | |
| " None,\n", | |
| " max_length= self.max_len,\n", | |
| " padding = 'max_length',\n", | |
| " add_special_tokens=True,\n", | |
| " return_token_type_ids= False,\n", | |
| " return_attention_mask= True,\n", | |
| " truncation=True,\n", | |
| " return_tensors = 'pt'\n", | |
| " )\n", | |
| " \n", | |
| " input_ids = inputs['input_ids'].flatten()\n", | |
| " attn_mask = inputs['attention_mask'].flatten()\n", | |
| " \n", | |
| " return {\n", | |
| " 'input_ids': input_ids ,\n", | |
| " 'attention_mask': attn_mask,\n", | |
| " 'label': torch.tensor(labels, dtype=torch.float) \n", | |
| " }" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "MwaT7gKoZOUk" | |
| }, | |
| "source": [ | |
| "class QuoteTagDataModule (pl.LightningDataModule):\n", | |
| " \n", | |
| " def __init__(self, train_data, val_data, test_data,tokenizer,train_batch_size=8, val_batch_size=8, test_batch_size=8, max_token_len=150):\n", | |
| " super().__init__()\n", | |
| " self.train_data = train_data\n", | |
| " self.test_data = test_data\n", | |
| " self.val_data = val_data\n", | |
| " self.tokenizer = tokenizer\n", | |
| " self.train_batch_size = train_batch_size\n", | |
| " self.test_batch_size = test_batch_size\n", | |
| " self.val_batch_size = val_batch_size\n", | |
| " self.max_token_len = max_token_len\n", | |
| "\n", | |
| " def setup(self):\n", | |
| " self.train_dataset = QuoteTagDataset(data=self.train_data, tokenizer=self.tokenizer,max_len = self.max_token_len)\n", | |
| " self.val_dataset = QuoteTagDataset(data=self.val_data,tokenizer=self.tokenizer,max_len = self.max_token_len)\n", | |
| " self.test_dataset = QuoteTagDataset(data=self.test_data,tokenizer=self.tokenizer,max_len = self.max_token_len)\n", | |
| " \n", | |
| " \n", | |
| " def train_dataloader(self):\n", | |
| " return DataLoader (self.train_dataset, batch_size = self.train_batch_size, shuffle = True , num_workers=0)\n", | |
| "\n", | |
| " def val_dataloader(self):\n", | |
| " return DataLoader (self.val_dataset,batch_size=self.val_batch_size)\n", | |
| "\n", | |
| " def test_dataloader(self):\n", | |
| " return DataLoader (self.test_dataset,batch_size=self.test_batch_size)" | |
| ], | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "FnHdoUkocPVl", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 145, | |
| "referenced_widgets": [ | |
| "7acb1814e2e340b2837b5373c30a1e77", | |
| "9a15ee583d7541ec86fa8e08c54dce93", | |
| "e6ae74eb35ae4af4b36c2678b7009ca4", | |
| "dbf8263b0a774b969f48d020c0a11dca", | |
| "4a0ee3c951604e13b8f9152f54be4126", | |
| "e2be1cf10bb4470aad1fa8e4cdff4628", | |
| "1a22dc1116b640e0ac267946df2ea555", | |
| "713d5e01242a410dac9059f6a0666718", | |
| "785ad773e15a4fe29bb9403e399605ac", | |
| "89382f329b574871b33106859eaf8e01", | |
| "90f6e024d082434a8d56b038323bc44b", | |
| "3a51e7205624486fb7981f7e4b49e891", | |
| "0311cad8bb924c109a709e416ea0748b", | |
| "35ba2247fb6f4939818a8bc58ea373e3", | |
| "c1efb98ea2904bef92abfb40712e5449", | |
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| }, | |
| "outputId": "82c59754-a44e-4533-b543-ff10398c58f1" | |
| }, | |
| "source": [ | |
| "# Initialize the Bert tokenizer\n", | |
| "BERT_MODEL_NAME = 'bert-base-cased'\n", | |
| "Bert_tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_NAME)\n", | |
| "\n", | |
| "# Initialize the parameters that will be use for training\n", | |
| "TRAIN_BATCH_SIZE = 8\n", | |
| "TEST_BATCH_SIZE = 8\n", | |
| "VAL_BATCH_SIZE = 8\n", | |
| "MAX_LEN = 128\n", | |
| "\n", | |
| "# Instantiate and set up the data_module\n", | |
| "data_module = QuoteTagDataModule(train_data,val_data,test_data,Bert_tokenizer,TRAIN_BATCH_SIZE,VAL_BATCH_SIZE,TEST_BATCH_SIZE,MAX_LEN)\n", | |
| "data_module.setup()" | |
| ], | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "metadata": {} | |
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| { | |
| "output_type": "display_data", | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "bc7347de03234db88e4ac55541ed6ef2", | |
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| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1S60h_uBceTD" | |
| }, | |
| "source": [ | |
| "## Train the Model" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "JbYVBfHucXIH" | |
| }, | |
| "source": [ | |
| "class QuoteTagClassifier(pl.LightningModule):\n", | |
| " \n", | |
| " def __init__(self, n_classes=15, steps_per_epoch=None, n_epochs=3, lr=2e-5 ):\n", | |
| " super().__init__()\n", | |
| "\n", | |
| " self.bert = BertModel.from_pretrained(BERT_MODEL_NAME, return_dict=True)\n", | |
| " self.classifier = nn.Linear(self.bert.config.hidden_size,n_classes) # outputs = number of labels\n", | |
| " self.steps_per_epoch = steps_per_epoch\n", | |
| " self.n_epochs = n_epochs\n", | |
| " self.lr = lr\n", | |
| " self.criterion = nn.BCEWithLogitsLoss()\n", | |
| " \n", | |
| " def forward(self,input_ids, attn_mask):\n", | |
| " output = self.bert(input_ids = input_ids ,attention_mask = attn_mask)\n", | |
| " output = self.classifier(output.pooler_output)\n", | |
| " return output\n", | |
| " \n", | |
| " \n", | |
| " def training_step(self,batch,batch_idx):\n", | |
| " input_ids = batch['input_ids']\n", | |
| " attention_mask = batch['attention_mask']\n", | |
| " labels = batch['label']\n", | |
| " \n", | |
| " outputs = self(input_ids,attention_mask)\n", | |
| " loss = self.criterion(outputs,labels)\n", | |
| " self.log('train_loss',loss , prog_bar=True,logger=True)\n", | |
| " \n", | |
| " return {\"loss\" :loss, \"predictions\":outputs, \"labels\": labels }\n", | |
| "\n", | |
| "\n", | |
| " def validation_step(self,batch,batch_idx):\n", | |
| " input_ids = batch['input_ids']\n", | |
| " attention_mask = batch['attention_mask']\n", | |
| " labels = batch['label']\n", | |
| " \n", | |
| " outputs = self(input_ids,attention_mask)\n", | |
| " loss = self.criterion(outputs,labels)\n", | |
| " self.log('val_loss',loss , prog_bar=True,logger=True)\n", | |
| " \n", | |
| " return loss\n", | |
| "\n", | |
| " def test_step(self,batch,batch_idx):\n", | |
| " input_ids = batch['input_ids']\n", | |
| " attention_mask = batch['attention_mask']\n", | |
| " labels = batch['label']\n", | |
| " outputs = self(input_ids,attention_mask)\n", | |
| " loss = self.criterion(outputs,labels)\n", | |
| " self.log('test_loss',loss , prog_bar=True,logger=True)\n", | |
| " return loss\n", | |
| " \n", | |
| " \n", | |
| " def configure_optimizers(self):\n", | |
| " optimizer = AdamW(self.parameters() , lr=self.lr)\n", | |
| " warmup_steps = self.steps_per_epoch//3\n", | |
| " total_steps = self.steps_per_epoch * self.n_epochs - warmup_steps\n", | |
| " scheduler = get_linear_schedule_with_warmup(optimizer,warmup_steps,total_steps)\n", | |
| " return [optimizer], [scheduler]" | |
| ], | |
| "execution_count": 14, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 117, | |
| "referenced_widgets": [ | |
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| "id": "mEOaH4jQcjj4", | |
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| }, | |
| "source": [ | |
| "N_EPOCHS = 20\n", | |
| "LR = 2e-05\n", | |
| "steps_per_epoch = len(train_data)//TRAIN_BATCH_SIZE\n", | |
| "\n", | |
| "model = QuoteTagClassifier(n_classes=len(classes), steps_per_epoch=steps_per_epoch,n_epochs=N_EPOCHS,lr=LR)" | |
| ], | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "505b59b77e1744ec89841c927450afa2", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Downloading: 0%| | 0.00/416M [00:00<?, ?B/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight']\n", | |
| "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
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| ] | |
| }, | |
| "id": "2SNlSYxjc1ui", | |
| "outputId": "1b243293-6a1c-442d-be74-0e3f792768a1" | |
| }, | |
| "source": [ | |
| "# Instantiate the Model Trainer\n", | |
| "trainer = pl.Trainer(max_epochs = N_EPOCHS , gpus = 1, progress_bar_refresh_rate = 20)\n", | |
| "# Train the Classifier Model\n", | |
| "trainer.fit(model, data_module)" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "GPU available: True, used: True\n", | |
| "TPU available: False, using: 0 TPU cores\n", | |
| "IPU available: False, using: 0 IPUs\n", | |
| "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/datamodule.py:424: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n", | |
| " f\"DataModule.{name} has already been called, so it will not be called again. \"\n", | |
| "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", | |
| "\n", | |
| " | Name | Type | Params\n", | |
| "-------------------------------------------------\n", | |
| "0 | bert | BertModel | 108 M \n", | |
| "1 | classifier | Linear | 11.5 K\n", | |
| "2 | criterion | BCEWithLogitsLoss | 0 \n", | |
| "-------------------------------------------------\n", | |
| "108 M Trainable params\n", | |
| "0 Non-trainable params\n", | |
| "108 M Total params\n", | |
| "433.287 Total estimated model params size (MB)\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "a89bc3ed28e54adc8af999d0530c2ee4", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validation sanity check: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "cd77fc51f2ed45479d70f76a59bdf5fb", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Training: -1it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:406: LightningDeprecationWarning: One of the returned values {'labels', 'predictions'} has a `grad_fn`. We will detach it automatically but this behaviour will change in v1.6. Please detach it manually: `return {'loss': ..., 'something': something.detach()}`\n", | |
| " f\"One of the returned values {set(extra.keys())} has a `grad_fn`. We will detach it automatically\"\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "41022e02170c439692857abb46f22bb8", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "4bdca7bd3b7a42bd9f753eb4dce7c1a4", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "1630f6fcd4cb46e2821bda6f8ae02a68", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "61e1378a1445476ab7590a5eb9d8cbb7", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "43cd71466374425da14ef160f2127d6f", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "63e63ee41dd74ddc931e2439222a346a", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "6f42c686696c425ab057e3df27bde927", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "0ce42a3488724a5fb57de9633ff4b23b", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "5580ba19bca24263b014d6f4a36acf70", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "6be5bc67e6134dbf98a36f4525b10537", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "c4e5e99159c44e4f90f085a42e240a77", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "fb3bbebb788a4b59ace664bbbbfb4589", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "052c9139efa24d7cb4e6ed34c8793834", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "93d046cc08614ffb8879900c7cec15b0", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "0ca6296f155a4880b51c2a0cceeb8717", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "d6f8c53b870249b9bc87826e5f0a9393", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "988936f2a82146d395328ec8d4c838b0", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "172691ddfd01469a8e48bbd4db1685d7", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "a00dc50ba9494ed68a844a437d71a3a0", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "f0a74e1390ec466f940a2020dcba9148", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Validating: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "pOCxFLuArjpM", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 231, | |
| "referenced_widgets": [ | |
| "4cac59ca79d84dcaaa7f05554cb53682", | |
| "8bd51ddf01534bb9858904ffab6b3bef", | |
| "202a94cc850d4dcf8749be59ab788585", | |
| "3544b4f79a534f2793f66ccd6c284127", | |
| "b5d00f94290f47ec8e605d70aceba88b", | |
| "0a1d56868ce5483aadd7d1cc665474ce", | |
| "3fa0b104ccb142bc9a25865a3f5ed39e", | |
| "36e2812ded85412e9c20892135641c1c", | |
| "3834b5867c1e4899ad5710de14c77bf1", | |
| "81bfd5f2b9814761ad2f436a4001cd4c", | |
| "df4b97bd698b4dd5b17544eeff53496b" | |
| ] | |
| }, | |
| "outputId": "0640649d-accf-4669-fbf2-f3db598e3edc" | |
| }, | |
| "source": [ | |
| "# Evaluate the model performance on the test dataset\n", | |
| "trainer.test(model,datamodule=data_module)" | |
| ], | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/datamodule.py:424: LightningDeprecationWarning: DataModule.prepare_data has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.prepare_data.\n", | |
| " f\"DataModule.{name} has already been called, so it will not be called again. \"\n", | |
| "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/core/datamodule.py:424: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.\n", | |
| " f\"DataModule.{name} has already been called, so it will not be called again. \"\n", | |
| "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "application/vnd.jupyter.widget-view+json": { | |
| "model_id": "4cac59ca79d84dcaaa7f05554cb53682", | |
| "version_minor": 0, | |
| "version_major": 2 | |
| }, | |
| "text/plain": [ | |
| "Testing: 0it [00:00, ?it/s]" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--------------------------------------------------------------------------------\n", | |
| "DATALOADER:0 TEST RESULTS\n", | |
| "{'test_loss': 0.16399289667606354}\n", | |
| "--------------------------------------------------------------------------------\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[{'test_loss': 0.16399289667606354}]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 17 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Y1F5qwftrqSu" | |
| }, | |
| "source": [ | |
| "## Evaluate Model Performance on Test Set" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "CgKDvVw0rtPP" | |
| }, | |
| "source": [ | |
| "from torch.utils.data import TensorDataset\n", | |
| "\n", | |
| "# Tokenize all quotes in test_data\n", | |
| "input_ids = []\n", | |
| "attention_masks = []\n", | |
| "\n", | |
| "\n", | |
| "for quote in test_data.Quote:\n", | |
| " encoded_quote = Bert_tokenizer.encode_plus(\n", | |
| " quote,\n", | |
| " None,\n", | |
| " add_special_tokens=True,\n", | |
| " max_length= MAX_LEN,\n", | |
| " padding = 'max_length',\n", | |
| " return_token_type_ids= False,\n", | |
| " return_attention_mask= True,\n", | |
| " truncation=True,\n", | |
| " return_tensors = 'pt' \n", | |
| " )\n", | |
| " \n", | |
| " input_ids.append(encoded_quote['input_ids'])\n", | |
| " attention_masks.append(encoded_quote['attention_mask'])\n", | |
| " \n", | |
| "# Now convert the lists into tensors.\n", | |
| "input_ids = torch.cat(input_ids, dim=0)\n", | |
| "attention_masks = torch.cat(attention_masks, dim=0)\n", | |
| "labels = torch.tensor(test_data[classes].values)\n", | |
| "\n", | |
| "# Create the DataLoader.\n", | |
| "pred_data = TensorDataset(input_ids, attention_masks, labels)\n", | |
| "pred_sampler = SequentialSampler(pred_data)\n", | |
| "pred_dataloader = DataLoader(pred_data, sampler=pred_sampler, batch_size=TEST_BATCH_SIZE)" | |
| ], | |
| "execution_count": 18, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "kydtWtpor-nk" | |
| }, | |
| "source": [ | |
| "flat_pred_outs = 0\n", | |
| "flat_true_labels = 0" | |
| ], | |
| "execution_count": 19, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "vRIHdPATsC52" | |
| }, | |
| "source": [ | |
| "# Put model in evaluation mode\n", | |
| "model = model.to(device) # moving model to cuda\n", | |
| "model.eval()\n", | |
| "\n", | |
| "# Tracking variables \n", | |
| "pred_outs, true_labels = [], []\n", | |
| "#i=0\n", | |
| "# Predict \n", | |
| "for batch in pred_dataloader:\n", | |
| " # Add batch to GPU\n", | |
| " batch = tuple(t.to(device) for t in batch)\n", | |
| " \n", | |
| " # Unpack the inputs from our dataloader\n", | |
| " b_input_ids, b_attn_mask, b_labels = batch\n", | |
| " \n", | |
| " with torch.no_grad():\n", | |
| " pred_out = model(b_input_ids,b_attn_mask)\n", | |
| " pred_out = torch.sigmoid(pred_out)\n", | |
| " pred_out = pred_out.detach().cpu().numpy()\n", | |
| " label_ids = b_labels.to('cpu').numpy()\n", | |
| "\n", | |
| " pred_outs.append(pred_out)\n", | |
| " true_labels.append(label_ids)" | |
| ], | |
| "execution_count": 20, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "JpxPqnJhsJ0Z" | |
| }, | |
| "source": [ | |
| "flat_pred_outs = np.concatenate(pred_outs, axis=0)\n", | |
| "flat_true_labels = np.concatenate(true_labels, axis=0)" | |
| ], | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Mmp2piPhsV68" | |
| }, | |
| "source": [ | |
| "## Predictions of Tags in Test set\n", | |
| "\n", | |
| "First of all we need to identify the threshdol that performs the best for the test dataset." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "OoI6UfRfsSBq", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "7c1f672c-521a-4b6b-ebbe-d6b346c6b21e" | |
| }, | |
| "source": [ | |
| "threshold = np.arange(0.4,0.51,0.01)\n", | |
| "threshold" | |
| ], | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([0.4 , 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 22 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "AT6X1ld7sbnK" | |
| }, | |
| "source": [ | |
| "Let's define a function that takes a threshold value and uses it to convert probabilities into 1 or 0." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "F3TtQ8k5scKo" | |
| }, | |
| "source": [ | |
| "# convert probabilities into 0 or 1 based on a threshold value\n", | |
| "def classify(pred_prob,thresh):\n", | |
| " y_pred = []\n", | |
| "\n", | |
| " for tag_label_row in pred_prob:\n", | |
| " temp=[]\n", | |
| " for tag_label in tag_label_row:\n", | |
| " if tag_label >= thresh:\n", | |
| " temp.append(1) \n", | |
| " else:\n", | |
| " temp.append(0)\n", | |
| " y_pred.append(temp)\n", | |
| "\n", | |
| " return y_pred" | |
| ], | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "30kZNVXJseC5" | |
| }, | |
| "source": [ | |
| "from sklearn import metrics\n", | |
| "scores=[] # Store the list of f1 scores for prediction on each threshold\n", | |
| "\n", | |
| "#convert labels to 1D array\n", | |
| "y_true = flat_true_labels.ravel() \n", | |
| "\n", | |
| "for thresh in threshold:\n", | |
| " \n", | |
| " #classes for each threshold\n", | |
| " pred_bin_label = classify(flat_pred_outs,thresh) \n", | |
| "\n", | |
| " #convert to 1D array\n", | |
| " y_pred = np.array(pred_bin_label).ravel()\n", | |
| "\n", | |
| " scores.append(metrics.f1_score(y_true,y_pred))" | |
| ], | |
| "execution_count": 24, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "CIoqnDPpsir-", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "3e7c901f-0206-442a-81c2-202d9558b593" | |
| }, | |
| "source": [ | |
| "# find the optimal threshold\n", | |
| "opt_thresh = threshold[scores.index(max(scores))]\n", | |
| "print(f'Optimal Threshold Value = {opt_thresh}')" | |
| ], | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Optimal Threshold Value = 0.4\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "NZecOkhLsnbj" | |
| }, | |
| "source": [ | |
| "## Performance Score Evaluation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "DUSFhV_8sm2T" | |
| }, | |
| "source": [ | |
| "#predictions for optimal threshold\n", | |
| "y_pred_labels = classify(flat_pred_outs,opt_thresh)\n", | |
| "y_pred = np.array(y_pred_labels).ravel() # Flatten" | |
| ], | |
| "execution_count": 26, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "DZw1X0cystnl", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "c1744d30-583f-428d-b612-c771ccd1e11f" | |
| }, | |
| "source": [ | |
| "print(metrics.classification_report(y_true,y_pred))" | |
| ], | |
| "execution_count": 27, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " precision recall f1-score support\n", | |
| "\n", | |
| " 0 0.96 0.98 0.97 4151\n", | |
| " 1 0.74 0.54 0.62 349\n", | |
| "\n", | |
| " accuracy 0.95 4500\n", | |
| " macro avg 0.85 0.76 0.80 4500\n", | |
| "weighted avg 0.94 0.95 0.95 4500\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "uuRNtR67syUX", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "outputId": "1cdb9441-a76d-407a-a386-b35a315f8f2b" | |
| }, | |
| "source": [ | |
| "from sklearn.preprocessing import MultiLabelBinarizer\n", | |
| "\n", | |
| "mlb = MultiLabelBinarizer()\n", | |
| "yt = mlb.fit_transform([classes])\n", | |
| "yt.shape\n", | |
| "\n", | |
| "y_pred = mlb.inverse_transform(np.array(y_pred_labels))\n", | |
| "y_act = mlb.inverse_transform(flat_true_labels)\n", | |
| "\n", | |
| "df = pd.DataFrame({'Body':test_data['Quote'],'Actual Tags':y_act,'Predicted Tags':y_pred})\n", | |
| "df.sample(40)" | |
| ], | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Body</th>\n", | |
| " <th>Actual Tags</th>\n", | |
| " <th>Predicted Tags</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>4327</th>\n", | |
| " <td>I learned that the problems that we have are n...</td>\n", | |
| " <td>(god,)</td>\n", | |
| " <td>(god,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2347</th>\n", | |
| " <td>Nobody has ever measured, not even poets, how ...</td>\n", | |
| " <td>(love,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1961</th>\n", | |
| " <td>There is no philosophy without the art of igno...</td>\n", | |
| " <td>(philosophy,)</td>\n", | |
| " <td>()</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>498</th>\n", | |
| " <td>Every picture shows a spot with which the arti...</td>\n", | |
| " <td>(love,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3006</th>\n", | |
| " <td>Each moment of a happy lover's hour is worth a...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2063</th>\n", | |
| " <td>I hope somebody falls in love with me - other ...</td>\n", | |
| " <td>(love,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4088</th>\n", | |
| " <td>Resist much, obey little.</td>\n", | |
| " <td>(poetry,)</td>\n", | |
| " <td>(inspirational,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2880</th>\n", | |
| " <td>It'll be the Internet and piracy that will kil...</td>\n", | |
| " <td>(philosophy,)</td>\n", | |
| " <td>()</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5296</th>\n", | |
| " <td>Success is getting what you want, happiness is...</td>\n", | |
| " <td>(happiness,)</td>\n", | |
| " <td>(happiness,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6988</th>\n", | |
| " <td>Come sleep with me: We won't make Love,Love wi...</td>\n", | |
| " <td>(love, relationships)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1072</th>\n", | |
| " <td>Humor has been the balm of my life, but it's b...</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2455</th>\n", | |
| " <td>A friend of mine said something powerful at hi...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>(life,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6915</th>\n", | |
| " <td>Sickness, insanity and death were the angels t...</td>\n", | |
| " <td>(death,)</td>\n", | |
| " <td>()</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>100</th>\n", | |
| " <td>It is never too late to be what you might have...</td>\n", | |
| " <td>(inspirational,)</td>\n", | |
| " <td>(inspirational,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7670</th>\n", | |
| " <td>To me, the Seventies were very inspirational a...</td>\n", | |
| " <td>(love,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1855</th>\n", | |
| " <td>There is only one success - to be able to spen...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>(life,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7344</th>\n", | |
| " <td>Never underestimate the power of stupid people...</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4631</th>\n", | |
| " <td>All the darkness in the world cannot extinguis...</td>\n", | |
| " <td>(inspirational,)</td>\n", | |
| " <td>(inspirational,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7848</th>\n", | |
| " <td>Success in life comes not from holding a good ...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>(life,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4191</th>\n", | |
| " <td>Only a true best friend can protect you from y...</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " <td>()</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1802</th>\n", | |
| " <td>Reading 'Youth in Revolt' might have ruined my...</td>\n", | |
| " <td>(truth,)</td>\n", | |
| " <td>(truth,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6376</th>\n", | |
| " <td>Life is a series of natural and spontaneous ch...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>(life,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4634</th>\n", | |
| " <td>There is no friendship, no love, like that of ...</td>\n", | |
| " <td>(love,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7474</th>\n", | |
| " <td>Music is ... A higher revelation than all Wisd...</td>\n", | |
| " <td>(philosophy, wisdom)</td>\n", | |
| " <td>()</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6787</th>\n", | |
| " <td>When someone is in your heart, they're never t...</td>\n", | |
| " <td>(love,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4735</th>\n", | |
| " <td>HopeSmiles from the threshold of the year to c...</td>\n", | |
| " <td>(happiness, hope, inspirational)</td>\n", | |
| " <td>(happiness,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3892</th>\n", | |
| " <td>Nobody realizes that some people expend tremen...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>()</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1840</th>\n", | |
| " <td>If you wish to succeed in life, make persevera...</td>\n", | |
| " <td>(hope, life)</td>\n", | |
| " <td>(life,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7919</th>\n", | |
| " <td>I do have a lot of time for people in my life,...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>(life,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>770</th>\n", | |
| " <td>I wish it need not have happened in my time,\"s...</td>\n", | |
| " <td>(death,)</td>\n", | |
| " <td>()</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5007</th>\n", | |
| " <td>To live in this world, you must be able to do ...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4797</th>\n", | |
| " <td>It is a curious thought, but it is only when y...</td>\n", | |
| " <td>(love,)</td>\n", | |
| " <td>(love,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6309</th>\n", | |
| " <td>The longer and more carefully we look at a fun...</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " <td>()</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7317</th>\n", | |
| " <td>There are a lot of times that we feel like our...</td>\n", | |
| " <td>(faith, god)</td>\n", | |
| " <td>(god,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>51</th>\n", | |
| " <td>All that is gold does not glitter,Not all thos...</td>\n", | |
| " <td>(poetry,)</td>\n", | |
| " <td>(inspirational,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3879</th>\n", | |
| " <td>You haven't got a letter on yours,\"George obse...</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>683</th>\n", | |
| " <td>And, when you want something, all the universe...</td>\n", | |
| " <td>(inspirational,)</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>374</th>\n", | |
| " <td>Imagine all the people living life in peace. Y...</td>\n", | |
| " <td>(life,)</td>\n", | |
| " <td>(life,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2168</th>\n", | |
| " <td>Never doubt that a small group of thoughtful, ...</td>\n", | |
| " <td>(inspirational, life, philosophy)</td>\n", | |
| " <td>(inspirational,)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4599</th>\n", | |
| " <td>How do you feel, Georgie?\"whispered Mrs. Weasl...</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " <td>(humor,)</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Body ... Predicted Tags\n", | |
| "4327 I learned that the problems that we have are n... ... (god,)\n", | |
| "2347 Nobody has ever measured, not even poets, how ... ... (love,)\n", | |
| "1961 There is no philosophy without the art of igno... ... ()\n", | |
| "498 Every picture shows a spot with which the arti... ... (love,)\n", | |
| "3006 Each moment of a happy lover's hour is worth a... ... (love,)\n", | |
| "2063 I hope somebody falls in love with me - other ... ... (love,)\n", | |
| "4088 Resist much, obey little. ... (inspirational,)\n", | |
| "2880 It'll be the Internet and piracy that will kil... ... ()\n", | |
| "5296 Success is getting what you want, happiness is... ... (happiness,)\n", | |
| "6988 Come sleep with me: We won't make Love,Love wi... ... (love,)\n", | |
| "1072 Humor has been the balm of my life, but it's b... ... (humor,)\n", | |
| "2455 A friend of mine said something powerful at hi... ... (life,)\n", | |
| "6915 Sickness, insanity and death were the angels t... ... ()\n", | |
| "100 It is never too late to be what you might have... ... (inspirational,)\n", | |
| "7670 To me, the Seventies were very inspirational a... ... (love,)\n", | |
| "1855 There is only one success - to be able to spen... ... (life,)\n", | |
| "7344 Never underestimate the power of stupid people... ... (humor,)\n", | |
| "4631 All the darkness in the world cannot extinguis... ... (inspirational,)\n", | |
| "7848 Success in life comes not from holding a good ... ... (life,)\n", | |
| "4191 Only a true best friend can protect you from y... ... ()\n", | |
| "1802 Reading 'Youth in Revolt' might have ruined my... ... (truth,)\n", | |
| "6376 Life is a series of natural and spontaneous ch... ... (life,)\n", | |
| "4634 There is no friendship, no love, like that of ... ... (love,)\n", | |
| "7474 Music is ... A higher revelation than all Wisd... ... ()\n", | |
| "6787 When someone is in your heart, they're never t... ... (love,)\n", | |
| "4735 HopeSmiles from the threshold of the year to c... ... (happiness,)\n", | |
| "3892 Nobody realizes that some people expend tremen... ... ()\n", | |
| "1840 If you wish to succeed in life, make persevera... ... (life,)\n", | |
| "7919 I do have a lot of time for people in my life,... ... (life,)\n", | |
| "770 I wish it need not have happened in my time,\"s... ... ()\n", | |
| "5007 To live in this world, you must be able to do ... ... (love,)\n", | |
| "4797 It is a curious thought, but it is only when y... ... (love,)\n", | |
| "6309 The longer and more carefully we look at a fun... ... ()\n", | |
| "7317 There are a lot of times that we feel like our... ... (god,)\n", | |
| "51 All that is gold does not glitter,Not all thos... ... (inspirational,)\n", | |
| "3879 You haven't got a letter on yours,\"George obse... ... (humor,)\n", | |
| "683 And, when you want something, all the universe... ... (humor,)\n", | |
| "374 Imagine all the people living life in peace. Y... ... (life,)\n", | |
| "2168 Never doubt that a small group of thoughtful, ... ... (inspirational,)\n", | |
| "4599 How do you feel, Georgie?\"whispered Mrs. Weasl... ... (humor,)\n", | |
| "\n", | |
| "[40 rows x 3 columns]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 28 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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