LLM_024_text_similarity.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/micah-shull/LLMs/blob/main/LLM_024_text_similarity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load Libraries\n",
        "\n",
        "### 1. **`nltk.corpus.stopwords` (`from nltk.corpus import stopwords`)**\n",
        "   - **Purpose**: NLTK (Natural Language Toolkit) is a library for working with human language data (text). The `stopwords` module in NLTK provides lists of common words (like \"the\", \"is\", etc.) that are often removed from text during preprocessing in NLP tasks.\n",
        "   - **Common Uses**:\n",
        "     - Removing stop words from text to focus on meaningful words.\n",
        "     - Preprocessing text data for tasks like text classification, sentiment analysis, or keyword extraction.\n",
        "\n",
        "### 2. **`nltk.download('stopwords')`**\n",
        "   - **Purpose**: This downloads the list of stop words from the NLTK corpus, which is necessary before using `stopwords.words()` in the code.\n",
        "   - **Common Uses**: Ensures that stop words are available for text preprocessing.\n",
        "\n",
        "### 3. **`re` (`import re`)**\n",
        "   - **Purpose**: The `re` module provides support for working with regular expressions in Python. Regular expressions are used for pattern matching, searching, and manipulating strings.\n",
        "   - **Common Uses**:\n",
        "     - Searching for patterns within text.\n",
        "     - Replacing or removing specific patterns (e.g., removing special characters, validating formats like emails, etc.).\n",
        "\n",
        "### 4. **`TfidfVectorizer` (`from sklearn.feature_extraction.text import TfidfVectorizer`)**\n",
        "   - **Purpose**: `TfidfVectorizer` from the `scikit-learn` library is used to convert a collection of raw text documents into a matrix of TF-IDF (Term Frequency-Inverse Document Frequency) features. It helps quantify the importance of words in a document relative to a corpus.\n",
        "   - **Common Uses**:\n",
        "     - Converting text data into numerical features for machine learning models.\n",
        "     - Measuring the importance of words in text documents for tasks like text classification or information retrieval.\n",
        "\n",
        "### 5. **`cosine_similarity` (`from sklearn.metrics.pairwise import cosine_similarity`)**\n",
        "   - **Purpose**: This function computes the cosine similarity between two sets of vectors. Cosine similarity measures the cosine of the angle between two vectors, making it useful for comparing text documents based on the similarity of their content.\n",
        "   - **Common Uses**:\n",
        "     - Finding similarity between text documents (e.g., finding related documents).\n",
        "     - Clustering or comparing documents in NLP tasks.\n",
        "\n",
        "### 6. **`euclidean_distances` (`from sklearn.metrics.pairwise import euclidean_distances`)**\n",
        "   - **Purpose**: This function computes the Euclidean distance between two sets of vectors. Euclidean distance is the straight-line distance between two points in multi-dimensional space, often used for measuring the difference between text documents or data points.\n",
        "   - **Common Uses**:\n",
        "     - Measuring the distance between documents or data points in machine learning tasks.\n",
        "     - Comparing how different two text documents or vectors are in a numerical space.\n",
        "\n"
      ],
      "metadata": {
        "id": "MU4-QUxUeks0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install --upgrade nltk"
      ],
      "metadata": {
        "id": "xwOSJnNpbHDg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FE6yXm8ybFlA",
        "outputId": "9db5f69c-827a-448b-cc84-4b346248177e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from nltk.corpus import stopwords\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "import re\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.metrics.pairwise import euclidean_distances"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample corpus\n",
        "documents = ['Machine learning is the study of computer algorithms that improve automatically through experience.\\\n",
        "              Machine learning algorithms build a mathematical model based on sample data, known as training data.\\\n",
        "              The discipline of machine learning employs various approaches to teach computers to accomplish tasks \\\n",
        "              where no fully satisfactory algorithm is available.',\n",
        "              'Machine learning is closely related to computational statistics, which focuses on making predictions using computers.\\\n",
        "              The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.',\n",
        "              'Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so. \\\n",
        "              It involves computers learning from data provided so that they carry out certain tasks.',\n",
        "              'Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the \"signal\"\\\n",
        "              or \"feedback\" available to the learning system: Supervised, Unsupervised and Reinforcement',\n",
        "              'Software engineering is the systematic application of engineering approaches to the development of software.\\\n",
        "              Software engineering is a computing discipline.',\n",
        "              'A software engineer creates programs based on logic for the computer to execute. A software engineer has to be more concerned\\\n",
        "              about the correctness of the program in all the cases. Meanwhile, a data scientist is comfortable with uncertainty and variability.\\\n",
        "              Developing a machine learning application is more iterative and explorative process than software engineering.'\n",
        "]"
      ],
      "metadata": {
        "id": "fIjhPjmMbpjV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Load the documents into a DataFrame\n",
        "The code starts by loading the `documents` (which is assumed to be a list or a collection of text data) into a pandas DataFrame.\n",
        "\n",
        "- **What this does**: Creates a DataFrame `documents_df` with a single column named `'documents'`, where each row corresponds to a document from the `documents` list."
      ],
      "metadata": {
        "id": "woCPePU9dc0Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "documents_df=pd.DataFrame(documents,columns=['documents'])\n",
        "for d in documents_df.documents:\n",
        "  print(d)"
      ],
      "metadata": {
        "id": "LJq6siO5dg0w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "495789f4-ebf5-4467-edd8-07ad2674b228"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Machine learning is the study of computer algorithms that improve automatically through experience.              Machine learning algorithms build a mathematical model based on sample data, known as training data.              The discipline of machine learning employs various approaches to teach computers to accomplish tasks               where no fully satisfactory algorithm is available.\n",
            "Machine learning is closely related to computational statistics, which focuses on making predictions using computers.              The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.\n",
            "Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.               It involves computers learning from data provided so that they carry out certain tasks.\n",
            "Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the \"signal\"              or \"feedback\" available to the learning system: Supervised, Unsupervised and Reinforcement\n",
            "Software engineering is the systematic application of engineering approaches to the development of software.              Software engineering is a computing discipline.\n",
            "A software engineer creates programs based on logic for the computer to execute. A software engineer has to be more concerned              about the correctness of the program in all the cases. Meanwhile, a data scientist is comfortable with uncertainty and variability.              Developing a machine learning application is more iterative and explorative process than software engineering.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Clean the text by removing special characters and stop words\n",
        "\n",
        "- **Goal**: This step aims to clean the text by:\n",
        "  - Removing special characters (anything that is not a letter).\n",
        "  - Converting text to lowercase.\n",
        "  - Removing stop words (common words like \"the,\" \"is,\" etc.) based on the stop word list `stop_words_l`.\n",
        "\n",
        "- **What needs cleaning**:\n",
        "  - The use of `re.sub()` inside the `if` statement is redundant since it's being applied twice. We can clean this up."
      ],
      "metadata": {
        "id": "4U81Vt-5dqLf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# Create a list of English stop words\n",
        "stop_words_l = stopwords.words('english')\n",
        "\n",
        "# Function to clean text\n",
        "def clean_text(doc):\n",
        "    # Split the document into words, remove non-alphabetical characters, convert to lowercase, and remove stop words\n",
        "    return \" \".join(\n",
        "        re.sub(r'[^a-zA-Z]', ' ', word).lower()\n",
        "        for word in doc.split()\n",
        "        if re.sub(r'[^a-zA-Z]', ' ', word).lower() not in stop_words_l\n",
        "    )\n",
        "\n",
        "# Apply the clean_text function to the documents\n",
        "documents_df['documents_cleaned'] = documents_df['documents'].apply(clean_text)"
      ],
      "metadata": {
        "id": "AvnsJ7W_dxD3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words_l = stopwords.words('english')\n",
        "\n",
        "documents_df['documents_cleaned'] = documents_df.documents.apply(\n",
        "    lambda x: \" \".join(\n",
        "        re.sub(r'[^a-zA-Z]', ' ', w).lower()\n",
        "        for w in x.split()\n",
        "        if re.sub(r'[^a-zA-Z]', ' ', w).lower() not in stop_words_l\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "id": "el_DRaW6dlBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: TF-IDF Vectorization\n",
        "\n",
        "- **Explanation**:\n",
        "  - `TfidfVectorizer()` is used to convert the cleaned documents into **TF-IDF vectors**.\n",
        "  - The `fit()` method learns the vocabulary from the cleaned documents.\n",
        "  - The `transform()` method converts the cleaned documents into a sparse matrix of TF-IDF scores (one vector per document).\n",
        "\n",
        "- **What this does**: The `tfidf_vectors` matrix represents the term frequency-inverse document frequency (TF-IDF) score of each word in each document, allowing for further analysis like similarity or distance calculations."
      ],
      "metadata": {
        "id": "W9EdraCpdzsF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tfidfvectoriser = TfidfVectorizer()\n",
        "tfidfvectoriser.fit(documents_df['documents_cleaned'])\n",
        "tfidf_vectors = tfidfvectoriser.transform(documents_df['documents_cleaned'])"
      ],
      "metadata": {
        "id": "w1pgV-Fnd2wa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Calculate Pairwise Similarities and Differences\n",
        "\n",
        "- **Cosine Similarity**:\n",
        "  - `np.dot(tfidf_vectors, tfidf_vectors.T).toarray()` computes the **cosine similarity** between the TF-IDF vectors. This gives a similarity matrix where each entry is the cosine similarity between two documents.\n",
        "  - Cosine similarity measures the angle between two vectors, effectively capturing how similar the content of two documents is (ignoring magnitude).\n",
        "\n",
        "- **Euclidean Distance**:\n",
        "  - `euclidean_distances(tfidf_vectors)` computes the Euclidean distance between each pair of TF-IDF vectors. Euclidean distance measures the straight-line distance between two vectors (documents).\n",
        "\n",
        "- **Result**: You now have two matrices:\n",
        "  - `pairwise_similarities`: Contains cosine similarity scores between documents.\n",
        "  - `pairwise_differences`: Contains Euclidean distances between documents."
      ],
      "metadata": {
        "id": "x1lUH3vtd9aL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pairwise_similarities = np.dot(tfidf_vectors, tfidf_vectors.T).toarray()\n",
        "pairwise_differences = euclidean_distances(tfidf_vectors)"
      ],
      "metadata": {
        "id": "JklDM29kd7Fg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5: Function to Find the Most Similar Documents\n",
        "\n",
        "\n",
        "- **Explanation**:\n",
        "  - This function prints out the document with the given `doc_id` and finds other documents that are similar based on the provided similarity or distance matrix.\n",
        "  - It sorts the documents either by **cosine similarity** (descending order, higher is better) or **Euclidean distance** (ascending order, lower is better).\n",
        "  - It then prints the most similar documents based on the metric."
      ],
      "metadata": {
        "id": "HEb1YMG9eEob"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def most_similar(doc_id, similarity_matrix, matrix_type):\n",
        "    print(f'Document: {documents_df.iloc[doc_id][\"documents\"]}')\n",
        "    print('\\nSimilar Documents:')\n",
        "\n",
        "    # Determine the sorting direction based on similarity or distance\n",
        "    if matrix_type == 'Cosine Similarity':\n",
        "        similar_ix = np.argsort(similarity_matrix[doc_id])[::-1]  # Sort in descending order (higher similarity is better)\n",
        "    elif matrix_type == 'Euclidean Distance':\n",
        "        similar_ix = np.argsort(similarity_matrix[doc_id])  # Sort in ascending order (lower distance is better)\n",
        "\n",
        "    # Print similar documents\n",
        "    for ix in similar_ix:\n",
        "        if ix == doc_id:  # Skip the document itself\n",
        "            continue\n",
        "        print('\\n')\n",
        "        print(f'Document: {documents_df.iloc[ix][\"documents\"]}')\n",
        "        print(f'{matrix_type}: {similarity_matrix[doc_id][ix]}')"
      ],
      "metadata": {
        "id": "SWsa7ulneChD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "### Step 6: Testing the Similarity Function\n",
        "\n",
        "- **What this does**:\n",
        "  - The function `most_similar(0, ...)` is called with document ID `0` to find and print the documents most similar to the first document.\n",
        "  - It does this twice: once using cosine similarity and once using Euclidean distance.\n",
        "\n",
        "### Cleaned and Explained Final Code:\n",
        "\n",
        "### Explanation Summary:\n",
        "1. **Text Cleaning**: The code cleans the documents by removing non-alphabetic characters and stop words, then converts the text to lowercase.\n",
        "2. **TF-IDF Vectorization**: It converts the cleaned text into numerical vectors representing the importance of words in the document using TF-IDF.\n",
        "3. **Similarity/Difference Calculation**: It calculates pairwise similarities (cosine similarity) and differences (Euclidean distance) between documents.\n",
        "4. **Most Similar Documents**: It prints documents most similar to a selected document based on cosine similarity or Euclidean distance.\n",
        "\n"
      ],
      "metadata": {
        "id": "SH0OoxLydKgD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "most_similar(0, pairwise_similarities, 'Cosine Similarity')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VqNhO6zbeKMM",
        "outputId": "8110a74e-4c61-4b2e-946f-3128d28cc410"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document: Machine learning is the study of computer algorithms that improve automatically through experience.              Machine learning algorithms build a mathematical model based on sample data, known as training data.              The discipline of machine learning employs various approaches to teach computers to accomplish tasks               where no fully satisfactory algorithm is available.\n",
            "\n",
            "Similar Documents:\n",
            "\n",
            "\n",
            "Document: Machine learning is closely related to computational statistics, which focuses on making predictions using computers.              The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.\n",
            "Cosine Similarity: 0.8262172937393188\n",
            "\n",
            "\n",
            "Document: A software engineer creates programs based on logic for the computer to execute. A software engineer has to be more concerned              about the correctness of the program in all the cases. Meanwhile, a data scientist is comfortable with uncertainty and variability.              Developing a machine learning application is more iterative and explorative process than software engineering.\n",
            "Cosine Similarity: 0.8164832592010498\n",
            "\n",
            "\n",
            "Document: Software engineering is the systematic application of engineering approaches to the development of software.              Software engineering is a computing discipline.\n",
            "Cosine Similarity: 0.6500377655029297\n",
            "\n",
            "\n",
            "Document: Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.               It involves computers learning from data provided so that they carry out certain tasks.\n",
            "Cosine Similarity: 0.6039087772369385\n",
            "\n",
            "\n",
            "Document: Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the \"signal\"              or \"feedback\" available to the learning system: Supervised, Unsupervised and Reinforcement\n",
            "Cosine Similarity: 0.4798518717288971\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "most_similar(0, pairwise_differences, 'Euclidean Distance')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ckG42ckG_7dn",
        "outputId": "4a7d4d5a-1f07-474a-ee2b-56a0430a0ba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document: Machine learning is the study of computer algorithms that improve automatically through experience.              Machine learning algorithms build a mathematical model based on sample data, known as training data.              The discipline of machine learning employs various approaches to teach computers to accomplish tasks               where no fully satisfactory algorithm is available.\n",
            "\n",
            "Similar Documents:\n",
            "\n",
            "\n",
            "Document: Machine learning is closely related to computational statistics, which focuses on making predictions using computers.              The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.\n",
            "Euclidean Distance: 9.6488618850708\n",
            "\n",
            "\n",
            "Document: A software engineer creates programs based on logic for the computer to execute. A software engineer has to be more concerned              about the correctness of the program in all the cases. Meanwhile, a data scientist is comfortable with uncertainty and variability.              Developing a machine learning application is more iterative and explorative process than software engineering.\n",
            "Euclidean Distance: 9.767533302307129\n",
            "\n",
            "\n",
            "Document: Software engineering is the systematic application of engineering approaches to the development of software.              Software engineering is a computing discipline.\n",
            "Euclidean Distance: 13.767190933227539\n",
            "\n",
            "\n",
            "Document: Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.               It involves computers learning from data provided so that they carry out certain tasks.\n",
            "Euclidean Distance: 14.188263893127441\n",
            "\n",
            "\n",
            "Document: Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the \"signal\"              or \"feedback\" available to the learning system: Supervised, Unsupervised and Reinforcement\n",
            "Euclidean Distance: 16.023740768432617\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Full Code"
      ],
      "metadata": {
        "id": "AaL9XtNQeMLi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import euclidean_distances\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# Load documents into a DataFrame\n",
        "documents_df = pd.DataFrame(documents, columns=['documents'])\n",
        "\n",
        "# Create a list of English stop words\n",
        "stop_words_l = stopwords.words('english')\n",
        "\n",
        "# Function to clean text by removing special characters and stop words\n",
        "def clean_text(doc):\n",
        "    return \" \".join(\n",
        "        re.sub(r'[^a-zA-Z]', ' ', word).lower()\n",
        "        for word in doc.split()\n",
        "        if re.sub(r'[^a-zA-Z]', ' ', word).lower() not in stop_words_l\n",
        "    )\n",
        "\n",
        "# Clean the documents\n",
        "documents_df['documents_cleaned'] = documents_df['documents'].apply(clean_text)\n",
        "\n",
        "# TF-IDF vectorization\n",
        "tfidfvectoriser = TfidfVectorizer()\n",
        "tfidfvectoriser.fit(documents_df['documents_cleaned'])\n",
        "tfidf_vectors = tfidfvectoriser.transform(documents_df['documents_cleaned'])\n",
        "\n",
        "# Compute pairwise cosine similarities and Euclidean distances\n",
        "pairwise_similarities = np.dot(tfidf_vectors, tfidf_vectors.T).toarray()\n",
        "pairwise_differences = euclidean_distances(tfidf_vectors)\n",
        "\n",
        "# Function to print the most similar documents\n",
        "def most_similar(doc_id, similarity_matrix, matrix_type):\n",
        "    print(f'Document: {documents_df.iloc[doc_id][\"documents\"]}')\n",
        "    print('\\nSimilar Documents:')\n",
        "\n",
        "    if matrix_type == 'Cosine Similarity':\n",
        "        similar_ix = np.argsort(similarity_matrix[doc_id])[::-1]  # Sort descending for similarity\n",
        "    elif matrix_type == 'Euclidean Distance':\n",
        "        similar_ix = np.argsort(similarity_matrix[doc_id])  # Sort ascending for distance\n",
        "\n",
        "    for ix in similar_ix:\n",
        "        if ix == doc_id:  # Skip the original document\n",
        "            continue\n",
        "        print('\\n')\n",
        "        print(f'Document: {documents_df.iloc[ix][\"documents\"]}')\n",
        "        print(f'{matrix_type}: {similarity_matrix[doc_id][ix]}')\n",
        "\n",
        "# Test: Find documents similar to the first document\n",
        "most_similar(0, pairwise_similarities, 'Cosine Similarity')\n",
        "most_similar(0, pairwise_differences, 'Euclidean Distance')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdRnVL0ydRuS",
        "outputId": "59af3577-1b42-4e9a-bef9-031b3e77e4f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document: Machine learning is the study of computer algorithms that improve automatically through experience.              Machine learning algorithms build a mathematical model based on sample data, known as training data.              The discipline of machine learning employs various approaches to teach computers to accomplish tasks               where no fully satisfactory algorithm is available.\n",
            "\n",
            "Similar Documents:\n",
            "\n",
            "\n",
            "Document: Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.               It involves computers learning from data provided so that they carry out certain tasks.\n",
            "Cosine Similarity: 0.2282251849266025\n",
            "\n",
            "\n",
            "Document: Machine learning is closely related to computational statistics, which focuses on making predictions using computers.              The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.\n",
            "Cosine Similarity: 0.2072169372318548\n",
            "\n",
            "\n",
            "Document: Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the \"signal\"              or \"feedback\" available to the learning system: Supervised, Unsupervised and Reinforcement\n",
            "Cosine Similarity: 0.15970535959056292\n",
            "\n",
            "\n",
            "Document: A software engineer creates programs based on logic for the computer to execute. A software engineer has to be more concerned              about the correctness of the program in all the cases. Meanwhile, a data scientist is comfortable with uncertainty and variability.              Developing a machine learning application is more iterative and explorative process than software engineering.\n",
            "Cosine Similarity: 0.12539932728160036\n",
            "\n",
            "\n",
            "Document: Software engineering is the systematic application of engineering approaches to the development of software.              Software engineering is a computing discipline.\n",
            "Cosine Similarity: 0.050317088144109255\n",
            "Document: Machine learning is the study of computer algorithms that improve automatically through experience.              Machine learning algorithms build a mathematical model based on sample data, known as training data.              The discipline of machine learning employs various approaches to teach computers to accomplish tasks               where no fully satisfactory algorithm is available.\n",
            "\n",
            "Similar Documents:\n",
            "\n",
            "\n",
            "Document: Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.               It involves computers learning from data provided so that they carry out certain tasks.\n",
            "Euclidean Distance: 1.2423967281616588\n",
            "\n",
            "\n",
            "Document: Machine learning is closely related to computational statistics, which focuses on making predictions using computers.              The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.\n",
            "Euclidean Distance: 1.259192648301399\n",
            "\n",
            "\n",
            "Document: Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the \"signal\"              or \"feedback\" available to the learning system: Supervised, Unsupervised and Reinforcement\n",
            "Euclidean Distance: 1.296375439762291\n",
            "\n",
            "\n",
            "Document: A software engineer creates programs based on logic for the computer to execute. A software engineer has to be more concerned              about the correctness of the program in all the cases. Meanwhile, a data scientist is comfortable with uncertainty and variability.              Developing a machine learning application is more iterative and explorative process than software engineering.\n",
            "Euclidean Distance: 1.322573758032723\n",
            "\n",
            "\n",
            "Document: Software engineering is the systematic application of engineering approaches to the development of software.              Software engineering is a computing discipline.\n",
            "Euclidean Distance: 1.378174816092567\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sentence Transformers"
      ],
      "metadata": {
        "id": "J0zyt6lqgslC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbdLSRzQgq2W",
        "outputId": "886840cd-4298-44b3-f78d-cccada0d219c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.2.1)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.44.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.6)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.5.0+cu121)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.5.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.13.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.24.7)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (10.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (3.16.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2024.10.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2.32.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.1.4)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.11.0->sentence-transformers) (1.3.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (1.26.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (2024.9.11)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.41.0->sentence-transformers) (0.19.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (3.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2024.8.30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "sbert_model = SentenceTransformer('bert-base-nli-mean-tokens')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594,
          "referenced_widgets": [
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          ]
        },
        "id": "oZRydA0WhCLl",
        "outputId": "b2b7bda1-a1be-4b47-c231-6e1d61adcfb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/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": [
              "modules.json:   0%|          | 0.00/229 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e514db290a974fd4b4e3cd22dca573ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/122 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70fba8efddc94f148c681bd2e0d29354"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/3.99k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "572599d0a3e04b5aa56f6cbcec86022a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "886ab5eff06d445c93f5e9dff47dfa0f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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        {
          "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"
          ]
        },
        {
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    {
      "cell_type": "code",
      "source": [
        "document_embeddings = sbert_model.encode(documents_df['documents_cleaned'])\n",
        "\n",
        "pairwise_similarities=cosine_similarity(document_embeddings)\n",
        "pairwise_differences=euclidean_distances(document_embeddings)\n",
        "\n",
        "most_similar(0,pairwise_similarities,'Cosine Similarity')\n",
        "most_similar(0,pairwise_differences,'Euclidean Distance')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0WxSMMOgrrW",
        "outputId": "d4e81cb6-e06a-446a-d19d-ce21d904267e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Document: Machine learning is the study of computer algorithms that improve automatically through experience.              Machine learning algorithms build a mathematical model based on sample data, known as training data.              The discipline of machine learning employs various approaches to teach computers to accomplish tasks               where no fully satisfactory algorithm is available.\n",
            "\n",
            "Similar Documents:\n",
            "\n",
            "\n",
            "Document: Machine learning is closely related to computational statistics, which focuses on making predictions using computers.              The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.\n",
            "Cosine Similarity: 0.8262172937393188\n",
            "\n",
            "\n",
            "Document: A software engineer creates programs based on logic for the computer to execute. A software engineer has to be more concerned              about the correctness of the program in all the cases. Meanwhile, a data scientist is comfortable with uncertainty and variability.              Developing a machine learning application is more iterative and explorative process than software engineering.\n",
            "Cosine Similarity: 0.8164832592010498\n",
            "\n",
            "\n",
            "Document: Software engineering is the systematic application of engineering approaches to the development of software.              Software engineering is a computing discipline.\n",
            "Cosine Similarity: 0.6500377655029297\n",
            "\n",
            "\n",
            "Document: Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.               It involves computers learning from data provided so that they carry out certain tasks.\n",
            "Cosine Similarity: 0.6039087772369385\n",
            "\n",
            "\n",
            "Document: Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the \"signal\"              or \"feedback\" available to the learning system: Supervised, Unsupervised and Reinforcement\n",
            "Cosine Similarity: 0.4798518717288971\n",
            "Document: Machine learning is the study of computer algorithms that improve automatically through experience.              Machine learning algorithms build a mathematical model based on sample data, known as training data.              The discipline of machine learning employs various approaches to teach computers to accomplish tasks               where no fully satisfactory algorithm is available.\n",
            "\n",
            "Similar Documents:\n",
            "\n",
            "\n",
            "Document: Machine learning is closely related to computational statistics, which focuses on making predictions using computers.              The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning.\n",
            "Euclidean Distance: 9.6488618850708\n",
            "\n",
            "\n",
            "Document: A software engineer creates programs based on logic for the computer to execute. A software engineer has to be more concerned              about the correctness of the program in all the cases. Meanwhile, a data scientist is comfortable with uncertainty and variability.              Developing a machine learning application is more iterative and explorative process than software engineering.\n",
            "Euclidean Distance: 9.767533302307129\n",
            "\n",
            "\n",
            "Document: Software engineering is the systematic application of engineering approaches to the development of software.              Software engineering is a computing discipline.\n",
            "Euclidean Distance: 13.767190933227539\n",
            "\n",
            "\n",
            "Document: Machine learning involves computers discovering how they can perform tasks without being explicitly programmed to do so.               It involves computers learning from data provided so that they carry out certain tasks.\n",
            "Euclidean Distance: 14.188263893127441\n",
            "\n",
            "\n",
            "Document: Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the \"signal\"              or \"feedback\" available to the learning system: Supervised, Unsupervised and Reinforcement\n",
            "Euclidean Distance: 16.023740768432617\n"
          ]
        }
      ]
    }
  ]
}