comparison of embedding models' performance.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hq39x_Jfveo-"
      },
      "source": [
        "# Comparison of multilingual embedding models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LbjyHLiFh6tr"
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      "outputs": [],
      "source": [
        "from IPython.display import HTML, display\n",
        "\n",
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        "get_ipython().events.register('pre_run_cell', set_css)"
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          "name": "stdout",
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          "text": [
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed dataclasses-json-0.6.3 deprecated-1.2.14 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 llama-index-0.9.38 marshmallow-3.20.2 mypy-extensions-1.0.0 openai-1.10.0 tiktoken-0.5.2 typing-extensions-4.9.0 typing-inspect-0.9.0\n"
          ]
        }
      ],
      "source": [
        "pip install llama-index"
      ]
    },
    {
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      "metadata": {
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          "metadata": {},
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      ],
      "source": [
        "from llama_index.finetuning import (\n",
        "    generate_qa_embedding_pairs,\n",
        "    EmbeddingQAFinetuneDataset,\n",
        ")\n",
        "from llama_index.llms import OpenAI\n",
        "fr_dataset = EmbeddingQAFinetuneDataset.from_json(\"/content/fr_dataset.json\")\n",
        "it_dataset = EmbeddingQAFinetuneDataset.from_json(\"/content/it_dataset.json\")\n",
        "keys_to_delete = [key for key in it_dataset.corpus.keys() if it_dataset.corpus[key] == '']\n",
        "for key in keys_to_delete:\n",
        "    del it_dataset.corpus[key]\n",
        "keys_to_delete = [key for key in fr_dataset.corpus.keys() if fr_dataset.corpus[key] == '']\n",
        "for key in keys_to_delete:\n",
        "    del fr_dataset.corpus[key]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyVqU_pXg0kl"
      },
      "source": [
        "# Italian dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "dmh5jqx7g2Y2",
        "outputId": "ac2eaf70-fb6b-416b-dc14-ea2c7f5560f7"
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['queries', 'corpus', 'relevant_docs', 'mode'])\n",
            "Qual è il titolo del documento di lavoro dei servizi della Commissione che accompagna la Raccomandazione del Consiglio sul programma nazionale di riforma 2023 dell'Italia?\n",
            "['1dc6e9c6-90f3-419c-9d03-01ea745f0f18']\n",
            "IT   IT \n",
            " \n",
            " \n",
            " COMMISSIONE  \n",
            "EUROPEA   \n",
            "Bruxelles, 24.5.2023  \n",
            "SWD(2023) 612 final  \n",
            " \n",
            "DOCUMENTO DI LAVORO DEI SERVIZI DELLA COMMISSIONE  \n",
            "Relazione per paese 2023 - Italia  \n",
            "che accompagna il documento  \n",
            "Raccomandazione di RACCOMANDAZIONE DEL CONSIGLIO  \n",
            "sul programma nazionale di riforma 2023 dell'Italia e che formula un parere del \n",
            "Consiglio sul programma di stabilità 2023 dell'Italia  \n",
            "{COM(2023)  612 final}\n"
          ]
        }
      ],
      "source": [
        "print(it_dataset.__dict__.keys())\n",
        "print(it_dataset.queries[\"26c7b899-bb79-4dfe-bcf6-0a69649932aa\"])\n",
        "print(it_dataset.relevant_docs[\"26c7b899-bb79-4dfe-bcf6-0a69649932aa\"])\n",
        "print(it_dataset.corpus[\"1dc6e9c6-90f3-419c-9d03-01ea745f0f18\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hSRf-slRoylp"
      },
      "source": [
        "# French dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "GHHkL8LikGid",
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          "output_type": "stream",
          "text": [
            "dict_keys(['queries', 'corpus', 'relevant_docs', 'mode'])\n",
            "Quelle est la date du document de travail des services de la Commission pour la France ?\n",
            "['eacf897e-c2b7-4666-b905-d7bb2a05a2e4']\n",
            "FR   FR \n",
            " \n",
            " \n",
            " COMMISSION  \n",
            "EUROPÉENNE   \n",
            "Bruxelles, le 24.5.2023  \n",
            "SWD(2023) 610 final  \n",
            " \n",
            "DOCUMENT DE TRAVAIL DES SERVICES DE LA COMMISSION  \n",
            "Rapport 2023 pour la France  \n",
            "accompagnant le document  \n",
            "Recommandation de recommandation du Conseil  \n",
            " \n",
            "concernant le programme national de réforme de la France pour 2023 et portant avis du \n",
            "Conseil sur le programme de stabilité de la France pour 2023  \n",
            "{COM(2023)  610 final}\n"
          ]
        }
      ],
      "source": [
        "print(fr_dataset.__dict__.keys())\n",
        "print(fr_dataset.queries[\"132b18e9-cb9c-4c24-9a81-2b5b1dec3478\"])\n",
        "print(fr_dataset.relevant_docs[\"132b18e9-cb9c-4c24-9a81-2b5b1dec3478\"])\n",
        "print(fr_dataset.corpus[\"eacf897e-c2b7-4666-b905-d7bb2a05a2e4\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KaI8GawNo2mi"
      },
      "source": [
        "## Evaluation functions:\n",
        "- Hit Rate\n",
        "- MRR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 17
        },
        "id": "ingENMTEey7q",
        "outputId": "2706af55-8c8c-4d38-fe55-9fd7999c9f10"
      },
      "outputs": [
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      ],
      "source": [
        "from tqdm.notebook import tqdm\n",
        "import pandas as pd\n",
        "from llama_index.evaluation import RetrieverEvaluator\n",
        "import numpy as np\n",
        "from llama_index.schema import TextNode\n",
        "from llama_index import ServiceContext, VectorStoreIndex\n",
        "from llama_index.embeddings import OpenAIEmbedding\n",
        "import os\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"sk-\"\n",
        "def evaluate(\n",
        "    dataset,\n",
        "    embed_model,\n",
        "    top_k=5,\n",
        "    verbose=False,\n",
        "):\n",
        "    corpus = dataset.corpus\n",
        "    queries = dataset.queries\n",
        "    relevant_docs = dataset.relevant_docs\n",
        "\n",
        "    service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
        "    nodes = [TextNode(id_=id_, text=text) for id_, text in corpus.items()]\n",
        "    index = VectorStoreIndex(\n",
        "        nodes, service_context=service_context, show_progress=True\n",
        "    )\n",
        "    retriever = index.as_retriever(similarity_top_k=top_k)\n",
        "\n",
        "    eval_results = []\n",
        "    for query_id, query in tqdm(queries.items()):\n",
        "        retrieved_nodes = retriever.retrieve(query)\n",
        "        retrieved_ids = [node.node.node_id for node in retrieved_nodes]\n",
        "        expected_id = relevant_docs[query_id][0]\n",
        "        is_hit = expected_id in retrieved_ids  # assume 1 relevant doc\n",
        "        if is_hit:\n",
        "            rank = retrieved_ids.index(expected_id) + 1\n",
        "            mrr = 1 / rank\n",
        "        else:\n",
        "            mrr = 0\n",
        "\n",
        "        eval_result = {\n",
        "            \"is_hit\": is_hit,\n",
        "            \"mrr\": mrr,\n",
        "            \"retrieved\": retrieved_ids,\n",
        "            \"expected\": expected_id,\n",
        "            \"query\": query_id,\n",
        "        }\n",
        "        eval_results.append(eval_result)\n",
        "    return eval_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MPwETUrNcvT6"
      },
      "source": [
        "# text-embedding-3-large"
      ]
    },
    {
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              "<div id=\"df-411b69b1-d115-45a4-894b-6e81ae9480d4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-411b69b1-d115-45a4-894b-6e81ae9480d4')\"\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-411b69b1-d115-45a4-894b-6e81ae9480d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         Hit Rate       MRR\n",
              "Italian  0.844007  0.646962\n",
              "French   0.808495  0.608371"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from llama_index.embeddings import OpenAIEmbedding\n",
        "\n",
        "text_embedding_3_large = OpenAIEmbedding(model=\"text-embedding-3-large\")\n",
        "\n",
        "# results on the Italian dataset\n",
        "text_embedding_3_large_results_it = evaluate(it_dataset,text_embedding_3_large)\n",
        "df_text_embedding_3_large_results_it = pd.DataFrame(text_embedding_3_large_results_it)\n",
        "\n",
        "hit_rate_text_embedding_3_large_it = df_text_embedding_3_large_results_it[\"is_hit\"].mean()\n",
        "mrr_text_embedding_3_large_it = df_text_embedding_3_large_results_it[\"mrr\"].mean()\n",
        "\n",
        "# results on the French dataset\n",
        "text_embedding_3_large_results_fr = evaluate(fr_dataset,text_embedding_3_large)\n",
        "df_text_embedding_3_large_results_fr = pd.DataFrame(text_embedding_3_large_results_fr)\n",
        "\n",
        "hit_rate_text_embedding_3_large_fr = df_text_embedding_3_large_results_fr[\"is_hit\"].mean()\n",
        "mrr_text_embedding_3_large_fr = df_text_embedding_3_large_results_fr[\"mrr\"].mean()\n",
        "\n",
        "# Now, create a DataFrame with both Italian and French results\n",
        "results = {\n",
        "    \"Hit Rate\": [hit_rate_text_embedding_3_large_it, hit_rate_text_embedding_3_large_fr],\n",
        "    \"MRR\": [mrr_text_embedding_3_large_it, mrr_text_embedding_3_large_fr]\n",
        "}\n",
        "df_results_text_embedding_3_large = pd.DataFrame(results, index=[\"Italian\", \"French\"])\n",
        "df_results_text_embedding_3_large"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfE50fXxQ6EF"
      },
      "source": [
        "# text-embedding-3-large reduced to 256 dimensions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
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        },
        "id": "r-bCrGw9q2Sc",
        "outputId": "ae645cc5-9c7c-4dcb-849b-3dd9ba7cd126"
      },
      "outputs": [
        {
          "data": {
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              "    pre {\n",
              "        white-space: pre-wrap;\n",
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              "Generating embeddings:   0%|          | 0/179 [00:00<?, ?it/s]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hit Rate</th>\n",
              "      <th>MRR</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Italian</th>\n",
              "      <td>0.789819</td>\n",
              "      <td>0.582649</td>\n",
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              "                                                    [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",
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              "\n",
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              "    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",
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              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
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              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         Hit Rate       MRR\n",
              "Italian  0.789819  0.582649\n",
              "French   0.749627  0.554347"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from llama_index.embeddings import OpenAIEmbedding\n",
        "\n",
        "text_embedding_3_large_256 = OpenAIEmbedding(model=\"text-embedding-3-large\", dimensions = 256)\n",
        "\n",
        "# results on the Italian dataset\n",
        "text_embedding_3_large_256_results_it = evaluate(it_dataset,text_embedding_3_large_256)\n",
        "df_text_embedding_3_large_256_results_it = pd.DataFrame(text_embedding_3_large_256_results_it)\n",
        "\n",
        "hit_rate_text_embedding_3_large_256_it = df_text_embedding_3_large_256_results_it[\"is_hit\"].mean()\n",
        "mrr_text_embedding_3_large_256_it = df_text_embedding_3_large_256_results_it[\"mrr\"].mean()\n",
        "\n",
        "# results on the French dataset\n",
        "text_embedding_3_large_256_results_fr = evaluate(fr_dataset,text_embedding_3_large_256)\n",
        "df_text_embedding_3_large_256_results_fr = pd.DataFrame(text_embedding_3_large_256_results_fr)\n",
        "\n",
        "hit_rate_text_embedding_3_large_256_fr = df_text_embedding_3_large_256_results_fr[\"is_hit\"].mean()\n",
        "mrr_text_embedding_3_large_256_fr = df_text_embedding_3_large_256_results_fr[\"mrr\"].mean()\n",
        "\n",
        "# Now, create a DataFrame with both Italian and French results\n",
        "results = {\n",
        "    \"Hit Rate\": [hit_rate_text_embedding_3_large_256_it, hit_rate_text_embedding_3_large_256_fr],\n",
        "    \"MRR\": [mrr_text_embedding_3_large_256_it, mrr_text_embedding_3_large_256_fr]\n",
        "}\n",
        "df_results_text_embedding_3_large_256 = pd.DataFrame(results, index=[\"Italian\", \"French\"])\n",
        "df_results_text_embedding_3_large_256"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLU-g0tpfQ8y"
      },
      "source": [
        "## Ada-002 embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
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            "c3bd8e49c69d47ceb46fd8aeb13b20e2",
            "e258fb63fb5f4173ac96efc3ed957859"
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        },
        "id": "cC2cDHmnfT_Q",
        "outputId": "323c481f-1d6d-4ef0-eb99-f1b2186071ff"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Hit Rate</th>\n",
              "      <th>MRR</th>\n",
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              "      <th>Italian</th>\n",
              "      <td>0.758621</td>\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-aea0a18d-8f85-44c6-96e2-4d3b830a6044');\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",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "         Hit Rate       MRR\n",
              "Italian  0.758621  0.609524\n",
              "French   0.772727  0.600025"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ada = OpenAIEmbedding()\n",
        "\n",
        "# results on the Italian dataset\n",
        "ada_results_it = evaluate(it_dataset, ada)\n",
        "df_ada_results_it = pd.DataFrame(ada_results_it)\n",
        "\n",
        "hit_rate_ada_it = df_ada_results_it[\"is_hit\"].mean()\n",
        "mrr_ada_it = df_ada_results_it[\"mrr\"].mean()\n",
        "\n",
        "# results on the French dataset\n",
        "ada_results_fr = evaluate(fr_dataset, ada)\n",
        "df_ada_results_fr = pd.DataFrame(ada_results_fr)\n",
        "\n",
        "hit_rate_ada_fr = df_ada_results_fr[\"is_hit\"].mean()\n",
        "mrr_ada_fr = df_ada_results_fr[\"mrr\"].mean()\n",
        "\n",
        "# Now, create a DataFrame with both Italian and French results\n",
        "results = {\n",
        "    \"Hit Rate\": [hit_rate_ada_it, hit_rate_ada_fr],\n",
        "    \"MRR\": [mrr_ada_it, mrr_ada_fr]\n",
        "}\n",
        "\n",
        "df_results_ada = pd.DataFrame(results, index=[\"Italian\", \"French\"])\n",
        "df_results_ada"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TvD2qRhefZL0"
      },
      "source": [
        "## Cohere/Cohere-embed-multilingual-v3.0\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YVaugUJLuaUD"
      },
      "outputs": [],
      "source": [
        "pip install cohere"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
            "e8a0d3531c7b417c98ce32586c1229d9",
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            "text/plain": [
              "         Hit Rate       MRR\n",
              "Italian  0.830870  0.647345\n",
              "French   0.782414  0.583358"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "from llama_index.embeddings.cohereai import CohereEmbedding\n",
        "\n",
        "cohere_api_key = \"\"\n",
        "\n",
        "embed_model_co = CohereEmbedding(\n",
        "    cohere_api_key=cohere_api_key,\n",
        "    model_name=\"embed-multilingual-v3.0\",\n",
        "    input_type=\"search_document\",\n",
        ")\n",
        "\n",
        "# Italian dataset evaluation\n",
        "cohere_results_it = evaluate(it_dataset, embed_model_co)\n",
        "df_cohere_results_it = pd.DataFrame(cohere_results_it)\n",
        "hit_rate_co_it = df_cohere_results_it[\"is_hit\"].mean()\n",
        "mrr_co_it = df_cohere_results_it[\"mrr\"].mean()\n",
        "\n",
        "# French dataset evaluation\n",
        "cohere_results_fr = evaluate(fr_dataset, embed_model_co)\n",
        "df_cohere_results_fr = pd.DataFrame(cohere_results_fr)\n",
        "hit_rate_co_fr = df_cohere_results_fr[\"is_hit\"].mean()\n",
        "mrr_co_fr = df_cohere_results_fr[\"mrr\"].mean()\n",
        "\n",
        "# DataFrame with both Italian and French results for Cohere\n",
        "results_cohere = {\n",
        "    \"Hit Rate\": [hit_rate_co_it, hit_rate_co_fr],\n",
        "    \"MRR\": [mrr_co_it, mrr_co_fr]\n",
        "}\n",
        "\n",
        "df_results_cohere = pd.DataFrame(results_cohere, index=[\"Italian\", \"French\"])\n",
        "df_results_cohere"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZNjymWzfoWh"
      },
      "source": [
        "## intfloat/multilingual-e5-large"
      ]
    },
    {
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
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          "text": [
            "Hit rate e5 large italian: 0.7323481116584565\n",
            "MRR e5 large italian: 0.5658456486042693\n"
          ]
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          "text": [
            "Hit rate e5 large french: 0.6229508196721312\n",
            "MRR e5 large french: 0.4499503229011425\n"
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            ],
            "text/plain": [
              "         Hit Rate       MRR\n",
              "Italian  0.732348  0.565846\n",
              "French   0.622951  0.449950"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "\n",
        "embed_model_e5 = HuggingFaceEmbedding(model_name=\"intfloat/multilingual-e5-large\", max_length=512)\n",
        "\n",
        "# Italian dataset evaluation\n",
        "e5_large_results_it = evaluate(it_dataset, embed_model_e5)\n",
        "df_e5_large_results_it = pd.DataFrame(e5_large_results_it)\n",
        "hit_rate_e5_large_it = df_e5_large_results_it[\"is_hit\"].mean()\n",
        "mrr_e5_large_it = df_e5_large_results_it[\"mrr\"].mean()\n",
        "print(f\"Hit rate e5 large italian: {hit_rate_e5_large_it}\")\n",
        "print(f\"MRR e5 large italian: {mrr_e5_large_it}\")\n",
        "\n",
        "# French dataset evaluation\n",
        "e5_large_results_fr = evaluate(fr_dataset, embed_model_e5)\n",
        "df_e5_large_results_fr = pd.DataFrame(e5_large_results_fr)\n",
        "hit_rate_e5_large_fr = df_e5_large_results_fr[\"is_hit\"].mean()\n",
        "mrr_e5_large_fr = df_e5_large_results_fr[\"mrr\"].mean()\n",
        "print(f\"Hit rate e5 large french: {hit_rate_e5_large_fr}\")\n",
        "print(f\"MRR e5 large french: {mrr_e5_large_fr}\")\n",
        "\n",
        "# DataFrame with both Italian and French results\n",
        "results_e5_large = {\n",
        "    \"Hit Rate\": [hit_rate_e5_large_it, hit_rate_e5_large_fr],\n",
        "    \"MRR\": [mrr_e5_large_it, mrr_e5_large_fr]\n",
        "}\n",
        "\n",
        "df_results_e5_large = pd.DataFrame(results_e5_large, index=[\"Italian\", \"French\"])\n",
        "df_results_e5_large"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQd9ivSNgLhQ"
      },
      "source": [
        "## Cohere/Cohere-embed-multilingual-light-v3.0\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312,
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              "Generating embeddings:   0%|          | 0/179 [00:00<?, ?it/s]"
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            "Hit rate cohere light italian: 0.7980295566502463\n",
            "MRR cohere light italian: 0.6211001642036125\n"
          ]
        },
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            "Hit rate cohere light french: 0.7690014903129657\n",
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            "text/plain": [
              "         Hit Rate      MRR\n",
              "Italian  0.798030  0.62110\n",
              "French   0.769001  0.58306"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embed_model_co_light = CohereEmbedding(\n",
        "    cohere_api_key=cohere_api_key,\n",
        "    model_name=\"embed-multilingual-light-v3.0\",\n",
        "    input_type=\"search_document\",\n",
        ")\n",
        "\n",
        "# Italian dataset evaluation\n",
        "cohere_light_results_it = evaluate(it_dataset, embed_model_co_light)\n",
        "df_cohere_light_results_it = pd.DataFrame(cohere_light_results_it)\n",
        "hit_rate_co_light_it = df_cohere_light_results_it[\"is_hit\"].mean()\n",
        "mrr_co_light_it = df_cohere_light_results_it[\"mrr\"].mean()\n",
        "print(f\"Hit rate cohere light italian: {hit_rate_co_light_it}\")\n",
        "print(f\"MRR cohere light italian: {mrr_co_light_it}\")\n",
        "\n",
        "# French dataset evaluation\n",
        "cohere_light_results_fr = evaluate(fr_dataset, embed_model_co_light)\n",
        "df_cohere_light_results_fr = pd.DataFrame(cohere_light_results_fr)\n",
        "hit_rate_co_light_fr = df_cohere_light_results_fr[\"is_hit\"].mean()\n",
        "mrr_co_light_fr = df_cohere_light_results_fr[\"mrr\"].mean()\n",
        "print(f\"Hit rate cohere light french: {hit_rate_co_light_fr}\")\n",
        "print(f\"MRR cohere light french: {mrr_co_light_fr}\")\n",
        "\n",
        "# DataFrame with both Italian and French results\n",
        "results_cohere_light = {\n",
        "    \"Hit Rate\": [hit_rate_co_light_it, hit_rate_co_light_fr],\n",
        "    \"MRR\": [mrr_co_light_it, mrr_co_light_fr]\n",
        "}\n",
        "\n",
        "df_results_cohere_light = pd.DataFrame(results_cohere_light, index=[\"Italian\", \"French\"])\n",
        "df_results_cohere_light"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_Aw1sfDiv7V"
      },
      "source": [
        "## sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
      ]
    },
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            "Hit rate paraphrase l12 italian: 0.24630541871921183\n",
            "MRR paraphrase l12 italian: 0.15158730158730158\n"
          ]
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          "text": [
            "Hit rate paraphrase l12 french: 0.23472429210134127\n",
            "MRR paraphrase l12 french: 0.15500496770988573\n"
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            "text/plain": [
              "         Hit Rate       MRR\n",
              "Italian  0.246305  0.151587\n",
              "French   0.234724  0.155005"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embed_model_paraphrase_l12 = HuggingFaceEmbedding(model_name=\"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\")\n",
        "\n",
        "# Italian dataset evaluation\n",
        "paraphrase_l12_results_it = evaluate(it_dataset, embed_model_paraphrase_l12)\n",
        "df_paraphrase_l12_results_it = pd.DataFrame(paraphrase_l12_results_it)\n",
        "hit_paraphrase_l12_results_it = df_paraphrase_l12_results_it[\"is_hit\"].mean()\n",
        "mrr_paraphrase_l12_results_it = df_paraphrase_l12_results_it[\"mrr\"].mean()\n",
        "print(f\"Hit rate paraphrase l12 italian: {hit_paraphrase_l12_results_it}\")\n",
        "print(f\"MRR paraphrase l12 italian: {mrr_paraphrase_l12_results_it}\")\n",
        "\n",
        "# French dataset evaluation\n",
        "paraphrase_l12_results_fr = evaluate(fr_dataset, embed_model_paraphrase_l12)\n",
        "df_paraphrase_l12_results_fr = pd.DataFrame(paraphrase_l12_results_fr)\n",
        "hit_paraphrase_l12_results_fr = df_paraphrase_l12_results_fr[\"is_hit\"].mean()\n",
        "mrr_paraphrase_l12_results_fr = df_paraphrase_l12_results_fr[\"mrr\"].mean()\n",
        "print(f\"Hit rate paraphrase l12 french: {hit_paraphrase_l12_results_fr}\")\n",
        "print(f\"MRR paraphrase l12 french: {mrr_paraphrase_l12_results_fr}\")\n",
        "\n",
        "# DataFrame with both Italian and French results\n",
        "results_paraphrase_l12 = {\n",
        "    \"Hit Rate\": [hit_paraphrase_l12_results_it, hit_paraphrase_l12_results_fr],\n",
        "    \"MRR\": [mrr_paraphrase_l12_results_it, mrr_paraphrase_l12_results_fr]\n",
        "}\n",
        "\n",
        "df_results_paraphrase_l12 = pd.DataFrame(results_paraphrase_l12, index=[\"Italian\", \"French\"])\n",
        "df_results_paraphrase_l12"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qV-rGkWZr6mp"
      },
      "source": [
        "# izhx/udever-bloom-1b1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/",
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            "Hit rate Bloom italian: 0.024630541871921183\n",
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            "Hit rate Bloom french: 0.03353204172876304\n",
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            "text/plain": [
              "         Hit Rate       MRR\n",
              "Italian  0.024631  0.006787\n",
              "French   0.033532  0.014295"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "embed_model_bloom_1b = HuggingFaceEmbedding(model_name=\"izhx/udever-bloom-1b1\", max_length=512)\n",
        "\n",
        "# Italian dataset evaluation\n",
        "bloom_results_it = evaluate(it_dataset, embed_model_bloom_1b)\n",
        "df_bloom_results_it = pd.DataFrame(bloom_results_it)\n",
        "hit_bloom_results_it = df_bloom_results_it[\"is_hit\"].mean()\n",
        "mrr_bloom_results_it = df_bloom_results_it[\"mrr\"].mean()\n",
        "print(f\"Hit rate Bloom italian: {hit_bloom_results_it}\")\n",
        "print(f\"MRR Bloom italian: {mrr_bloom_results_it}\")\n",
        "\n",
        "# French dataset evaluation\n",
        "bloom_results_fr = evaluate(fr_dataset, embed_model_bloom_1b)\n",
        "df_bloom_results_fr = pd.DataFrame(bloom_results_fr)\n",
        "hit_bloom_results_fr = df_bloom_results_fr[\"is_hit\"].mean()\n",
        "mrr_bloom_results_fr = df_bloom_results_fr[\"mrr\"].mean()\n",
        "print(f\"Hit rate Bloom french: {hit_bloom_results_fr}\")\n",
        "print(f\"MRR Bloom french: {mrr_bloom_results_fr}\")\n",
        "\n",
        "# DataFrame with both Italian and French results\n",
        "results_bloom = {\n",
        "    \"Hit Rate\": [hit_bloom_results_it, hit_bloom_results_fr],\n",
        "    \"MRR\": [mrr_bloom_results_it, mrr_bloom_results_fr]\n",
        "}\n",
        "\n",
        "df_results_bloom = pd.DataFrame(results_bloom, index=[\"Italian\", \"French\"])\n",
        "df_results_bloom"
      ]
    }
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