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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEzvEdwBCu8j"
      },
      "source": [
        "# PyTerrier ECIR 2021 Tutorial Notebook - Part 4.3 - ColBERT\n",
        "\n",
        "This notebook provides experiences to attendees for building transformer pipelines in [PyTerrier](https://github.com/terrier-org/pyterrier). \n",
        "\n",
        "This notebook aims to demonstrate use of the [ColBERT dense retrieval](https://github.com/stanford-futuredata/ColBERT/tree/v0.2) for end-to-end indexing and retrieval in PyTerrier, as provided by the [*Pyterrier ColBERT*](https://github.com/terrierteam/pyterrier_colbert) plugin.\n",
        "\n",
        "In this notebook, you will experience indexing and retrieval using pyterrier_colbert.\n",
        "\n",
        "NB: ColBERT is memory hungry. For this reason, we are not able to demonstate ColBERT on corpora larger than Vaswani (11k abstracts) within the tight constraints of a Google Colab environment."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8Wk1OOx2R0-"
      },
      "source": [
        "# Setup\n",
        "\n",
        "In the following, we will set up the libraries required to execute the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMT4zyo2C0f7"
      },
      "source": [
        "## Python packages installation\n",
        "\n",
        "The following packages are installed to avoid warnings/errors during [PyTerrier](https://github.com/terrier-org/pyterrier) installation. Note that the current release of [*Pyterrier ColBERT*](https://github.com/terrierteam/pyterrier_colbert) works only with the following Python packages:\n",
        "\n",
        "* `transfomers`, version 3.0.2\n",
        "* `faiss-gpu`, version 1.6.3\n",
        "\n",
        "> You can safely ignore the message about runtime restart."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56dHbyAWCwyY",
        "outputId": "0f5902d8-dca5-4caa-873f-0359998ecd32"
      },
      "source": [
        "!apt install --upgrade libomp-dev\n",
        "\n",
        "!pip install --upgrade transformers==3.0.2\n",
        "!pip install --upgrade faiss-gpu==1.6.3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeCzaedsCu5t"
      },
      "source": [
        "## Pyterrier installation\n",
        "\n",
        "The following cell installs the latest release of the [PyTerrier](https://github.com/terrier-org/pyterrier) package."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSUDPlRbDAHL",
        "outputId": "39dd1a95-95c6-44c5-dedb-074f183aafdb"
      },
      "source": [
        "!pip install python-terrier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxnKwBEoCu2_"
      },
      "source": [
        "## Pyterrier plugins installation\n",
        "\n",
        "We install the official version of the [*Pyterrier ColBERT*](https://github.com/terrierteam/pyterrier_colbert) plugin. You can safely ignore the package versioning errors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCE7EXbvIfFd",
        "outputId": "d62d7c00-0606-498a-c26a-64bfe41a5cfc"
      },
      "source": [
        "!pip install --upgrade git+https://github.com/terrierteam/pyterrier_colbert.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-o4Mx9IXIopW"
      },
      "source": [
        "## Trained model download\n",
        "\n",
        "This downloads the [ColBERT](https://github.com/stanford-futuredata/ColBERT) model checkpoint. This will be downloaded the first time it is used - download takes less than 1 minute in our experience."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iywOz_nfIqC_"
      },
      "source": [
        "checkpoint=\"http://www.dcs.gla.ac.uk/~craigm/ecir2021-tutorial/colbert_model_checkpoint.zip\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8fum4I4Cu0a"
      },
      "source": [
        "# Preliminary steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gja2vig3Cuyl"
      },
      "source": [
        "## [PyTerrier](https://github.com/terrier-org/pyterrier) initialization\n",
        "\n",
        "Lets get [PyTerrier](https://github.com/terrier-org/pyterrier) started. This will download the latest version of the [Terrier](http://terrier.org) IR platform."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRXwUvXvJFGk",
        "outputId": "20d2b35e-2e1f-469f-cc4c-2a5483693224"
      },
      "source": [
        "import pyterrier as pt\n",
        "\n",
        "if not pt.started():\n",
        "    pt.init()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GUjIYGfCuu8"
      },
      "source": [
        "## [Vaswani](http://ir.dcs.gla.ac.uk/resources/test_collections/npl/) Dataset download\n",
        "\n",
        "The following cell downloads the [Vaswani](http://ir.dcs.gla.ac.uk/resources/test_collections/npl/) dataset that we will use in the reamining of the tutorial.\n",
        "\n",
        " We limit queries to just 50 topics to avoid RAM issues with ColBERT on Colab. ColBERT is **very** memory-hungry."
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            "504040161c334d7489d915ca7d06dea3",
            "c9df03fb759148308cc704c00ffaab1d"
          ]
        },
        "outputId": "36f06557-3721-478e-f556-47f1e340ad67"
      },
      "source": [
        "dataset = pt.get_dataset(\"vaswani\")\n",
        "topics = dataset.get_topics().head(50)\n",
        "qrels = dataset.get_qrels()\n",
        "\n",
        "index = dataset.get_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eS7k0tmCurm"
      },
      "source": [
        "# [ColBERT](https://github.com/stanford-futuredata/ColBERT) indexing\n",
        "\n",
        "We are going to index the [Vaswani](http://ir.dcs.gla.ac.uk/resources/test_collections/npl/) collection with [ColBERT](https://github.com/stanford-futuredata/ColBERT).\n",
        "\n",
        "The construction of this index takes some time. The following code:\n",
        "* downloads some additional BERT models;\n",
        "* processes the whole collection to compute the document embeddings, e.g, at most 180 embeddings per document;\n",
        "* performs the *training* of the IVFPQ [FAISS](https://github.com/facebookresearch/faiss) index supporting approximate nearest neightbour search.\n",
        "\n",
        "For 11,429 documents, the code computes 581,496 document embeddings, ~50.8 embeddings per document, in approximatively **5 minutes**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "3qn9p5dOJ6To",
        "outputId": "7b26795a-057f-48a1-f7fd-565526161c60"
      },
      "source": [
        "!rm -rf /content/colbert_index\n",
        "\n",
        "import pyterrier_colbert.indexing\n",
        "\n",
        "colbert_indexer = pyterrier_colbert.indexing.ColBERTIndexer(checkpoint=checkpoint, \n",
        "                                                            index_root=\"/content\",\n",
        "                                                            index_name=\"colbert_index\",\n",
        "                                                            chunksize=3)\n",
        "colbert_indexer.index(dataset.get_corpus_iter())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kHX8LPJQXL1I"
      },
      "source": [
        "Lets give a look at the files created."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_QHOgO6XOew",
        "outputId": "c3bd85ae-9a85-4954-a350-066088832030"
      },
      "source": [
        "!ls -lh /content/colbert_index/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7HEyTdJXSxT"
      },
      "source": [
        "So we have a few files that have been generated. Firstly, note that ColBERT indexes into chunks - Vaswani is small enough to only need a single chunk, so we have only `0.pt` and no `1.pt` etc :\n",
        " - $x$ `.pt` - the document embeddings for each chunk\n",
        " - $x$ `.sample` - a sample of the document embeddings in that chunk - used for training FAISS, not needed at retrieval time\n",
        " - `doclens.` $x$ `.json` - the number of document embeddings per document.\n",
        " - `ivfpq.` $y$ `.faiss` - the FAISS index for all document embeddings\n",
        " - `docnos.pkl.gz` - the docno document metadata, used by PyTerrier_ColBERT to return docnos.\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3kRN1i3CunH"
      },
      "source": [
        "# Retrieval experiments\n",
        "\n",
        "Now that indexing/downloading has completed, we can load in the index and the learned model (which we will need for encoding queries). Index loading can take some time, as the [FAISS](https://github.com/facebookresearch/faiss) index needs to be loaded in main memory, as well as the document embeddings index.\n",
        "\n",
        "Lets prepare an experiment. Firstly, lets create in a BM25 baseline transformer, and the [ColBERT](https://github.com/stanford-futuredata/ColBERT) retrieve transformer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134,
          "referenced_widgets": [
            "3aafe35ed3ec4b48a38d9f7166afa3f9",
            "d6191e2d8a2e40de9cdb33012e78275f",
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          ]
        },
        "id": "-2UAASpBMl4x",
        "outputId": "10be0457-ab9a-4160-8e9a-78f4dd2128df"
      },
      "source": [
        "bm25_retriever = pt.BatchRetrieve(index, wmodel=\"BM25\")\n",
        "\n",
        "colbert_retriever = colbert_indexer.ranking_factory().end_to_end()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVHdEDtsCuki"
      },
      "source": [
        "Now we are ready to run the experiments. We are going to retrieve the top 10 ranked documents for the official topics, and compute several effectiveness metrics. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjNydtH1MwcH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "762683a4-92cc-413a-b4d6-32a083a75fd9"
      },
      "source": [
        "pt.Experiment(\n",
        "    [bm25_retriever % 10, colbert_retriever % 10], \n",
        "    topics,\n",
        "    qrels,\n",
        "    eval_metrics=[\"map\", \"recip_rank\", \"P_10\", \"ndcg_cut_10\", \"mrt\"],\n",
        "    names=['BM25', 'ColBERT'],\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SGHIbd6yhLR"
      },
      "source": [
        "So for this small dataset, ColBERT achieves a MAP is similar to BM25, a marginally higher P@10, but a lower MRR."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7f4S96E2DbY"
      },
      "source": [
        "#  That's all folks\n",
        "\n",
        "Once you have finished all of the Part 4 notebooks, please dont forget to complete our exit quiz: https://forms.office.com/r/2WbpLiQmWV"
      ]
    }
  ]
}