examples/nq/sparse_retrieval_FiD_FlanT5.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_o70WfKQFTd"
      },
      "source": [
        "# Sparse Retrieval for Natural Questions RAG\n",
        "\n",
        "This notebook demonstrates [PyTerrier](https://github.com/terrier-org/pyterrier) and [PyTerrier-RAG](https://github.com/terrierteam/pyterrier_rag). This notebooks runs on Google Colab if you select a Runtime with a T4 GPU (these are free to use), and will take approx. 12 minutes to execute fully."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6iZau7_QP9v"
      },
      "source": [
        "## Installation\n",
        "\n",
        "Lets install what we need:\n",
        " - PyTerrier - core platform\n",
        " - PyTerrier_t5 - MonoT5 reranker\n",
        " - pyterrier_rag - Support for RAG datasets and answer generators (aka readers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uatJzx1kQ7_J",
        "outputId": "347f52f9-6439-4151-e098-c85994e8d81e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.4/163.4 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m347.9/347.9 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m53.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m75.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m287.9/287.9 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.4/119.4 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.2/119.2 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.4/45.4 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for chest (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for warc3-wet-clueweb09 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for cbor (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "%pip install -q python-terrier pyterrier_t5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvFoJULYRAqq",
        "outputId": "a52b7574-5fa7-4d21-ebcb-f77afe4b06d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.5/9.5 MB\u001b[0m \u001b[31m71.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m72.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pyterrier-rag (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "%pip install -q git+https://github.com/terrierteam/pyterrier_rag.git@stable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "kHFMfW1uPZhS"
      },
      "outputs": [],
      "source": [
        "import pyterrier as pt\n",
        "import pyterrier_rag"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrI7rYO4Qeb2"
      },
      "source": [
        "## Retrievers\n",
        "\n",
        "Lets load a sparse index of Wikipedia. Conveniently, we've stored this as a [Huggingface dataset](https://huggingface.co/datasets/pyterrier/ragwiki-terrier). This is 12GB in size (it also contains the text of the documents) - downloading takes about 10 minutes on Google Colab.\n",
        "\n",
        "We'll use that index to get a BM25 retriever (you can see how this was created at https://huggingface.co/datasets/pyterrier/ragwiki-terrier#reproduction). \n",
        "\n",
        "Terrier doesnt like question marks in queries, so we'll strip these and restore them after.\n",
        "\n",
        "Finally, lets make a monoT5 reranker, we can use that to rerank BM25,\n"
      ]
    },
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        },
        "id": "BybUPDh6Qy33",
        "outputId": "9c859371-a566-496a-ebeb-19d4b84f848e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6390c0d2ef0e4ca5acf1530bc958ccae",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "https://huggingface.co/datasets/pyterrier/ragwiki-terrier/resolve/main/artifact.tar.lz4:   0%|          | 0.00…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "extracting data.direct.bf [1.9 GB]\n",
            "extracting data.document.fsarrayfile [340.7 MB]\n",
            "extracting data.inverted.bf [1.5 GB]\n",
            "extracting data.lexicon.fsomapfile [330.0 MB]\n",
            "extracting data.lexicon.fsomaphash [1017 B]\n",
            "extracting data.lexicon.fsomapid [15.3 MB]\n",
            "extracting data.meta-0.fsomapfile [1.3 GB]\n",
            "extracting data.meta.idx [160.3 MB]\n",
            "extracting data.meta.zdata [8.2 GB]\n",
            "extracting data.properties [4.1 KB]\n",
            "extracting pt_meta.json [79 B]\n",
            "terrier-assemblies 5.11 jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "terrier-python-helper 0.0.8 jar not found, downloading to /root/.pyterrier...\n",
            "Done\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Java started (triggered by tokenise) and loaded: pyterrier.java, pyterrier.terrier.java [version=5.11 (build: craig.macdonald 2025-01-13 21:29), helper_version=0.0.8]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "14:59:59.364 [main] WARN org.terrier.structures.BaseCompressingMetaIndex -- Structure meta reading lookup file directly from disk (SLOW) - try index.meta.index-source=fileinmem in the index properties file. 160.3 MiB of memory would be required.\n",
            "14:59:59.442 [main] WARN org.terrier.structures.BaseCompressingMetaIndex -- Structure meta reading data file directly from disk (SLOW) - try index.meta.data-source=fileinmem in the index properties file. 8.2 GiB of memory would be required.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/pyterrier/terrier/retriever.py:208: UserWarning: Multi-threaded retrieval is experimental, YMMV.\n",
            "  warn(\n",
            "/usr/local/lib/python3.11/dist-packages/pyterrier/terrier/retriever.py:215: UserWarning: Upgrading indexref /root/.pyterrier/artifacts/ebfd80cc597a31719f11ab5cd11ad8f441bc460f760c82ff66413ba9fb06943f/data.properties to be concurrent\n",
            "  warn(\n",
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "model_id": "d2aee0e3177d455483a2ac8382c3762b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
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        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d7687d62fba0436382a3a731e299df57",
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            "text/plain": [
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            ]
          },
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          "output_type": "display_data"
        },
        {
          "data": {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
            "/usr/local/lib/python3.11/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
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              "model_id": "4a1d22c856574b04a2f0045b26435edc",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from pyterrier_t5 import MonoT5ReRanker\n",
        "\n",
        "sparse_index = pt.Artifact.from_hf('pyterrier/ragwiki-terrier')\n",
        "\n",
        "bm25_ret = pt.rewrite.tokenise() >> sparse_index.bm25(include_fields=['docno', 'text', 'title'], threads=5) >> pt.rewrite.reset()\n",
        "monoT5 = MonoT5ReRanker(batch_size=64, verbose=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5j2yqwJ-RJXJ"
      },
      "source": [
        "Lets formulate our monoT5 reranking pipeline - we'll take the top 10 documents from BM25 and rerank those using BM25. Here we are using two PyTerrier operators to make a pipeline:\n",
        " - `%` - apply a rank cutoff to the left.\n",
        " - `>>` - compose (aka. then), which means apply the right handside on the output of the left hand side."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "5cMn5JvMRItr"
      },
      "outputs": [],
      "source": [
        "monoT5_ret = bm25_ret % 10 >> monoT5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ns_W7XucRmew"
      },
      "source": [
        "Lets compare the results..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "S8idFu1bRfh6",
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              "        document.querySelector('#df-8c30e6e1-aabc-4695-b260-ebf669286aa7 button.colab-df-convert');\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8c30e6e1-aabc-4695-b260-ebf669286aa7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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            ],
            "text/plain": [
              "  qid    docid    docno                                               text  \\\n",
              "1   1   860125   860125  Chemical reaction A chemical reaction is a pro...   \n",
              "0   1    53321    53321  are called reactants or reagents. Chemical rea...   \n",
              "2   1  3147077  3147077  the course of a reaction. Reaction mechanisms ...   \n",
              "\n",
              "                 title                         query     score  rank  \n",
              "1  \"Chemical reaction\"  What are chemical reactions? -0.029645     0  \n",
              "0  \"Chemical reaction\"  What are chemical reactions? -0.080680     1  \n",
              "2            Chemistry  What are chemical reactions? -0.658414     2  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(monoT5_ret%3).search(\"What are chemical reactions?\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnPLza6uRnzW"
      },
      "source": [
        "Interestingly, the re-ranking had some impact - 860125 was 3rd under BM25, but became first under monoT5 - while order many not matter so much for our readers, the inclusion of 3147077 and removal of 1027780 would likely change the reader's generated answer.\n",
        "\n",
        "\n",
        "You'll see that all of our retrievers give as output the same columns:\n",
        " - qid - unique identifier of the question\n",
        " - query - text of the question\n",
        " - docno - unique identifier of the passage\n",
        " - title and text (of the passage)\n",
        " - score and rank - to invoke an ordering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuYGD1c4RCdx"
      },
      "source": [
        "## Readers\n",
        "\n",
        "### Fusion in Decoder\n",
        "\n",
        "Lets now see the readers that will generate the answers. The first one we use is Fusion in Decoder - a T5-based model that encodes each document separately, but combines these representations in the decoder step.\n",
        "\n",
        "In PyTerrier terms, a reader takes as input the following columns:\n",
        " - qid\n",
        " - query\n",
        " - docno\n",
        " - title & text\n",
        "\n",
        "And returns:\n",
        " - qid\n",
        " - query\n",
        " - qanswer\n",
        "\n",
        "We provide a checkpoint trained for NQ on Huggingface at terrierteam/t5fid_base_nq.\n",
        "\n",
        "We further formulate two RAG pipelines - one using BM25 and one using monoT5 as input to FiD."
      ]
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      "outputs": [
        {
          "data": {
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      ],
      "source": [
        "import pyterrier_rag.readers\n",
        "fid = pyterrier_rag.readers.T5FiD(\"terrierteam/t5fid_base_nq\")\n",
        "\n",
        "bm25_fid = bm25_ret %3 >> fid\n",
        "monot5_fid = monoT5_ret %3 >> fid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rVm--ixWhyP"
      },
      "source": [
        "When we invoke search on this pipeline, we now have a qanswer column that contains the answer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
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        "id": "YXIGgxY0W6H9",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6abd209d-c434-4c94-8124-7af429f6e315 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6abd209d-c434-4c94-8124-7af429f6e315');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  qid                         query             qanswer\n",
              "0   1  What are chemical reactions?  chemical equations"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "monot5_fid.search(\"What are chemical reactions?\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6P8VZannWokl"
      },
      "source": [
        "### FlanT5\n",
        "\n",
        "Our second reader is FlanT5 - an instruction-tuned model - we'll use it zero-shot."
      ]
    },
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        "id": "lFnukKkP80_A",
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          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "  qid                         query  \\\n",
              "0   1  What are chemical reactions?   \n",
              "\n",
              "                                             qanswer  \n",
              "0  chemical transformation of one set of chemical...  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "flant5 = pyterrier_rag.readers.Seq2SeqLMReader(model_name_or_path='google/flan-t5-base', model=None)\n",
        "monoT5_flant5 =  bm25_ret % 10 >> monoT5 %3 >> flant5\n",
        "monoT5_flant5.search(\"What are chemical reactions?\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUIocpDYvLY6"
      },
      "source": [
        "Interesting to see that the answer by FlanT5 is a bit longer and detailed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "APGF5E99RrlR"
      },
      "source": [
        "# Datasets & Experiments\n",
        "\n",
        "Lets compare the effectiveness of these three approaches on the Natural Questions dataset. These topics are automatically downloaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "W--MmMYZSqSP",
        "outputId": "7a72e880-1eb8-4bc0-c60c-1a69595688d5"
      },
      "outputs": [
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9c756fda-e67e-4e60-8832-ff5152425843 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9c756fda-e67e-4e60-8832-ff5152425843');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-465b2043-115f-4886-8ef5-267eebd13ad7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-465b2043-115f-4886-8ef5-267eebd13ad7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-465b2043-115f-4886-8ef5-267eebd13ad7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     qid                                             query\n",
              "0  dev_0               who sings does he love me with reba\n",
              "1  dev_1  how many pages is invisible man by ralph ellison"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = pt.get_dataset('rag:nq')\n",
        "dataset.get_topics('dev').head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scU0jppYSz3C"
      },
      "source": [
        "And their corresponding gold truth answers:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "ESvmPzxYSwr4",
        "outputId": "6af066c0-0c0a-4968-81b9-e44ce5af7322"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"qid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"dev_1\",\n          \"dev_0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"query\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"how many pages is invisible man by ralph ellison\",\n          \"who sings does he love me with reba\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gold_answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"581 (second edition)\",\n          \"Linda Davis\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
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              "  <div id=\"df-1ecabbcb-7051-4cf9-9398-adee53a06e9c\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "      <th>gold_answer</th>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
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              "        document.querySelector('#df-1ecabbcb-7051-4cf9-9398-adee53a06e9c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1ecabbcb-7051-4cf9-9398-adee53a06e9c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-08cef480-b9c9-4498-b79a-c7456f87196f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "     qid                                             query  \\\n",
              "0  dev_0               who sings does he love me with reba   \n",
              "1  dev_1  how many pages is invisible man by ralph ellison   \n",
              "\n",
              "            gold_answer  \n",
              "0           Linda Davis  \n",
              "1  581 (second edition)  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.get_topics('dev').head(2).merge(dataset.get_answers('dev'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0I13hlRSubW"
      },
      "source": [
        "Now lets run an experiment using Natural Questions.\n",
        "\n",
        "They first four arguments correspond closely to main details our our experiment - specifically, we're going to compare: `bm25_fid`, `monot5_fid`, `monoT5_flant5`, on 100 dev topics (this take about 2 minutes). We'll evaluate our answers using Exact Match and F1.\n",
        "\n",
        "The additional arguments are:\n",
        " - `batch_size` - how many queries to run and evalate at once. Not always necessary, but makes the progress bars more granular\n",
        " - `verbose` - display progress bars for this experiment\n",
        " - `precompute_prefix` - optimise the experiment such that BM25 is only computed once.\n",
        " - `names` - for naming rows in the output dataframe\n",
        " - `baseline` - we'll compare to monoT5 with FiD, to see how much it helps compared to BM25, and how much FlanT5 does better than FiD."
      ]
    },
    {
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        "outputId": "c32984f4-2390-4819-d974-c6443f1a7bce"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Precomputing results of 100 topics on shared pipeline component (pt.apply.query() >> TerrierRetr(BM25) >> pt.apply.generic())\n",
            "/usr/local/lib/python3.11/dist-packages/pyterrier/pipelines.py:229: UserWarning: precompute_prefix with batch_size is very experimental. Please report any problems\n",
            "  warn(\"precompute_prefix with batch_size is very experimental. Please report any problems\")\n"
          ]
        },
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            "text/plain": [
              "pt.Experiment precomputation:   0%|          | 0/4 [00:00<?, ?batches/s]"
            ]
          },
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            "text/plain": [
              "pt.Experiment:   0%|          | 0/12 [00:00<?, ?batches/s]"
            ]
          },
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            "text/plain": [
              "               name        F1    EM  F1 +  F1 -  F1 p-value  EM +  EM -  \\\n",
              "0          bm25 fid  0.265524  0.17   4.0  18.0    0.020276   3.0  11.0   \n",
              "1        monoT5_fid  0.351333  0.25   NaN   NaN         NaN   NaN   NaN   \n",
              "2  monoT5 FlanT5 0z  0.298913  0.21   5.0  13.0    0.061812   3.0   7.0   \n",
              "\n",
              "   EM p-value  \n",
              "0    0.031801  \n",
              "1         NaN  \n",
              "2    0.207501  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pt.Experiment(\n",
        "    [bm25_fid, monot5_fid, monoT5_flant5],\n",
        "    dataset.get_topics('dev').head(100), # NB: remove .head(100) to run on all dev topics\n",
        "    dataset.get_answers('dev'),\n",
        "    [pyterrier_rag.measures.F1, pyterrier_rag.measures.EM],\n",
        "    batch_size=25,\n",
        "    verbose=True,\n",
        "    precompute_prefix=True,\n",
        "    names=['bm25 fid', 'monoT5_fid', 'monoT5 FlanT5 0z'],\n",
        "    baseline=1\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5BOsFFLQCbT"
      },
      "source": [
        "From the results, we can see that MonoT5 with FiD was the most effective answer generator - 0.35 F1, 0.2 EM. Applying monoT5 to rerank the top 10 passages of BM25 improved the answers to 18 questions compared to raw BM25 (see F1- column). The improvement brought by monoT5 is significant for both F1 and EM (see the calculated p-values).\n",
        "\n",
        "FlanT5 gave a better answer than FiD for 5 questions (F1+), but degraded for 13 (F1-). FiD is likely better as it has been fine-tuned on the NQ dataset, while FlanT5 is used zero-shot. However, the difference is not statistically significant for either F1 nor EM (according to a paired t-test).\n",
        "\n",
        "FiD can be take more passages than just 3, as its context length is not limited. Let's see how well it does with a context length of 100 passages selected by monoT5. This experiments takes about 10 minutes on a Colab T4 GPU. Again, the BM25 results are pre-computed and reused for both pipelines."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
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      },
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Precomputing results of 100 topics on shared pipeline component (pt.apply.query() >> TerrierRetr(BM25) >> pt.apply.generic())\n",
            "/usr/local/lib/python3.11/dist-packages/pyterrier/pipelines.py:229: UserWarning: precompute_prefix with batch_size is very experimental. Please report any problems\n",
            "  warn(\"precompute_prefix with batch_size is very experimental. Please report any problems\")\n"
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              "pt.Experiment precomputation:   0%|          | 0/4 [00:00<?, ?batches/s]"
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              "pt.Experiment:   0%|          | 0/8 [00:00<?, ?batches/s]"
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            "text/plain": [
              "              name        F1    EM  F1 +  F1 -  F1 p-value  EM +  EM -  \\\n",
              "0    monoT5 3p FiD  0.351333  0.25   NaN   NaN         NaN   NaN   NaN   \n",
              "1  monoT5 100p FiD  0.547905  0.40  29.0   8.0    0.000013  20.0   5.0   \n",
              "\n",
              "   EM p-value  \n",
              "0         NaN  \n",
              "1    0.002304  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pt.Experiment(\n",
        "    [monot5_fid, bm25_ret % 200 >> monoT5 % 100 >> fid],\n",
        "    dataset.get_topics('dev').head(100), # NB: remove .head(100) to run on all dev topics\n",
        "    dataset.get_answers('dev'),\n",
        "    [pyterrier_rag.measures.F1, pyterrier_rag.measures.EM],\n",
        "    batch_size=25,\n",
        "    verbose=True,\n",
        "    precompute_prefix=True,\n",
        "    names=['monoT5 3p FiD', 'monoT5 100p FiD'],\n",
        "    baseline=0\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BG5qdxExxPAN"
      },
      "source": [
        "Great! According to F1, giving FiD 100 passages gives a significant improvement $(p<0.05)$ compared to 3 passages, improving the answers of 29 queries.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLYuzyouyXGW"
      },
      "source": [
        "# That's all folks.\n",
        "\n",
        "There are lots of other retrievers possible in PyTerrier - for instance [query expansion](https://pyterrier.readthedocs.io/en/latest/rewrite.html), [doc2query](https://github.com/terrierteam/pyterrier_doc2query), [SPLADE learned sparse](https://github.com/cmacdonald/pyt_splade) or [dense retrieval](https://github.com/terrierteam/pyterrier_dr) (including the [ColBERT](https://github.com/terrierteam/pyterrier_colbert) multi-representation dense model).\n",
        "\n",
        "PyTerrier-RAG also provides easy access to lots of other datasets."
      ]
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      "source": []
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