vaswani.ipynb

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        "# PyTerrier ColBERT Demo Notebook - Vaswani\n",
        "\n",
        "This notebook demonstrates use of [PyTerrier plugin for ColBERT](https:/terrierteam/pyterrier_colbert) for dense passage retrieval. \n",
        "\n",
        "[ColBERT](https:/stanford-futuredata/ColBERT/tree/v0.2) is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. ColBERT relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings. Then at search time, it embeds every query into another matrix of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. \n",
        "\n",
        "\n",
        "[ColBERT](https://arxiv.org/abs/2004.12832) is built on top of [BERT](https://arxiv.org/abs/1810.04805). ColBERT surpasses the quality of single-vector representation models, while scaling efficiently to large corpora. \n",
        "\n",
        "The corpus used in this demo is the [Vaswani NPL corpus](http://ir.dcs.gla.ac.uk/resources/test_collections/npl/), a corpus of 11,429 scientific abstract, with corresponding queries and relevance assessments."
      ]
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    {
      "cell_type": "markdown",
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        "id": "WVdiFSfQPWpr"
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      "source": [
        "## Installation \n",
        "\n",
        "We need to install [PyTerrier](https:/terrier-org/pyterrier)."
      ]
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    {
      "cell_type": "code",
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        "id": "6szZMYozPdw8",
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        "outputId": "a4241a6e-66ff-4cd6-e654-f56add78e82b"
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      "source": [
        "!pip install python-terrier"
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b3/0c/e3b7bf52e23345d5f9a6a3ff6de0cad419c96491893ab60cbbe9161644a8/ijson-3.1.4-cp37-cp37m-manylinux2010_x86_64.whl (126kB)\n",
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            "Collecting zlib-state>=0.1.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/05/95df36be761a5ff85a39b438d87937f02762c19fea95c0f10b679a259f14/zlib_state-0.1.3-cp37-cp37m-manylinux2010_x86_64.whl (72kB)\n",
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            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->deprecation->python-terrier) (2.4.7)\n",
            "Collecting cbor>=1.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/9b/99/01c6a987c920500189eb74a291bd3a388e6c7cf85736bb6b066d9833315e/cbor-1.0.0.tar.gz\n",
            "Building wheels for collected packages: python-terrier, wget, pytrec-eval, chest, warc3-wet-clueweb09, cbor\n",
            "  Building wheel for python-terrier (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-terrier: filename=python_terrier-0.5.0-cp37-none-any.whl size=79538 sha256=712ffb1252635f06ed567370f5b69c326ea4f3aa765242db7a14484d36e5e8d0\n",
            "  Stored in directory: /root/.cache/pip/wheels/b4/4d/75/ab103d23ae843881ff3adb2d75a5b8d624e22adade8d5f7506\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-cp37-none-any.whl size=9681 sha256=5d644a4b5854a18e835193f247da1ee2d3bb119211079e8d2d796886b963bb58\n",
            "  Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n",
            "  Building wheel for pytrec-eval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pytrec-eval: filename=pytrec_eval-0.5-cp37-cp37m-linux_x86_64.whl size=264888 sha256=2eb15638d8bb5cf9aab025e60ee7b2ee0d1f04bda6ce7806eb06f3fcb949c49d\n",
            "  Stored in directory: /root/.cache/pip/wheels/55/66/40/1779aa0a8eb66e088669befe286f695cdfe420ba91ce662127\n",
            "  Building wheel for chest (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for chest: filename=chest-0.2.3-cp37-none-any.whl size=7623 sha256=047dc44ff3748d57a1ad49d6b2a9be94c77c438b9cc9edad3101b2bef6566ca8\n",
            "  Stored in directory: /root/.cache/pip/wheels/f9/52/97/3f0eb272609dc1b7a20fb678a45003301b51f03cf766f6237f\n",
            "  Building wheel for warc3-wet-clueweb09 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for warc3-wet-clueweb09: filename=warc3_wet_clueweb09-0.2.5-cp37-none-any.whl size=18909 sha256=c1977077c19c8705d6a91e686f3709a630c98e73e391edb433d082b4de6db6f4\n",
            "  Stored in directory: /root/.cache/pip/wheels/09/da/a9/ec9816edf7f789eab3fea2e57abe37bf7d6ab65f8ef8ee7f31\n",
            "  Building wheel for cbor (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cbor: filename=cbor-1.0.0-cp37-cp37m-linux_x86_64.whl size=51259 sha256=0677d3a615f4d0e2a163f783ac9693104e3fb210eee5efb73d2ed95a46baa4bc\n",
            "  Stored in directory: /root/.cache/pip/wheels/40/5c/a5/e6d629446a6a687ba328c55f1589234c29b99ef35b1a65dbaa\n",
            "Successfully built python-terrier wget pytrec-eval chest warc3-wet-clueweb09 cbor\n",
            "Installing collected packages: wget, pytrec-eval, pyjnius, multiset, matchpy, deprecation, chest, typish, nptyping, cbor, trec-car-tools, warc3-wet-clueweb09, pyyaml, lz4, warc3-wet, lxml, ijson, zlib-state, ir-datasets, python-terrier\n",
            "  Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed cbor-1.0.0 chest-0.2.3 deprecation-2.1.0 ijson-3.1.4 ir-datasets-0.3.3 lxml-4.6.3 lz4-3.1.3 matchpy-0.5.4 multiset-2.1.1 nptyping-1.4.1 pyjnius-1.3.0 python-terrier-0.5.0 pytrec-eval-0.5 pyyaml-5.4.1 trec-car-tools-2.5.4 typish-1.9.1 warc3-wet-0.2.3 warc3-wet-clueweb09-0.2.5 wget-3.2 zlib-state-0.1.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiKQMcrEPdkC"
      },
      "source": [
        "This installs the [PyTerrier plugin for ColBERT](https:/terrierteam/pyterrier_colbert). It supplies an indexer and a retrieval transformer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxDlO_Z2PdAZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f65c63b-8d22-4f92-d29b-a3510b8109c3"
      },
      "source": [
        "!pip install --upgrade git+https:/terrierteam/pyterrier_colbert.git"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https:/terrierteam/pyterrier_colbert.git\n",
            "  Cloning https:/terrierteam/pyterrier_colbert.git to /tmp/pip-req-build-v69a3sw_\n",
            "  Running command git clone -q https:/terrierteam/pyterrier_colbert.git /tmp/pip-req-build-v69a3sw_\n",
            "Requirement already satisfied, skipping upgrade: python-terrier in /usr/local/lib/python3.7/dist-packages (from pyterrier-colbert==0.0.1) (0.5.0)\n",
            "Requirement already satisfied, skipping upgrade: pandas in /usr/local/lib/python3.7/dist-packages (from pyterrier-colbert==0.0.1) (1.1.5)\n",
            "Collecting ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT\n",
            "  Cloning https:/cmacdonald/ColBERT.git (to revision v0.2) to /tmp/pip-install-gvrh73e2/ColBERT\n",
            "  Running command git clone -q https:/cmacdonald/ColBERT.git /tmp/pip-install-gvrh73e2/ColBERT\n",
            "  Running command git checkout -b v0.2 --track origin/v0.2\n",
            "  Switched to a new branch 'v0.2'\n",
            "  Branch 'v0.2' set up to track remote branch 'v0.2' from 'origin'.\n",
            "Requirement already satisfied, skipping upgrade: nptyping in /usr/local/lib/python3.7/dist-packages (from python-terrier->pyterrier-colbert==0.0.1) (1.4.1)\n",
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            "Collecting transformers==3.0.2\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: tensorboard in /usr/local/lib/python3.7/dist-packages (from ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (2.4.1)\n",
            "Requirement already satisfied, skipping upgrade: torch in /usr/local/lib/python3.7/dist-packages (from ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (1.8.0+cu101)\n",
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            "Requirement already satisfied, skipping upgrade: heapdict in /usr/local/lib/python3.7/dist-packages (from chest->python-terrier->pyterrier-colbert==0.0.1) (1.0.1)\n",
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            "Requirement already satisfied, skipping upgrade: trec-car-tools>=2.5.4 in /usr/local/lib/python3.7/dist-packages (from ir-datasets>=0.2.0->python-terrier->pyterrier-colbert==0.0.1) (2.5.4)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/02/59/68c7e3833f535615fb97d33ffcb7b30bbf62bc7477a9c59cd19ad8535d72/tokenizers-0.8.1rc1-cp37-cp37m-manylinux1_x86_64.whl (3.0MB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (3.0.12)\n",
            "Collecting sentencepiece!=0.1.92\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/99/e0808cb947ba10f575839c43e8fafc9cc44e4a7a2c8f79c60db48220a577/sentencepiece-0.1.95-cp37-cp37m-manylinux2014_x86_64.whl (1.2MB)\n",
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            "Collecting querystring-parser\n",
            "  Downloading https://files.pythonhosted.org/packages/88/6b/572b2590fd55114118bf08bde63c0a421dcc82d593700f3e2ad89908a8a9/querystring_parser-1.2.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from mlflow->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (3.12.4)\n",
            "Requirement already satisfied, skipping upgrade: click>=7.0 in /usr/local/lib/python3.7/dist-packages (from mlflow->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (7.1.2)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bc/af/631375abc29e59cedfa4467a5f7755503ba19898890751e1f2636ef02f92/databricks-cli-0.14.3.tar.gz (54kB)\n",
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            "Collecting alembic<=1.4.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e0/e9/359dbb77c35c419df0aedeb1d53e71e7e3f438ff64a8fdb048c907404de3/alembic-1.4.1.tar.gz (1.1MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a6/99/98019716955ba243657daedd1de8f3a88ca1f5b75057c38e959db22fb87b/GitPython-3.1.14-py3-none-any.whl (159kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/ca/926f7cd3a2014b16870086b2d0fdc84a9e49473c68a8dff8b57f7c156f43/gunicorn-20.0.4-py2.py3-none-any.whl (77kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 11.7MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: sqlparse>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from mlflow->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.4.1)\n",
            "Requirement already satisfied, skipping upgrade: cloudpickle in /usr/local/lib/python3.7/dist-packages (from mlflow->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (1.3.0)\n",
            "Collecting docker>=4.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c4/22/410313ad554477e87ec406d38d85f810e61ddb0d2fc44e64994857476de9/docker-4.4.4-py2.py3-none-any.whl (147kB)\n",
            "\u001b[K     |████████████████████████████████| 153kB 62.3MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: entrypoints in /usr/local/lib/python3.7/dist-packages (from mlflow->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.3)\n",
            "Collecting prometheus-flask-exporter\n",
            "  Downloading https://files.pythonhosted.org/packages/4c/d5/8a046d683c2cc084b6a502812827ede69b1064f95d93f94b83f809b21723/prometheus_flask_exporter-0.18.1.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (1.27.1)\n",
            "Requirement already satisfied, skipping upgrade: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.4.3)\n",
            "Requirement already satisfied, skipping upgrade: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (54.1.2)\n",
            "Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (3.3.4)\n",
            "Requirement already satisfied, skipping upgrade: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (1.8.0)\n",
            "Requirement already satisfied, skipping upgrade: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.36.2)\n",
            "Requirement already satisfied, skipping upgrade: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (1.32.0)\n",
            "Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (3.7.4.3)\n",
            "Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn->python-terrier->pyterrier-colbert==0.0.1) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: cbor>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from trec-car-tools>=2.5.4->ir-datasets>=0.2.0->python-terrier->pyterrier-colbert==0.0.1) (1.0.0)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->deprecation->python-terrier->pyterrier-colbert==0.0.1) (2.4.7)\n",
            "Requirement already satisfied, skipping upgrade: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from databricks-cli>=0.8.7->mlflow->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.8.9)\n",
            "Requirement already satisfied, skipping upgrade: itsdangerous>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask->mlflow->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (1.1.0)\n",
            "Collecting Mako\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f3/54/dbc07fbb20865d3b78fdb7cf7fa713e2cba4f87f71100074ef2dc9f9d1f7/Mako-1.1.4-py2.py3-none-any.whl (75kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 9.7MB/s \n",
            "\u001b[?25hCollecting python-editor>=0.3\n",
            "  Downloading https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f35513717/python_editor-1.0.4-py3-none-any.whl\n",
            "Collecting gitdb<5,>=4.0.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/58/1a/b53069576a6b48a203ec8b7971ae5e455b1d801f5b7d086b986b8fc17186/gitdb-4.0.6-py3-none-any.whl (63kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 9.8MB/s \n",
            "\u001b[?25hCollecting websocket-client>=0.32.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/33/80e0d4f60e84a1ddd9a03f340be1065a2a363c47ce65c4bd3bae65ce9631/websocket_client-0.58.0-py2.py3-none-any.whl (61kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 9.2MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: prometheus_client in /usr/local/lib/python3.7/dist-packages (from prometheus-flask-exporter->mlflow->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.9.0)\n",
            "Requirement already satisfied, skipping upgrade: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (4.7.2)\n",
            "Requirement already satisfied, skipping upgrade: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (4.2.1)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.2.8)\n",
            "Requirement already satisfied, skipping upgrade: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (1.3.0)\n",
            "Requirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (3.7.2)\n",
            "Collecting smmap<4,>=3.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/d5/1e/6130925131f639b2acde0f7f18b73e33ce082ff2d90783c436b52040af5a/smmap-3.0.5-py2.py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (0.4.8)\n",
            "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:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (3.1.0)\n",
            "Requirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->ColBERT@ git+https:/cmacdonald/ColBERT.git@v0.2#egg=ColBERT->pyterrier-colbert==0.0.1) (3.4.1)\n",
            "Building wheels for collected packages: pyterrier-colbert, ColBERT, sacremoses, databricks-cli, alembic, prometheus-flask-exporter\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=12043 sha256=c909dc583b8efa94d704eea0df226cf2cbd76b6c7e2f52396f9c2e8936eec61b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-h6_wcwzl/wheels/7d/23/87/59bcb24958d35319315084fe0b193e9b0c15a1d384199dbaf3\n",
            "  Building wheel for ColBERT (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ColBERT: filename=ColBERT-0.2.0-cp37-none-any.whl size=48403 sha256=1e5466b8ae9feab7010794c26c8b7dba12d09881567c00b1baa6b6935e0a989e\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-h6_wcwzl/wheels/5e/13/f2/a62b29b499781f128bb73f5672c2166928170a0f256d98c565\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp37-none-any.whl size=893262 sha256=5ffb306c816da71ee44a63435df8323d67c7266afcdf90e85aeb8c2e0aaa560a\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "  Building wheel for databricks-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for databricks-cli: filename=databricks_cli-0.14.3-cp37-none-any.whl size=100557 sha256=f4fa480b5bf1c69a25d679b55dea465ee4f7c4cb18c23170708cebdeb6b9a416\n",
            "  Stored in directory: /root/.cache/pip/wheels/5b/24/f3/34d8e3964dac4ba849d844273c49a679111b00d5799ebb934a\n",
            "  Building wheel for alembic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alembic: filename=alembic-1.4.1-py2.py3-none-any.whl size=158155 sha256=62d76fa3558385fa8406996d156f1c124be912988465f6f65bf0093f5ccdb034\n",
            "  Stored in directory: /root/.cache/pip/wheels/84/07/f7/12f7370ca47a66030c2edeedcc23dec26ea0ac22dcb4c4a0f3\n",
            "  Building wheel for prometheus-flask-exporter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prometheus-flask-exporter: filename=prometheus_flask_exporter-0.18.1-cp37-none-any.whl size=17159 sha256=66f41dc41f63ae14edc32a473ca1d816e57ad42cae567cf3b76f7c56b151449a\n",
            "  Stored in directory: /root/.cache/pip/wheels/b4/1f/b8/66bd9bc3a9d6c6987ff6c4dfeb6f1fe97b5a0e5ed5849c0437\n",
            "Successfully built pyterrier-colbert ColBERT sacremoses databricks-cli alembic prometheus-flask-exporter\n",
            "Installing collected packages: sacremoses, tokenizers, sentencepiece, transformers, ujson, querystring-parser, databricks-cli, Mako, python-editor, alembic, smmap, gitdb, gitpython, gunicorn, websocket-client, docker, prometheus-flask-exporter, mlflow, ColBERT, pyterrier-colbert\n",
            "Successfully installed ColBERT-0.2.0 Mako-1.1.4 alembic-1.4.1 databricks-cli-0.14.3 docker-4.4.4 gitdb-4.0.6 gitpython-3.1.14 gunicorn-20.0.4 mlflow-1.14.1 prometheus-flask-exporter-0.18.1 pyterrier-colbert-0.0.1 python-editor-1.0.4 querystring-parser-1.2.4 sacremoses-0.0.43 sentencepiece-0.1.95 smmap-3.0.5 tokenizers-0.8.1rc1 transformers-3.0.2 ujson-4.0.2 websocket-client-0.58.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssDoJmlxQJzX"
      },
      "source": [
        "This installs [FAISS](https:/facebookresearch/faiss), a library for efficient similarity search and clustering of dense vectors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WP-LDkbx9iRv",
        "outputId": "23c81839-d572-4104-815d-aa46f9daccfd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import sys\n",
        "\n",
        "COLAB='google.colab' in sys.modules\n",
        "\n",
        "try:\n",
        "  import faiss\n",
        "  faiss.get_num_gpus()\n",
        "except:\n",
        "  if COLAB:\n",
        "    print('Installing faiss-gpu from pip ')\n",
        "    !pip install faiss-gpu==1.6.3\n",
        "  else:\n",
        "    print('Installing faiss-gpu via Conda')\n",
        "    !conda install -c pytorch faiss-gpu\n",
        "\n",
        "import faiss\n",
        "assert faiss.get_num_gpus() > 0"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing faiss-gpu from pip \n",
            "Collecting faiss-gpu==1.6.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1c/43/f33a7d59e7b367f34e1ab61db70a5a75194c848ea2df940c49f7aedc95d9/faiss_gpu-1.6.3-cp37-cp37m-manylinux2010_x86_64.whl (35.5MB)\n",
            "\u001b[K     |████████████████████████████████| 35.5MB 143kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from faiss-gpu==1.6.3) (1.19.5)\n",
            "Installing collected packages: faiss-gpu\n",
            "Successfully installed faiss-gpu-1.6.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wih-SWCAP8tL"
      },
      "source": [
        "# Setup\n",
        "\n",
        "Lets get [PyTerrier](https:/terrier-org/pyterrier) started. This will download the latest version of the [Terrier](http://terrier.org) IR platform."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IAyLRo62QCbU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3efc15c1-6c02-4c21-9bf9-d26e232fc045"
      },
      "source": [
        "import pyterrier as pt\n",
        "pt.init()"
      ],
      "execution_count": 4,
      "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": [
            "terrier-assemblies 5.4  jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "terrier-python-helper 0.0.5  jar not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "PyTerrier 0.5.0 has loaded Terrier 5.4 (built by craigm on 2021-01-16 14:17)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLADCxA2S4gu"
      },
      "source": [
        "This is the ColBERT checkpoint generated by Craig Macdonald. and used in our TREC 2020 Participation. It will be downloaded first time it is used. Downloading time varies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZPzASx85tiit"
      },
      "source": [
        "checkpoint=\"http://www.dcs.gla.ac.uk/~craigm/colbert.dnn.zip\""
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCImH1CJTzG0"
      },
      "source": [
        "# Indexing\n",
        "\n",
        "This indexes the [Vaswani dataset](http://ir.dcs.gla.ac.uk/resources/test_collections/npl/). Indexing takes about 3 minutes using a Colab GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "cc88fe38bfaf4fb29ffd56b20966c163",
            "05e48ebe97e24017ba883b48d39dadb1",
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            "206edc961cbf4837b9bbe2b60504349d",
            "517d141df78448cca0d7054516f38cd0",
            "27cf0dc3e658483fa1cd0730105a5dfa",
            "7f37153f5e3c4fd7b7b3e0b184855a8f",
            "732f5f49182b4b009cb2506fa2e84e5e",
            "727582b0699b4352b3e9451b2313a7d5",
            "13a1db40b3df49c6ad2f152e669ee2ec",
            "b235415b788241cb94549eccabcac729",
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            "866a6e1dbf85415fb13d6360885899bd",
            "b361a649f7ce460eb83169682a5f3578",
            "2b6791a7871548f487df30be886003f0",
            "97268ea359ad4a55a9d92ae6879fb6a1",
            "aed13c7b97684b03a09be0aaa3ee8144",
            "235dc82297c5486da9db2377481dfdff",
            "ac1940685f3a498fa6bbb4b06d0e21ef",
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            "1e7a8f5919bf466abfa81f4a2899119f",
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            "f5c75ec6bd6644b38fd4dddf711ff2ec",
            "57110f214e714a5394d440d98f983db6",
            "a8348864244c44e7a97b458c5343d7f7",
            "5ad3e63d935b45989da6f0fc5834e9ea",
            "8a01041b36f84bda82552f9a06553bef",
            "1f8921d70a554e02b2c211cce8972385",
            "1868c3cb2ded4efa97f49313deb0dbff",
            "1e26e9a19a7649fe8f6a89599d3521e2"
          ]
        },
        "id": "bgRH9z0hT8_Z",
        "outputId": "c77385ac-3770-4c82-c85a-ccbacbe03a91"
      },
      "source": [
        "!rm -rf /content/colbertindex\n",
        "\n",
        "import pyterrier_colbert.indexing\n",
        "\n",
        "indexer = pyterrier_colbert.indexing.ColBERTIndexer(checkpoint, \"/content\", \"colbertindex\", chunksize=3)\n",
        "indexer.index(pt.get_dataset(\"irds:vaswani\").get_corpus_iter())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] [starting] building docstore\n",
            "[INFO] If you have a local copy of http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz, you can symlink it here to avoid downloading it again: /root/.ir_datasets/downloads/23e5607081191b153738e81fbd834680\n",
            "[INFO] [starting] http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz\n",
            "docs_iter: 0it [00:01, ?it/s]\n",
            "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: 0.0%| 0.00/2.13M [00:00<?, ?B/s]\u001b[A\n",
            "\n",
            "\u001b[A[INFO] [finished] http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: [00:00] [2.13MB] [14.7MB/s]\n",
            "docs_iter: 0it [00:01, ?it/s]\n",
            "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: [00:00] [2.13MB] [13.1MB/s]\u001b[A\n",
            "docs_iter: 11429it [00:01, 7974.55it/s]\n",
            "[INFO] [finished] docs_iter: [00:01] [11429it] [7969.76it/s]\n",
            "[INFO] [finished] building docstore [1.44s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "[Mar 26, 13:28:07] [0] \t\t #> Local args.bsize = 128\n",
            "[Mar 26, 13:28:07] [0] \t\t #> args.index_root = /content\n",
            "[Mar 26, 13:28:07] [0] \t\t #> self.possible_subset_sizes = [69905]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[INFO] Lock 140466134063632 acquired on /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.7156163d5fdc189c3016baca0775ffce230789d7fa2a42ef516483e4ca884517.lock\n",
            "[INFO] https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpvqzv286k\n"
          ],
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        },
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            "\n",
            "\r\r\r\r\r\r\r"
          ],
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        },
        {
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          "text": [
            "[INFO] storing https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json in cache at /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.7156163d5fdc189c3016baca0775ffce230789d7fa2a42ef516483e4ca884517\n",
            "[INFO] creating metadata file for /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.7156163d5fdc189c3016baca0775ffce230789d7fa2a42ef516483e4ca884517\n",
            "[INFO] Lock 140466134063632 released on /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.7156163d5fdc189c3016baca0775ffce230789d7fa2a42ef516483e4ca884517.lock\n",
            "[INFO] loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json from cache at /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.7156163d5fdc189c3016baca0775ffce230789d7fa2a42ef516483e4ca884517\n",
            "[INFO] Model config BertConfig {\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "[INFO] Lock 140466134064080 acquired on /root/.cache/torch/transformers/f2ee78bdd635b758cc0a12352586868bef80e47401abe4c4fcc3832421e7338b.36ca03ab34a1a5d5fa7bc3d03d55c4fa650fed07220e2eeebc06ce58d0e9a157.lock\n",
            "[INFO] https://cdn.huggingface.co/bert-base-uncased-pytorch_model.bin not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpp6hmlgu1\n"
          ],
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            "\n",
            "\r\r\r\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[INFO] storing https://cdn.huggingface.co/bert-base-uncased-pytorch_model.bin in cache at /root/.cache/torch/transformers/f2ee78bdd635b758cc0a12352586868bef80e47401abe4c4fcc3832421e7338b.36ca03ab34a1a5d5fa7bc3d03d55c4fa650fed07220e2eeebc06ce58d0e9a157\n",
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            "[INFO] Lock 140466134064080 released on /root/.cache/torch/transformers/f2ee78bdd635b758cc0a12352586868bef80e47401abe4c4fcc3832421e7338b.36ca03ab34a1a5d5fa7bc3d03d55c4fa650fed07220e2eeebc06ce58d0e9a157.lock\n",
            "[INFO] loading weights file https://cdn.huggingface.co/bert-base-uncased-pytorch_model.bin from cache at /root/.cache/torch/transformers/f2ee78bdd635b758cc0a12352586868bef80e47401abe4c4fcc3832421e7338b.36ca03ab34a1a5d5fa7bc3d03d55c4fa650fed07220e2eeebc06ce58d0e9a157\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\r\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[WARNING] 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",
            "[WARNING] 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 26, 13:28:29] #> Loading model checkpoint.\n",
            "[Mar 26, 13:28:29] #> Loading checkpoint http://www.dcs.gla.ac.uk/~craigm/colbert.dnn.zip\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"http://www.dcs.gla.ac.uk/~craigm/colbert.dnn.zip\" to /root/.cache/torch/hub/checkpoints/colbert.dnn.zip\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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          "text": [
            "\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 26, 13:29:07] #> checkpoint['epoch'] = 0\n",
            "[Mar 26, 13:29:07] #> checkpoint['batch'] = 44500\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[INFO] Lock 140466131921552 acquired on /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084.lock\n",
            "[INFO] https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpc0nnn0ma\n"
          ],
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        },
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          "output_type": "display_data",
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          "output_type": "stream",
          "text": [
            "\n",
            "\r\r\r\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[INFO] storing https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt in cache at /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n",
            "[INFO] creating metadata file for /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n",
            "[INFO] Lock 140466131921552 released on /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084.lock\n",
            "[INFO] loading file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\r\n",
            "\n",
            "[Mar 26, 13:29:09] #> Note: Output directory /content already exists\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "[Mar 26, 13:29:09] #> Creating directory /content/colbertindex \n",
            "\n",
            "\n",
            "\n",
            "docFromText on 11429 documents\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "tokens doc 0: 180\n",
            "total tokens 2057220\n",
            "lenD = 11429 \n",
            "11429/content/colbertindex/0.pt\n",
            "\n",
            "[Mar 26, 13:30:11] [0] \t\t #> Completed batch #0 (starting at passage #0) \t\tPassages/min: 10.9k (overall),  10.9k (this encoding),  1423.2M (this saving)\n",
            "[Mar 26, 13:30:11] [0] \t\t [NOTE] Done with local share.\n",
            "[Mar 26, 13:30:12] [0] \t\t #> Joining saver thread.\n",
            "[Mar 26, 13:30:12] [0] \t\t #> Saved batch #0 to /content/colbertindex/0.pt \t\t Saving Throughput = 2.5M passages per minute.\n",
            "\n",
            "#> num_embeddings = 581496\n",
            "[Mar 26, 13:30:12] #> Starting..\n",
            "[Mar 26, 13:30:12] #> Processing slice #1 of 1 (range 0..1).\n",
            "[Mar 26, 13:30:12] #> Will write to /content/colbertindex/ivfpq.100.faiss.\n",
            "[Mar 26, 13:30:12] #> Loading /content/colbertindex/0.sample ...\n",
            "#> Sample has shape (29074, 128)\n",
            "[Mar 26, 13:30:12] Preparing resources for 1 GPUs.\n",
            "[Mar 26, 13:30:12] #> Training with the vectors...\n",
            "[Mar 26, 13:30:12] #> Training now (using 1 GPUs)...\n",
            "36.77404522895813\n",
            "26.64493465423584\n",
            "0.0008385181427001953\n",
            "[Mar 26, 13:31:15] Done training!\n",
            "\n",
            "[Mar 26, 13:31:15] #> Indexing the vectors...\n",
            "[Mar 26, 13:31:15] #> Loading ('/content/colbertindex/0.pt', None, None) (from queue)...\n",
            "[Mar 26, 13:31:15] #> Processing a sub_collection with shape (581496, 128)\n",
            "[Mar 26, 13:31:15] Add data with shape (581496, 128) (offset = 0)..\n",
            "524288/581496 (1.234 s)   Flush indexes to CPU\n",
            "add(.) time: 1.360 s \t\t--\t\t index.ntotal = 581496\n",
            "[Mar 26, 13:31:17] Done indexing!\n",
            "[Mar 26, 13:31:17] Writing index to /content/colbertindex/ivfpq.100.faiss ...\n",
            "[Mar 26, 13:31:17] \n",
            "\n",
            "Done! All complete (for slice #1 of 1)!\n",
            "#> Faiss encoding complete\n",
            "#> Indexing complete, Time elapsed 190.58 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KX_a_6M1U5pv"
      },
      "source": [
        "The indexing procedure generates the document embeddings index and a [FAISS](https:/facebookresearch/faiss) index, together with some additional files."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cz39leTbUzRO",
        "outputId": "78c1626b-5bd4-47ce-88e0-cb4fae47b157",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!ls -ltrh /content/colbertindex"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 163M\n",
            "-rw-r--r-- 1 root root 142M Mar 26 13:30 0.pt\n",
            "-rw-r--r-- 1 root root 7.1M Mar 26 13:30 0.sample\n",
            "-rw-r--r-- 1 root root  35K Mar 26 13:30 doclens.0.json\n",
            "-rw-r--r-- 1 root root  36K Mar 26 13:30 docnos.pkl.gz\n",
            "-rw-r--r-- 1 root root  14M Mar 26 13:31 ivfpq.100.faiss\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZW9HGAcV40i"
      },
      "source": [
        "# Retrieval\n",
        "\n",
        "Now that indexing has completed, we can load in the index and the checkpoint model (which we will need for encoding queries). Index loading can take some times, as the [FAISS](https:/facebookresearch/faiss) index needs to be loaded in main memory, as well as the document embeddings index.\n",
        "\n",
        "Since we indexed a collection from scratch and the data structures are already loaded in main memory, we reuse the data structures for retrieval.\n",
        "\n",
        "In the case the indexing was done offline, the following ColBERT factory can be used:\n",
        "\n",
        "```python\n",
        "pyterrier_colbert_factory = pyterrier_colbert.ranking.ColBERTFactory(checkpoint, \"/content\", \"colbertindex\")\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxxWlc6gVosw",
        "outputId": "2fbc101e-f329-4b1f-dee2-0586d9e43ce0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
            "012121b7cedd42fb80086b12edb30600",
            "00f8bf321ad241739cdbddf5e86491e4",
            "3d895f7334f344c7a2cd7e82925f522d",
            "5eb01dae22e646ae90546e08db21b4a8",
            "d05eb6d54c82400e8339c60368462b03",
            "97a509cf0bd343c3a187e7889b68c65a",
            "1c30719fa7244e5f899e442801f07469",
            "1c1ed6e0e3a44d2db8a69ba86a9febe1"
          ]
        }
      },
      "source": [
        "pyterrier_colbert_factory = indexer.ranking_factory()\n",
        "\n",
        "colbert_e2e = pyterrier_colbert_factory.end_to_end()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[Mar 26, 13:31:18] #> Loading the FAISS index from /content/colbertindex/ivfpq.100.faiss ..\n",
            "[Mar 26, 13:31:18] #> Building the emb2pid mapping..\n",
            "[Mar 26, 13:31:18] len(self.emb2pid) = 581496\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[INFO] loading file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Loading reranking index, memtype=mem\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Loading index shards to memory', max=1.0, style=ProgressS…"
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          "text": [
            "\n"
          ],
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_IJE0fqWSBd"
      },
      "source": [
        "Here we can ask [PyTerrier](https:/terrier-org/pyterrier) to search the [ColBERT](https:/stanford-futuredata/ColBERT/tree/v0.2) index for `'chemical reactions'`, returning the top 10 scored documents."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaamDhjsmHde",
        "outputId": "7ec4f282-8faf-4a33-dd48-458a019e98b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        }
      },
      "source": [
        "(colbert_e2e % 10).search(\"chemical reactions\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] NumExpr defaulting to 2 threads.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>qid</th>\n",
              "      <th>query</th>\n",
              "      <th>docid</th>\n",
              "      <th>query_toks</th>\n",
              "      <th>query_embs</th>\n",
              "      <th>docno</th>\n",
              "      <th>score</th>\n",
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              "      <td>1</td>\n",
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              "      <td>2421</td>\n",
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              "      <td>chemical reactions</td>\n",
              "      <td>4292</td>\n",
              "      <td>[tensor(101), tensor(1), tensor(5072), tensor(...</td>\n",
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              "     qid               query  docid  ...  docno      score rank\n",
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              "555    1  chemical reactions   9373  ...   9374  17.140938    3\n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sl5qfF77W1E5"
      },
      "source": [
        "# Run an experiment\n",
        "\n",
        "Lets prepare an experiment. Firstly, lets create in a BM25 baseline transformer."
      ]
    },
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      "source": [
        "dataset = pt.get_dataset(\"vaswani\")\n",
        "\n",
        "bm25 = pt.BatchRetrieve(dataset.get_index(), wmodel=\"BM25\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading vaswani index to /root/.pyterrier/corpora/vaswani/index\n"
          ],
          "name": "stdout"
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            "\n"
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      "source": [
        "Finally, lets evaluate our performance. We also load in an BM25 index for the same corpus for comparison reasons. We limit our experiments to just 50 queries."
      ]
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      },
      "source": [
        "pt.Experiment(\n",
        "    [bm25, colbert_e2e],\n",
        "    dataset.get_topics().head(50),\n",
        "    dataset.get_qrels(),\n",
        "    eval_metrics=[\"map\", \"recip_rank\", \"mrt\"],\n",
        "    names = [\"BM25\", \"ColBERT\"]\n",
        ")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading vaswani topics to /root/.pyterrier/corpora/vaswani/query-text.trec\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3c4a8e5e510b4779a2213777a8c26324",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='query-text.trec', max=3121.0, style=ProgressStyle(descrip…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Downloading vaswani qrels to /root/.pyterrier/corpora/vaswani/qrels\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90593fd759c44304bbaeb8d83b9be657",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='qrels', max=6793.0, style=ProgressStyle(description_width…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>map</th>\n",
              "      <th>recip_rank</th>\n",
              "      <th>mrt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BM25</td>\n",
              "      <td>0.338941</td>\n",
              "      <td>0.808238</td>\n",
              "      <td>44.980815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ColBERT</td>\n",
              "      <td>0.333021</td>\n",
              "      <td>0.762643</td>\n",
              "      <td>780.633069</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name       map  recip_rank         mrt\n",
              "0     BM25  0.338941    0.808238   44.980815\n",
              "1  ColBERT  0.333021    0.762643  780.633069"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gx37vThbkiGa"
      },
      "source": [
        ""
      ],
      "execution_count": 11,
      "outputs": []
    }
  ]
}