fine_tune_multi.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EtSdhnyTr5Uq"
      },
      "outputs": [],
      "source": [
        "!pip install sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "itF8GS1_r4Yx"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import SentenceTransformer, InputExample, losses, evaluation, util , SentencesDataset\n",
        "from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator\n",
        "from torch.utils.data import DataLoader\n",
        "import torch\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from torch.utils.data import DataLoader\n",
        "import random\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "VlDXIzC7sUxX"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"df.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "_zWqNJJHsWDb"
      },
      "outputs": [],
      "source": [
        "n = 5000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "5sEGRJucsXgG"
      },
      "outputs": [],
      "source": [
        "all_q = []\n",
        "all_des = []\n",
        "all_title = []\n",
        "all_index = []\n",
        "\n",
        "for i in range(n):\n",
        "    if(\"(Novel)\" in df.loc[i, \"title\"] or \"(Light Novel)\" in df.loc[i, \"title\"] or \"(Webcomic)\" in df.loc[i, \"title\"] or \"(Doujinshi)\" in df.loc[i, \"title\"]):\n",
        "        continue\n",
        "    if(df.loc[i, \"q\"]):\n",
        "        all_q.append(df.loc[i, \"q\"].split('\\n')[0])\n",
        "        all_des.append(str(df.loc[i, \"description\"]))\n",
        "        all_title.append(str(df.loc[i, \"title\"]))\n",
        "        all_index.append(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "RiNv-JdccuHG"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(all_q, all_des, test_size=0.25, random_state=42)"
      ]
    },
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        {
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        {
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          },
          "metadata": {},
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      ],
      "source": [
        "model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ACc2h9KG3kmM"
      },
      "outputs": [],
      "source": [
        "train_examples = []\n",
        "for i in range(len(x_train)):\n",
        "  train_examples.append(InputExample(texts=[str(x_train[i]), str(y_train[i])], label=1.0))\n",
        "# make unrelated data\n",
        "for i in range(len(x_train)):\n",
        "  rand_idx_des = random.randint(0, len(x_train)-1)\n",
        "  rand_idx_q = random.randint(0, len(y_train)-1)\n",
        "  while(rand_idx_des == rand_idx_q):\n",
        "    rand_idx_q = random.randint(0, len(y_train)-1)\n",
        "  train_examples.append(InputExample(texts=[str(x_train[rand_idx_des]), str(y_train[rand_idx_q])], label=0.0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T50zY5G5in7N",
        "outputId": "abe97a39-966f-4078-d136-69d27912e2bd"
      },
      "outputs": [
        {
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              "6358"
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          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "len(train_examples)"
      ]
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    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "b2L5e0am1wmJ"
      },
      "outputs": [],
      "source": [
        "train_dataloader = DataLoader(train_examples, batch_size=16)\n",
        "train_loss = losses.CosineSimilarityLoss(model)"
      ]
    },
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        },
        "id": "XZ7EPoO25Ag3",
        "outputId": "6d13e0e1-6592-455b-9e71-2c8d44623c4d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "967f87873d374261abe9c5491bd57a65",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Epoch:   0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fbc1399737344c389db410afdd780038",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Iteration:   0%|          | 0/398 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2b2fa8140fd24b06812641f2bdd79a2c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Iteration:   0%|          | 0/398 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2a4acdde9d34b0d93c1dfa307a65036",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Iteration:   0%|          | 0/398 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6acc2337a1d54799aaf83f8a48be597c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Iteration:   0%|          | 0/398 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1027b1465c90403195cddbce14fa9f10",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Iteration:   0%|          | 0/398 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model.fit(train_objectives=[(train_dataloader, train_loss)],\n",
        "          epochs=5, warmup_steps=100, show_progress_bar=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "1p24R5H951Cs"
      },
      "outputs": [],
      "source": [
        "model.save(\"./model.pt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "iFljWwtD0lJk"
      },
      "outputs": [],
      "source": [
        "path = './model.pt'\n",
        "model = SentenceTransformer(path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hz20X2Cp4u9w"
      },
      "outputs": [],
      "source": [
        "passage_embedding = model.encode(['Detective', 'นักสืบ'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VPIyGZfk_Gkf",
        "outputId": "154ac0d6-5567-4f9a-cb6c-c1b8dc30e119"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "384"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(passage_embedding[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Upv-woaK9gEW"
      },
      "outputs": [],
      "source": [
        "cosine_scores = util.cos_sim(passage_embedding[0], passage_embedding[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MGkfTXu_bqO",
        "outputId": "43788ab2-f837-459b-c708-9dd6085b765e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor(0.6290)"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cosine_scores[0][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "W6sR_9ni_toF"
      },
      "outputs": [],
      "source": [
        "val_q = x_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "_5oQzXCu_3s2"
      },
      "outputs": [],
      "source": [
        "embeddings_des = model.encode(all_des, convert_to_tensor=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "PI4zFqYo_vOl"
      },
      "outputs": [],
      "source": [
        "all_val_q = []\n",
        "label = []\n",
        "for i in range(len(val_q)):\n",
        "    embedding_q = model.encode(val_q[i], convert_to_tensor=True)\n",
        "    idxx = all_q.index(val_q[i])\n",
        "    label.append(idxx)\n",
        "    all_val_q.append(embedding_q)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xEfGN7h2ATpp",
        "outputId": "f9fb2f43-9729-435c-eda5-2a07549850df"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4239"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(embeddings_des)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3TpmZG_LI85",
        "outputId": "53fa4184-d595-494b-85fd-3662671b35f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1060"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(all_val_q)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PLOxdwmL4EQ",
        "outputId": "3df2b505-4fad-4848-fba4-05cb813072a3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(list, torch.Tensor)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(all_val_q), type(all_val_q[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "QgaBujI__min"
      },
      "outputs": [],
      "source": [
        "acc = 0\n",
        "n_val = len(all_val_q)\n",
        "topk = 5\n",
        "\n",
        "for i in range(n_val):\n",
        "    cosine_scores = util.cos_sim(all_val_q[i], embeddings_des)\n",
        "    #print(len(cosine_scores[0]))\n",
        "    #max_val = max(cosine_scores[0])\n",
        "    all_idx = torch.topk(cosine_scores[0], topk).indices\n",
        "    ans = []\n",
        "    for j in all_idx:\n",
        "        ans.append(all_index[int(j)])\n",
        "\n",
        "    #check idex in all_des==idx\n",
        "    if(all_index[label[i]] in ans):\n",
        "        acc += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LA_I9bY_q3o",
        "outputId": "421fb83c-6885-4b7b-aa64-883a9835ac21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "top1 acc : 0.0\n"
          ]
        }
      ],
      "source": [
        "print(\"top1 acc :\", acc/len(all_val_q))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Amu8c2CNXK-s",
        "outputId": "3ae1ace7-0e42-482d-be48-6557d033dd89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "top3 acc : 0.0\n"
          ]
        }
      ],
      "source": [
        "print(\"top3 acc :\", acc/len(all_val_q))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zS7ykzjbXnv9",
        "outputId": "b9a1e9fa-db6a-4650-813f-bc437a78c4eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "top5 acc : 0.0009433962264150943\n"
          ]
        }
      ],
      "source": [
        "print(\"top5 acc :\", acc/len(all_val_q))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "-yTjohv-pw2o"
      },
      "source": [
        "#Test test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AC3Z48IKpy3Q"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0c340zoJp0lJ"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"df.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wkjS0FImp1xQ"
      },
      "outputs": [],
      "source": [
        "n = 5000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0oqEQ8kTp32C"
      },
      "outputs": [],
      "source": [
        "all_q = []\n",
        "all_des = []\n",
        "all_title = []\n",
        "all_index = []\n",
        "\n",
        "for i in range(n):\n",
        "    if(\"(Novel)\" in df.loc[i, \"title\"] or \"(Light Novel)\" in df.loc[i, \"title\"] or \"(Webcomic)\" in df.loc[i, \"title\"] or \"(Doujinshi)\" in df.loc[i, \"title\"]):\n",
        "        continue\n",
        "    if(df.loc[i, \"q\"]):\n",
        "        all_q.append(str(df.loc[i, \"q\"]).split('\\n')[0])\n",
        "        all_des.append(str(df.loc[i, \"description\"]))\n",
        "        all_title.append(str(df.loc[i, \"title\"]))\n",
        "        all_index.append(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
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        },
        "id": "AdaVG2XFp4p2",
        "outputId": "e4393fe1-580a-4154-ecbf-1e2aacef821d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4c53666d8e5c4a419f0bf17fc943f41f",
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              "model_id": "ba60d401db2941bab4ee3116dc581fbe",
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        },
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              "model_id": "79ba47f1029549bb9f3f451ee618c358",
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          },
          "metadata": {},
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        },
        {
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              "model_id": "a67e49e3e40d42cda1d86873c42323dc",
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              "model_id": "9631faa524b04a7e958aff458fc87a55",
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        },
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        },
        {
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              "model_id": "002657e86e494206a181936c16aee412",
              "version_major": 2,
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          },
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        },
        {
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              "model_id": "1a1cb062f97d4e679088c64198384c23",
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          },
          "metadata": {},
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        },
        {
          "data": {
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              "model_id": "02a4c687f340478398e0fff69a9c09f5",
              "version_major": 2,
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          "metadata": {},
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        },
        {
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              "model_id": "60598f1f901b41ccb29574d640ac46b5",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "3435459e9082460caf3bd19b34c51ca9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)ff6066015c/vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "bc818e50fefc4e598081a4f1b1c75634",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sentence_transformers import SentenceTransformer, util\n",
        "model = SentenceTransformer('distiluse-base-multilingual-cased-v2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sSUHT1rZqBWH"
      },
      "outputs": [],
      "source": [
        "embeddings_des = model.encode(all_des, convert_to_tensor=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KnAwUuBJqDqQ"
      },
      "outputs": [],
      "source": [
        "val_q = all_q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "SPHuUT0EqD_3",
        "outputId": "5527e107-4d00-45fb-ce7f-b170ca48b983"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-259a50aca85c>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mall_val_q\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_q\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0membedding_q\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_q\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0midxx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mall_q\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_q\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'val_q' is not defined"
          ]
        }
      ],
      "source": [
        "all_val_q = []\n",
        "label = []\n",
        "for i in range(len(val_q)):\n",
        "    embedding_q = model.encode(val_q[i], convert_to_tensor=True)\n",
        "    idxx = all_q.index(val_q[i])\n",
        "    label.append(idxx)\n",
        "    all_val_q.append(embedding_q)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MbqdyDJsqHKe"
      },
      "outputs": [],
      "source": [
        "acc = 0\n",
        "n_val = len(all_val_q)\n",
        "topk = 5\n",
        "\n",
        "for i in range(n_val):\n",
        "    cosine_scores = util.cos_sim(all_val_q[i], embeddings_des)\n",
        "    #print(len(cosine_scores[0]))\n",
        "    #max_val = max(cosine_scores[0])\n",
        "    all_idx = torch.topk(cosine_scores[0], topk).indices\n",
        "    ans = []\n",
        "    for j in all_idx:\n",
        "        ans.append(all_index[int(j)])\n",
        "\n",
        "    #check idex in all_des==idx\n",
        "    if(all_index[label[i]] in ans):\n",
        "        acc += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65dowcZFrD7h",
        "outputId": "dd544fd3-9dcf-4965-b846-b98fbfaa5d40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "top1 acc : 0.4170794998820477\n"
          ]
        }
      ],
      "source": [
        "print(\"top1 acc :\", acc/len(all_val_q))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hyg78rAzrEVU",
        "outputId": "48e19e56-5736-4ffb-cdc8-b55c2cfe1e58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "top3 acc : 0.5119131870724227\n"
          ]
        }
      ],
      "source": [
        "print(\"top3 acc :\", acc/len(all_val_q))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "id": "WFL0LKWsqmRn",
        "outputId": "08eca7fd-9cc8-4ba1-c0a2-dc6b02005a62"
      },
      "outputs": [
        {
          "ename": "ZeroDivisionError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-7de60bbbfb6f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"top5 acc :\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_val_q\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
          ]
        }
      ],
      "source": [
        "print(\"top5 acc :\", acc/len(all_val_q))"
      ]
    }
  ],
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