notebooks/11_Sentiment_Analysis_with_Transformers.ipynb

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        "66b7eadcfc8444fe988696c3eed4bb2e": {
          "model_module": "@jupyter-widgets/controls",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Sentiment Analysis with Transformers using Python, PyTorch, and Hugging Face\n",
        "\n",
        "## Introduction\n",
        "Sentiment analysis is a natural language processing (NLP) task where the aim is to determine the sentiment behind a piece of text. With the advent of transformer models like BERT, GPT, and others, sentiment analysis has become more accurate and nuanced.\n",
        "\n",
        "In this tutorial, we'll use Python, PyTorch, and the Hugging Face Transformers library to perform sentiment analysis. We'll be using Google Colab notebooks, which provide a free and powerful environment to run deep learning experiments.\n",
        "\n",
        "## Setting Up the Environment\n",
        "First, ensure you are in a Google Colab environment. Then, install the necessary packages:"
      ],
      "metadata": {
        "id": "JGl3vxz-XELD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q torch transformers"
      ],
      "metadata": {
        "id": "KiB4L5U42g9s"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "After installation, let's check the versions of these packages for reproducibility:\n"
      ],
      "metadata": {
        "id": "WUwCVhkWXPyk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import transformers\n",
        "\n",
        "print(\"Torch version:\", torch.__version__)\n",
        "print(\"Transformers version:\", transformers.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjVlfwYkXLlg",
        "outputId": "36683517-3c19-4c78-9687-71eb595352c2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Torch version: 2.1.0+cu118\n",
            "Transformers version: 4.35.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Necessary Libraries"
      ],
      "metadata": {
        "id": "47uZGj8gXWX5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "from torch.nn.functional import softmax\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "2DYrTi2RXUZI"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the Pre-trained Model and Tokenizer\n",
        "We'll use a pre-trained model from Hugging Face. For this tutorial, let's use bert-base-uncased, which is a BERT model trained on English language:"
      ],
      "metadata": {
        "id": "hIaAI1yiXaTM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        "id": "-79zKIQeXZtm",
        "outputId": "35a30d82-cd63-42ba-bf1d-8f138262da54"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The Transformer Model\n",
        "Transformer models like BERT use attention mechanisms to understand the context of words in a sentence. Unlike traditional models, they can handle word relationships regardless of their position.\n",
        "\n",
        "## Preparing the Data\n",
        "The tokenizer converts text into a format that the model can understand:"
      ],
      "metadata": {
        "id": "mHhChxLRXfw6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(sentences):\n",
        "    return tokenizer(sentences, padding=True, truncation=True, return_tensors=\"pt\")"
      ],
      "metadata": {
        "id": "csC7yAVoXfG5"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sentiment Analysis Function\n",
        "We'll define a function to perform sentiment analysis:"
      ],
      "metadata": {
        "id": "Eg6CEgXhXliy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sentiment_analysis(sentence, model, tokenizer):\n",
        "    tokens = tokenize([sentence])\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**tokens)\n",
        "    predictions = softmax(outputs.logits, dim=-1)\n",
        "    return predictions"
      ],
      "metadata": {
        "id": "dEthxyvHXkos"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualization Function\n",
        "Visualizing results can be helpful:"
      ],
      "metadata": {
        "id": "f6I5zvxLXr4l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def show_sentiments(sentences, model, tokenizer):\n",
        "    fig, axs = plt.subplots(len(sentences), 1, figsize=(5, len(sentences) * 2))\n",
        "    for i, sentence in enumerate(sentences):\n",
        "        sentiments = sentiment_analysis(sentence, model, tokenizer).numpy()[0]\n",
        "        axs[i].bar(['Negative', 'Positive'], sentiments)\n",
        "        axs[i].set_title(sentence)\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "Jjp-1xm0Xpua"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Running the Analysis\n",
        "Let's analyze some sentences:"
      ],
      "metadata": {
        "id": "0WaPaarxXwX8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentences = [\"I love this product!\", \"I hate this product!\"]\n",
        "show_sentiments(sentences, model, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "Epl11OuGXuvr",
        "outputId": "402ae14a-e833-4714-80e8-1ed0e8df0a3c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Understanding the Code\n",
        "1. Tokenization: Converts raw text into a format that the model can understand (input IDs, attention masks).\n",
        "2. Model Prediction: The model predicts the sentiment scores for each sentence.\n",
        "3. Softmax: Converts logits to probabilities.\n",
        "4. Visualization: Bar plots to display sentiment scores.\n",
        "\n",
        "## Conclusion\n",
        "We've seen how to use a transformer model for sentiment analysis. This approach provides state-of-the-art results for NLP tasks.\n",
        "\n"
      ],
      "metadata": {
        "id": "LWZba-E4X1X4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "erNMkDj-XyZ2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}