colbert_text_and_explain.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "colbert-text-and-explain.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poqzBNP0ddGl"
      },
      "source": [
        "#PyTerrier Text Scoring Explanation\n",
        "\n",
        "This notebook demonstrates the use of an interaction diagram for PyTerrier_ColBERT."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KJQC5H9mdm01"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_xjEAH_fsH0D",
        "outputId": "19bfc207-2c9e-4c40-acf6-0fa4f8d84b12"
      },
      "source": [
        "!pip install python-terrier\n",
        "!pip install --upgrade git+https://github.com/terrierteam/pyterrier_colbert.git\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "  Cloning https://github.com/terrierteam/pyterrier_colbert.git to /tmp/pip-req-build-6ayzu4pc\n",
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            "Requirement already satisfied, skipping upgrade: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->ColBERT@ git+https://github.com/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (3.1.0)\n",
            "Building wheels for collected packages: pyterrier-colbert\n",
            "  Building wheel for pyterrier-colbert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyterrier-colbert: filename=pyterrier_colbert-0.0.1-cp37-none-any.whl size=12044 sha256=f2309f2471fbeb994dd14420625996f4a5f4e480e5d7a6ed058144330dcd8893\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-pswirv9n/wheels/7d/23/87/59bcb24958d35319315084fe0b193e9b0c15a1d384199dbaf3\n",
            "Successfully built pyterrier-colbert\n",
            "Installing collected packages: pyterrier-colbert\n",
            "  Found existing installation: pyterrier-colbert 0.0.1\n",
            "    Uninstalling pyterrier-colbert-0.0.1:\n",
            "      Successfully uninstalled pyterrier-colbert-0.0.1\n",
            "Successfully installed pyterrier-colbert-0.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0k0VtyRsnVz",
        "outputId": "9955d600-49d0-4f4c-cae1-6d8a67eb1e46"
      },
      "source": [
        "import pyterrier as pt\n",
        "pt.init()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tqdm/std.py:658: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
            "  from pandas import Panel\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "PyTerrier 0.4.0 has loaded Terrier 5.4 (built by craigm on 2021-01-16 14:17)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfEvhR2EdpTB"
      },
      "source": [
        "## PyTerrier_ColBERT \n",
        "\n",
        "Here, we load a ColBERT checkpoint created at the University of Glasgow on the MSMARCO passage ranking triples file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYAP_1ZnsKKe",
        "outputId": "8e5608da-6b31-4330-e1bd-1d15ec2bb43a"
      },
      "source": [
        "checkpoint=\"http://www.dcs.gla.ac.uk/~craigm/colbert.dnn.zip\"\n",
        "import pyterrier_colbert.ranking\n",
        "factory = pyterrier_colbert.ranking.ColBERTFactory(checkpoint, None, None)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyterrier_colbert/ranking.py:221: UserWarning: No index_root and index_name specified - no index ranking possible\n",
            "  warn(\"No index_root and index_name specified - no index ranking possible\")\n",
            "/usr/local/lib/python3.7/dist-packages/pyterrier_colbert/ranking.py:237: UserWarning: Faiss not installed. You cannot do retrieval\n",
            "  warn(\"Faiss not installed. You cannot do retrieval\")\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing ColBERT: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing ColBERT from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n",
            "- This IS NOT expected if you are initializing ColBERT from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of ColBERT were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['linear.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Mar 24, 18:17:58] #> Loading model checkpoint.\n",
            "[Mar 24, 18:17:58] #> Loading checkpoint http://www.dcs.gla.ac.uk/~craigm/colbert.dnn.zip\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/hub.py:452: UserWarning: Falling back to the old format < 1.6. This support will be deprecated in favor of default zipfile format introduced in 1.6. Please redo torch.save() to save it in the new zipfile format.\n",
            "  warnings.warn('Falling back to the old format < 1.6. This support will be '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Mar 24, 18:18:10] #> checkpoint['epoch'] = 0\n",
            "[Mar 24, 18:18:10] #> checkpoint['batch'] = 44500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rr79-7n2d9PO"
      },
      "source": [
        "## Interactions Figure\n",
        "\n",
        "This interaction figure shows how a query and a document interact. In particular: \n",
        " - the top sub-plot shows the contribution each query wordpiece to the document's score.\n",
        " - In the heatmap, darker colours indicate higher similarity between the query emebdding and the document embedding. \n",
        " - ColBERT uses max_sim operator - for each query embedding, only the most similar document embedding contributes to the final score of the document. For each query embedding, we put an \"X\" mark in the row of document embedding that is the source of that maximum similarity for that query embedding.\n",
        " - \\[MASK\\] tokens are extra tokens added to the query by ColBERT. We can observe which document embeddings these match with.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqyp6hEAsO10",
        "outputId": "6f8fb427-b1de-47b3-f28e-01b614703706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        }
      },
      "source": [
        "factory.explain_text(\"why did the us voluntarily enter ww1\", \"the USA entered ww2 because of pearl harbor\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}