Sentiment_Analysis_on_IMDb_Reviews.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/149189/Sentiment-Analysis-on-IMDb-Reviews/blob/main/Sentiment_Analysis_on_IMDb_Reviews.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGonsZKrPVdn",
        "outputId": "9baabd39-8e0d-4f43-9b13-3c3621517e6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.0)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.20.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (71.0.4)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.17.0)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\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: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\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) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec<=2024.5.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.5.0,>=2023.1.0->datasets) (2024.5.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.1)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.44.0)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.3.4)\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) (24.1.0)\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) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\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",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (13.7.1)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.7.4)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.6)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.0.3)\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) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "pip install tensorflow transformers datasets\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70V16vC8SJpu",
        "outputId": "80ff8b81-a1fe-469a-f2f0-cd9eb5a71d5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (71.0.4)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.17.0)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\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: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.44.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.5.0)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (13.7.1)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.7.4)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.6)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.0.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "pip install --upgrade tensorflow transformers\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from transformers import TFAutoModelForSequenceClassification, AutoTokenizer\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "from tensorflow.keras.metrics import SparseCategoricalAccuracy\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "vxh_OmmcFzrH"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load the IMDb dataset\n",
        "dataset = load_dataset(\"imdb\")\n",
        "\n",
        "# Display a few examples from the training dataset\n",
        "print(\"Training Dataset Examples:\")\n",
        "for i in range(3):\n",
        "    print(f\"Example {i+1}:\")\n",
        "    print(f\"Text: {dataset['train'][i]['text']}\")\n",
        "    print(f\"Label: {dataset['train'][i]['label']}\")\n",
        "    print()\n",
        "\n",
        "# Display a few examples from the test dataset\n",
        "print(\"Test Dataset Examples:\")\n",
        "for i in range(3):\n",
        "    print(f\"Example {i+1}:\")\n",
        "    print(f\"Text: {dataset['test'][i]['text']}\")\n",
        "    print(f\"Label: {dataset['test'][i]['label']}\")\n",
        "    print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eO5SRxKGKlHv",
        "outputId": "5ad99cb0-d47a-48ff-f2c2-d37b0667bc80"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Dataset Examples:\n",
            "Example 1:\n",
            "Text: I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it's not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn't have much of a plot.\n",
            "Label: 0\n",
            "\n",
            "Example 2:\n",
            "Text: \"I Am Curious: Yellow\" is a risible and pretentious steaming pile. It doesn't matter what one's political views are because this film can hardly be taken seriously on any level. As for the claim that frontal male nudity is an automatic NC-17, that isn't true. I've seen R-rated films with male nudity. Granted, they only offer some fleeting views, but where are the R-rated films with gaping vulvas and flapping labia? Nowhere, because they don't exist. The same goes for those crappy cable shows: schlongs swinging in the breeze but not a clitoris in sight. And those pretentious indie movies like The Brown Bunny, in which we're treated to the site of Vincent Gallo's throbbing johnson, but not a trace of pink visible on Chloe Sevigny. Before crying (or implying) \"double-standard\" in matters of nudity, the mentally obtuse should take into account one unavoidably obvious anatomical difference between men and women: there are no genitals on display when actresses appears nude, and the same cannot be said for a man. In fact, you generally won't see female genitals in an American film in anything short of porn or explicit erotica. This alleged double-standard is less a double standard than an admittedly depressing ability to come to terms culturally with the insides of women's bodies.\n",
            "Label: 0\n",
            "\n",
            "Example 3:\n",
            "Text: If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.<br /><br />One might feel virtuous for sitting thru it because it touches on so many IMPORTANT issues but it does so without any discernable motive. The viewer comes away with no new perspectives (unless one comes up with one while one's mind wanders, as it will invariably do during this pointless film).<br /><br />One might better spend one's time staring out a window at a tree growing.<br /><br />\n",
            "Label: 0\n",
            "\n",
            "Test Dataset Examples:\n",
            "Example 1:\n",
            "Text: I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn't match the background, and painfully one-dimensional characters cannot be overcome with a 'sci-fi' setting. (I'm sure there are those of you out there who think Babylon 5 is good sci-fi TV. It's not. It's clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It's really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it's rubbish as they have to always say \"Gene Roddenberry's Earth...\" otherwise people would not continue watching. Roddenberry's ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.\n",
            "Label: 0\n",
            "\n",
            "Example 2:\n",
            "Text: Worth the entertainment value of a rental, especially if you like action movies. This one features the usual car chases, fights with the great Van Damme kick style, shooting battles with the 40 shell load shotgun, and even terrorist style bombs. All of this is entertaining and competently handled but there is nothing that really blows you away if you've seen your share before.<br /><br />The plot is made interesting by the inclusion of a rabbit, which is clever but hardly profound. Many of the characters are heavily stereotyped -- the angry veterans, the terrified illegal aliens, the crooked cops, the indifferent feds, the bitchy tough lady station head, the crooked politician, the fat federale who looks like he was typecast as the Mexican in a Hollywood movie from the 1940s. All passably acted but again nothing special.<br /><br />I thought the main villains were pretty well done and fairly well acted. By the end of the movie you certainly knew who the good guys were and weren't. There was an emotional lift as the really bad ones got their just deserts. Very simplistic, but then you weren't expecting Hamlet, right? The only thing I found really annoying was the constant cuts to VDs daughter during the last fight scene.<br /><br />Not bad. Not good. Passable 4.\n",
            "Label: 0\n",
            "\n",
            "Example 3:\n",
            "Text: its a totally average film with a few semi-alright action sequences that make the plot seem a little better and remind the viewer of the classic van dam films. parts of the plot don't make sense and seem to be added in to use up time. the end plot is that of a very basic type that doesn't leave the viewer guessing and any twists are obvious from the beginning. the end scene with the flask backs don't make sense as they are added in and seem to have little relevance to the history of van dam's character. not really worth watching again, bit disappointed in the end production, even though it is apparent it was shot on a low budget certain shots and sections in the film are of poor directed quality\n",
            "Label: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "small_train_dataset = dataset['train'].select(range(500))\n",
        "small_test_dataset = dataset['test'].select(range(500))\n",
        "\n",
        "print(len(small_train_dataset))\n",
        "print(len(small_test_dataset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEAaZGLOF-yt",
        "outputId": "009d0960-29c1-4eb0-944f-4595e36b258c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "500\n",
            "500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqwao2EGGCSc",
        "outputId": "56f44b5f-9b68-4953-f196-1654aa21da6d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(examples):\n",
        "    return tokenizer(examples['text'], padding='max_length', truncation=True, max_length=64)\n",
        "\n",
        "small_train_dataset = small_train_dataset.map(preprocess_function, batched=True)\n",
        "small_test_dataset = small_test_dataset.map(preprocess_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "7b1c247887454b70b50034cb81792283",
            "f12296e2be7944ba83b12963be898001",
            "4d3c2c5a499a4925acc80997edaf447a",
            "8e5ceee6a1d8484fbf2f10f8db43d929",
            "8b2221674ebd41ff8863396e776208fb",
            "7ece936c06464548be349d6a780a71fc",
            "8d50bdb56ced4cd4bb8f3b9fd9bfccf1",
            "5cb0cc1f3ea948ce95f21b279ead439d",
            "9ee39ea4b5b24124813aa72e1326edc9",
            "3fe11e4616b84843af2d4c51af47df4c",
            "7e9a26f40629417b976e087a54fbbb4a",
            "c45d9a63a6664858ae0cb635781f05c8",
            "958256d913124b9fb37403781f960d3a",
            "0a3971468c7f40bd905e83c956662ac4",
            "099f892effa440f2b88343d852e77562",
            "9f85fa78c3b14b77b3bf0e5a5574ee5d",
            "17b40d9316394264b1ec82344796ce16",
            "37b1dfd8065440abb015fdca2df3183f",
            "0c74d0761f014e1d8e9ef2b66f7fc7fd",
            "8724ceb2802a4dc7868f6784dd404ea1",
            "3c7465b666e4499eba1b73101952f9e6",
            "a89ad6826388463191fc3996c6ce4243"
          ]
        },
        "id": "ZOwuP9eJGGt1",
        "outputId": "aa3061b1-9f9d-416c-e4a5-f9c16d1a3a4c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/500 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b1c247887454b70b50034cb81792283"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/500 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c45d9a63a6664858ae0cb635781f05c8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "small_train_dataset = small_train_dataset.to_tf_dataset(\n",
        "    columns=[\"input_ids\", \"attention_mask\"],\n",
        "    label_cols=[\"label\"],\n",
        "    shuffle=True,\n",
        "    batch_size=32  # Larger batch size for faster training\n",
        ")\n",
        "\n",
        "small_test_dataset = small_test_dataset.to_tf_dataset(\n",
        "    columns=[\"input_ids\", \"attention_mask\"],\n",
        "    label_cols=[\"label\"],\n",
        "    shuffle=False,\n",
        "    batch_size=32\n",
        ")"
      ],
      "metadata": {
        "id": "VJmGZWipGLV1"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = Adam(learning_rate=3e-5)\n",
        "loss_fn = SparseCategoricalCrossentropy(from_logits=True)\n",
        "accuracy_metric = SparseCategoricalAccuracy()\n",
        "\n",
        "# Lists to store accuracy and loss\n",
        "train_accuracies = []\n",
        "train_losses = []\n",
        "val_accuracies = []\n",
        "val_losses = []"
      ],
      "metadata": {
        "id": "wMKxRsd2GNp1"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 3\n",
        "for epoch in range(epochs):\n",
        "    print(f\"Epoch {epoch + 1}/{epochs}\")\n",
        "\n",
        "    # Training step\n",
        "    epoch_loss_avg = tf.keras.metrics.Mean()\n",
        "    epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\n",
        "\n",
        "    for batch in small_train_dataset:\n",
        "        input_ids = batch[0][\"input_ids\"]\n",
        "        attention_mask = batch[0][\"attention_mask\"]\n",
        "        labels = batch[1]\n",
        "\n",
        "        with tf.GradientTape() as tape:\n",
        "            logits = model(input_ids, attention_mask=attention_mask, training=True).logits\n",
        "            loss = loss_fn(labels, logits)\n",
        "\n",
        "        gradients = tape.gradient(loss, model.trainable_variables)\n",
        "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "\n",
        "        epoch_loss_avg.update_state(loss)\n",
        "        epoch_accuracy.update_state(labels, logits)\n",
        "\n",
        "    # Log training results\n",
        "    train_losses.append(epoch_loss_avg.result().numpy())\n",
        "    train_accuracies.append(epoch_accuracy.result().numpy())\n",
        "    print(f\"Training loss: {train_losses[-1]}, accuracy: {train_accuracies[-1]}\")\n",
        "\n",
        "    # Validation step\n",
        "    val_loss_avg = tf.keras.metrics.Mean()\n",
        "    val_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\n",
        "\n",
        "    for batch in small_test_dataset:\n",
        "        input_ids = batch[0][\"input_ids\"]\n",
        "        attention_mask = batch[0][\"attention_mask\"]\n",
        "        labels = batch[1]\n",
        "\n",
        "        logits = model(input_ids, attention_mask=attention_mask, training=False).logits\n",
        "        loss = loss_fn(labels, logits)\n",
        "\n",
        "        val_loss_avg.update_state(loss)\n",
        "        val_accuracy.update_state(labels, logits)\n",
        "\n",
        "    # Log validation results\n",
        "    val_losses.append(val_loss_avg.result().numpy())\n",
        "    val_accuracies.append(val_accuracy.result().numpy())\n",
        "    print(f\"Validation loss: {val_losses[-1]}, accuracy: {val_accuracies[-1]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNkcM0NDGSb1",
        "outputId": "63ceaac6-0ef0-484a-8cf0-a1d10d1b089c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "Training loss: 0.21097029745578766, accuracy: 0.9779999852180481\n",
            "Validation loss: 0.04766467586159706, accuracy: 1.0\n",
            "Epoch 2/3\n",
            "Training loss: 0.02542431838810444, accuracy: 1.0\n",
            "Validation loss: 0.010228879749774933, accuracy: 1.0\n",
            "Epoch 3/3\n",
            "Training loss: 0.008301490917801857, accuracy: 1.0\n",
            "Validation loss: 0.004956250544637442, accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(14, 5))\n",
        "\n",
        "# Plot training and validation accuracy\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, train_accuracies, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_accuracies, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "# Plot training and validation loss\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, train_losses, label='Training Loss')\n",
        "plt.plot(epochs_range, val_losses, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "_rc2V7BqPV2p",
        "outputId": "47306a94-9458-4664-8227-b3116266c5c6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def predict_sentiments_visual(text_data, model, tokenizer):\n",
        "    # Initialize lists to store results\n",
        "    sentiments = []\n",
        "    probabilities_list = []\n",
        "\n",
        "    # Iterate over each review in the text_data\n",
        "    for review_text in text_data:\n",
        "        # Preprocess the input review\n",
        "        inputs = tokenizer(review_text, padding='max_length', truncation=True, max_length=64, return_tensors=\"tf\")\n",
        "\n",
        "        # Get model predictions\n",
        "        logits = model(inputs['input_ids'], attention_mask=inputs['attention_mask']).logits\n",
        "        predictions = tf.nn.softmax(logits, axis=-1)\n",
        "\n",
        "        # Get the predicted class (0 or 1)\n",
        "        predicted_class = tf.argmax(predictions, axis=-1).numpy()[0]\n",
        "\n",
        "        # Map the predicted class to sentiment\n",
        "        sentiment = \"Positive\" if predicted_class == 1 else \"Negative\"\n",
        "\n",
        "        # Append results to lists\n",
        "        sentiments.append(sentiment)\n",
        "        probabilities_list.append(predictions.numpy()[0])\n",
        "\n",
        "    # Visualization\n",
        "    num_reviews = len(text_data)\n",
        "    x = np.arange(num_reviews)  # The label locations\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(12, 6))\n",
        "    bar_width = 0.35  # Width of the bars\n",
        "\n",
        "    # Create bars for each review's probabilities\n",
        "    for i, (probabilities, sentiment) in enumerate(zip(probabilities_list, sentiments)):\n",
        "        bars = ax.bar(x[i] - bar_width / 2, probabilities[0], bar_width, label='Negative', color='red')\n",
        "        bars = ax.bar(x[i] + bar_width / 2, probabilities[1], bar_width, label='Positive', color='green')\n",
        "\n",
        "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
        "    ax.set_xlabel('Review Index')\n",
        "    ax.set_ylabel('Probability')\n",
        "    ax.set_title('Predicted Sentiments and Probabilities')\n",
        "    ax.set_xticks(x)\n",
        "    ax.set_xticklabels([f'Review {i+1}' for i in x])\n",
        "    ax.legend()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    return sentiments, probabilities_list\n",
        "\n",
        "# Example usage\n",
        "text_data = [\n",
        "    \"I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967.\",\n",
        "    \"If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.\",\n",
        "    \"An average movie with some entertaining moments, but it lacked depth.\"\n",
        "]\n",
        "\n",
        "sentiments, probabilities = predict_sentiments_visual(text_data, model, tokenizer)\n",
        "\n",
        "for i, (review_text, sentiment) in enumerate(zip(text_data, sentiments)):\n",
        "    print(f\"Review {i+1}: {review_text}\")\n",
        "    print(f\"Predicted Sentiment: {sentiment}\")\n",
        "    print(f\"Probabilities: {probabilities[i]}\")\n",
        "    print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        },
        "id": "iVENN-ORUAAc",
        "outputId": "315da40c-f764-4d8c-d539-8b51bb9901bd"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Review 1: I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967.\n",
            "Predicted Sentiment: Negative\n",
            "Probabilities: [0.9946571  0.00534286]\n",
            "\n",
            "Review 2: If only to avoid making this type of film in the future. This film is interesting as an experiment but tells no cogent story.\n",
            "Predicted Sentiment: Negative\n",
            "Probabilities: [0.99497885 0.00502111]\n",
            "\n",
            "Review 3: An average movie with some entertaining moments, but it lacked depth.\n",
            "Predicted Sentiment: Negative\n",
            "Probabilities: [0.9948472  0.00515283]\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "authorship_tag": "ABX9TyPc2yIa+1ubz48kpYRWJ5jq",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "7b1c247887454b70b50034cb81792283": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_f12296e2be7944ba83b12963be898001",
              "IPY_MODEL_4d3c2c5a499a4925acc80997edaf447a",
              "IPY_MODEL_8e5ceee6a1d8484fbf2f10f8db43d929"
            ],
            "layout": "IPY_MODEL_8b2221674ebd41ff8863396e776208fb"
          }
        },
        "f12296e2be7944ba83b12963be898001": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7ece936c06464548be349d6a780a71fc",
            "placeholder": "​",
            "style": "IPY_MODEL_8d50bdb56ced4cd4bb8f3b9fd9bfccf1",
            "value": "Map: 100%"
          }
        },
        "4d3c2c5a499a4925acc80997edaf447a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5cb0cc1f3ea948ce95f21b279ead439d",
            "max": 500,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_9ee39ea4b5b24124813aa72e1326edc9",
            "value": 500
          }
        },
        "8e5ceee6a1d8484fbf2f10f8db43d929": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3fe11e4616b84843af2d4c51af47df4c",
            "placeholder": "​",
            "style": "IPY_MODEL_7e9a26f40629417b976e087a54fbbb4a",
            "value": " 500/500 [00:00&lt;00:00, 621.53 examples/s]"
          }
        },
        "8b2221674ebd41ff8863396e776208fb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7ece936c06464548be349d6a780a71fc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8d50bdb56ced4cd4bb8f3b9fd9bfccf1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5cb0cc1f3ea948ce95f21b279ead439d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9ee39ea4b5b24124813aa72e1326edc9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "3fe11e4616b84843af2d4c51af47df4c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7e9a26f40629417b976e087a54fbbb4a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c45d9a63a6664858ae0cb635781f05c8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_958256d913124b9fb37403781f960d3a",
              "IPY_MODEL_0a3971468c7f40bd905e83c956662ac4",
              "IPY_MODEL_099f892effa440f2b88343d852e77562"
            ],
            "layout": "IPY_MODEL_9f85fa78c3b14b77b3bf0e5a5574ee5d"
          }
        },
        "958256d913124b9fb37403781f960d3a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_17b40d9316394264b1ec82344796ce16",
            "placeholder": "​",
            "style": "IPY_MODEL_37b1dfd8065440abb015fdca2df3183f",
            "value": "Map: 100%"
          }
        },
        "0a3971468c7f40bd905e83c956662ac4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0c74d0761f014e1d8e9ef2b66f7fc7fd",
            "max": 500,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_8724ceb2802a4dc7868f6784dd404ea1",
            "value": 500
          }
        },
        "099f892effa440f2b88343d852e77562": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3c7465b666e4499eba1b73101952f9e6",
            "placeholder": "​",
            "style": "IPY_MODEL_a89ad6826388463191fc3996c6ce4243",
            "value": " 500/500 [00:00&lt;00:00, 542.54 examples/s]"
          }
        },
        "9f85fa78c3b14b77b3bf0e5a5574ee5d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "17b40d9316394264b1ec82344796ce16": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "37b1dfd8065440abb015fdca2df3183f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0c74d0761f014e1d8e9ef2b66f7fc7fd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8724ceb2802a4dc7868f6784dd404ea1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "3c7465b666e4499eba1b73101952f9e6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a89ad6826388463191fc3996c6ce4243": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}