experiments/reranking_others.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbTUYoH4FeAf"
      },
      "source": [
        "RE-RANKING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMS-ANL3D7vd",
        "outputId": "9c1dba6f-d52b-4bac-e787-935d130ba3f7"
      },
      "outputs": [],
      "source": [
        "!pip install -U beir\n",
        "!pip install 'elasticsearch<7.14.0'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPHS4YFuFoma",
        "outputId": "53da4f30-4df0-4d69-8aba-7d51520f4d49"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/beir/util.py:2: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm\n"
          ]
        }
      ],
      "source": [
        "import pathlib, os\n",
        "import time\n",
        "import pandas as pd\n",
        "from beir import util\n",
        "from beir.datasets.data_loader import GenericDataLoader\n",
        "from beir.retrieval.search.lexical import BM25Search as BM25\n",
        "from beir.retrieval.evaluation import EvaluateRetrieval\n",
        "from beir.reranking.models import CrossEncoder\n",
        "from beir.reranking import Rerank"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kh8g3a0PGSz-",
        "outputId": "a7433edd-0a13-4d60-cbfe-fb2f9bbe1166"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-12-07 05:43:31--  https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz\n",
            "Resolving artifacts.elastic.co (artifacts.elastic.co)... 34.120.127.130, 2600:1901:0:1d7::\n",
            "Connecting to artifacts.elastic.co (artifacts.elastic.co)|34.120.127.130|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 229941304 (219M) [application/x-gzip]\n",
            "Saving to: ‘elasticsearch-oss-7.9.2-linux-x86_64.tar.gz’\n",
            "\n",
            "elasticsearch-oss-7 100%[===================>] 219.29M  5.92MB/s    in 38s     \n",
            "\n",
            "2021-12-07 05:44:09 (5.80 MB/s) - ‘elasticsearch-oss-7.9.2-linux-x86_64.tar.gz’ saved [229941304/229941304]\n",
            "\n",
            "--2021-12-07 05:44:09--  https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512\n",
            "Resolving artifacts.elastic.co (artifacts.elastic.co)... 34.120.127.130, 2600:1901:0:1d7::\n",
            "Connecting to artifacts.elastic.co (artifacts.elastic.co)|34.120.127.130|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 173 [application/octet-stream]\n",
            "Saving to: ‘elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512’\n",
            "\n",
            "elasticsearch-oss-7 100%[===================>]     173  --.-KB/s    in 0s      \n",
            "\n",
            "2021-12-07 05:44:09 (53.0 MB/s) - ‘elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512’ saved [173/173]\n",
            "\n",
            "elasticsearch-oss-7.9.2-linux-x86_64.tar.gz: OK\n"
          ]
        }
      ],
      "source": [
        "if not os.path.exists('elasticsearch-oss-7.9.2-linux-x86_64.tar.gz'):\n",
        "  !wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz\n",
        "  !wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512\n",
        "  !tar -xzf elasticsearch-oss-7.9.2-linux-x86_64.tar.gz\n",
        "  !sudo chown -R daemon:daemon elasticsearch-7.9.2/\n",
        "  !shasum -a 512 -c elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8qPjo9Rbl0FU",
        "outputId": "10fa7c75-1721-4c0f-cbcc-f5b141d710d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting job # 0 in a separate thread.\n"
          ]
        }
      ],
      "source": [
        "%%bash --bg\n",
        "\n",
        "sudo -H -u daemon elasticsearch-7.9.2/bin/elasticsearch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "He91N2HJl77m"
      },
      "outputs": [],
      "source": [
        "time.sleep(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ys-n3vSmA06",
        "outputId": "524bc05f-42b2-4d76-a8b4-9d4cae628dff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "root         418     416  0 05:44 ?        00:00:00 sudo -H -u daemon elasticsearch-7.9.2/bin/elasticsearch\n",
            "daemon       419     418 99 05:44 ?        00:00:18 /content/elasticsearch-7.9.2/jdk/bin/java -Xshare:auto -Des.networkaddress.cache.ttl=60 -Des.networkaddress.cache.negative.ttl=10 -XX:+AlwaysPreTouch -Xss1m -Djava.awt.headless=true -Dfile.encoding=UTF-8 -Djna.nosys=true -XX:-OmitStackTraceInFastThrow -XX:+ShowCodeDetailsInExceptionMessages -Dio.netty.noUnsafe=true -Dio.netty.noKeySetOptimization=true -Dio.netty.recycler.maxCapacityPerThread=0 -Dio.netty.allocator.numDirectArenas=0 -Dlog4j.shutdownHookEnabled=false -Dlog4j2.disable.jmx=true -Djava.locale.providers=SPI,COMPAT -Xms1g -Xmx1g -XX:+UseG1GC -XX:G1ReservePercent=25 -XX:InitiatingHeapOccupancyPercent=30 -Djava.io.tmpdir=/tmp/elasticsearch-11391299929225202314 -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=data -XX:ErrorFile=logs/hs_err_pid%p.log -Xlog:gc*,gc+age=trace,safepoint:file=logs/gc.log:utctime,pid,tags:filecount=32,filesize=64m -XX:MaxDirectMemorySize=536870912 -Des.path.home=/content/elasticsearch-7.9.2 -Des.path.conf=/content/elasticsearch-7.9.2/config -Des.distribution.flavor=oss -Des.distribution.type=tar -Des.bundled_jdk=true -cp /content/elasticsearch-7.9.2/lib/* org.elasticsearch.bootstrap.Elasticsearch\n",
            "root         658      86  0 05:44 ?        00:00:00 /bin/bash -c ps -ef | grep elasticsearch\n",
            "root         660     658  0 05:44 ?        00:00:00 grep elasticsearch\n"
          ]
        }
      ],
      "source": [
        "!ps -ef | grep elasticsearch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Fgd-5d6SmFo_"
      },
      "outputs": [],
      "source": [
        "!curl -sX GET \"localhost:9200/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "nuO6Se4svb8d"
      },
      "outputs": [],
      "source": [
        "def retrieve_lexical(dataset):\n",
        "  hostname = 'localhost' \n",
        "  url = 'https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip'\n",
        "  out_dir = os.path.join(os.getcwd(), 'datasets')\n",
        "  dataset_r = dataset\n",
        "  index_r = dataset_r\n",
        "  data_path_r = util.download_and_unzip(url.format(dataset_r), out_dir)\n",
        "  corpus_r, queries_r, qrels_r = GenericDataLoader(data_path_r).load(split='test') # 'test', 'train', 'dev'\n",
        "  model_r = BM25(index_name=index_r, hostname=hostname, initialize=True) # initialize=True : reindex\n",
        "  retriever_r = EvaluateRetrieval(model_r)\n",
        "  results_r = retriever_r.retrieve(corpus_r, queries_r)\n",
        "  return corpus_r, queries_r, qrels_r, retriever_r, results_r"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "icq5FvhN5eXy"
      },
      "outputs": [],
      "source": [
        "def rerank_model(reranker, corpus, queries, qrels, retriever, results, top_k):\n",
        "  rerank_results = reranker.rerank(corpus, queries, results, top_k=top_k)\n",
        "  return EvaluateRetrieval.evaluate(qrels, rerank_results, retriever.k_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Uej16KOhmTCP"
      },
      "outputs": [],
      "source": [
        "def evaluate(name, ndcg, _map, recall, precision):\n",
        "  c_map = 'MAP@10'\n",
        "  c_map_h = 'MAP@100'\n",
        "  c_map_t = 'MAP@1000'\n",
        "  c_ndcg = 'NDCG@10'\n",
        "  c_ndcg_h = 'NDCG@100'\n",
        "  c_ndcg_t = 'NDCG@1000'\n",
        "  c_pre = 'P@10'\n",
        "  c_pre_h = 'P@100'\n",
        "  c_pre_t = 'P@1000'\n",
        "  c_rec = 'Recall@10'\n",
        "  c_rec_h = 'Recall@100'\n",
        "  c_rec_t = 'Recall@1000'\n",
        "  eval_dict = {\n",
        "      c_map: [_map[c_map]], \n",
        "      c_map_h: [_map[c_map_h]],\n",
        "      c_map_t: [_map[c_map_t]],\n",
        "      c_ndcg: [ndcg[c_ndcg]], \n",
        "      c_ndcg_h: [ndcg[c_ndcg_h]],\n",
        "      c_ndcg_t: [ndcg[c_ndcg_t]],\n",
        "      c_pre: [precision[c_pre]], \n",
        "      c_pre_h: [precision[c_pre_h]],\n",
        "      c_pre_t: [precision[c_pre_t]],\n",
        "      c_rec: [recall[c_rec]],\n",
        "      c_rec_h: [recall[c_rec_h]],\n",
        "      c_rec_t: [recall[c_rec_t]]\n",
        "      }\n",
        "  eval_df = pd.DataFrame(data=eval_dict)\n",
        "  eval_df.index = [name]\n",
        "  return eval_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "bDAOzlRR-15G"
      },
      "outputs": [],
      "source": [
        "def evaluate_rerank(rerank, corpus, queries, qrels, retriever, results, top_k):\n",
        "  r_ndcg, r_map, r_recall, r_precision = rerank_model(rerank['rr'], corpus, queries, qrels, retriever, results, top_k)\n",
        "  return evaluate(rerank['name'], r_ndcg, r_map, r_recall, r_precision)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "yntClnX2COPz"
      },
      "outputs": [],
      "source": [
        "def run_all_models(models, corpus, queries, qrels, retriever, results, top_k):\n",
        "  rr_evals = []\n",
        "  for model in models:\n",
        "      rr_evals.append(evaluate_rerank(model, corpus, queries, qrels, retriever, results, top_k))\n",
        "  return rr_evals"
      ]
    },
    {
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              "/content/datasets/quora.zip:   0%|          | 0.00/15.1M [00:00<?, ?iB/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cf23361c03f743889138581cf00d04bd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/522931 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 0/522931 [00:00<?, ?docs/s]\n",
            "que: 100%|██████████| 79/79 [03:13<00:00,  2.45s/it]\n"
          ]
        }
      ],
      "source": [
        "fq_d = 'fiqa'\n",
        "ag_d = 'arguana'\n",
        "tu_d = 'webis-touche2020'\n",
        "qa_d = 'quora'\n",
        "fq_corpus, fq_queries, fq_qrels, fq_retriever, fq_results = retrieve_lexical(fq_d)\n",
        "fq_ndcg, fq_map, fq_recall, fq_precision = fq_retriever.evaluate(fq_qrels, fq_results, fq_retriever.k_values)\n",
        "ag_corpus, ag_queries, ag_qrels, ag_retriever, ag_results = retrieve_lexical(ag_d)\n",
        "ag_ndcg, ag_map, ag_recall, ag_precision = ag_retriever.evaluate(ag_qrels, ag_results, ag_retriever.k_values)\n",
        "tu_corpus, tu_queries, tu_qrels, tu_retriever, tu_results = retrieve_lexical(tu_d)\n",
        "tu_ndcg, tu_map, tu_recall, tu_precision = tu_retriever.evaluate(tu_qrels, tu_results, tu_retriever.k_values)\n",
        "qa_corpus, qa_queries, qa_qrels, qa_retriever, qa_results = retrieve_lexical(qa_d)\n",
        "qa_ndcg, qa_map, qa_recall, qa_precision = qa_retriever.evaluate(qa_qrels, qa_results, qa_retriever.k_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "sql5LYi78mhF"
      },
      "outputs": [],
      "source": [
        "lexical_eval = []\n",
        "lexical_eval.append(evaluate(fq_d, fq_ndcg, fq_map, fq_recall, fq_precision))\n",
        "lexical_eval.append(evaluate(ag_d, ag_ndcg, ag_map, ag_recall, ag_precision))\n",
        "lexical_eval.append(evaluate(tu_d, tu_ndcg, tu_map, tu_recall, tu_precision))\n",
        "lexical_eval.append(evaluate(qa_d, qa_ndcg, qa_map, qa_recall, qa_precision))"
      ]
    },
    {
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            "edf01b51cc86405aa871458c6c2e640d",
            "f408bc2585e044b3b9547d25769da2d1",
            "8627215528ee41978e0b7a354492018d",
            "6d41c66b836f4ab3b0a87c15fcb88dfe"
          ]
        },
        "id": "QEL-wEbQerxj",
        "outputId": "d0857dc6-b9bb-4c2b-8775-074ee567e17d"
      },
      "outputs": [],
      "source": [
        "batch_size = 128\n",
        "top_k = 100\n",
        "cmtb = 'cross-encoder/ms-marco-TinyBERT-L-2-v2'\n",
        "cmlm = 'cross-encoder/ms-marco-MiniLM-L-6-v2'\n",
        "cmeb = 'cross-encoder/ms-marco-electra-base'\n",
        "rr_cmtb = {'name': cmtb, 'rr': Rerank(CrossEncoder(cmtb), batch_size=batch_size)}\n",
        "rr_cmlm = {'name': cmlm, 'rr': Rerank(CrossEncoder(cmlm), batch_size=batch_size)}\n",
        "rr_cmeb = {'name': cmeb, 'rr': Rerank(CrossEncoder(cmeb), batch_size=batch_size)}\n",
        "models = [rr_cmtb, rr_cmlm, rr_cmeb]\n",
        "fq_res = run_all_models(models, fq_corpus, fq_queries, fq_qrels, fq_retriever, fq_results, top_k)\n",
        "ag_res = run_all_models(models, ag_corpus, ag_queries, ag_qrels, ag_retriever, ag_results, top_k)\n",
        "tu_res = run_all_models(models, tu_corpus, tu_queries, tu_qrels, tu_retriever, tu_results, top_k)\n",
        "qa_res = run_all_models(models, qa_corpus, qa_queries, qa_qrels, qa_retriever, qa_results, top_k)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttWN3PaM_nNE"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0k8q-Ld_pmO"
      },
      "source": [
        "LEXICAL SEARCH (BM25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "id": "jiibaVjDg9Ba",
        "outputId": "1009a242-3b2b-4031-aee5-a8d27af35469"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAP@10</th>\n",
              "      <th>MAP@100</th>\n",
              "      <th>MAP@1000</th>\n",
              "      <th>NDCG@10</th>\n",
              "      <th>NDCG@100</th>\n",
              "      <th>NDCG@1000</th>\n",
              "      <th>P@10</th>\n",
              "      <th>P@100</th>\n",
              "      <th>P@1000</th>\n",
              "      <th>Recall@10</th>\n",
              "      <th>Recall@100</th>\n",
              "      <th>Recall@1000</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fiqa</th>\n",
              "      <td>0.19108</td>\n",
              "      <td>0.20437</td>\n",
              "      <td>0.20607</td>\n",
              "      <td>0.25370</td>\n",
              "      <td>0.31299</td>\n",
              "      <td>0.34747</td>\n",
              "      <td>0.07022</td>\n",
              "      <td>0.01306</td>\n",
              "      <td>0.00192</td>\n",
              "      <td>0.32440</td>\n",
              "      <td>0.54886</td>\n",
              "      <td>0.75883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arguana</th>\n",
              "      <td>0.38402</td>\n",
              "      <td>0.39444</td>\n",
              "      <td>0.39465</td>\n",
              "      <td>0.47184</td>\n",
              "      <td>0.51672</td>\n",
              "      <td>0.52189</td>\n",
              "      <td>0.07539</td>\n",
              "      <td>0.00952</td>\n",
              "      <td>0.00099</td>\n",
              "      <td>0.75391</td>\n",
              "      <td>0.95164</td>\n",
              "      <td>0.99147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>webis-touche2020</th>\n",
              "      <td>0.13872</td>\n",
              "      <td>0.22110</td>\n",
              "      <td>0.23848</td>\n",
              "      <td>0.34707</td>\n",
              "      <td>0.45483</td>\n",
              "      <td>0.55796</td>\n",
              "      <td>0.33061</td>\n",
              "      <td>0.09531</td>\n",
              "      <td>0.01635</td>\n",
              "      <td>0.21220</td>\n",
              "      <td>0.56093</td>\n",
              "      <td>0.88200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quora</th>\n",
              "      <td>0.75962</td>\n",
              "      <td>0.76689</td>\n",
              "      <td>0.76721</td>\n",
              "      <td>0.80771</td>\n",
              "      <td>0.82773</td>\n",
              "      <td>0.83123</td>\n",
              "      <td>0.12175</td>\n",
              "      <td>0.01447</td>\n",
              "      <td>0.00154</td>\n",
              "      <td>0.90219</td>\n",
              "      <td>0.97698</td>\n",
              "      <td>0.99573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   MAP@10  MAP@100  ...  Recall@100  Recall@1000\n",
              "fiqa              0.19108  0.20437  ...     0.54886      0.75883\n",
              "arguana           0.38402  0.39444  ...     0.95164      0.99147\n",
              "webis-touche2020  0.13872  0.22110  ...     0.56093      0.88200\n",
              "quora             0.75962  0.76689  ...     0.97698      0.99573\n",
              "\n",
              "[4 rows x 12 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat(lexical_eval)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AflT9sHxDJEh"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ccIWU-yW7TB"
      },
      "source": [
        "**FIQA** - [Homepage](https://sites.google.com/view/fiqa/home) - [Paper](https://www.researchgate.net/publication/324629350_WWW'18_Open_Challenge_Financial_Opinion_Mining_and_Question_Answering)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "xzY5UqcuDBc0",
        "outputId": "9070b249-ab01-4598-c491-626546b7f2d8"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAP@10</th>\n",
              "      <th>MAP@100</th>\n",
              "      <th>MAP@1000</th>\n",
              "      <th>NDCG@10</th>\n",
              "      <th>NDCG@100</th>\n",
              "      <th>NDCG@1000</th>\n",
              "      <th>P@10</th>\n",
              "      <th>P@100</th>\n",
              "      <th>P@1000</th>\n",
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              "      <th>Recall@100</th>\n",
              "      <th>Recall@1000</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-TinyBERT-L-2-v2</th>\n",
              "      <td>0.21365</td>\n",
              "      <td>0.22741</td>\n",
              "      <td>0.22741</td>\n",
              "      <td>0.28076</td>\n",
              "      <td>0.33540</td>\n",
              "      <td>0.33540</td>\n",
              "      <td>0.07639</td>\n",
              "      <td>0.01306</td>\n",
              "      <td>0.00131</td>\n",
              "      <td>0.35040</td>\n",
              "      <td>0.54886</td>\n",
              "      <td>0.54886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-MiniLM-L-6-v2</th>\n",
              "      <td>0.27601</td>\n",
              "      <td>0.28581</td>\n",
              "      <td>0.28581</td>\n",
              "      <td>0.34879</td>\n",
              "      <td>0.38578</td>\n",
              "      <td>0.38578</td>\n",
              "      <td>0.09444</td>\n",
              "      <td>0.01306</td>\n",
              "      <td>0.00131</td>\n",
              "      <td>0.41484</td>\n",
              "      <td>0.54886</td>\n",
              "      <td>0.54886</td>\n",
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              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-electra-base</th>\n",
              "      <td>0.27795</td>\n",
              "      <td>0.28748</td>\n",
              "      <td>0.28748</td>\n",
              "      <td>0.34969</td>\n",
              "      <td>0.38706</td>\n",
              "      <td>0.38706</td>\n",
              "      <td>0.09506</td>\n",
              "      <td>0.01306</td>\n",
              "      <td>0.00131</td>\n",
              "      <td>0.41044</td>\n",
              "      <td>0.54886</td>\n",
              "      <td>0.54886</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         MAP@10  ...  Recall@1000\n",
              "cross-encoder/ms-marco-TinyBERT-L-2-v2  0.21365  ...      0.54886\n",
              "cross-encoder/ms-marco-MiniLM-L-6-v2    0.27601  ...      0.54886\n",
              "cross-encoder/ms-marco-electra-base     0.27795  ...      0.54886\n",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat(fq_res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSgelj92pqr0"
      },
      "source": [
        "**ARGUANA** - [Homepage](http://argumentation.bplaced.net/arguana/data) - [Paper](https://aclanthology.org/P18-1023.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "l9Y_h6Lh_8mN",
        "outputId": "fd5334cb-b69a-4116-a16c-110eac3107cf"
      },
      "outputs": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAP@10</th>\n",
              "      <th>MAP@100</th>\n",
              "      <th>MAP@1000</th>\n",
              "      <th>NDCG@10</th>\n",
              "      <th>NDCG@100</th>\n",
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              "      <th>Recall@1000</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-TinyBERT-L-2-v2</th>\n",
              "      <td>0.26694</td>\n",
              "      <td>0.28279</td>\n",
              "      <td>0.28279</td>\n",
              "      <td>0.34016</td>\n",
              "      <td>0.41913</td>\n",
              "      <td>0.41913</td>\n",
              "      <td>0.05782</td>\n",
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              "      <td>0.57824</td>\n",
              "      <td>0.95164</td>\n",
              "      <td>0.95164</td>\n",
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              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-MiniLM-L-6-v2</th>\n",
              "      <td>0.33404</td>\n",
              "      <td>0.34773</td>\n",
              "      <td>0.34773</td>\n",
              "      <td>0.41645</td>\n",
              "      <td>0.47680</td>\n",
              "      <td>0.47680</td>\n",
              "      <td>0.06842</td>\n",
              "      <td>0.00952</td>\n",
              "      <td>0.00095</td>\n",
              "      <td>0.68421</td>\n",
              "      <td>0.95164</td>\n",
              "      <td>0.95164</td>\n",
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              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-electra-base</th>\n",
              "      <td>0.24336</td>\n",
              "      <td>0.26152</td>\n",
              "      <td>0.26152</td>\n",
              "      <td>0.31069</td>\n",
              "      <td>0.40055</td>\n",
              "      <td>0.40055</td>\n",
              "      <td>0.05306</td>\n",
              "      <td>0.00952</td>\n",
              "      <td>0.00095</td>\n",
              "      <td>0.53058</td>\n",
              "      <td>0.95164</td>\n",
              "      <td>0.95164</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         MAP@10  ...  Recall@1000\n",
              "cross-encoder/ms-marco-TinyBERT-L-2-v2  0.26694  ...      0.95164\n",
              "cross-encoder/ms-marco-MiniLM-L-6-v2    0.33404  ...      0.95164\n",
              "cross-encoder/ms-marco-electra-base     0.24336  ...      0.95164\n",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat(ag_res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDMITwtJVgrM"
      },
      "source": [
        "**TOUCHÉ** - [Homepage](https://webis.de/events/touche-20/) - [Paper](https://www.researchgate.net/publication/344371868_Overview_of_Touche_2020_Argument_Retrieval)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "-1v55l6lVhKi",
        "outputId": "a26c34b0-8c75-46b9-b752-770a6418b679"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAP@10</th>\n",
              "      <th>MAP@100</th>\n",
              "      <th>MAP@1000</th>\n",
              "      <th>NDCG@10</th>\n",
              "      <th>NDCG@100</th>\n",
              "      <th>NDCG@1000</th>\n",
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              "      <th>P@100</th>\n",
              "      <th>P@1000</th>\n",
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              "      <th>Recall@1000</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-TinyBERT-L-2-v2</th>\n",
              "      <td>0.09835</td>\n",
              "      <td>0.17556</td>\n",
              "      <td>0.17556</td>\n",
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              "      <td>0.40985</td>\n",
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              "      <td>0.24286</td>\n",
              "      <td>0.09531</td>\n",
              "      <td>0.00953</td>\n",
              "      <td>0.17087</td>\n",
              "      <td>0.56093</td>\n",
              "      <td>0.56093</td>\n",
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              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-MiniLM-L-6-v2</th>\n",
              "      <td>0.11157</td>\n",
              "      <td>0.19242</td>\n",
              "      <td>0.19242</td>\n",
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              "      <td>0.42348</td>\n",
              "      <td>0.42348</td>\n",
              "      <td>0.24898</td>\n",
              "      <td>0.09531</td>\n",
              "      <td>0.00953</td>\n",
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              "      <td>0.56093</td>\n",
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              "    <tr>\n",
              "      <th>cross-encoder/ms-marco-electra-base</th>\n",
              "      <td>0.10467</td>\n",
              "      <td>0.18296</td>\n",
              "      <td>0.18296</td>\n",
              "      <td>0.27678</td>\n",
              "      <td>0.41752</td>\n",
              "      <td>0.41752</td>\n",
              "      <td>0.26939</td>\n",
              "      <td>0.09531</td>\n",
              "      <td>0.00953</td>\n",
              "      <td>0.18802</td>\n",
              "      <td>0.56093</td>\n",
              "      <td>0.56093</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         MAP@10  ...  Recall@1000\n",
              "cross-encoder/ms-marco-TinyBERT-L-2-v2  0.09835  ...      0.56093\n",
              "cross-encoder/ms-marco-MiniLM-L-6-v2    0.11157  ...      0.56093\n",
              "cross-encoder/ms-marco-electra-base     0.10467  ...      0.56093\n",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat(tu_res)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyMlCEVqVhlJ"
      },
      "source": [
        "**QUORA** - [Homepage](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) - [Paper](https://arxiv.org/abs/1907.01041)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "80NZtDujVh82",
        "outputId": "1d8a6cfc-84b1-4119-c6ca-f42920915b99"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>MAP@10</th>\n",
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              "                                         MAP@10  ...  Recall@1000\n",
              "cross-encoder/ms-marco-TinyBERT-L-2-v2  0.75451  ...      0.97739\n",
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              "\n",
              "[3 rows x 12 columns]"
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        "end of fun."
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