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| "metadata": { | |
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| }, | |
| "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>" | |
| ] | |
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| "# Install And Import Required Libraries" | |
| ], | |
| "metadata": { | |
| "id": "JqzjJrri-Zc6" | |
| } | |
| }, | |
| { | |
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| }, | |
| "collapsed": true, | |
| "id": "Y_8xMePV8FrL", | |
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| }, | |
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| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.3)\n", | |
| "Collecting datasets\n", | |
| " Downloading datasets-3.4.0-py3-none-any.whl.metadata (19 kB)\n", | |
| "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.17.0)\n", | |
| "Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.28.1)\n", | |
| "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (1.26.4)\n", | |
| "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (24.2)\n", | |
| "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (6.0.2)\n", | |
| "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (2024.11.6)\n", | |
| "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from transformers) (2.32.3)\n", | |
| "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.21.0)\n", | |
| "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.5.3)\n", | |
| "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.11/dist-packages (from transformers) (4.67.1)\n", | |
| "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (18.1.0)\n", | |
| "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n", | |
| " Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n", | |
| "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n", | |
| "Collecting xxhash (from datasets)\n", | |
| " Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n", | |
| "Collecting multiprocess<0.70.17 (from datasets)\n", | |
| " Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n", | |
| "Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets) (2024.10.0)\n", | |
| "Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.13)\n", | |
| "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.6.1)\n", | |
| "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n", | |
| "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (25.2.0)\n", | |
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| "\u001b[?25hDownloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n", | |
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| "\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n", | |
| "Successfully installed datasets-3.4.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n", | |
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| "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch)\n", | |
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| "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch)\n", | |
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| "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch)\n", | |
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| "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch)\n", | |
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| "Collecting nvidia-curand-cu12==10.3.5.147 (from torch)\n", | |
| " Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n", | |
| "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch)\n", | |
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| "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch)\n", | |
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| "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch)\n", | |
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| "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n", | |
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| "\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", | |
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| "Requirement already satisfied: dill in /usr/local/lib/python3.11/dist-packages (from evaluate) (0.3.8)\n", | |
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| "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.11/dist-packages (from evaluate) (4.67.1)\n", | |
| "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from evaluate) (3.5.0)\n", | |
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| "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->evaluate) (1.17.0)\n", | |
| "Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\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": [ | |
| "fb481a1440b74cac9456a2cf43e88d5b", | |
| "dc543f9b149f47fda6580d533ba4d040", | |
| "74a27db6355e4d1ca119bb4470102d52", | |
| "0f9de8f8b35347e29c4727e41d5fe891", | |
| "e8e4858c56884ad381daf53d8a487bb3", | |
| "8a1ea8ffe5064763a23c733d79d60494", | |
| "d0611b1a13f448efbf2f04f8c681bdf1", | |
| "f69edbd83e29462ca5375ccecc4d5746", | |
| "855e66f269eb44c7a749984b71671f81", | |
| "27133dc22889410a991b271715982fa1", | |
| "45cf02e631a34e5db0c1faefbedd7add", | |
| "0932957e14ad4400958eb3e9ac488e5d", | |
| "b5bd9b357bbe4024ae067033060b07d2", | |
| "bb5cfcef1c8a4ccfa7847e0ec3367304", | |
| "7822a930105740f4a2d9e83aaa89322c", | |
| "27a36710ec7e498caec9702fbf804641", | |
| "dfff27cb9ddc47c2991e4652eab3cb9e", | |
| "1ce8bc4ac8fd4490ab0f2d8bc45cb4ab", | |
| "c32df1df9aee4b168b2440d96b90a904", | |
| "6c7a0a2430e948d9a1586dcff8dcd8a6" | |
| ] | |
| }, | |
| "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.sv…" | |
| ], | |
| "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, | |
| "referenced_widgets": [ | |
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| { | |
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| "/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", | |
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| "Generating train split: 0%| | 0/394 [00:00<?, ? examples/s]" | |
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| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "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, | |
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| { | |
| "output_type": "execute_result", | |
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| " title published_date \\\n", | |
| "0 Norwich: Post-mortems to take place after four... 2024-01-21 \n", | |
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| "2 Norwich: Two of four found dead in house were ... 2024-01-21 \n", | |
| "3 Dubai Desert Classic: Rory McIlroy wins tourna... 2024-01-21 \n", | |
| "4 Covid Inquiry has my informal messages, says S... 2024-01-21 \n", | |
| "\n", | |
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| "\n", | |
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| "\n", | |
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| "2 http://www.bbc.co.uk/news/uk-england-norfolk-6... \n", | |
| "3 http://www.bbc.co.uk/sport/golf/68047211 \n", | |
| "4 http://www.bbc.co.uk/news/uk-scotland-68042064 \n", | |
| "\n", | |
| " top_image \n", | |
| "0 https://ichef.bbci.co.uk/news/976/cpsprodpb/52... \n", | |
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| "3 https://ichef.bbci.co.uk/news/1024/cpsprodpb/1... \n", | |
| "4 https://ichef.bbci.co.uk/news/976/cpsprodpb/BE... " | |
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| " <td>2024-01-21</td>\n", | |
| " <td>[https://www.facebook.com/bbcnews]</td>\n", | |
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| " <td>http://www.bbc.co.uk/news/uk-scotland-68042064</td>\n", | |
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falling debris in the garage, while the camera also seems to pick up a dog trying to get away from danger.\\n\\nThere are unconfirmed reports of deaths and the full extent of the damage in Vanuatu remains unclear.\\n\\nRead more on this story:\",\n \"That's all from us in London, thanks for landing on our live page during this momentous occasion for Japan and the global space community.\\n\\nWe'll be closely monitoring developments as officials learn more about the fate of Slim.\\n\\nIf you want to read more about today's Moon mission, our science correspondent Jonathan Amos has you covered.\\n\\nThis page was edited by Andrew Humphrey and Alex Therrien, and written by Gem O'Reilly, Ali Abbas Ahmadi, Jonathan Amos, and Jake Lapham.\",\n \"An investigation is launched after six prisoners died at Parc Prison in Bridgend within three weeks\\n\\nAn investigation has been launched after six inmates in a Welsh prison died in the space of three weeks.\\n\\nSecurity firm G4S, which runs HMP Parc in Bridgend, said the deaths occurred between 27 February and 19 March.\\n\\nTwo of the men have been named as John Rose and Jason Hussey, both of whom died on the same day.\\n\\nThe others are expected to be named later this week, with the Prisons and Probation Ombudsman launching investigation.\\n\\nThe six deaths are among a total of 20 deaths at the prison the body has been notified of, and begun investigating, since January 2022.\\n\\nOmbudsman Adrian Usher said: \\\"Naturally, I am very concerned about the number of deaths which have occurred in such a short space of time and I offer my deepest condolences to the family and friends of those affected.\\n\\n\\\"However we are not making any assumptions and we are keeping an open mind as to whether or not there is a causal link behind these deaths.\\\"\\n\\nMr Usher added: \\\"My team of investigators have begun their work, and we are in contact with HMP Parc so that we can promptly and thoroughly investigate all of the circumstances surrounding each of these deaths.\\n\\n\\\"I am unable to provide further information at present as I do not want to prejudice the inquest, but I will provide my final reports publicly once the inquests have concluded.\\\"\\n\\nAn HMP Parc spokesperson said: \\\"Our thoughts are with the families and friends of the prisoners who have died recently at Parc.\\n\\n\\\"As with all deaths in custody, the Prison and Probation Ombudsman will investigate.\\\"\\n\\nOpened in November 1997, HMP Parc is a category B local prison holding convicted male adult and young offenders and convicted and remand sex offenders.\\n\\nAt the time of a 2019 inspection it held 1,612 prisoners, making it one of the largest prisons in the UK.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"link\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 23527,\n \"samples\": [\n \"http://www.bbc.co.uk/news/articles/c047xg5gedko\",\n 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| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.tail()" | |
| ], | |
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| "35941 Strabane: Stephen Holmes dies days after being... 2025-02-09 \n", | |
| "\n", | |
| " authors \\\n", | |
| "35937 https://www.facebook.com/bbcnews \n", | |
| "35938 https://www.facebook.com/bbcnews \n", | |
| "35939 https://www.facebook.com/bbcnews \n", | |
| "35940 https://www.facebook.com/bbcnews \n", | |
| "35941 https://www.facebook.com/bbcnews \n", | |
| "\n", | |
| " description section \\\n", | |
| "35937 Concerns were raised over the condition of fre... Middle East \n", | |
| "35938 The family of Kevin Holding fear they may neve... England \n", | |
| "35939 Four families who say their children died afte... Technology \n", | |
| "35940 As a young real estate developer in New York, ... World \n", | |
| "35941 The 31 year old's sister describes him as \"eve... Northern Ireland \n", | |
| "\n", | |
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| "35938 Man's pop-up urinal death may never be explain... \n", | |
| "35939 Parents suing TikTok over children's deaths sa... \n", | |
| "35940 Donald and Ivana Trump disembark the Trump Pri... \n", | |
| "35941 Stephen Holmes was seriously injured in an att... \n", | |
| "\n", | |
| " link \\\n", | |
| "35937 http://www.bbc.co.uk/news/articles/clyz7124dppo \n", | |
| "35938 http://www.bbc.co.uk/news/articles/cp8q5mpqrvqo \n", | |
| "35939 http://www.bbc.co.uk/news/articles/c20pyn55v79o \n", | |
| "35940 http://www.bbc.co.uk/news/articles/c4gp5pw654lo \n", | |
| "35941 http://www.bbc.co.uk/news/articles/c62q1qz29e7o \n", | |
| "\n", | |
| " top_image \n", | |
| "35937 https://ichef.bbci.co.uk/ace/standard/3840/cps... \n", | |
| "35938 https://ichef.bbci.co.uk/ace/standard/3840/cps... \n", | |
| "35939 https://ichef.bbci.co.uk/ace/standard/1800/cps... \n", | |
| "35940 https://ichef.bbci.co.uk/ace/standard/976/cpsp... \n", | |
| "35941 https://ichef.bbci.co.uk/ace/standard/3840/cps... " | |
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| " document.querySelector('#df-60d0f35f-e329-4ac1-a3fd-b088c0e3d012 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "repr_error": "0" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.isnull().sum()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 335 | |
| }, | |
| "id": "r6i6cAz1MWp7", | |
| "outputId": "483adce2-962a-446c-a446-25f2d9b79270" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "title 0\n", | |
| "published_date 0\n", | |
| "authors 6542\n", | |
| "description 112\n", | |
| "section 7428\n", | |
| "content 0\n", | |
| "link 0\n", | |
| "top_image 0\n", | |
| "dtype: int64" | |
| ], | |
| "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>0</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>title</th>\n", | |
| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
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| " <th>authors</th>\n", | |
| " <td>6542</td>\n", | |
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| " <th>description</th>\n", | |
| " <td>112</td>\n", | |
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| " <td>7428</td>\n", | |
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| " <td>0</td>\n", | |
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| "</div><br><label><b>dtype:</b> int64</label>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Data Preprocessing:\n", | |
| "\n", | |
| "\n", | |
| "1. Handle Missing Values\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "ZmI2p_o7Oqjt" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Fill null description with corresponding titles\n", | |
| "df['description'] = df['description'].fillna(df['title'])\n", | |
| "\n", | |
| "# Fill null authors with 'https://www.facebook.com/bbcnews'\n", | |
| "df['authors'] = df['authors'].fillna('https://www.facebook.com/bbcnews')\n", | |
| "\n", | |
| "# Fill null section with 'Unknown'\n", | |
| "df['section'] = df['section'].fillna('Unknown')" | |
| ], | |
| "metadata": { | |
| "id": "dK57onOpOzzF" | |
| }, | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| " 2. Clean text by lowercasing, removing source name etc. from title and description\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "PV2KnHrjZmwS" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['title'] = df['title'].str.lower()\n", | |
| "df['title'] = df['title'].str.replace(r' - bbc.*$', '', regex=True).str.replace(r'[^a-z0-9 ]', '', regex=True)\n", | |
| "\n", | |
| "df['description'] = df['description'].str.lower()\n", | |
| "df['description'] = df['description'].str.replace(r'[^a-z0-9 ]', '', regex=True)" | |
| ], | |
| "metadata": { | |
| "id": "mp2dz3vPa81a" | |
| }, | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Data Visualization:\n", | |
| "* Draw Histogram For Title And Description Length\n", | |
| "\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "hrbTxKVnP5YG" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "text_length = df['title'].str.len()\n", | |
| "description_length = df['description'].str.len()\n", | |
| "\n", | |
| "plt.figure(figsize=(10,5))\n", | |
| "\n", | |
| "# Histogram for title length distribution\n", | |
| "plt.subplot(1,2,1)\n", | |
| "sns.histplot(text_length, bins=15, kde=True)\n", | |
| "plt.title('Title Length Distribution')\n", | |
| "plt.xlabel('Length')\n", | |
| "plt.ylabel('Frequency')\n", | |
| "\n", | |
| "# Histogram for description length distribution\n", | |
| "plt.subplot(1,2,2)\n", | |
| "sns.histplot(description_length, bins=15, kde=True)\n", | |
| "plt.title('Description Length Distribution')\n", | |
| "plt.xlabel('Length')\n", | |
| "plt.ylabel('Frequency')\n", | |
| "\n", | |
| "plt.tight_layout()\n", | |
| "plt.show()\n", | |
| "\n", | |
| "# Calculate the mean variance of the 'title'\n", | |
| "title_mean = df['title'].str.len().mean()\n", | |
| "title_variance = df['title'].str.len().var()\n", | |
| "print(f\"The mean of title is: {title_mean}\")\n", | |
| "print(f\"The variance of title is: {title_variance}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 543 | |
| }, | |
| "collapsed": true, | |
| "id": "i3Z1u_iRQSys", | |
| "outputId": "6b0d4eef-c654-4368-8429-937bef914f31" | |
| }, | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1000x500 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "The mean of title is: 60.945467697957824\n", | |
| "The variance of title is: 173.61668057893962\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Lable Data Using RoBERTa:\n", | |
| "\n", | |
| "\n", | |
| "* Load and run a pretrained RoBERTa sentiment model on news titles.\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "eTPDmpM9jdIS" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "classifier = pipeline(\"sentiment-analysis\", model=\"cardiffnlp/twitter-xlm-roberta-base-sentiment\")\n", | |
| "news_titles = df['title'].tolist()\n", | |
| "predictions = classifier(news_titles)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 195, | |
| "referenced_widgets": [ | |
| "3b287007724944cb8f2908ea0bf21c77", | |
| "a4a0efc8ffd94ef5aea07df67a9a3d01", | |
| "367ebd2562ae494a9a85529091deaa33", | |
| "acfbe4f5f6b9405db67edefe2d626d14", | |
| "913be29f6e8043adb79d47d5a66de4f8", | |
| "c18c3f5f0c194fdab07364f5ee5883f8", | |
| "a58775577b3e486e92ac04a2a2f92429", | |
| "9774bbd3aaff4347bda71616b0234b40", | |
| "5b31688479f94d3380ae1646b8cf7bcc", | |
| "82f0847bc4164e3b832b0ae7739c25af", | |
| "d5dfc93d8e19433aa90d2470ebd63d68", | |
| "117f871ca9c04d3bb081f01b4d9b8ae3", | |
| "765f78e20d9343d2a4a3dcac57bf4e98", | |
| "80acb7ebe1ca4fa18bf3adc35b09b556", | |
| "ebe7045ae7194336909e8dda72e0ff75", | |
| "d4545a8599bd485c8afacd78e486a7fa", | |
| "38e113e771704cff8792a1f47121a92c", | |
| "2d4f3a2354d84ad5b65fcc67aae4fbac", | |
| "e266cd306bfe4940b393d48fcba9395b", | |
| "cc5f5eb69dca47a4997b79b70e1a6125", | |
| "6259a20c9f90441680a2d8bdcf340406", | |
| "2192d13e97754d25aca9e439a10de103", | |
| "c2f11b5b10e04181b65fb5f07a8c8c44", | |
| "2a06baa0309846e888d401c0733f9056", | |
| "bb61914b9df142d280d3aa6b91c74ca9", | |
| "6fcc7dde696e4a3b928477e75aa69104", | |
| "3a534efc36b74ab49dd99af335bd8e3a", | |
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| "4156eac059a648eda55d82ea179c50af", | |
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| "d2ccc04737ee4a96966503771fe052ac", | |
| "35d2f35b7c3d488c8abe6c5982cd5fbd", | |
| "fb27615990714d7b9ec619c5a197be94", | |
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| "76d5571085d54efeba9be798e45f675a", | |
| "adbbdf1eb28f4d258c9717cc37c9b8aa", | |
| "a00c8c3f0bbe48528a2b1976d2db8a31", | |
| "a5351c33803a49d9b8ac35e2b1a703f6", | |
| "b132ee82fb664575b4b9585b0b8658e6", | |
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| "3379c5a438bb49c28014ab93c44815ac", | |
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| "4bd4ec274ee14ffb8decaa8049b1d1fb", | |
| "567a83314835452e86dc642dc6f5a94a", | |
| "5e226776661c4c97abe8a830eaef99de", | |
| "147c675ddd4c4a879e6a81da8f34c9ab" | |
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| "id": "aeBUAj8Gjcdp", | |
| "outputId": "7941b293-d981-4888-c59b-77e6e10b27cd" | |
| }, | |
| "execution_count": 13, | |
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| { | |
| "output_type": "display_data", | |
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| "text/plain": [ | |
| "config.json: 0%| | 0.00/841 [00:00<?, ?B/s]" | |
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| "metadata": {} | |
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| "output_type": "display_data", | |
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| "application/vnd.jupyter.widget-view+json": { | |
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| "metadata": {} | |
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| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "sentencepiece.bpe.model: 0%| | 0.00/5.07M [00:00<?, ?B/s]" | |
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| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| "metadata": {} | |
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| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "model.safetensors: 0%| | 0.00/1.11G [00:00<?, ?B/s]" | |
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| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "fb27615990714d7b9ec619c5a197be94" | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "special_tokens_map.json: 0%| | 0.00/150 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "fbdac5ff3085401e8b1c93023532a092" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Device set to use cuda:0\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Print 1st prediction to check output format\n", | |
| "print(predictions[0])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Ofi-8LQHmlgW", | |
| "outputId": "963a1511-a0c7-4345-ec3a-e7c2cd37349c" | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "{'label': 'neutral', 'score': 0.7302418351173401}\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "* Save the unfiltered labeled data" | |
| ], | |
| "metadata": { | |
| "id": "Jzlftq0GsX5H" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "unfiltered_predictions = [(title, predct[\"label\"], predct[\"score\"]) for title, predct in zip(news_titles, predictions)]\n", | |
| "df_unfiltered = pd.DataFrame(unfiltered_predictions, columns=[\"title\", \"label\", \"score\"])\n", | |
| "df_unfiltered.to_csv(\"/content/drive/MyDrive/ColabFiles/unfiltered_labeled_news.csv\", index=False)" | |
| ], | |
| "metadata": { | |
| "id": "hNw7OZLrsheM" | |
| }, | |
| "execution_count": 17, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "* Load unfiltered labeled data from drive." | |
| ], | |
| "metadata": { | |
| "id": "eqL5hiavJoq0" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df_unfiltered = pd.read_csv('/content/drive/MyDrive/ColabFiles/unfiltered_labeled_news.csv', dtype={'score': float})\n", | |
| "\n", | |
| "print(df_unfiltered.isnull().sum())\n", | |
| "df_unfiltered.dropna(inplace=True)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "YUd5d2kFJ4D6", | |
| "outputId": "20f10baa-32bb-4921-f71f-09a53ba39ba8" | |
| }, | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "title 3\n", | |
| "label 0\n", | |
| "score 0\n", | |
| "dtype: int64\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "* Check Sentiment Distribution" | |
| ], | |
| "metadata": { | |
| "id": "CTdNnwngIOFm" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "unfiltered_label_counts = df_unfiltered[\"label\"].value_counts()\n", | |
| "\n", | |
| "plt.figure(figsize=(8, 6))\n", | |
| "\n", | |
| "plt.pie(unfiltered_label_counts, labels=unfiltered_label_counts.index, autopct='%1.1f%%', startangle=90, colors=sns.color_palette(\"pastel\"))\n", | |
| "plt.title('Label Distribution (Unfiltered)')" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 539 | |
| }, | |
| "id": "ELr85xY-IUai", | |
| "outputId": "32fee67c-412f-4c9e-e2f2-9b67c9752b5d" | |
| }, | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Text(0.5, 1.0, 'Label Distribution (Unfiltered)')" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 19 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 800x600 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "* We'll keep only high-confidence predictions to reduce noise.\n", | |
| "* To keep data balanced we'll keep negative sentiment prediction with confidence >= 0.80 and positive sentiment prediction with confidence >= 0.65" | |
| ], | |
| "metadata": { | |
| "id": "4FZhu8r5mhrE" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "titels = df_unfiltered['title'].tolist()\n", | |
| "labels = df_unfiltered['label'].tolist()\n", | |
| "scores = df_unfiltered['score'].tolist()\n", | |
| "\n", | |
| "filtered_predictions = [(title, label) for title, label, score in zip(titels, labels, scores)\n", | |
| " if (label == \"positive\" and score >= 0.65) or\n", | |
| " (label != \"positive\" and score >= 0.8)]" | |
| ], | |
| "metadata": { | |
| "id": "GkquWSKCpLe_" | |
| }, | |
| "execution_count": 20, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "* Save labeled data as `labeled_news.csv` for training." | |
| ], | |
| "metadata": { | |
| "id": "77irgzmZpIxP" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df_filtered = pd.DataFrame(filtered_predictions, columns=[\"title\", \"label\"])\n", | |
| "df_filtered.to_csv(\"/content/drive/MyDrive/ColabFiles/labeled_news.csv\", index=False)" | |
| ], | |
| "metadata": { | |
| "id": "9oYci9wepzj7" | |
| }, | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Visualize Sentiment Data" | |
| ], | |
| "metadata": { | |
| "id": "z2H6cL-QtPH1" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "unfiltered_label_counts = df_unfiltered[\"label\"].value_counts()\n", | |
| "filtered_label_counts = df_filtered[\"label\"].value_counts()\n", | |
| "\n", | |
| "plt.figure(figsize=(12, 6))\n", | |
| "\n", | |
| "# Pie chart for unfiltered predictions\n", | |
| "plt.subplot(1, 2, 1)\n", | |
| "plt.pie(unfiltered_label_counts, labels=unfiltered_label_counts.index, autopct='%1.1f%%', startangle=90, colors=sns.color_palette(\"pastel\"))\n", | |
| "plt.title('Label Distribution (Unfiltered)')\n", | |
| "\n", | |
| "# Pie chart for filtered predictions\n", | |
| "plt.subplot(1, 2, 2)\n", | |
| "plt.pie(filtered_label_counts, labels=filtered_label_counts.index, autopct='%1.1f%%', startangle=0, colors=sns.color_palette(\"pastel\"))\n", | |
| "plt.title('Label Distribution (Filtered)')\n", | |
| "\n", | |
| "plt.tight_layout()\n", | |
| "plt.show()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 589 | |
| }, | |
| "id": "j6ZyCWEutWyt", | |
| "outputId": "3c120b0b-c615-4ada-a130-ff47bc984be2" | |
| }, | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1200x600 with 2 Axes>" | |
| ], | |
| "image/png": 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| |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Analyse And Lable AG News Data:\n", | |
| "* The BBC News Data is very unbalanced and contains only 9% positive titles.\n", | |
| "* The idea is to integrate some positive titles from AG News and create a balanced unified dataset." | |
| ], | |
| "metadata": { | |
| "id": "y-L6Z1-43cgR" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Load Dataset From Hugging Face:\n", | |
| "* The dataset contains real time monthly articles from BBC News.\n", | |
| "* The dataset contains 127.6k train rows." | |
| ], | |
| "metadata": { | |
| "id": "5WpGfD1n4pEh" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "AG_DATASET_PATH = \"fancyzhx/ag_news\"\n", | |
| "\n", | |
| "dataset = load_dataset(path = AG_DATASET_PATH)\n", | |
| "df = pd.DataFrame(dataset['train'])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "referenced_widgets": [ | |
| "ef5a94ef622a4d869bf3dd7af7b568b4", | |
| "e527d8a9f56d425ca53346c13c182915", | |
| "d9847bc732b347a3947fd1e92e0bad74", | |
| "8042db7c5d754f2e98f9d0d6ec133edc", | |
| "b212779fcb57451bb79ef1c8c168bc87", | |
| "1787a028b84f49a8bcd4f9b7bba5f3af", | |
| "70395cb17132434789da3a0f7f8e9e88", | |
| "45094b3bf90a4b579c6fe3cb8ef75565", | |
| "d6ea36e49b294efabdd40dd72d7469b3", | |
| "9cf3c208d01540368a4155de0a8c330d", | |
| "a3ac5bc4665d4e1da77ce007069102c9", | |
| "e2093257383d44f7bb19fe998bf0af2e", | |
| "adcc751562d4430ebaeff4d892e65608", | |
| "6e34c6443bf34b93b1d520b5a56e9af3", | |
| "40f71d93cc0c42beb2d0394b78523bb6", | |
| "efd0cb1dff3c48049e786bbbd3aa5015", | |
| "406202915ee34b9595b23589e1b9ea25", | |
| "e3d742fb417c4ac6aadbcda72d314c76", | |
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| { | |
| "output_type": "display_data", | |
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| "text/plain": [ | |
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| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Save Unlabeled Dataset as CSV In Drive" | |
| ], | |
| "metadata": { | |
| "id": "bbx_B5sk6eCY" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.to_csv(\"/content/drive/MyDrive/ColabFiles/ag_news.csv\", index=False)" | |
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| "id": "ql_TvLQ66gqC" | |
| }, | |
| "execution_count": 24, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Exploratory Data Analysis" | |
| ], | |
| "metadata": { | |
| "id": "BFyTtcB1-lB1" | |
| } | |
| }, | |
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| "cell_type": "code", | |
| "source": [ | |
| "df.head()" | |
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| "0 Wall St. Bears Claw Back Into the Black (Reute... 2\n", | |
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| "2 Oil and Economy Cloud Stocks' Outlook (Reuters... 2\n", | |
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| " <td>1</td>\n", | |
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| " <tr>\n", | |
| " <th>119998</th>\n", | |
| " <td>Today's NFL games PITTSBURGH at NY GIANTS Time...</td>\n", | |
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| " <th>119999</th>\n", | |
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| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
| "\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad58c441-59ef-4e4b-a982-d92ef90d687e')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
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| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
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| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
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| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
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| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
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| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
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| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-ad58c441-59ef-4e4b-a982-d92ef90d687e');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
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| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
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| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
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| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
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| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
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| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
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| " background-color: var(--disabled-bg-color);\n", | |
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| " box-shadow: none;\n", | |
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| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
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| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
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| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
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| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
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| "\n", | |
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| " async function quickchart(key) {\n", | |
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| " document.querySelector('#' + key + ' button');\n", | |
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| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
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| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-63a6eaa0-b815-4a8a-9234-e2f431a21fc3 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
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| "\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "dataframe", | |
| "summary": "{\n \"name\": \"df\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Renteria signing a top-shelf deal Red Sox general manager Theo Epstein acknowledged Edgar Renteria was more a luxury for the 2005 Red Sox than a necessity. But there's nothing wrong with getting the keys to a BMW, and that's what the four-time All-Star and two-time Gold Glover is in the eyes of the Red Sox.\",\n \"Nets get Carter from Raptors INDIANAPOLIS -- All-Star Vince Carter was traded by the Toronto Raptors to the New Jersey Nets for Alonzo Mourning, Eric Williams, Aaron Williams, and a pair of first-round draft picks yesterday.\",\n \"Saban not going to Dolphins yet The Miami Dolphins will put their courtship of LSU coach Nick Saban on hold to comply with the NFL's hiring policy by interviewing at least one minority candidate, a team source told The Associated Press last night.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 26 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.isnull().sum()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 147 | |
| }, | |
| "id": "2kFugEEd_dQU", | |
| "outputId": "74bb7ac2-fe3e-48f7-e04b-710c0d62151d" | |
| }, | |
| "execution_count": 30, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "text 0\n", | |
| "label 0\n", | |
| "dtype: int64" | |
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| "metadata": {}, | |
| "execution_count": 30 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Data Preprocessing And Visualization" | |
| ], | |
| "metadata": { | |
| "id": "lCeCv0oqBO6v" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df['text'] = df['text'].str.lower()\n", | |
| "df['text'] = df['text'].str.replace(r'[^a-z0-9 ]', '', regex=True)" | |
| ], | |
| "metadata": { | |
| "id": "FD9aoP96BSLy" | |
| }, | |
| "execution_count": 31, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "text_length = df['text'].str.len()\n", | |
| "\n", | |
| "sns.histplot(text_length, bins=15, kde=True)\n", | |
| "plt.title('Text Length Distribution')\n", | |
| "plt.xlabel('Length')\n", | |
| "plt.ylabel('Frequency')" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 490 | |
| }, | |
| "id": "i0G2JuIcCIyH", | |
| "outputId": "2a710f58-8281-4b90-93fe-2620e22a02e1" | |
| }, | |
| "execution_count": 32, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Text(0, 0.5, 'Frequency')" | |
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| "metadata": {}, | |
| "execution_count": 32 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
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| "image/png": 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