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sentiment.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/yatiksihag01/e1f95a22cd71ec76b9117557b0d7dc87/sentiment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Install And Import Required Libraries"
],
"metadata": {
"id": "JqzjJrri-Zc6"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"collapsed": true,
"id": "Y_8xMePV8FrL",
"outputId": "1e2edd65-d6ab-4936-8fef-b5b74798cc2e"
},
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{
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"name": "stdout",
"text": [
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"\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n",
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"\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m63.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
" Attempting uninstall: nvidia-nvjitlink-cu12\n",
" Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
" Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
" Attempting uninstall: nvidia-curand-cu12\n",
" Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
" Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
" Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
" Attempting uninstall: nvidia-cufft-cu12\n",
" Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
" Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
" Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
" Attempting uninstall: nvidia-cuda-runtime-cu12\n",
" Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
" Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-cupti-cu12\n",
" Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cublas-cu12\n",
" Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
" Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
" Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
" Attempting uninstall: nvidia-cusparse-cu12\n",
" Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
" Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
" Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
" Attempting uninstall: nvidia-cudnn-cu12\n",
" Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
" Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
" Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
" Attempting uninstall: nvidia-cusolver-cu12\n",
" Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
" Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
" Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
"Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n",
"Collecting evaluate\n",
" Downloading evaluate-0.4.3-py3-none-any.whl.metadata (9.2 kB)\n",
"Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from evaluate) (3.4.0)\n",
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"Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)\n",
"\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: evaluate\n",
"Successfully installed evaluate-0.4.3\n"
]
}
],
"source": [
"%pip install transformers datasets\n",
"%pip install torch\n",
"%pip install evaluate"
]
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset, Dataset\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline\n",
"from transformers import TrainingArguments, Trainer\n",
"from transformers import DistilBertTokenizer, DistilBertForSequenceClassification\n",
"import evaluate\n",
"import torch\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
],
"metadata": {
"id": "uHNwXBBeAyM0"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Login to Hugging Face Account"
],
"metadata": {
"id": "9kk7X8LP8NKk"
}
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17,
"referenced_widgets": [
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},
"id": "xY01O5My8eqZ",
"outputId": "3ac55eda-5385-4c7e-f281-03ea2d729223"
},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svโ€ฆ"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "fb481a1440b74cac9456a2cf43e88d5b"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Analyse And Label BBC News Data"
],
"metadata": {
"id": "VT9ZJ5ik1-rb"
}
},
{
"cell_type": "markdown",
"source": [
"## Load Dataset From Hugging Face:\n",
"* The dataset contains real time monthly articles from BBC News.\n",
"* Data collected every month from 2017 to current.\n",
"* We are using 10 months data containing ~29k rows combined.\n",
"\n"
],
"metadata": {
"id": "-NSspb-rBgWH"
}
},
{
"cell_type": "code",
"source": [
"BBC_DATASET_PATH = \"RealTimeData/bbc_news_alltime\"\n",
"COULMN_NAME = \"title\" # Column to keep\n",
"CONFIG_NAMES = ['2024-01', '2024-02', '2024-03', '2024-04', '2024-05',\n",
" '2024-06', '2024-07', '2024-08', '2024-09', '2024-10',\n",
" '2024-11', '2024-12', '2025-01', '2025-02']\n",
"\n",
"# Load each configuration\n",
"dfs = []\n",
"for config_name in CONFIG_NAMES:\n",
" dataset = load_dataset(path = BBC_DATASET_PATH, name = config_name)\n",
" df = pd.DataFrame(dataset['train'])\n",
" dfs.append(df)\n",
"\n",
"# Concatenate all DataFrames intoa single DataFrame\n",
"df = pd.concat(dfs, ignore_index=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
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"id": "gYLZQR4HBaZy",
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},
"execution_count": 4,
"outputs": [
{
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"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
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{
"cell_type": "markdown",
"source": [
"### Save Unlabeled Dataset as CSV In Drive"
],
"metadata": {
"id": "glG6yaXl-lue"
}
},
{
"cell_type": "code",
"source": [
"df.to_csv(\"/content/drive/MyDrive/ColabFiles/unlabeled_raw_articles.csv\", index=False)"
],
"metadata": {
"id": "OglYN8jv-0oz"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Exploratory Data Analysis"
],
"metadata": {
"id": "oaE7i2DSJV0b"
}
},
{
"cell_type": "code",
"source": [
"df.head()"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 660
},
"id": "5SP59jSUL9DY",
"outputId": "d20671db-b326-4b51-bdf6-a2b58352c108"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
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" <td>Scotland's first minister is \"very willing\" to...</td>\n",
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