Sentence_Transformers_&_Datasets_&_Evaluate.ipynb

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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/pj2111/Assignments/blob/master/Sentence_Transformers_%26_Datasets_%26_Evaluate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following that understanding the Sentence Transformers library, and the models\n",
        "https://youtu.be/S_xT3kFW5Yc"
      ],
      "metadata": {
        "id": "VuzgPnkPNaWr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\\Datasets : To use the datasets that are available in Huggingface ecosystem\n",
        "https://youtu.be/i9XnOfyDvig"
      ],
      "metadata": {
        "id": "GTK1_SfqkQCK"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluation: In-Detail Walkthrough of Measurement, Metric & Comparators\n",
        "https://www.youtube.com/watch?v=AlqXKrdKqTQ\n"
      ],
      "metadata": {
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    },
    {
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        "outputId": "72911b06-600b-409f-ce29-87ab48634ce9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting transformers<5.0.0,>=4.6.0 (from sentence-transformers)\n",
            "  Downloading transformers-4.33.2-py3-none-any.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m50.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.1)\n",
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            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (3.8.1)\n",
            "Collecting sentencepiece (from sentence-transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m45.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.17.2)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m65.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m70.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.2.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence-transformers) (9.4.0)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=8b6efce6097842d3ecf0d1eb476d4b3a3231524938472af72bfbe3233eae0b08\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, transformers, sentence-transformers\n",
            "Successfully installed safetensors-0.3.3 sentence-transformers-2.2.2 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.33.2\n"
          ]
        }
      ],
      "source": [
        "!pip install -U sentence-transformers\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer,util\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n"
      ],
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        "colab": {
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      },
      "execution_count": null,
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentences = ['This framework generates embeddings for each input sentence',\n",
        "    'Sentences are passed as a list of string.']\n",
        "\n",
        "\n",
        "embeddings = model.encode(sentences)\n",
        "\n",
        "\n",
        "for sentence, embedding in zip(sentences, embeddings):\n",
        "    print(\"Sentence:\", sentence)\n",
        "    print(\"Embedding:\", embedding)\n",
        "    print(\"\")"
      ],
      "metadata": {
        "id": "Rhs4AkBPIy8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvE_ibhLJJWV",
        "outputId": "6bf47d19-82c1-43d7-9389-e6bad8868c6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 384)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emb1 = model.encode(\"I am eating Apple\")\n",
        "emb2 = model.encode(\"I like fruits\")\n",
        "cos_sim = util.cos_sim(emb1, emb2)\n",
        "print(\"Cosine-Similarity:\", cos_sim)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdyM9kSIJKEi",
        "outputId": "57fe53d2-a14b-4cf5-f7b5-f7ebbe8cb7fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cosine-Similarity: tensor([[0.5398]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cos_sim = util.cos_sim(embeddings[0], embeddings[1])\n",
        "print(\"Cosine-Similarity:\", cos_sim)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-eDaJUed1s9",
        "outputId": "a93ff54c-6ef1-468a-eb55-a73e23e328d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cosine-Similarity: tensor([[0.5381]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# embeddings[1]\n",
        "emb2"
      ],
      "metadata": {
        "id": "vQlhq2xjd9RH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute cosine similarity between all pairs\n",
        "\n",
        "sentences = ['A man is eating food.',\n",
        "          'A man is eating a piece of bread.',\n",
        "          'The girl is carrying a baby.',\n",
        "          'A man is riding a horse.',\n",
        "          'A woman is playing violin.',\n",
        "          'Two men pushed carts through the woods.',\n",
        "          'A man is riding a white horse on an enclosed ground.',\n",
        "          'A monkey is playing drums.',\n",
        "          'Someone in a gorilla costume is playing a set of drums.'\n",
        "          ]\n",
        "\n",
        "#Encode all sentences\n",
        "embeddings = model.encode(sentences)\n",
        "\n",
        "#Compute cosine similarity between all pairs\n",
        "cos_sim = util.cos_sim(embeddings, embeddings)\n",
        "\n",
        "cos_sim\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dc4h97Zxd_x7",
        "outputId": "586c3217-c04a-4366-9d5e-6f57afb92ba0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 1.0000,  0.7553, -0.1050,  0.2474, -0.0704, -0.0333,  0.1707,  0.0476,\n",
              "          0.0630],\n",
              "        [ 0.7553,  1.0000, -0.0610,  0.1442, -0.0809, -0.0216,  0.1157,  0.0362,\n",
              "          0.0216],\n",
              "        [-0.1050, -0.0610,  1.0000, -0.1088,  0.0217, -0.0413, -0.0928,  0.0231,\n",
              "          0.0247],\n",
              "        [ 0.2474,  0.1442, -0.1088,  1.0000, -0.0348,  0.0362,  0.7369,  0.0821,\n",
              "          0.1389],\n",
              "        [-0.0704, -0.0809,  0.0217, -0.0348,  1.0000, -0.1654, -0.0592,  0.1961,\n",
              "          0.2564],\n",
              "        [-0.0333, -0.0216, -0.0413,  0.0362, -0.1654,  1.0000,  0.0769, -0.0380,\n",
              "         -0.0895],\n",
              "        [ 0.1707,  0.1157, -0.0928,  0.7369, -0.0592,  0.0769,  1.0000,  0.0495,\n",
              "          0.1191],\n",
              "        [ 0.0476,  0.0362,  0.0231,  0.0821,  0.1961, -0.0380,  0.0495,  1.0000,\n",
              "          0.6433],\n",
              "        [ 0.0630,  0.0216,  0.0247,  0.1389,  0.2564, -0.0895,  0.1191,  0.6433,\n",
              "          1.0000]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_sentence_combinations=[]\n",
        "\n",
        "for i in range(len(cos_sim)-1):\n",
        "  for j in range(i+1, len(cos_sim)):\n",
        "    all_sentence_combinations.append((cos_sim[i][j], i, j))\n",
        "all_sentence_combinations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9vptMtbsjV-k",
        "outputId": "ac08d83c-abf5-4958-e863-9c1ba9816d80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(tensor(0.7553), 0, 1),\n",
              " (tensor(-0.1050), 0, 2),\n",
              " (tensor(0.2474), 0, 3),\n",
              " (tensor(-0.0704), 0, 4),\n",
              " (tensor(-0.0333), 0, 5),\n",
              " (tensor(0.1707), 0, 6),\n",
              " (tensor(0.0476), 0, 7),\n",
              " (tensor(0.0630), 0, 8),\n",
              " (tensor(-0.0610), 1, 2),\n",
              " (tensor(0.1442), 1, 3),\n",
              " (tensor(-0.0809), 1, 4),\n",
              " (tensor(-0.0216), 1, 5),\n",
              " (tensor(0.1157), 1, 6),\n",
              " (tensor(0.0362), 1, 7),\n",
              " (tensor(0.0216), 1, 8),\n",
              " (tensor(-0.1088), 2, 3),\n",
              " (tensor(0.0217), 2, 4),\n",
              " (tensor(-0.0413), 2, 5),\n",
              " (tensor(-0.0928), 2, 6),\n",
              " (tensor(0.0231), 2, 7),\n",
              " (tensor(0.0247), 2, 8),\n",
              " (tensor(-0.0348), 3, 4),\n",
              " (tensor(0.0362), 3, 5),\n",
              " (tensor(0.7369), 3, 6),\n",
              " (tensor(0.0821), 3, 7),\n",
              " (tensor(0.1389), 3, 8),\n",
              " (tensor(-0.1654), 4, 5),\n",
              " (tensor(-0.0592), 4, 6),\n",
              " (tensor(0.1961), 4, 7),\n",
              " (tensor(0.2564), 4, 8),\n",
              " (tensor(0.0769), 5, 6),\n",
              " (tensor(-0.0380), 5, 7),\n",
              " (tensor(-0.0895), 5, 8),\n",
              " (tensor(0.0495), 6, 7),\n",
              " (tensor(0.1191), 6, 8),\n",
              " (tensor(0.6433), 7, 8)]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sort list by the highest cosine similarity score\n",
        "all_sentence_combinations = sorted(all_sentence_combinations, key=lambda x: x[0], reverse=True)\n",
        "\n",
        "print(\"Top-5 most similar pairs:\")\n",
        "for score, i, j in all_sentence_combinations[0:5]:\n",
        "    print(\"{} \\t {} \\t {:.4f}\".format(sentences[i], sentences[j], cos_sim[i][j]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjYd53zgjqbB",
        "outputId": "7a8f8b17-0884-466d-84f4-750e17a081ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top-5 most similar pairs:\n",
            "A man is eating food. \t A man is eating a piece of bread. \t 0.7553\n",
            "A man is riding a horse. \t A man is riding a white horse on an enclosed ground. \t 0.7369\n",
            "A monkey is playing drums. \t Someone in a gorilla costume is playing a set of drums. \t 0.6433\n",
            "A woman is playing violin. \t Someone in a gorilla costume is playing a set of drums. \t 0.2564\n",
            "A man is eating food. \t A man is riding a horse. \t 0.2474\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://twitter.com/akshay_pachaar/status/1709908854123925692?s=46&t=daA7yY2GpvrwdYemrOcUeg"
      ],
      "metadata": {
        "id": "zNYpquMNNiPt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\\Datasets : To use the datasets that are available in Huggingface ecosystem\n",
        "https://youtu.be/i9XnOfyDvig\n"
      ],
      "metadata": {
        "id": "en5kk2dBOJlD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " pip install datasets"
      ],
      "metadata": {
        "id": "cATYQN0MjuGb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "squad_datasets = load_dataset(\"squad\")"
      ],
      "metadata": {
        "id": "UMzY-q5eBK9J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "squad_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJzkShw7Bt6k",
        "outputId": "319ceccf-35d7-4448-c34a-bde3ae0e487e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 87599\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 10570\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "squad_datasets[\"train\"][0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ut4CmOd0Bz3k",
        "outputId": "f92b1497-a13a-4d9a-fa79-3d1894c44719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': ['5733be284776f41900661182',\n",
              "  '5733be284776f4190066117f',\n",
              "  '5733be284776f41900661180',\n",
              "  '5733be284776f41900661181',\n",
              "  '5733be284776f4190066117e'],\n",
              " 'title': ['University_of_Notre_Dame',\n",
              "  'University_of_Notre_Dame',\n",
              "  'University_of_Notre_Dame',\n",
              "  'University_of_Notre_Dame',\n",
              "  'University_of_Notre_Dame'],\n",
              " 'context': ['Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',\n",
              "  'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',\n",
              "  'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',\n",
              "  'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',\n",
              "  'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.'],\n",
              " 'question': ['To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',\n",
              "  'What is in front of the Notre Dame Main Building?',\n",
              "  'The Basilica of the Sacred heart at Notre Dame is beside to which structure?',\n",
              "  'What is the Grotto at Notre Dame?',\n",
              "  'What sits on top of the Main Building at Notre Dame?'],\n",
              " 'answers': [{'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]},\n",
              "  {'text': ['a copper statue of Christ'], 'answer_start': [188]},\n",
              "  {'text': ['the Main Building'], 'answer_start': [279]},\n",
              "  {'text': ['a Marian place of prayer and reflection'], 'answer_start': [381]},\n",
              "  {'text': ['a golden statue of the Virgin Mary'], 'answer_start': [92]}]}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "squad_train=load_dataset(\"squad\",split='train')\n",
        "squad_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1LsaWSICCDa",
        "outputId": "ff2fe078-1169-4779-a8e6-42cb33c6d8a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '5733be284776f41900661182',\n",
              " 'title': 'University_of_Notre_Dame',\n",
              " 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',\n",
              " 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',\n",
              " 'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "squad_train[0]['question']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "JP61m9w2LZ1V",
        "outputId": "0c022843-ae11-416b-963b-20cc96478ed5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "squad_train[\"question\"][:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pq_nB2A6Lg2T",
        "outputId": "ffb61e39-c030-4b2d-9c3d-d0bd20ee253d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',\n",
              " 'What is in front of the Notre Dame Main Building?']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_squad = squad_train.sort(\"id\")"
      ],
      "metadata": {
        "id": "o8a-2Ji2LoCv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shuffled_squad = sorted_squad.shuffle(seed=42)"
      ],
      "metadata": {
        "id": "JbWtVxFUR8b2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filter_1=squad_train.select([0,5,79,6])"
      ],
      "metadata": {
        "id": "w1HPMslYSFOK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filter_1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7eQ9zWYTx0C",
        "outputId": "f94c2c10-69d0-491e-8cb6-d3e374af8ddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "    num_rows: 4\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(filter_1['context'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRl2nLfZTzy2",
        "outputId": "1bca9dd3-7734-4106-cf73-935c8b1306f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "695"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l1=0\n",
        "l2=0\n",
        "for i in filter_1['context']:\n",
        "  print(len(i))\n",
        "  print(len(i)/50)\n",
        "  l1=l1+len(i)\n",
        "print(\"sum=\",l1)\n",
        "print(\"avg=\",l1/50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QbbFIJMYWTPR",
        "outputId": "0edc8498-3147-48d1-d639-8e310f690b76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "695\n",
            "13.9\n",
            "1405\n",
            "28.1\n",
            "1191\n",
            "23.82\n",
            "1405\n",
            "28.1\n",
            "sum= 4696\n",
            "avg= 93.92\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cw63HLFMRdWa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filter_1['context']"
      ],
      "metadata": {
        "id": "xxH8fAyYWoPQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "has_len_50 = squad_train.filter(lambda example:len(example['context'])>1000)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "845be7cf09ad44dd9c3e0e15f885ec55",
            "db5d670a0f0b4804876f4b61e5d05490",
            "a944cdb1c1c54232ab76dceadcb84751",
            "51dbcae82f90480d9e305094e5f08426",
            "b7b40c3ccab2439b85b28b8a366c7448",
            "24431e70962c49158a521d66277ff4bb",
            "822b9c47cbcc4e2eb686284a80c0f476",
            "79bb2d73f9c4485890bea48da81f04ea",
            "ce70b99afa7740899d0c71095901ec6d",
            "3d83642fedf6468b8e437810b2e1d457",
            "ef9d78b0f51d45f8be161c5cd055e096"
          ]
        },
        "id": "nxw4SgwBWtcp",
        "outputId": "a595b635-be53-40bc-fb20-06c77bb34c43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/87599 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "845be7cf09ad44dd9c3e0e15f885ec55"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "has_len_50"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpYsdaYDZFvB",
        "outputId": "41beffde-2e23-4274-dba2-dd878c8b66d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "    num_rows: 15123\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "has_len_50.train_test_split()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lz3qA-l7ZICg",
        "outputId": "73602aed-6967-4ee9-d5d9-0b2a1778b348"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 11342\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 3781\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "has_len_50.shard(num_shards=4,index=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdQ6vFLob4ie",
        "outputId": "e5137b8d-ffb6-4aa3-e197-ba8842e1349e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "    num_rows: 3781\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "has_len_50.rename_column(\"id\",\"index\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wB2biVvEchjk",
        "outputId": "d921f070-8fd6-4ced-e2a8-0f4f1b1a4c42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['index', 'title', 'context', 'question', 'answers'],\n",
              "    num_rows: 15123\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "has_len_50.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eKJZKwEykp9-",
        "outputId": "f2114773-ebab-4f3f-ab8d-df92ac3d1715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers.text', 'answers.answer_start'],\n",
              "    num_rows: 15123\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def add_prefix(example):\n",
        "  example['sentence_1']='New context:' + example['context']\n",
        "  return example"
      ],
      "metadata": {
        "id": "vrIJdUfakrhD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with_sentence1=has_len_50.map(add_prefix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "daff92f35318404fbf89d714aabefddd",
            "178badf2bfcf4b19b3990cae029d3983",
            "5a81b38bd809401bb1ca2d06f3829f0e",
            "97469bc9c22243fa8afd28a0162ab8c3",
            "a2bd1661256448caba6c6b36dff19843",
            "f29cc2070a1d4580a5988fdbb90499cf",
            "1f6cf93065e24660814782ef95f9ce08",
            "a0a4a13214064ff39278a0a100bc6f97",
            "6a12191a875c41fca7e3b66ba739c6ca",
            "76fba36fb37c4b3991086e7cc0b9f308",
            "ba387549fce24f6e89afae07a853f26e"
          ]
        },
        "id": "jFi2Fsrs8CK8",
        "outputId": "2e3f4d03-f871-4f50-be58-696cae85a44b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/15123 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "daff92f35318404fbf89d714aabefddd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with_sentence1[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4C6tEPt80Ah",
        "outputId": "46a6e132-8997-4c23-e2b3-017761022a06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '5733bf84d058e614000b61be',\n",
              " 'title': 'University_of_Notre_Dame',\n",
              " 'context': \"As at most other universities, Notre Dame's students run a number of news media outlets. The nine student-run outlets include three newspapers, both a radio and television station, and several magazines and journals. Begun as a one-page journal in September 1876, the Scholastic magazine is issued twice monthly and claims to be the oldest continuous collegiate publication in the United States. The other magazine, The Juggler, is released twice a year and focuses on student literature and artwork. The Dome yearbook is published annually. The newspapers have varying publication interests, with The Observer published daily and mainly reporting university and other news, and staffed by students from both Notre Dame and Saint Mary's College. Unlike Scholastic and The Dome, The Observer is an independent publication and does not have a faculty advisor or any editorial oversight from the University. In 1987, when some students believed that The Observer began to show a conservative bias, a liberal newspaper, Common Sense was published. Likewise, in 2003, when other students believed that the paper showed a liberal bias, the conservative paper Irish Rover went into production. Neither paper is published as often as The Observer; however, all three are distributed to all students. Finally, in Spring 2008 an undergraduate journal for political science research, Beyond Politics, made its debut.\",\n",
              " 'question': 'When did the Scholastic Magazine of Notre dame begin publishing?',\n",
              " 'answers': {'text': ['September 1876'], 'answer_start': [248]},\n",
              " 'sentence_1': \"New context:As at most other universities, Notre Dame's students run a number of news media outlets. The nine student-run outlets include three newspapers, both a radio and television station, and several magazines and journals. Begun as a one-page journal in September 1876, the Scholastic magazine is issued twice monthly and claims to be the oldest continuous collegiate publication in the United States. The other magazine, The Juggler, is released twice a year and focuses on student literature and artwork. The Dome yearbook is published annually. The newspapers have varying publication interests, with The Observer published daily and mainly reporting university and other news, and staffed by students from both Notre Dame and Saint Mary's College. Unlike Scholastic and The Dome, The Observer is an independent publication and does not have a faculty advisor or any editorial oversight from the University. In 1987, when some students believed that The Observer began to show a conservative bias, a liberal newspaper, Common Sense was published. Likewise, in 2003, when other students believed that the paper showed a liberal bias, the conservative paper Irish Rover went into production. Neither paper is published as often as The Observer; however, all three are distributed to all students. Finally, in Spring 2008 an undergraduate journal for political science research, Beyond Politics, made its debut.\"}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def chunk_examples(examples):\n",
        "  chunks=[]\n",
        "  for sentence in examples['context']:\n",
        "    chunks += [sentence [i:i +50] for i in range(0,len(sentence),50)]\n",
        "  return {\"chunks\":chunks}"
      ],
      "metadata": {
        "id": "9beJA2yb800c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chunked_ds = filter_1.map(chunk_examples,\n",
        "                            batched=True,\n",
        "                            remove_columns=filter_1.column_names)"
      ],
      "metadata": {
        "id": "jd07Rn4p-3bg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def chunk_examples(examples):\n",
        "  \"\"\"Splits long sentences into small\"\"\"\n",
        "  chunks = []\n",
        "  for sentence in examples[\"context\"]:\n",
        "      chunks += [sentence[i:i + 50] for i in range(0, len(sentence), 50)]\n",
        "  return {\"chunks\": chunks}\n",
        "\n",
        "\n",
        "chunked_ds = has_len_50.map(chunk_examples,\n",
        "                            batched=True,\n",
        "                            remove_columns=has_len_50.column_names)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bdd9e4caa77846d59ff3259c3a44dd7d",
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            "2a717d770d0948059ce9f66b99569665",
            "2446abbf34d0475ba3122b8245515b9b",
            "fe20db295ace45c599b37bf14b9fbda3",
            "b7836a401cfc43c18105bdc8a4ab27a3",
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        "outputId": "74214d57-bbeb-47dc-aaef-633d3b9a3993"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/15123 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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    },
    {
      "cell_type": "code",
      "source": [
        "chunked_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FgxAutxZFqco",
        "outputId": "60096abc-f7b5-4ef2-bd5a-015f70f55b53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['chunks'],\n",
              "    num_rows: 96\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in chunked_ds:\n",
        "  print((i))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54tvch_8SR0k",
        "outputId": "dad26706-4e7c-4631-ec77-584e7f0e01b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'chunks': 'Architecturally, the school has a Catholic charact'}\n",
            "{'chunks': \"er. Atop the Main Building's gold dome is a golden\"}\n",
            "{'chunks': ' statue of the Virgin Mary. Immediately in front o'}\n",
            "{'chunks': 'f the Main Building and facing it, is a copper sta'}\n",
            "{'chunks': 'tue of Christ with arms upraised with the legend \"'}\n",
            "{'chunks': 'Venite Ad Me Omnes\". Next to the Main Building is '}\n",
            "{'chunks': 'the Basilica of the Sacred Heart. Immediately behi'}\n",
            "{'chunks': 'nd the basilica is the Grotto, a Marian place of p'}\n",
            "{'chunks': 'rayer and reflection. It is a replica of the grott'}\n",
            "{'chunks': 'o at Lourdes, France where the Virgin Mary reputed'}\n",
            "{'chunks': 'ly appeared to Saint Bernadette Soubirous in 1858.'}\n",
            "{'chunks': ' At the end of the main drive (and in a direct lin'}\n",
            "{'chunks': 'e that connects through 3 statues and the Gold Dom'}\n",
            "{'chunks': 'e), is a simple, modern stone statue of Mary.'}\n",
            "{'chunks': \"As at most other universities, Notre Dame's studen\"}\n",
            "{'chunks': 'ts run a number of news media outlets. The nine st'}\n",
            "{'chunks': 'udent-run outlets include three newspapers, both a'}\n",
            "{'chunks': ' radio and television station, and several magazin'}\n",
            "{'chunks': 'es and journals. Begun as a one-page journal in Se'}\n",
            "{'chunks': 'ptember 1876, the Scholastic magazine is issued tw'}\n",
            "{'chunks': 'ice monthly and claims to be the oldest continuous'}\n",
            "{'chunks': ' collegiate publication in the United States. The '}\n",
            "{'chunks': 'other magazine, The Juggler, is released twice a y'}\n",
            "{'chunks': 'ear and focuses on student literature and artwork.'}\n",
            "{'chunks': ' The Dome yearbook is published annually. The news'}\n",
            "{'chunks': 'papers have varying publication interests, with Th'}\n",
            "{'chunks': 'e Observer published daily and mainly reporting un'}\n",
            "{'chunks': 'iversity and other news, and staffed by students f'}\n",
            "{'chunks': \"rom both Notre Dame and Saint Mary's College. Unli\"}\n",
            "{'chunks': 'ke Scholastic and The Dome, The Observer is an ind'}\n",
            "{'chunks': 'ependent publication and does not have a faculty a'}\n",
            "{'chunks': 'dvisor or any editorial oversight from the Univers'}\n",
            "{'chunks': 'ity. In 1987, when some students believed that The'}\n",
            "{'chunks': ' Observer began to show a conservative bias, a lib'}\n",
            "{'chunks': 'eral newspaper, Common Sense was published. Likewi'}\n",
            "{'chunks': 'se, in 2003, when other students believed that the'}\n",
            "{'chunks': ' paper showed a liberal bias, the conservative pap'}\n",
            "{'chunks': 'er Irish Rover went into production. Neither paper'}\n",
            "{'chunks': ' is published as often as The Observer; however, a'}\n",
            "{'chunks': 'll three are distributed to all students. Finally,'}\n",
            "{'chunks': ' in Spring 2008 an undergraduate journal for polit'}\n",
            "{'chunks': 'ical science research, Beyond Politics, made its d'}\n",
            "{'chunks': 'ebut.'}\n",
            "{'chunks': 'About 80% of undergraduates and 20% of graduate st'}\n",
            "{'chunks': 'udents live on campus. The majority of the graduat'}\n",
            "{'chunks': 'e students on campus live in one of four graduate '}\n",
            "{'chunks': 'housing complexes on campus, while all on-campus u'}\n",
            "{'chunks': 'ndergraduates live in one of the 29 residence hall'}\n",
            "{'chunks': 's. Because of the religious affiliation of the uni'}\n",
            "{'chunks': 'versity, all residence halls are single-sex, with '}\n",
            "{'chunks': '15 male dorms and 14 female dorms. The university '}\n",
            "{'chunks': 'maintains a visiting policy (known as parietal hou'}\n",
            "{'chunks': 'rs) for those students who live in dormitories, sp'}\n",
            "{'chunks': 'ecifying times when members of the opposite sex ar'}\n",
            "{'chunks': \"e allowed to visit other students' dorm rooms; how\"}\n",
            "{'chunks': 'ever, all residence halls have 24-hour social spac'}\n",
            "{'chunks': 'es for students regardless of gender. Many residen'}\n",
            "{'chunks': 'ce halls have at least one nun and/or priest as a '}\n",
            "{'chunks': 'resident. There are no traditional social fraterni'}\n",
            "{'chunks': 'ties or sororities at the university, but a majori'}\n",
            "{'chunks': 'ty of students live in the same residence hall for'}\n",
            "{'chunks': ' all four years. Some intramural sports are based '}\n",
            "{'chunks': 'on residence hall teams, where the university offe'}\n",
            "{'chunks': 'rs the only non-military academy program of full-c'}\n",
            "{'chunks': 'ontact intramural American football. At the end of'}\n",
            "{'chunks': ' the intramural season, the championship game is p'}\n",
            "{'chunks': 'layed on the field in Notre Dame Stadium.'}\n",
            "{'chunks': \"As at most other universities, Notre Dame's studen\"}\n",
            "{'chunks': 'ts run a number of news media outlets. The nine st'}\n",
            "{'chunks': 'udent-run outlets include three newspapers, both a'}\n",
            "{'chunks': ' radio and television station, and several magazin'}\n",
            "{'chunks': 'es and journals. Begun as a one-page journal in Se'}\n",
            "{'chunks': 'ptember 1876, the Scholastic magazine is issued tw'}\n",
            "{'chunks': 'ice monthly and claims to be the oldest continuous'}\n",
            "{'chunks': ' collegiate publication in the United States. The '}\n",
            "{'chunks': 'other magazine, The Juggler, is released twice a y'}\n",
            "{'chunks': 'ear and focuses on student literature and artwork.'}\n",
            "{'chunks': ' The Dome yearbook is published annually. The news'}\n",
            "{'chunks': 'papers have varying publication interests, with Th'}\n",
            "{'chunks': 'e Observer published daily and mainly reporting un'}\n",
            "{'chunks': 'iversity and other news, and staffed by students f'}\n",
            "{'chunks': \"rom both Notre Dame and Saint Mary's College. Unli\"}\n",
            "{'chunks': 'ke Scholastic and The Dome, The Observer is an ind'}\n",
            "{'chunks': 'ependent publication and does not have a faculty a'}\n",
            "{'chunks': 'dvisor or any editorial oversight from the Univers'}\n",
            "{'chunks': 'ity. In 1987, when some students believed that The'}\n",
            "{'chunks': ' Observer began to show a conservative bias, a lib'}\n",
            "{'chunks': 'eral newspaper, Common Sense was published. Likewi'}\n",
            "{'chunks': 'se, in 2003, when other students believed that the'}\n",
            "{'chunks': ' paper showed a liberal bias, the conservative pap'}\n",
            "{'chunks': 'er Irish Rover went into production. Neither paper'}\n",
            "{'chunks': ' is published as often as The Observer; however, a'}\n",
            "{'chunks': 'll three are distributed to all students. Finally,'}\n",
            "{'chunks': ' in Spring 2008 an undergraduate journal for polit'}\n",
            "{'chunks': 'ical science research, Beyond Politics, made its d'}\n",
            "{'chunks': 'ebut.'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chunked_ds[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLxSAYl0F0rr",
        "outputId": "41057d2e-7def-4fe5-bb2f-d7659c270d07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'chunks': [\"As at most other universities, Notre Dame's studen\",\n",
              "  'ts run a number of news media outlets. The nine st',\n",
              "  'udent-run outlets include three newspapers, both a',\n",
              "  ' radio and television station, and several magazin',\n",
              "  'es and journals. Begun as a one-page journal in Se']}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "has_len_50[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRjj6z0bJe2M",
        "outputId": "f9a243b3-8848-4d7e-8a0a-370af2142583"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '5733bf84d058e614000b61be',\n",
              " 'title': 'University_of_Notre_Dame',\n",
              " 'context': \"As at most other universities, Notre Dame's students run a number of news media outlets. The nine student-run outlets include three newspapers, both a radio and television station, and several magazines and journals. Begun as a one-page journal in September 1876, the Scholastic magazine is issued twice monthly and claims to be the oldest continuous collegiate publication in the United States. The other magazine, The Juggler, is released twice a year and focuses on student literature and artwork. The Dome yearbook is published annually. The newspapers have varying publication interests, with The Observer published daily and mainly reporting university and other news, and staffed by students from both Notre Dame and Saint Mary's College. Unlike Scholastic and The Dome, The Observer is an independent publication and does not have a faculty advisor or any editorial oversight from the University. In 1987, when some students believed that The Observer began to show a conservative bias, a liberal newspaper, Common Sense was published. Likewise, in 2003, when other students believed that the paper showed a liberal bias, the conservative paper Irish Rover went into production. Neither paper is published as often as The Observer; however, all three are distributed to all students. Finally, in Spring 2008 an undergraduate journal for political science research, Beyond Politics, made its debut.\",\n",
              " 'question': 'When did the Scholastic Magazine of Notre dame begin publishing?',\n",
              " 'answers': {'text': ['September 1876'], 'answer_start': [248]}}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for sentence in has_5['context']:\n",
        "  print(1)\n",
        "  print(sentence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qtlfY3ZJn5c",
        "outputId": "d49e681d-d926-43ec-d3f6-d112b4ac42c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "As at most other universities, Notre Dame's students run a number of news media outlets. The nine student-run outlets include three newspapers, both a radio and television station, and several magazines and journals. Begun as a one-page journal in September 1876, the Scholastic magazine is issued twice monthly and claims to be the oldest continuous collegiate publication in the United States. The other magazine, The Juggler, is released twice a year and focuses on student literature and artwork. The Dome yearbook is published annually. The newspapers have varying publication interests, with The Observer published daily and mainly reporting university and other news, and staffed by students from both Notre Dame and Saint Mary's College. Unlike Scholastic and The Dome, The Observer is an independent publication and does not have a faculty advisor or any editorial oversight from the University. In 1987, when some students believed that The Observer began to show a conservative bias, a liberal newspaper, Common Sense was published. Likewise, in 2003, when other students believed that the paper showed a liberal bias, the conservative paper Irish Rover went into production. Neither paper is published as often as The Observer; however, all three are distributed to all students. Finally, in Spring 2008 an undergraduate journal for political science research, Beyond Politics, made its debut.\n",
            "1\n",
            "The university first offered graduate degrees, in the form of a Master of Arts (MA), in the 1854–1855 academic year. The program expanded to include Master of Laws (LL.M.) and Master of Civil Engineering in its early stages of growth, before a formal graduate school education was developed with a thesis not required to receive the degrees. This changed in 1924 with formal requirements developed for graduate degrees, including offering Doctorate (PhD) degrees. Today each of the five colleges offer graduate education. Most of the departments from the College of Arts and Letters offer PhD programs, while a professional Master of Divinity (M.Div.) program also exists. All of the departments in the College of Science offer PhD programs, except for the Department of Pre-Professional Studies. The School of Architecture offers a Master of Architecture, while each of the departments of the College of Engineering offer PhD programs. The College of Business offers multiple professional programs including MBA and Master of Science in Accountancy programs. It also operates facilities in Chicago and Cincinnati for its executive MBA program. Additionally, the Alliance for Catholic Education program offers a Master of Education program where students study at the university during the summer and teach in Catholic elementary schools, middle schools, and high schools across the Southern United States for two school years.\n",
            "1\n",
            "George Gipp was the school's legendary football player during 1916–20. He played semiprofessional baseball and smoked, drank, and gambled when not playing sports. He was also humble, generous to the needy, and a man of integrity. It was in 1928 that famed coach Knute Rockne used his final conversation with the dying Gipp to inspire the Notre Dame team to beat the Army team and \"win one for the Gipper.\" The 1940 film, Knute Rockne, All American, starred Pat O'Brien as Knute Rockne and Ronald Reagan as Gipp. Today the team competes in Notre Dame Stadium, an 80,795-seat stadium on campus. The current head coach is Brian Kelly, hired from the University of Cincinnati on December 11, 2009. Kelly's record in midway through his sixth season at Notre Dame is 52–21. In 2012, Kelly's Fighting Irish squad went undefeated and played in the BCS National Championship Game. Kelly succeeded Charlie Weis, who was fired in November 2009 after five seasons. Although Weis led his team to two Bowl Championship Series bowl games, his overall record was 35–27, mediocre by Notre Dame standards, and the 2007 team had the most losses in school history. The football team generates enough revenue to operate independently while $22.1 million is retained from the team's profits for academic use. Forbes named the team as the most valuable in college football, worth a total of $101 million in 2007.\n",
            "1\n",
            "The university first offered graduate degrees, in the form of a Master of Arts (MA), in the 1854–1855 academic year. The program expanded to include Master of Laws (LL.M.) and Master of Civil Engineering in its early stages of growth, before a formal graduate school education was developed with a thesis not required to receive the degrees. This changed in 1924 with formal requirements developed for graduate degrees, including offering Doctorate (PhD) degrees. Today each of the five colleges offer graduate education. Most of the departments from the College of Arts and Letters offer PhD programs, while a professional Master of Divinity (M.Div.) program also exists. All of the departments in the College of Science offer PhD programs, except for the Department of Pre-Professional Studies. The School of Architecture offers a Master of Architecture, while each of the departments of the College of Engineering offer PhD programs. The College of Business offers multiple professional programs including MBA and Master of Science in Accountancy programs. It also operates facilities in Chicago and Cincinnati for its executive MBA program. Additionally, the Alliance for Catholic Education program offers a Master of Education program where students study at the university during the summer and teach in Catholic elementary schools, middle schools, and high schools across the Southern United States for two school years.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "has_5 = has_len_50.select([0,5,79,6])\n",
        "has_5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfBwnT49PN5D",
        "outputId": "884f4270-d3a7-424f-bff5-e021f69106fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "    num_rows: 4\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "has_5['context']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4JO7Q5TdPOiF",
        "outputId": "6a95e9cc-d547-4aa0-c686-e18a6b576d48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"As at most other universities, Notre Dame's students run a number of news media outlets. The nine student-run outlets include three newspapers, both a radio and television station, and several magazines and journals. Begun as a one-page journal in September 1876, the Scholastic magazine is issued twice monthly and claims to be the oldest continuous collegiate publication in the United States. The other magazine, The Juggler, is released twice a year and focuses on student literature and artwork. The Dome yearbook is published annually. The newspapers have varying publication interests, with The Observer published daily and mainly reporting university and other news, and staffed by students from both Notre Dame and Saint Mary's College. Unlike Scholastic and The Dome, The Observer is an independent publication and does not have a faculty advisor or any editorial oversight from the University. In 1987, when some students believed that The Observer began to show a conservative bias, a liberal newspaper, Common Sense was published. Likewise, in 2003, when other students believed that the paper showed a liberal bias, the conservative paper Irish Rover went into production. Neither paper is published as often as The Observer; however, all three are distributed to all students. Finally, in Spring 2008 an undergraduate journal for political science research, Beyond Politics, made its debut.\",\n",
              " 'The university first offered graduate degrees, in the form of a Master of Arts (MA), in the 1854–1855 academic year. The program expanded to include Master of Laws (LL.M.) and Master of Civil Engineering in its early stages of growth, before a formal graduate school education was developed with a thesis not required to receive the degrees. This changed in 1924 with formal requirements developed for graduate degrees, including offering Doctorate (PhD) degrees. Today each of the five colleges offer graduate education. Most of the departments from the College of Arts and Letters offer PhD programs, while a professional Master of Divinity (M.Div.) program also exists. All of the departments in the College of Science offer PhD programs, except for the Department of Pre-Professional Studies. The School of Architecture offers a Master of Architecture, while each of the departments of the College of Engineering offer PhD programs. The College of Business offers multiple professional programs including MBA and Master of Science in Accountancy programs. It also operates facilities in Chicago and Cincinnati for its executive MBA program. Additionally, the Alliance for Catholic Education program offers a Master of Education program where students study at the university during the summer and teach in Catholic elementary schools, middle schools, and high schools across the Southern United States for two school years.',\n",
              " 'George Gipp was the school\\'s legendary football player during 1916–20. He played semiprofessional baseball and smoked, drank, and gambled when not playing sports. He was also humble, generous to the needy, and a man of integrity. It was in 1928 that famed coach Knute Rockne used his final conversation with the dying Gipp to inspire the Notre Dame team to beat the Army team and \"win one for the Gipper.\" The 1940 film, Knute Rockne, All American, starred Pat O\\'Brien as Knute Rockne and Ronald Reagan as Gipp. Today the team competes in Notre Dame Stadium, an 80,795-seat stadium on campus. The current head coach is Brian Kelly, hired from the University of Cincinnati on December 11, 2009. Kelly\\'s record in midway through his sixth season at Notre Dame is 52–21. In 2012, Kelly\\'s Fighting Irish squad went undefeated and played in the BCS National Championship Game. Kelly succeeded Charlie Weis, who was fired in November 2009 after five seasons. Although Weis led his team to two Bowl Championship Series bowl games, his overall record was 35–27, mediocre by Notre Dame standards, and the 2007 team had the most losses in school history. The football team generates enough revenue to operate independently while $22.1 million is retained from the team\\'s profits for academic use. Forbes named the team as the most valuable in college football, worth a total of $101 million in 2007.',\n",
              " 'The university first offered graduate degrees, in the form of a Master of Arts (MA), in the 1854–1855 academic year. The program expanded to include Master of Laws (LL.M.) and Master of Civil Engineering in its early stages of growth, before a formal graduate school education was developed with a thesis not required to receive the degrees. This changed in 1924 with formal requirements developed for graduate degrees, including offering Doctorate (PhD) degrees. Today each of the five colleges offer graduate education. Most of the departments from the College of Arts and Letters offer PhD programs, while a professional Master of Divinity (M.Div.) program also exists. All of the departments in the College of Science offer PhD programs, except for the Department of Pre-Professional Studies. The School of Architecture offers a Master of Architecture, while each of the departments of the College of Engineering offer PhD programs. The College of Business offers multiple professional programs including MBA and Master of Science in Accountancy programs. It also operates facilities in Chicago and Cincinnati for its executive MBA program. Additionally, the Alliance for Catholic Education program offers a Master of Education program where students study at the university during the summer and teach in Catholic elementary schools, middle schools, and high schools across the Southern United States for two school years.']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PyTorch 101: Dataset Class clearly explained!\n",
        "\n",
        "Understanding the Dataset Class is crucial for efficiently managing & preprocessing your data while building models in PyTorch!\n",
        "\n",
        "https://twitter.com/akshay_pachaar/status/1709908854123925692?s=46&t=daA7yY2GpvrwdYemrOcUeg"
      ],
      "metadata": {
        "id": "8FxbWwDLeuCR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from nltk. tokenize import word_tokenize\n",
        "# Define the PyTorch Dataset class\n",
        "class TokenizedTextDataset (Dataset) :\n",
        "  def __init__(self, texts, labels):\n",
        "    self.texts = texts\n",
        "    self. labels - labels\n",
        "  def __Len__(self):\n",
        "    return len(self.texts)\n",
        "  def __getitem__(self, idx):\n",
        "    text = self.texts[idx]\n",
        "    label = self.labels(idx)\n",
        "# Tokenize the text\n",
        "    tokenized_text = word_tokenize(text)\n",
        "    return tokenized_text,label\n",
        "\n",
        "# Sample sentiment text data\n",
        "\n",
        "sample_data = [\n",
        "\"I love this product!\",\n",
        "\"I hate this service.\"\n",
        "]\n",
        "\n",
        "sample_labels = [1, 0]\n",
        "\n",
        "tokenized_text_dataset = TokenizedTextDataset(sample_data, sample_labels)\n",
        "\n",
        "first_item = tokonized_text_dataset[0]\n",
        "first_item\n",
        ""
      ],
      "metadata": {
        "id": "Q2QqphNIQFwM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "7611196c-6c26-4317-f293-a9662c394e6f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-c4713d08df06>\u001b[0m in \u001b[0;36m<cell line: 27>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0msample_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mtokenized_text_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTokenizedTextDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0mfirst_item\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokonized_text_dataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-5-c4713d08df06>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, texts, labels)\u001b[0m\n\u001b[1;32m      6\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtexts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtexts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__Len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'TokenizedTextDataset' object has no attribute 'labels'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LaLiYwNjfcfN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluation: In-Detail Walkthrough of Measurement, Metric & Comparators\n",
        "https://www.youtube.com/watch?v=AlqXKrdKqTQ\n",
        "https://github.com/insightbuilder/python_de_learners_data/blob/main/code_script_notebooks/projects/huggingface_AWS/evaluate_library_intro_ver01.ipynb"
      ],
      "metadata": {
        "id": "C3Ijpt-Tj8V_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers datasets evaluate > /dev/null\n"
      ],
      "metadata": {
        "id": "5O2fV83_kTcP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2755c3cd31b341aeb119e725a1147968",
            "58fc076c962a4b7ea886a853abc079d7",
            "387b4b29374945ce9b0b5b500d18244d",
            "e8933036fbbd482c85c41f4b638cd490",
            "23784c6679b043f7813524718bdcbab3",
            "d827e0c759574b86920dcb6746a98c47",
            "4a0f2b9b4c0b4b60969be337fa518577",
            "6396ea289818403c9d4287f149cd6ea9",
            "4b40d883058b44649f52f6fe61ac40ae",
            "f3706fd5d28a4aef97d8c270eed8efe5",
            "a951446a9af64ade820b96a6a23a29c3"
          ]
        },
        "id": "SxoTXzlFkViX",
        "outputId": "94c59442-6681-4ac9-9e63-57bcbb71ed5c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2755c3cd31b341aeb119e725a1147968"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "meteor = evaluate.load(\"meteor\",module_type='metric')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "JSb_0cX0kcHd",
        "outputId": "2a3c5568-da49-40ef-f398-0cfd1c43c0b8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-c3a8f0987299>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmeteor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"meteor\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodule_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'metric'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evaluate/loading.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(path, config_name, module_type, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, **init_kwargs)\u001b[0m\n\u001b[1;32m    732\u001b[0m         \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodule_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodule_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrevision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdownload_mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m     )\n\u001b[0;32m--> 734\u001b[0;31m     \u001b[0mevaluation_cls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_main_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluation_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m     evaluation_instance = evaluation_cls(\n\u001b[1;32m    736\u001b[0m         \u001b[0mconfig_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/evaluate/loading.py\u001b[0m in \u001b[0;36mimport_main_class\u001b[0;34m(module_path)\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mimport_main_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mType\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mDatasetBuilder\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mType\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mEvaluationModule\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;34m\"\"\"Import a module at module_path and return its main class, a Metric by default\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m     \u001b[0mmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m     \u001b[0mmain_cls_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEvaluationModule\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m    124\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/importlib/_bootstrap_external.py\u001b[0m in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_call_with_frames_removed\u001b[0;34m(f, *args, **kwds)\u001b[0m\n",
            "\u001b[0;32m~/.cache/huggingface/modules/evaluate_modules/metrics/evaluate-metric--meteor/90426c9b4a2cf0bf10c6718aa8de221c7a50f46a694f4cc37bb3ee12fc9aa8c9/meteor.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimportlib_metadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmeteor_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'importlib_metadata' from 'datasets.config' (/usr/local/lib/python3.10/dist-packages/datasets/config.py)",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from datasets.config import importlib_metadata, version\n",
        "from nltk.translate import meteor_score\n"
      ],
      "metadata": {
        "id": "PeyANM1AkdBp"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from importlib_metadata import metadata\n",
        "\n"
      ],
      "metadata": {
        "id": "wB7-jzksl-X9"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python --version\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9a96ti8mEhL",
        "outputId": "87dc0a90-7428-4746-986d-c95459670591"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.10.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ex_match = evaluate.load(\"exact_match\",module_type='comparison')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "00aaaf52d8164c0fa11ff0a59997294a",
            "f6000aef6d1e44f58276de98a922655c",
            "4658b1f72457495c871acb2e7f384e20",
            "5b6df5a9bb18438b9e4feca49a3fa52b",
            "b74d33ffa1bd469280990ddb866d3153",
            "b66c7b9ec20c4a17b81d0af0261f9e7b",
            "1c7ef4abc10a45c9800ef91b72db458d",
            "2837d1af799b48d09aeecadc3c23327c",
            "e9ddca22935d4b838a13e6ea4fcc9041",
            "de190c729c364c8a960ca70279cf6b67",
            "341374e757f44e738a7be0a3c986ff95"
          ]
        },
        "id": "PRvht5FlnMS4",
        "outputId": "f9a62c83-7796-4ab9-cc32-79b5c5eebd97"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00aaaf52d8164c0fa11ff0a59997294a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_dist = evaluate.load(\"label_distribution\",\n",
        "                           module_type='measurement')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "d052589a39fb46c494a4fab603ce6305",
            "9bf127b534004cb08d0a47eef681b9ed",
            "76f12c6c749f40ffb117a3799b685d25",
            "74a31b25fb5d440d9e6b08f915899f8b",
            "b008a652c9e34261b900c14d0fdf0fa4",
            "58338b5f45674628a36dae5e94de6a14",
            "dbc90a90ac4a492fac1bf48fbed7742a",
            "d269521740c448c7bbc75d5ebde4310e",
            "964c9594f4e741229cad0cb0b63324a9",
            "6b45d96de1b44e6fb09d3ff8a22ba796",
            "2562465229fa49e6897cea8283130de1"
          ]
        },
        "id": "azDsGwoznkOD",
        "outputId": "ecc7c8da-d42f-4c97-8856-83cc43048029"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/3.88k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d052589a39fb46c494a4fab603ce6305"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#get the available evaluation functions inside a module\n",
        "\n",
        "metrics_list = evaluate.list_evaluation_modules(\n",
        "  module_type=\"metric\",\n",
        "  include_community=False,\n",
        "  with_details=True)\n",
        "\n",
        "\n",
        "len(metrics_list)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PboMqif6oLRt",
        "outputId": "5255ff26-380f-4708-9a45-9d6a2331b1c4"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "metrics_list[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nu6hzV99oZLt",
        "outputId": "880c1b41-22ce-42bd-b61a-c8434bc22eea"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'precision', 'type': 'metric', 'community': False, 'likes': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "comparer_list = evaluate.list_evaluation_modules(\n",
        "  module_type=\"comparison\",\n",
        "  include_community=False,\n",
        "  with_details=True)\n",
        "\n",
        "\n",
        "comparer_list[0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQXzBF_7odme",
        "outputId": "18e17145-3957-454e-a0b3-9b4674a82e2e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'mcnemar', 'type': 'comparison', 'community': False, 'likes': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "measure_list = evaluate.list_evaluation_modules(\n",
        "  module_type=\"measurement\",\n",
        "  include_community=False,\n",
        "  with_details=True)\n",
        "\n",
        "measure_list[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIq1JGPDogtf",
        "outputId": "b15a1fca-9e28-4ed5-eaaf-072b47c2f162"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'word_length', 'type': 'measurement', 'community': False, 'likes': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#attributes / methods\n",
        "dir(accuracy)"
      ],
      "metadata": {
        "id": "zQU9B6tJolD7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy.description)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9-c7DNsomvr",
        "outputId": "ed8186fb-d117-4915-eb50-5ffc5eae1e5e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\n",
            "Accuracy = (TP + TN) / (TP + TN + FP + FN)\n",
            " Where:\n",
            "TP: True positive\n",
            "TN: True negative\n",
            "FP: False positive\n",
            "FN: False negative\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy.features\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haUJZbq-pFtm",
        "outputId": "7b094bd4-d57f-41b5-e4c0-6c6f0555f271"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'predictions': Value(dtype='int32', id=None),\n",
              " 'references': Value(dtype='int32', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xXuXzOPwpJvQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}