diff --git "a/final_LED_model.ipynb" "b/final_LED_model.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "<div style='text-align: center;'>\n",
+ " <img src='https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQzJzIHdangJTrH2mFXFgsLjuLCjpfXXwbxg&usqp=CAU' width='100'/>\n",
+ " <h1>Sharif University of Technology</h1>\n",
+ " <h2>Natural Language Processing</h2>\n",
+ " <h3>Final Project</h3>\n",
+ " <h4>Spoiler classification and summary generation</h4>\n",
+ " <p><strong>Authors:</strong> Parnian Razavipour, Mobina Salimipanah</p>\n",
+ " <p><strong>(Equal Contribution)</strong></p>\n",
+ "</div>\n",
+ "<hr/>\n"
+ ],
+ "metadata": {
+ "id": "3kZY57mYHNea"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "VtuWHaKEQdEq",
+ "outputId": "0aaf7811-598b-48ba-80d9-623db23d6f0e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
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+ "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
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+ "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
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+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n",
+ "Collecting datasets\n",
+ " Downloading datasets-3.0.1-py3-none-any.whl.metadata (20 kB)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
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+ "Collecting pyarrow>=15.0.0 (from datasets)\n",
+ " Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
+ "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
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+ "Collecting xxhash (from datasets)\n",
+ " Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
+ "Collecting multiprocess (from datasets)\n",
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+ "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
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+ "\u001b[?25hInstalling collected packages: xxhash, pyarrow, dill, multiprocess, datasets\n",
+ " Attempting uninstall: pyarrow\n",
+ " Found existing installation: pyarrow 14.0.2\n",
+ " Uninstalling pyarrow-14.0.2:\n",
+ " Successfully uninstalled pyarrow-14.0.2\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0mSuccessfully installed datasets-3.0.1 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.4.1+cu121)\n",
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+ "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
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+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
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+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install transformers\n",
+ "!pip install datasets\n",
+ "!pip install torch\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1vXYwybdRDD_",
+ "outputId": "2a471522-0bd6-47a3-d924-4bf30899a6d7"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
+ "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (3.0.1)\n",
+ "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.4.1+cu121)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
+ "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
+ "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
+ "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
+ "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
+ "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
+ "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
+ "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.5.0)\n",
+ "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
+ "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
+ "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.5)\n",
+ "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
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+ "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
+ "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.0)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.11.1)\n",
+ "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
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+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
+ "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install transformers datasets torch\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**Download and Load Dataset**"
+ ],
+ "metadata": {
+ "id": "wJx5vLJFHlxT"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5tiNer2wNmKd",
+ "outputId": "d76c300d-1387-4491-f2e7-426a8a375652"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
+ "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
+ "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.8.30)\n",
+ "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.5)\n",
+ "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
+ "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.2.3)\n",
+ "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
+ "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
+ "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.10)\n",
+ "cp: cannot stat 'kaggle.json': No such file or directory\n",
+ "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n",
+ "Dataset URL: https://www.kaggle.com/datasets/rmisra/imdb-spoiler-dataset\n",
+ "License(s): Attribution 4.0 International (CC BY 4.0)\n",
+ "Downloading imdb-spoiler-dataset.zip to /content\n",
+ "100% 330M/331M [00:20<00:00, 20.6MB/s]\n",
+ "100% 331M/331M [00:20<00:00, 16.7MB/s]\n",
+ "Archive: imdb-spoiler-dataset.zip\n",
+ " inflating: IMDB_movie_details.json \n",
+ " inflating: IMDB_reviews.json \n",
+ "IMDB_movie_details.json IMDB_reviews.json imdb-spoiler-dataset.zip sample_data\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install kaggle\n",
+ "\n",
+ "!mkdir -p ~/.kaggle\n",
+ "!cp kaggle.json ~/.kaggle/\n",
+ "\n",
+ "!chmod 600 ~/.kaggle/kaggle.json\n",
+ "\n",
+ "!kaggle datasets download -d rmisra/imdb-spoiler-dataset\n",
+ "\n",
+ "!unzip imdb-spoiler-dataset.zip\n",
+ "\n",
+ "!ls"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "uXLdvqgVOL99",
+ "outputId": "658bd35a-2208-4869-b6f5-ea22d6eece6d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " movie_id plot_summary duration \\\n",
+ "0 tt0105112 Former CIA analyst, Jack Ryan is in England wi... 1h 57min \n",
+ "1 tt1204975 Billy (Michael Douglas), Paddy (Robert De Niro... 1h 45min \n",
+ "2 tt0243655 The setting is Camp Firewood, the year 1981. I... 1h 37min \n",
+ "3 tt0040897 Fred C. Dobbs and Bob Curtin, both down on the... 2h 6min \n",
+ "4 tt0126886 Tracy Flick is running unopposed for this year... 1h 43min \n",
+ "\n",
+ " genre rating release_date \\\n",
+ "0 [Action, Thriller] 6.9 1992-06-05 \n",
+ "1 [Comedy] 6.6 2013-11-01 \n",
+ "2 [Comedy, Romance] 6.7 2002-04-11 \n",
+ "3 [Adventure, Drama, Western] 8.3 1948-01-24 \n",
+ "4 [Comedy, Drama, Romance] 7.3 1999-05-07 \n",
+ "\n",
+ " plot_synopsis \n",
+ "0 Jack Ryan (Ford) is on a \"working vacation\" in... \n",
+ "1 Four boys around the age of 10 are friends in ... \n",
+ "2 \n",
+ "3 Fred Dobbs (Humphrey Bogart) and Bob Curtin (T... \n",
+ "4 Jim McAllister (Matthew Broderick) is a much-a... \n",
+ " review_date movie_id user_id is_spoiler \\\n",
+ "0 10 February 2006 tt0111161 ur1898687 True \n",
+ "1 6 September 2000 tt0111161 ur0842118 True \n",
+ "2 3 August 2001 tt0111161 ur1285640 True \n",
+ "3 1 September 2002 tt0111161 ur1003471 True \n",
+ "4 20 May 2004 tt0111161 ur0226855 True \n",
+ "\n",
+ " review_text rating \\\n",
+ "0 In its Oscar year, Shawshank Redemption (writt... 10 \n",
+ "1 The Shawshank Redemption is without a doubt on... 10 \n",
+ "2 I believe that this film is the best story eve... 8 \n",
+ "3 **Yes, there are SPOILERS here**This film has ... 10 \n",
+ "4 At the heart of this extraordinary movie is a ... 8 \n",
+ "\n",
+ " review_summary \n",
+ "0 A classic piece of unforgettable film-making. \n",
+ "1 Simply amazing. The best film of the 90's. \n",
+ "2 The best story ever told on film \n",
+ "3 Busy dying or busy living? \n",
+ "4 Great story, wondrously told and acted \n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "movie_details = pd.read_json('IMDB_movie_details.json', lines=True)\n",
+ "reviews = pd.read_json('IMDB_reviews.json', lines=True)\n",
+ "print(movie_details.head())\n",
+ "print(reviews.head())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "44kpxJ0iOjLz"
+ },
+ "outputs": [],
+ "source": [
+ "# Drop rows where 'plot_synopsis' or 'plot_summary' is missing\n",
+ "movie_details.dropna(subset=['plot_synopsis', 'plot_summary'], inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**training and test sets**"
+ ],
+ "metadata": {
+ "id": "2OUyhuoiH_QV"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "qaAPgonAPRGx"
+ },
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Split the data into training and test sets\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "train_data, temp_data = train_test_split(movie_details, test_size=0.3, random_state=42) # 70% for training, 30% for temp\n",
+ "\n",
+ "validation_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42) # Split the 30% into 15% validation and 15% test\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gewp_maVVU6g",
+ "outputId": "884c7f25-053a-4106-feba-960386884107"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(1100, 7)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ],
+ "source": [
+ "train_data.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6Y-syQ3ZPVDR",
+ "outputId": "85ed66a3-8bb4-4d33-b394-8c293b55ffdd"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "count 1572.000000\n",
+ "mean 1439.085242\n",
+ "std 1496.392929\n",
+ "min 0.000000\n",
+ "25% 493.750000\n",
+ "50% 1073.500000\n",
+ "75% 1920.000000\n",
+ "max 11396.000000\n",
+ "Name: synopsis_length, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Example exploration: Average length of plot_synopsis\n",
+ "movie_details['synopsis_length'] = movie_details['plot_synopsis'].apply(lambda x: len(x.split()))\n",
+ "print(movie_details['synopsis_length'].describe())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6lRJv7A7PX8x"
+ },
+ "outputs": [],
+ "source": [
+ "train_data.to_json('/content/train_data.json', orient='records', lines=True)\n",
+ "validation_data.to_json('/content/val_data.json', orient='records', lines=True)\n",
+ "test_data.to_json('/content/test_data.json', orient='records', lines=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "referenced_widgets": [
+ "f98bee5f48254d9eabd10431adf5aa9b",
+ "6da7932ca9d749ce860e69c2a38c2541",
+ "abf3f9738aa944f1b1cd615b0e3f4a25",
+ "dfbc0cc3e4624d3d9cb25f063548d43a",
+ "4eccac116b3c4175a328b28ed4d63e13",
+ "8c6f5315e19c42369cec6cab6af10f92",
+ "8e681408f4574d7c96f6403475c82a6f",
+ "0ed0e435a6494dc896534036f84513bd",
+ "bcb2b97307814249b005f05b74edcee9",
+ "76e9bb11a1e64b02a191b6c415a90c41",
+ "fafce82ad5504cf5ab6f49180ee12522",
+ "2e568c6632ee43819924eeeb7c8c5739",
+ "acd1d2b5fa534f72968f02110462b118",
+ "0ece36fed4174eb198b1b9882c67fd58",
+ "fe8439b6f5bc4b228d2a6a446614a0cb",
+ "83628ad7a12242778b8bc106fad8c40c",
+ "320973fd444141d7b78fcef4c03a174e",
+ "828a7ff0740a482fb0034fa8d92fee81",
+ "fbe47b01fead44c099e12920dc8efe78",
+ "9085fbc279254b35a1e3b0e95bfb9ac9",
+ "b59a14bf014b4365a9dbd16da1b0ca3e",
+ "0411f712b615428fa6377aa47eeb8f12",
+ "5870843299214a8988fd4caaaf61ab28",
+ "7e1bb2578c2e4aa7a0c6b5fcdb997e6e",
+ "03ded09b8b5a4ebb869aeb65c87aa10c",
+ "130bc93a5870428ea6ee440cb6e21830",
+ "fca1ce2fe9a6497d903866a566c85a9c",
+ "537e0076644145b889196d291bde4374",
+ "5e80aefc2bb9415cb9c25c5e5219e38d",
+ "3334c22eeadd426085be9ae64b34aa46",
+ "b5574fe8a4cb47fca826b5dae823eb11",
+ "c8409f30e0bb42f892ec697783caaeeb",
+ "ca114842ea594e8291bd0a58e4652b0c"
+ ]
+ },
+ "id": "yj5INXEXPhsr",
+ "outputId": "97f90268-e839-4106-9306-a221c4ca1b22"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f98bee5f48254d9eabd10431adf5aa9b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating test split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "2e568c6632ee43819924eeeb7c8c5739"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating val split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "5870843299214a8988fd4caaaf61ab28"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "data_files = {\n",
+ " 'train': '/content/train_data.json',\n",
+ " 'test': '/content/test_data.json',\n",
+ " 'val': '/content/val_data.json'\n",
+ "\n",
+ "\n",
+ "}\n",
+ "\n",
+ "# Loading the dataset from the JSON files\n",
+ "dataset = load_dataset('json', data_files=data_files, split={'train': 'train', 'test': 'test', 'val': 'val'})\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**Exploratory Data Analysis (EDA)**"
+ ],
+ "metadata": {
+ "id": "mPWl8kAFIjoG"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 411,
+ "referenced_widgets": [
+ "40415712030a409194c8e5d8016ac438",
+ "2ff91beaccf84d198af6dd09fdd45a84",
+ "91281d1677d54499a36a5b52c7a5deb3",
+ "b3c29ef77d5f4a609209963ddeeb957c",
+ "3a89f17eb147489e95b020b719b5e9b5",
+ "b037f88d1faa4fedae99f99b2934b99c",
+ "33d23975450440e885275a946a62fcd5",
+ "14ddf24706744ae99151e1ef2ec50184",
+ "fc9528e659914c2abed75db51e6dbc46",
+ "8578b9081c104ae09f76c7941c8d55ac",
+ "8f995c8a721841059f49a7751702a264",
+ "48e0e360e8bd496daf07b9f41f62ab4c",
+ "4655a7b4746d4dbb94f11bc4bf649cd2",
+ "824f9d74bd9043d69c5fbd7b8670f12b",
+ "c270c9de8d1e4f1ba69fc2d11f41c5da",
+ "8a2f0bf77c5244db861a91df114fbfe4",
+ "7967f8faea82454782f928f5953171aa",
+ "1e252e01d9d24f928370ec596c1d2dce",
+ "0a2b31a22ea64959b4be31046c927710",
+ "d9cbfca658cd42c6bf4b272ae586212d",
+ "9ef8c17ef4b9440db46a2fa02f7366c5",
+ "c761198e022d4c32a7d1b79dc04967fa",
+ "33bc1b435a9e42b7a8e40538e4f4f670",
+ "ceeaf991d8c44123b758e290bfcdd9d1",
+ "46de1b040239439aaa1819972a5f854a",
+ "f8f00b01f89b46f4841c4b7b72adf7e4",
+ "ab495b67bf124b9da726fc12c68aace9",
+ "d2446351d77f4b27a06de442f5186a5c",
+ "52735ac507c04d2bbaa4c60461cfeef3",
+ "553fe0d427154df38dedf9c93ecfed0e",
+ "6decf9a706a14e7b8df71e6bcab55a06",
+ "1e0cc3e5eab8408aa2506edf11daa581",
+ "9ff6312e60c84aca9e8f3d1478f6b665"
+ ]
+ },
+ "id": "GOe61CxiYkgv",
+ "outputId": "b5f95681-6609-4620-e64e-7b0309f2f14c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/2.32k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "40415712030a409194c8e5d8016ac438"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "spiece.model: 0%| | 0.00/792k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "48e0e360e8bd496daf07b9f41f62ab4c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/1.39M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "33bc1b435a9e42b7a8e40538e4f4f670"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "count 1572.000000\n",
+ "mean 2116.767812\n",
+ "std 2197.103130\n",
+ "min 0.000000\n",
+ "25% 729.000000\n",
+ "50% 1581.000000\n",
+ "75% 2814.750000\n",
+ "max 18103.000000\n",
+ "Name: token_length, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import T5Tokenizer\n",
+ "\n",
+ "\n",
+ "import pandas as pd\n",
+ "\n",
+ "movie_details = pd.read_json('/content/IMDB_movie_details.json', lines=True)\n",
+ "\n",
+ "tokenizer = T5Tokenizer.from_pretrained('t5-small')\n",
+ "\n",
+ "def calculate_token_length(text):\n",
+ " return len(tokenizer.tokenize(text))\n",
+ "\n",
+ "movie_details['token_length'] = movie_details['plot_synopsis'].apply(calculate_token_length)\n",
+ "\n",
+ "stats = movie_details['token_length'].describe()\n",
+ "print(stats)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "QHSO1Tz4Z5kc",
+ "outputId": "a13a07fa-69db-4429-97ff-6a9231f4e80e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "count 1572.000000\n",
+ "mean 151.191476\n",
+ "std 60.718672\n",
+ "min 20.000000\n",
+ "25% 103.000000\n",
+ "50% 142.000000\n",
+ "75% 195.250000\n",
+ "max 315.000000\n",
+ "Name: token_length, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Apply the function to the plot_synopsis column\n",
+ "movie_details['token_length'] = movie_details['plot_summary'].apply(calculate_token_length)\n",
+ "\n",
+ "# Display statistics about the token lengths\n",
+ "stats = movie_details['token_length'].describe()\n",
+ "print(stats)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VS4ROiIKnvLy"
+ },
+ "outputs": [],
+ "source": [
+ "device = 'cuda'\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dTcbyQKVQFFA"
+ },
+ "source": [
+ "##**Preprocess the Data**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import re\n",
+ "import torch\n",
+ "from transformers import LEDForConditionalGeneration, LEDTokenizer\n",
+ "\n",
+ "# Load tokenizer and model\n",
+ "tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384')\n",
+ "model = LEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')\n",
+ "\n",
+ "model = model.to(device)\n",
+ "\n",
+ "# Function to normalize text\n",
+ "def normalize_text(text):\n",
+ " text = text.lower() # Lowercase the text\n",
+ " text = re.sub(r'\\s+', ' ', text).strip() # Remove extra spaces and newlines\n",
+ " text = re.sub(r'[^\\w\\s]', '', text) # Remove non-alphanumeric characters\n",
+ " return text\n",
+ "\n",
+ "# Preprocess function with normalization\n",
+ "def preprocess_function(examples):\n",
+ " # Normalize the plot_synopsis and plot_summary\n",
+ " inputs = [\"summarize: \" + normalize_text(doc) for doc in examples[\"plot_synopsis\"]]\n",
+ " model_inputs = tokenizer(inputs, max_length=3000, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n",
+ "\n",
+ " # Normalize labels (plot_summary)\n",
+ " with tokenizer.as_target_tokenizer():\n",
+ " labels = tokenizer([normalize_text(doc) for doc in examples[\"plot_summary\"]], max_length=1024, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n",
+ "\n",
+ " # Replace -100 for padding tokens in labels\n",
+ " labels[\"input_ids\"] = [\n",
+ " [(label if label != tokenizer.pad_token_id else -100) for label in lab]\n",
+ " for lab in labels[\"input_ids\"]\n",
+ " ]\n",
+ "\n",
+ " model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
+ " return model_inputs"
+ ],
+ "metadata": {
+ "id": "eBxLkLaUHJLI",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 296,
+ "referenced_widgets": [
+ "4f57295463b143ec981c5b1d3a8e5a49",
+ "30bb9b323bb34b25b7bd94a57a08f947",
+ "f8f5ba727c104114b1aacbaf18be3b92",
+ "d787d652d4044ccb9529144d49e10c43",
+ "05fb81fc15954e0da2be232105a5fe34",
+ "06c3259f4c334ba19f8f3ec16c94dc71",
+ "ff91264eb09e470dbfab792e011b7c9d",
+ "66b831532b4e407687b49409b3f87767",
+ "3ef18fd318d04fb8b4c91b42707b4bd2",
+ "2885b8cfc2b741209c0caa0a8c9fa4c7",
+ "72f23b4514bf42b0a2475b98dfb7d7a7",
+ "daebf1aac7f54e05a22954dfc9ac8475",
+ "3502ffed2d904193a7f5cfbf9e46f941",
+ "052f3cd1f15d43d684b3f4bf1b315c46",
+ "b6000f50c0ae4023960afe1f54463203",
+ "58bfcde727374cb5b27e0eea0eae6579",
+ "cff8944496a9449a9d1e9269cbf6534f",
+ "c259b071ad53489d9dfa69283afc30e4",
+ "6820b5075d70492c8137a998c089ac05",
+ "0e6224d4897345e3be15d07e0a672de1",
+ "501ffe823844428085168dbb2adc9df8",
+ "af5d02cdb35a4061b8b45562c1fdc11e",
+ "63cc6928f2324ced805d8e94a94e16dc",
+ "f9578e0721f347d7aa26fbbb41c9e50a",
+ "08bc210b294f46718f9e2b05470c905d",
+ "93f990215b9645ff93075b963bcb012d",
+ "155b5d91def24487aab1cbd31112965a",
+ "0d57abcd6dcd41aeb92cdd86615f181a",
+ "4e782e5b17eb46d38b15c46de5260999",
+ "a5a7ad1bbad34587b312aa140ca09bb9",
+ "647ccaad654543f59d77d4a3ad789631",
+ "03f48376b86b4c2bbe698dbe6f908ac1",
+ "3e410bc98f834174ae3108a4b6eb6d0d",
+ "fd0f7478cf4946c3bdf5b597dc06b9a6",
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+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/27.0 [00:00<?, ?B/s]"
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+ },
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+ },
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+ ],
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+ },
+ "metadata": {}
+ },
+ {
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+ },
+ {
+ "output_type": "display_data",
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
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+ }
+ },
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+ },
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+ "output_type": "display_data",
+ "data": {
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "dda889cd884341e99555a04ca260c8d6"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "pytorch_model.bin: 0%| | 0.00/648M [00:00<?, ?B/s]"
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+ },
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+ "output_type": "display_data",
+ "data": {
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+ }
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+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NUPy3CuAQBGR",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 168,
+ "referenced_widgets": [
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+ ]
+ },
+ "outputId": "4410089e-0495-4ad8-98b3-7dd3ea77649e"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/1100 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8c66975cf6cd4dd394def8723c63deda"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:4126: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/236 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "824bd8f18b0f46dba1858baa7af60c2f"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/236 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f782a63861b94ea29c25b5b05da699f9"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "tokenized_datasets = dataset.map(preprocess_function, batched=True, remove_columns=dataset[\"train\"].column_names)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "naG4NIgFST5W"
+ },
+ "source": [
+ "##**Define the Model**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "gTR-Wzy2SS2l"
+ },
+ "outputs": [],
+ "source": [
+ "# from transformers import T5ForConditionalGeneration\n",
+ "import torch, gc\n",
+ "\n",
+ "gc.collect()\n",
+ "torch.cuda.empty_cache()\n",
+ "\n",
+ "# model = T5ForConditionalGeneration.from_pretrained('t5-small')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VUueLVLYu5C_"
+ },
+ "outputs": [],
+ "source": [
+ "import torch, gc\n",
+ "\n",
+ "gc.collect()\n",
+ "torch.cuda.empty_cache()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xCo8sJtDptW2",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "ed545194-7284-49d0-8b41-4f5a755bc819"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "using device: cuda\n"
+ ]
+ }
+ ],
+ "source": [
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "print(f\"using device: {device}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**Train the LED Model**"
+ ],
+ "metadata": {
+ "id": "p8hLnuPbIw6c"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "nuE7Nw58TQUs",
+ "outputId": "da1b22ae-b6dc-4b9d-90b9-ad05b4bc33f3"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import Trainer, TrainingArguments\n",
+ "import torch\n",
+ "\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=\"./results\",\n",
+ " evaluation_strategy=\"epoch\",\n",
+ " learning_rate=2e-5,\n",
+ " per_device_train_batch_size=1, # LED requires a lot of memory\n",
+ " per_device_eval_batch_size=1,\n",
+ " weight_decay=0.01,\n",
+ " save_total_limit=3,\n",
+ " num_train_epochs=3,\n",
+ " report_to=\"none\"\n",
+ ")\n",
+ "\n",
+ "# Initialize AdamW optimizer\n",
+ "optimizer = torch.optim.AdamW(model.parameters(), lr=training_args.learning_rate)\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=tokenized_datasets[\"train\"],\n",
+ " eval_dataset=tokenized_datasets[\"val\"],\n",
+ " tokenizer=tokenizer,\n",
+ " optimizers=(optimizer, None) # Pass the optimizer to the Trainer\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.train()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 311
+ },
+ "id": "xegd5GMMEY4R",
+ "outputId": "a7249a3f-393b-481f-cc48-6c5d47e81be0"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='2001' max='3300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [2001/3300 47:25 < 30:49, 0.70 it/s, Epoch 1.82/3]\n",
+ " </div>\n",
+ " <table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: left;\">\n",
+ " <th>Epoch</th>\n",
+ " <th>Training Loss</th>\n",
+ " <th>Validation Loss</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>2.925300</td>\n",
+ " <td>2.873685</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='3300' max='3300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [3300/3300 1:23:26, Epoch 3/3]\n",
+ " </div>\n",
+ " <table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: left;\">\n",
+ " <th>Epoch</th>\n",
+ " <th>Training Loss</th>\n",
+ " <th>Validation Loss</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>2.925300</td>\n",
+ " <td>2.873685</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>2.409600</td>\n",
+ " <td>2.883492</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>2.165300</td>\n",
+ " <td>2.920285</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=3300, training_loss=2.429696747750947, metrics={'train_runtime': 5007.437, 'train_samples_per_second': 0.659, 'train_steps_per_second': 0.659, 'total_flos': 6526373990400000.0, 'train_loss': 2.429696747750947, 'epoch': 3.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 21
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**Save the Model (3 epochs trained)**"
+ ],
+ "metadata": {
+ "id": "uBp1rPWNI8l9"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model_save_path = '/content/drive/MyDrive/summary_generation_Led_3'\n",
+ "trainer.save_model(model_save_path)"
+ ],
+ "metadata": {
+ "id": "kFuNP98wGaa2"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')\n",
+ "\n",
+ "# Load the trained model from Google Drive\n",
+ "model_save_path = '/content/drive/MyDrive/summary_generation_Led_3'\n",
+ "model = LEDForConditionalGeneration.from_pretrained(model_save_path)\n",
+ "\n",
+ "model = model.to(device)\n"
+ ],
+ "metadata": {
+ "id": "yopwoSEJgNkT"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**Continue training for 1 additional epoch**"
+ ],
+ "metadata": {
+ "id": "T8XIu4hFJSQk"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import Trainer, TrainingArguments\n",
+ "\n",
+ "# Reload the model from the saved checkpoint in Google Drive\n",
+ "model_save_path = '/content/drive/MyDrive/summary_generation' # Your saved path\n",
+ "model = LEDForConditionalGeneration.from_pretrained(model_save_path)\n",
+ "model = model.to(device) # Ensure the model is on the right device (GPU or CPU)\n",
+ "\n",
+ "# Define the training arguments for continuing training\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=\"./results\",\n",
+ " eval_strategy=\"epoch\",\n",
+ " save_strategy=\"epoch\",\n",
+ " learning_rate=2e-5,\n",
+ " per_device_train_batch_size=1, # LED is memory-intensive\n",
+ " per_device_eval_batch_size=1,\n",
+ " weight_decay=0.01,\n",
+ " save_total_limit=3, # Only keep the last 3 models saved\n",
+ " num_train_epochs=1, # Continue training for 1 additional epoch\n",
+ " report_to=\"none\",\n",
+ " logging_dir='./logs', # Directory for storing logs\n",
+ " logging_steps=500,\n",
+ " load_best_model_at_end=True,\n",
+ "\n",
+ "# Initialize the optimizer (AdamW) again for the new training run\n",
+ "optimizer = torch.optim.AdamW(model.parameters(), lr=training_args.learning_rate)\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=tokenized_datasets[\"train\"],\n",
+ " eval_dataset=tokenized_datasets[\"val\"],\n",
+ " tokenizer=tokenizer,\n",
+ " optimizers=(optimizer, None)\n",
+ ")\n",
+ "\n",
+ "trainer.train()\n",
+ "\n",
+ "trainer.save_model(\"/content/drive/MyDrive/summary_generation_Led_4\") # Save the new checkpoint\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 144
+ },
+ "id": "DnDXbarJm1YG",
+ "outputId": "90c5c5a8-bf06-4631-e868-17c6e7dc2f4a"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='1100' max='1100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [1100/1100 26:32, Epoch 1/1]\n",
+ " </div>\n",
+ " <table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: left;\">\n",
+ " <th>Epoch</th>\n",
+ " <th>Training Loss</th>\n",
+ " <th>Validation Loss</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>2.140500</td>\n",
+ " <td>2.964694</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "There were missing keys in the checkpoint model loaded: ['led.encoder.embed_tokens.weight', 'led.decoder.embed_tokens.weight', 'lm_head.weight'].\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**Evaluation on test data**"
+ ],
+ "metadata": {
+ "id": "ewfMVjdEJoWF"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7S7rMmsRzUDK"
+ },
+ "outputs": [],
+ "source": [
+ "pip install rouge_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "pip install nltk\n"
+ ],
+ "metadata": {
+ "id": "OFJmH2KUtdod"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch, gc\n",
+ "\n",
+ "gc.collect()\n",
+ "torch.cuda.empty_cache()\n"
+ ],
+ "metadata": {
+ "id": "SEGn9IQ4to0c"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import nltk\n",
+ "\n",
+ "nltk.download('punkt')\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FliiA5j_-bK6",
+ "outputId": "a1784ce0-e243-4f06-b2dc-554300ec8726"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+ "[nltk_data] Unzipping tokenizers/punkt.zip.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 20
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from datasets import load_metric\n",
+ "import nltk\n",
+ "\n",
+ "metric_rouge = load_metric(\"rouge\")\n",
+ "\n",
+ "def generate_summary(batch):\n",
+ " inputs = tokenizer(batch[\"plot_synopsis\"], max_length=3000, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n",
+ " inputs = inputs.to(device)\n",
+ " outputs = model.generate(inputs[\"input_ids\"], max_length=315, min_length=20, length_penalty=2.0, num_beams=4, early_stopping=True)\n",
+ "\n",
+ " batch[\"pred_summary\"] = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
+ " return batch\n",
+ "\n",
+ "results = dataset[\"test\"].map(generate_summary, batched=True, batch_size=8)\n",
+ "rouge_score = metric_rouge.compute(predictions=results[\"pred_summary\"], references=results[\"plot_summary\"])\n",
+ "print(\"ROUGE scores:\")\n",
+ "print(rouge_score)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 104,
+ "referenced_widgets": [
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+ "outputId": "4a6e0d20-74b4-4916-edf3-c7b0ef4b5117"
+ },
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+ "outputs": [
+ {
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+ "model_id": "ad570df7408d48d99eb69a50ad8f3201",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map: 0%| | 0/236 [00:00<?, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ROUGE scores:\n",
+ "{'rouge1': AggregateScore(low=Score(precision=0.2765937483910001, recall=0.28995076740292736, fmeasure=0.26035670057622456), mid=Score(precision=0.2921494324205486, recall=0.3074020206087751, fmeasure=0.2699305362452888), high=Score(precision=0.3059451759910771, recall=0.32368159175206357, fmeasure=0.27908123059966716)), 'rouge2': AggregateScore(low=Score(precision=0.05483977819595639, recall=0.05782616778822425, fmeasure=0.05115662969998852), mid=Score(precision=0.059651412474327724, recall=0.0629010316508109, fmeasure=0.05502744836975126), high=Score(precision=0.06481477377185653, recall=0.06911828260432601, fmeasure=0.05934541057407643)), 'rougeL': AggregateScore(low=Score(precision=0.17193896723048094, recall=0.17809953193399178, fmeasure=0.16095113303292502), mid=Score(precision=0.1815783962174589, recall=0.1874934529402342, fmeasure=0.16563763481163463), high=Score(precision=0.19066115263066233, recall=0.19614412850133803, fmeasure=0.1701542843468328)), 'rougeLsum': AggregateScore(low=Score(precision=0.17237558177544454, recall=0.17845137961598936, fmeasure=0.1609933753577488), mid=Score(precision=0.18171951919460577, recall=0.18763017833485315, fmeasure=0.16580293749438152), high=Score(precision=0.1909969265446938, recall=0.19646089352884588, fmeasure=0.1706179490049745))}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import nltk\n",
+ "from datasets import load_metric\n",
+ "nltk.download('punkt') # Ensure the NLTK tokenizer is available\n",
+ "\n",
+ "def calculate_bleu_scores(predictions, references):\n",
+ " # Load the BLEU metric\n",
+ " metric_bleu = load_metric(\"bleu\")\n",
+ "\n",
+ " # Tokenize the predictions and references\n",
+ " tokenized_predictions = [nltk.word_tokenize(pred) for pred in predictions]\n",
+ " tokenized_references = [[nltk.word_tokenize(ref)] for ref in references] # BLEU expects a list of list of references\n",
+ "\n",
+ " # Calculate BLEU scores\n",
+ " bleu_score = metric_bleu.compute(predictions=tokenized_predictions, references=tokenized_references)\n",
+ "\n",
+ " return bleu_score\n",
+ "\n",
+ "bleu_scores = calculate_bleu_scores(predictions=results[\"pred_summary\"], references=results[\"plot_summary\"])\n",
+ "\n",
+ "print(\"BLEU scores:\")\n",
+ "print(bleu_scores)\n"
+ ],
+ "metadata": {
+ "id": "ZaP_IEJPBDx8",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "36731e91-ec08-42ee-ad89-3879f57c450b"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+ "[nltk_data] Package punkt is already up-to-date!\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "BLEU scores:\n",
+ "{'bleu': 0.03487780615578816, 'precisions': [0.273911286562432, 0.05937059652418976, 0.016940597495189124, 0.005371388615198805], 'brevity_penalty': 1.0, 'length_ratio': 1.1596914314552467, 'translation_length': 32171, 'reference_length': 27741}\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import nltk\n",
+ "from datasets import load_metric\n",
+ "from prettytable import PrettyTable\n",
+ "\n",
+ "print(\"BLEU scores:\")\n",
+ "print(bleu_scores)\n",
+ "\n",
+ "# Prepare a pretty table to display the BLEU scores\n",
+ "table = PrettyTable()\n",
+ "table.field_names = [\"BLEU Score\", \"Value\"]\n",
+ "\n",
+ "# Assuming bleu_scores contains 'precisions' or equivalent\n",
+ "if 'precisions' in bleu_scores:\n",
+ " for i, score in enumerate(bleu_scores['precisions'], start=1):\n",
+ " table.add_row([f\"BLEU-{i}\", score])\n",
+ "\n",
+ "# Print the pretty table\n",
+ "print(\"BLEU scores:\")\n",
+ "print(table)\n"
+ ],
+ "metadata": {
+ "id": "DsTL-1BcB3JM",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5497b29f-b374-41af-e864-f72612627d93"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "BLEU scores:\n",
+ "{'bleu': 0.03487780615578816, 'precisions': [0.273911286562432, 0.05937059652418976, 0.016940597495189124, 0.005371388615198805], 'brevity_penalty': 1.0, 'length_ratio': 1.1596914314552467, 'translation_length': 32171, 'reference_length': 27741}\n",
+ "BLEU scores:\n",
+ "+------------+----------------------+\n",
+ "| BLEU Score | Value |\n",
+ "+------------+----------------------+\n",
+ "| BLEU-1 | 0.273911286562432 |\n",
+ "| BLEU-2 | 0.05937059652418976 |\n",
+ "| BLEU-3 | 0.016940597495189124 |\n",
+ "| BLEU-4 | 0.005371388615198805 |\n",
+ "+------------+----------------------+\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "jfIbuoxs7waf",
+ "outputId": "bede53de-18c0-40ac-b760-11f3d00fda6e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "+-----------+------------+-----------+--------+-----------+\n",
+ "| Metric | Confidence | Precision | Recall | F-Measure |\n",
+ "+-----------+------------+-----------+--------+-----------+\n",
+ "| ROUGE1 | Low | 0.2766 | 0.2900 | 0.2604 |\n",
+ "| ROUGE1 | Mid | 0.2921 | 0.3074 | 0.2699 |\n",
+ "| ROUGE1 | High | 0.3059 | 0.3237 | 0.2791 |\n",
+ "| ROUGE2 | Low | 0.0548 | 0.0578 | 0.0512 |\n",
+ "| ROUGE2 | Mid | 0.0597 | 0.0629 | 0.0550 |\n",
+ "| ROUGE2 | High | 0.0648 | 0.0691 | 0.0593 |\n",
+ "| ROUGEL | Low | 0.1719 | 0.1781 | 0.1610 |\n",
+ "| ROUGEL | Mid | 0.1816 | 0.1875 | 0.1656 |\n",
+ "| ROUGEL | High | 0.1907 | 0.1961 | 0.1702 |\n",
+ "| ROUGELSUM | Low | 0.1724 | 0.1785 | 0.1610 |\n",
+ "| ROUGELSUM | Mid | 0.1817 | 0.1876 | 0.1658 |\n",
+ "| ROUGELSUM | High | 0.1910 | 0.1965 | 0.1706 |\n",
+ "+-----------+------------+-----------+--------+-----------+\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datasets import load_metric\n",
+ "from prettytable import PrettyTable\n",
+ "\n",
+ "def print_rouge_table(rouge_scores):\n",
+ " table = PrettyTable()\n",
+ " table.field_names = [\"Metric\", \"Confidence\", \"Precision\", \"Recall\", \"F-Measure\"]\n",
+ "\n",
+ " # Iterate through each rouge type and its corresponding scores\n",
+ " for rouge_type, aggregate_scores in rouge_scores.items():\n",
+ " # Iterate through confidence levels: low, mid, high\n",
+ " for confidence_level in ['low', 'mid', 'high']:\n",
+ " score = getattr(aggregate_scores, confidence_level)\n",
+ " table.add_row([\n",
+ " rouge_type.upper(),\n",
+ " confidence_level.capitalize(),\n",
+ " f\"{score.precision:.4f}\",\n",
+ " f\"{score.recall:.4f}\",\n",
+ " f\"{score.fmeasure:.4f}\"\n",
+ " ])\n",
+ "\n",
+ " # Print the table\n",
+ " print(table)\n",
+ "\n",
+ "# Call the function to print the table\n",
+ "print_rouge_table(rouge_score)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "quABVJeaKqfn",
+ "outputId": "7b0b638f-028b-403f-e694-77929871f5fb"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2).to(device)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cGZbyKf8caaL"
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